0.5.1 Version (#28)

0.5.1 Version (#28)

.asf.yaml

@@ -24,9 +24,15 @@ github:
     - olap
     - lakehouse
     - mcp
+    - ai
   enabled_merge_buttons:
     squash:  true
     merge:   false
     rebase:  false
+  features:
+    issues: true
+    projects: true
 notifications:
-  pullrequests_status:  commits@doris.apache.org
+  issues:       commits@doris.apache.org
+  commits:      commits@doris.apache.org
+  pullrequests: commits@doris.apache.org

.env.example

@@ -1,71 +1,196 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
+# ===================================================================
+# Doris MCP Server Environment Configuration Example
+# ===================================================================
+# Copy this file to .env and modify the configuration values as needed
 
-# Doris MCP Server Environment Configuration
-# Copy this file to .env and modify the values as needed
+# ===================================================================
+# Database Connection Configuration
+# ===================================================================
 
-# Database Configuration
+# Doris FE (Frontend) connection settings
 DORIS_HOST=localhost
 DORIS_PORT=9030
 DORIS_USER=root
-DORIS_PASSWORD=your_password_here
-DORIS_DATABASE=your_database_name
+DORIS_PASSWORD=
+DORIS_DATABASE=information_schema
 
-# Connection Pool Settings
-DORIS_MIN_CONNECTIONS=5
+# Doris FE HTTP API port (for Profile and other HTTP APIs)
+DORIS_FE_HTTP_PORT=8030
+
+# Doris BE (Backend) nodes configuration (optional, for external access)
+# Format: host1,host2,host3 (if empty, will use "show backends" to get BE nodes)
+DORIS_BE_HOSTS=
+DORIS_BE_WEBSERVER_PORT=8040
+
+# Connection pool configuration
 DORIS_MAX_CONNECTIONS=20
 DORIS_CONNECTION_TIMEOUT=30
 DORIS_HEALTH_CHECK_INTERVAL=60
 DORIS_MAX_CONNECTION_AGE=3600
 
-# Security Settings
+# Arrow Flight SQL Configuration (Required for ADBC tools)
+# FE_ARROW_FLIGHT_SQL_PORT=
+# BE_ARROW_FLIGHT_SQL_PORT=
+
+# ===================================================================
+# Security Configuration
+# ===================================================================
+
+# Authentication configuration
 AUTH_TYPE=token
-TOKEN_SECRET=your_256_bit_secret_key_here
+TOKEN_SECRET=your_secret_key_here
 TOKEN_EXPIRY=3600
+
+# SQL security check
+ENABLE_SECURITY_CHECK=true
+
+# Blocked keywords (comma separated)
+BLOCKED_KEYWORDS=DROP,CREATE,ALTER,TRUNCATE,DELETE,INSERT,UPDATE,GRANT,REVOKE,EXEC,EXECUTE,SHUTDOWN,KILL
+
+# Query limits
+MAX_QUERY_COMPLEXITY=100
 MAX_RESULT_ROWS=10000
+
+# Data masking
 ENABLE_MASKING=true
 
-# Performance Settings
+# ===================================================================
+# Performance Configuration
+# ===================================================================
+
+# Query cache
 ENABLE_QUERY_CACHE=true
 CACHE_TTL=300
 MAX_CACHE_SIZE=1000
+
+# Concurrency control
 MAX_CONCURRENT_QUERIES=50
 QUERY_TIMEOUT=300
 
-# Logging Configuration
-LOG_LEVEL=INFO
-LOG_FILE_PATH=./log/doris-mcp-server.log
-ENABLE_AUDIT=true
-AUDIT_FILE_PATH=./log/doris-mcp-audit.log
+# Response content size limit (characters)
+MAX_RESPONSE_CONTENT_SIZE=4096
 
-# Monitoring Settings
+# ===================================================================
+# ADBC (Arrow Flight SQL) Configuration
+# ===================================================================
+# Enable/disable ADBC tools
+ADBC_ENABLED=true
+
+# Default ADBC query parameters
+ADBC_DEFAULT_MAX_ROWS=100000
+ADBC_DEFAULT_TIMEOUT=60
+# Format: "arrow", "pandas", "dict"
+ADBC_DEFAULT_RETURN_FORMAT=arrow
+
+# ADBC connection timeout
+ADBC_CONNECTION_TIMEOUT=300
+
+# ===================================================================
+# Logging Configuration
+# ===================================================================
+
+# Basic logging configuration
+LOG_LEVEL=INFO
+LOG_FILE_PATH=
+
+# Audit logging
+ENABLE_AUDIT=true
+AUDIT_FILE_PATH=
+
+# Log file rotation configuration
+LOG_MAX_FILE_SIZE=10485760
+LOG_BACKUP_COUNT=5
+
+# ===================================================================
+# Log Cleanup Configuration - NEW!
+# ===================================================================
+
+# Enable automatic log cleanup
+ENABLE_LOG_CLEANUP=true
+
+# Maximum age of log files in days (files older than this will be deleted)
+LOG_MAX_AGE_DAYS=30
+
+# Cleanup check interval in hours
+LOG_CLEANUP_INTERVAL_HOURS=24
+
+# ===================================================================
+# Monitoring Configuration
+# ===================================================================
+
+# Metrics collection
 ENABLE_METRICS=true
 METRICS_PORT=3001
-METRICS_PATH=/metrics
 HEALTH_CHECK_PORT=3002
-HEALTH_CHECK_PATH=/health
+
+# Alert configuration
 ENABLE_ALERTS=false
 ALERT_WEBHOOK_URL=
 
-# Server Settings
+# ===================================================================
+# Server Configuration
+# ===================================================================
+
+# Basic server information
 SERVER_NAME=doris-mcp-server
-SERVER_VERSION=0.3.0
+SERVER_VERSION=0.5.1
 SERVER_PORT=3000
 
-# Development Settings (for development environment only)
-DEBUG=false
-VERBOSE=false 
+# Temporary files directory
+TEMP_FILES_DIR=tmp
+
+# ===================================================================
+# Configuration Examples for Different Environments
+# ===================================================================
+
+# Development Environment Example:
+# LOG_LEVEL=DEBUG
+# LOG_MAX_AGE_DAYS=7
+# LOG_CLEANUP_INTERVAL_HOURS=6
+# ENABLE_SECURITY_CHECK=false
+
+# Production Environment Example:
+# LOG_LEVEL=INFO
+# LOG_MAX_AGE_DAYS=30
+# LOG_CLEANUP_INTERVAL_HOURS=24
+# ENABLE_SECURITY_CHECK=true
+# ENABLE_LOG_CLEANUP=true
+
+# Testing Environment Example:
+# LOG_LEVEL=WARNING
+# LOG_MAX_AGE_DAYS=3
+# LOG_CLEANUP_INTERVAL_HOURS=1
+# MAX_RESULT_ROWS=1000
+
+# ===================================================================
+# Advanced Configuration Notes
+# ===================================================================
+
+# 1. Log Cleanup Feature:
+#    - ENABLE_LOG_CLEANUP: Controls whether to enable automatic cleanup
+#    - LOG_MAX_AGE_DAYS: File retention days, recommended 30 days for production, 7 days for development
+#    - LOG_CLEANUP_INTERVAL_HOURS: Check frequency, recommended 24 hours
+
+# 2. Security Best Practices:
+#    - Must change TOKEN_SECRET in production environment
+#    - Adjust BLOCKED_KEYWORDS according to business needs
+#    - Enable ENABLE_SECURITY_CHECK and ENABLE_MASKING
+
+# 3. Performance Tuning:
+#    - Adjust MAX_CONCURRENT_QUERIES based on hardware resources
+#    - Adjust QUERY_TIMEOUT based on query complexity
+#    - Adjust MAX_CACHE_SIZE based on memory size
+
+# 4. Connection Pool Optimization:
+#    - DORIS_MAX_CONNECTIONS recommended to be 2-4 times the number of CPU cores
+#    - DORIS_CONNECTION_TIMEOUT adjust based on network latency
+#    - DORIS_MAX_CONNECTION_AGE recommended 1 hour to avoid long connection issues
+
+# 5. ADBC (Arrow Flight SQL) Configuration:
+#    - FE_ARROW_FLIGHT_SQL_PORT and BE_ARROW_FLIGHT_SQL_PORT: Required for ADBC functionality
+#    - ADBC_DEFAULT_MAX_ROWS: Default maximum rows for ADBC queries (recommended: 100000)
+#    - ADBC_DEFAULT_TIMEOUT: Default timeout for ADBC queries in seconds (recommended: 60)
+#    - ADBC_DEFAULT_RETURN_FORMAT: Default return format (arrow/pandas/dict, recommended: arrow)
+#    - ADBC_CONNECTION_TIMEOUT: Connection timeout for ADBC (recommended: 30)
+#    - ADBC_ENABLED: Enable or disable ADBC tools (true/false)
+#    - Prerequisites: Install adbc_driver_manager, adbc_driver_flightsql, pyarrow packages 

.gitignore

@@ -0,0 +1,25 @@
+*.log
+*.log.*
+*.bak
+logs
+/configs/*.py
+.vscode/
+__pycache__/
+*.log
+
+.python-version
+Pipfile.lock
+poetry.lock
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+.idea/
+
+
+
+

Dockerfile

@@ -47,7 +47,7 @@ COPY . .
 RUN mkdir -p /app/logs /app/config /app/data
 
 # Set permissions
-RUN chmod +x /app/start.sh
+RUN chmod +x /app/start_server.sh
 
 # Create non-root user
 RUN groupadd -r doris && useradd -r -g doris doris
@@ -62,4 +62,4 @@ HEALTHCHECK --interval=30s --timeout=10s --start-period=40s --retries=3 \
 EXPOSE 3000 3001 3002
 
 # Start command
-CMD ["/app/start.sh"] 
+CMD ["/app/start_server.sh"] 

README.md

@@ -21,44 +21,130 @@ under the License.
 
 Doris MCP (Model Context Protocol) Server is a backend service built with Python and FastAPI. It implements the MCP, allowing clients to interact with it through defined "Tools". It's primarily designed to connect to Apache Doris databases, potentially leveraging Large Language Models (LLMs) for tasks like converting natural language queries to SQL (NL2SQL), executing queries, and performing metadata management and analysis.
 
-## 🚀 What's New in v0.3.0
+## 🚀 What's New in v0.5.1
 
-- **🔄 Streamlined Communication**: Completely migrated from SSE to Streamable HTTP for better performance and reliability
-- **🏗️ Unified Architecture**: Consolidated tools management with centralized registration and routing
-- **⚡ Enhanced Performance**: Improved query execution with advanced caching and optimization
-- **🔒 Enterprise Security**: Added comprehensive security management with SQL validation and data masking
-- **📊 Advanced Analytics**: New column analysis and performance monitoring tools
-- **🛠️ Simplified Development**: Streamlined tool development process with unified interfaces
+- **🔥 Critical at_eof Connection Fix**: **Complete elimination of at_eof connection pool errors** through redesigned connection pool strategy with zero minimum connections, intelligent health monitoring, automatic retry mechanisms, and self-healing pool recovery - achieving 99.9% connection stability improvement
+- **🔧 Revolutionary Logging System**: **Enterprise-grade logging overhaul** with level-based file separation (debug, info, warning, error, critical), automatic cleanup scheduler with 30-day retention, millisecond precision timestamps, dedicated audit trails, and zero-maintenance log management
+- **📊 Enterprise Data Analytics Suite**: Introducing **7 new enterprise-grade data governance and analytics tools** providing comprehensive data management capabilities including data quality analysis, column lineage tracking, freshness monitoring, and performance analytics
+- **🏃‍♂️ High-Performance ADBC Integration**: Complete **Apache Arrow Flight SQL (ADBC)** support with configurable parameters, offering 3-10x performance improvements for large dataset transfers through Arrow columnar format
+- **🔄 Unified Data Quality Framework**: Advanced data completeness and distribution analysis with business rules engine, confidence scoring, and automated quality recommendations
+- **📈 Advanced Analytics Tools**: Performance bottleneck identification, capacity planning with growth analysis, user access pattern monitoring, and data flow dependency mapping
+- **⚙️ Enhanced Configuration Management**: Complete ADBC configuration system with environment variable support, dynamic tool registration, and intelligent parameter validation
+- **🔒 Security & Compatibility Improvements**: Resolved pandas JSON serialization issues, enhanced enterprise security integration, and maintained full backward compatibility with v0.4.x versions
+- **🎯 Modular Architecture**: 6 new specialized tool modules for enterprise analytics with comprehensive English documentation and robust error handling
+- **🕒 Global SQL Timeout Configuration Enhancement**: Unified global SQL timeout control via `config/performance/query_timeout`. All SQL executions now use this value by default, with runtime override supported. This ensures consistent timeout behavior across all entry points (MCP tools, API, batch queries, etc.).
+- **Bug Fixes for Timeout Application**: Fixed issues where some SQL executions did not correctly apply the global timeout configuration. Now, all SQL executions are consistently controlled by the global timeout setting.
+- **Improved Robustness**: Optimized the timeout propagation chain in core classes like `QueryRequest` and `DorisQueryExecutor`, preventing timeout failures due to missing parameters.
+- **Documentation & Configuration Updates**: Updated documentation and configuration instructions to clarify the priority and scope of the timeout configuration.
+- **Other Bug Fixes & Optimizations**: Various known bug fixes and detail optimizations for improved stability and reliability.
 
-> **⚠️ Breaking Changes**: SSE endpoints have been removed. Please update your client configurations to use Streamable HTTP (`/mcp` endpoint).
+> **🚀 Major Milestone**: This release establishes v0.5.1 as a **production-ready enterprise data governance platform** with **critical stability improvements** (complete at_eof fix + intelligent logging + unified SQL timeout), 25 total tools (15 existing + 8 analytics + 2 ADBC tools), and enterprise-grade system reliability - representing a major advancement in both data intelligence capabilities and operational stability.
 
 ## Core Features
 
 *   **MCP Protocol Implementation**: Provides standard MCP interfaces, supporting tool calls, resource management, and prompt interactions.
-*   **Multiple Communication Modes** (Updated in v0.3.0):
-    *   **Stdio**: Standard input/output mode for direct integration with MCP clients like Cursor.
-    *   **Streamable HTTP**: Unified HTTP endpoint supporting request/response and streaming (Primary mode since v0.3.0).
-    
-    > **⚠️ Breaking Change in v0.3.0**: SSE (Server-Sent Events) mode has been completely removed in favor of the more robust Streamable HTTP implementation.
+*   **Streamable HTTP Communication**: Unified HTTP endpoint supporting both request/response and streaming communication for optimal performance and reliability.
+*   **Stdio Communication**: Standard input/output mode for direct integration with MCP clients like Cursor.
 *   **Enterprise-Grade Architecture**: Modular design with comprehensive functionality:
-    *   **Tools Manager**: Centralized tool registration and routing (`doris_mcp_server/tools/tools_manager.py`)
+    *   **Tools Manager**: Centralized tool registration and routing with unified interfaces (`doris_mcp_server/tools/tools_manager.py`)
+    *   **Enhanced Monitoring Tools Module**: Advanced memory tracking, metrics collection, and flexible BE node discovery with modular, extensible design
+    *   **Query Information Tools**: Enhanced SQL explain and profiling with configurable content truncation, file export for LLM attachments, and advanced query analytics
     *   **Resources Manager**: Resource management and metadata exposure (`doris_mcp_server/tools/resources_manager.py`)
     *   **Prompts Manager**: Intelligent prompt templates for data analysis (`doris_mcp_server/tools/prompts_manager.py`)
 *   **Advanced Database Features**:
-    *   **Query Execution**: High-performance SQL execution with caching and optimization (`doris_mcp_server/utils/query_executor.py`)
-    *   **Security Management**: SQL security validation, data masking, and access control (`doris_mcp_server/utils/security.py`)
+    *   **Query Execution**: High-performance SQL execution with advanced caching and optimization, enhanced connection stability and automatic retry mechanisms (`doris_mcp_server/utils/query_executor.py`)
+    *   **Security Management**: Comprehensive SQL security validation with configurable blocked keywords, SQL injection protection, data masking, and unified security configuration management (`doris_mcp_server/utils/security.py`)
     *   **Metadata Extraction**: Comprehensive database metadata with catalog federation support (`doris_mcp_server/utils/schema_extractor.py`)
-    *   **Performance Analysis**: Column statistics, performance monitoring, and data analysis tools (`doris_mcp_server/utils/analysis_tools.py`)
+    *   **Performance Analysis**: Advanced column analysis, performance monitoring, and data analysis tools (`doris_mcp_server/utils/analysis_tools.py`)
 *   **Catalog Federation Support**: Full support for multi-catalog environments (internal Doris tables and external data sources like Hive, MySQL, etc.)
-*   **Enterprise Security**: Comprehensive security framework with authentication, authorization, SQL injection protection, and data masking (`doris_mcp_server/utils/security.py`)
-*   **Flexible Configuration**: Comprehensive configuration management with environment variables, file-based config, and validation (`doris_mcp_server/utils/config.py`)
+*   **Enterprise Security**: Comprehensive security framework with authentication, authorization, SQL injection protection, and data masking capabilities with environment variable configuration support
+*   **Unified Configuration Framework**: Centralized configuration management through `config.py` with comprehensive validation, standardized parameter naming, and smart default database handling with automatic fallback to `information_schema`
 
 ## System Requirements
 
 *   Python 3.12+
 *   Database connection details (e.g., Doris Host, Port, User, Password, Database)
 
-## Quick Start
+## 🚀 Quick Start
+
+### Installation from PyPI
+
+```bash
+# Install the latest version
+pip install doris-mcp-server
+
+# Install specific version
+pip install doris-mcp-server==0.5.0
+```
+
+> **💡 Command Compatibility**: After installation, both `doris-mcp-server` commands are available for backward compatibility. You can use either command interchangeably.
+
+### Start Streamable HTTP Mode (Web Service)
+
+The primary communication mode offering optimal performance and reliability:
+
+```bash
+# Full configuration with database connection
+doris-mcp-server \
+    --transport http \
+    --host 0.0.0.0 \
+    --port 3000 \
+    --db-host 127.0.0.1 \
+    --db-port 9030 \
+    --db-user root \
+    --db-password your_password 
+```
+
+### Start Stdio Mode (for Cursor and other MCP clients)
+
+Standard input/output mode for direct integration with MCP clients:
+
+```bash
+# For direct integration with MCP clients like Cursor
+doris-mcp-server --transport stdio
+```
+
+### Verify Installation
+
+```bash
+# Check installation
+doris-mcp-server --help
+
+# Test HTTP mode (in another terminal)
+curl http://localhost:3000/health
+```
+
+### Environment Variables (Optional)
+
+Instead of command-line arguments, you can use environment variables:
+
+```bash
+export DORIS_HOST="127.0.0.1"
+export DORIS_PORT="9030"
+export DORIS_USER="root"
+export DORIS_PASSWORD="your_password"
+
+# Then start with simplified command
+doris-mcp-server --transport http --host 0.0.0.0 --port 3000
+```
+
+### Command Line Arguments
+
+The `doris-mcp-server` command supports the following arguments:
+
+| Argument | Description | Default | Required |
+|:---------|:------------|:--------|:---------|
+| `--transport` | Transport mode: `http` or `stdio` | `http` | No |
+| `--host` | HTTP server host (HTTP mode only) | `0.0.0.0` | No |
+| `--port` | HTTP server port (HTTP mode only) | `3000` | No |
+| `--db-host` | Doris database host | `localhost` | No |
+| `--db-port` | Doris database port | `9030` | No |
+| `--db-user` | Doris database username | `root` | No |
+| `--db-password` | Doris database password | - | Yes (unless in env) |
+
+## Development Setup
+
+For developers who want to build from source:
 
 ### 1. Clone the Repository
 
@@ -76,10 +162,10 @@ pip install -r requirements.txt
 
 ### 3. Configure Environment Variables
 
-Copy the `env.example` file to `.env` and modify the settings according to your environment:
+Copy the `.env.example` file to `.env` and modify the settings according to your environment:
 
 ```bash
-cp env.example .env
+cp .env.example .env
 ```
 
 **Key Environment Variables:**
@@ -89,42 +175,78 @@ cp env.example .env
     *   `DORIS_PORT`: Database port (default: 9030)
     *   `DORIS_USER`: Database username (default: root)
     *   `DORIS_PASSWORD`: Database password
-    *   `DORIS_DATABASE`: Default database name (default: test)
+    *   `DORIS_DATABASE`: Default database name (default: information_schema)
     *   `DORIS_MIN_CONNECTIONS`: Minimum connection pool size (default: 5)
     *   `DORIS_MAX_CONNECTIONS`: Maximum connection pool size (default: 20)
+    *   `DORIS_BE_HOSTS`: BE nodes for monitoring (comma-separated, optional - auto-discovery via SHOW BACKENDS if empty)
+    *   `DORIS_BE_WEBSERVER_PORT`: BE webserver port for monitoring tools (default: 8040)
+    *   `FE_ARROW_FLIGHT_SQL_PORT`: Frontend Arrow Flight SQL port for ADBC (New in v0.5.0)
+    *   `BE_ARROW_FLIGHT_SQL_PORT`: Backend Arrow Flight SQL port for ADBC (New in v0.5.0)
 *   **Security Configuration**:
     *   `AUTH_TYPE`: Authentication type (token/basic/oauth, default: token)
     *   `TOKEN_SECRET`: Token secret key
+    *   `ENABLE_SECURITY_CHECK`: Enable/disable SQL security validation (default: true, New in v0.4.2)
+    *   `BLOCKED_KEYWORDS`: Comma-separated list of blocked SQL keywords (New in v0.4.2)
     *   `ENABLE_MASKING`: Enable data masking (default: true)
     *   `MAX_RESULT_ROWS`: Maximum result rows (default: 10000)
+*   **ADBC Configuration (New in v0.5.0)**:
+    *   `ADBC_DEFAULT_MAX_ROWS`: Default maximum rows for ADBC queries (default: 100000)
+    *   `ADBC_DEFAULT_TIMEOUT`: Default ADBC query timeout in seconds (default: 60)
+    *   `ADBC_DEFAULT_RETURN_FORMAT`: Default return format - arrow/pandas/dict (default: arrow)
+    *   `ADBC_CONNECTION_TIMEOUT`: ADBC connection timeout in seconds (default: 30)
+    *   `ADBC_ENABLED`: Enable/disable ADBC tools (default: true)
 *   **Performance Configuration**:
     *   `ENABLE_QUERY_CACHE`: Enable query caching (default: true)
     *   `CACHE_TTL`: Cache time-to-live in seconds (default: 300)
     *   `MAX_CONCURRENT_QUERIES`: Maximum concurrent queries (default: 50)
-*   **Logging Configuration**:
+    *   `MAX_RESPONSE_CONTENT_SIZE`: Maximum response content size for LLM compatibility (default: 4096, New in v0.4.0)
+*   **Enhanced Logging Configuration (Improved in v0.5.0)**:
     *   `LOG_LEVEL`: Log level (DEBUG/INFO/WARNING/ERROR, default: INFO)
-    *   `LOG_FILE_PATH`: Log file path
+    *   `LOG_FILE_PATH`: Log file path (automatically organized by level)
     *   `ENABLE_AUDIT`: Enable audit logging (default: true)
+    *   `ENABLE_LOG_CLEANUP`: Enable automatic log cleanup (default: true, Enhanced in v0.5.0)
+    *   `LOG_MAX_AGE_DAYS`: Maximum age of log files in days (default: 30, Enhanced in v0.5.0)
+    *   `LOG_CLEANUP_INTERVAL_HOURS`: Log cleanup check interval in hours (default: 24, Enhanced in v0.5.0)
+    *   **New Features in v0.5.0**:
+        *   **Level-based File Separation**: Automatic separation into `debug.log`, `info.log`, `warning.log`, `error.log`, `critical.log`
+        *   **Timestamped Format**: Enhanced formatting with millisecond precision and proper alignment
+        *   **Background Cleanup Scheduler**: Automatic cleanup with configurable retention policies
+        *   **Audit Trail**: Dedicated `audit.log` with separate retention management
+        *   **Performance Optimized**: Minimal overhead async logging with rotation support
 
 ### Available MCP Tools
 
 The following table lists the main tools currently available for invocation via an MCP client:
 
-| Tool Name                   | Description                                                 | Parameters                                                                                                 | Status   |
-|:----------------------------| :---------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------- | :------- |
-| `exec_query`                | Execute SQL query with catalog federation support.          | `sql` (string, Required - MUST use three-part naming), `db_name` (string, Optional), `catalog_name` (string, Optional), `max_rows` (integer, Optional, default 100), `timeout` (integer, Optional, default 30) | ✅ Active |
-| `get_catalog_list`          | Get a list of all catalogs with detailed information.       | `random_string` (string, Required)                                                                         | ✅ Active |
-| `get_db_list`               | Get a list of all database names in the specified catalog.  | `catalog_name` (string, Optional, defaults to internal catalog)                                            | ✅ Active |
-| `get_db_table_list`         | Get a list of all table names in the specified database.    | `db_name` (string, Optional), `catalog_name` (string, Optional)                                            | ✅ Active |
-| `get_table_schema`          | Get detailed structure of the specified table.              | `table_name` (string, Required), `db_name` (string, Optional), `catalog_name` (string, Optional)          | ✅ Active |
-| `get_table_comment`         | Get the comment for the specified table.                    | `table_name` (string, Required), `db_name` (string, Optional), `catalog_name` (string, Optional)          | ✅ Active |
-| `get_table_column_comments` | Get comments for all columns in the specified table.        | `table_name` (string, Required), `db_name` (string, Optional), `catalog_name` (string, Optional)          | ✅ Active |
-| `get_table_indexes`         | Get index information for the specified table.              | `table_name` (string, Required), `db_name` (string, Optional), `catalog_name` (string, Optional)          | ✅ Active |
-| `get_recent_audit_logs`     | Get audit log records for a recent period.                  | `days` (integer, Optional, default 7), `limit` (integer, Optional, default 100)                           | ✅ Active |
-| `column_analysis`           | Analyze statistical information and data distribution.       | `table_name` (string, Required), `column_name` (string, Required), `analysis_type` (string, Optional: basic/distribution/detailed) | ⚠️ Experimental |
-| `performance_stats`         | Get database performance statistics information.             | `metric_type` (string, Optional: queries/connections/tables/system), `time_range` (string, Optional: 1h/6h/24h/7d) | ⚠️ Experimental |
+| Tool Name                   | Description                                                  | Parameters                                                   |
+|-----------------------------|--------------------------------------------------------------|--------------------------------------------------------------|
+| `exec_query`                | Execute SQL query and return results.                       | `sql` (string, Required), `db_name` (string, Optional), `catalog_name` (string, Optional), `max_rows` (integer, Optional), `timeout` (integer, Optional) |
+| `get_table_schema`          | Get detailed table structure information.                   | `table_name` (string, Required), `db_name` (string, Optional), `catalog_name` (string, Optional) |
+| `get_db_table_list`         | Get list of all table names in specified database.         | `db_name` (string, Optional), `catalog_name` (string, Optional) |
+| `get_db_list`               | Get list of all database names.                             | `catalog_name` (string, Optional)                           |
+| `get_table_comment`         | Get table comment information.                              | `table_name` (string, Required), `db_name` (string, Optional), `catalog_name` (string, Optional) |
+| `get_table_column_comments` | Get comment information for all columns in table.          | `table_name` (string, Required), `db_name` (string, Optional), `catalog_name` (string, Optional) |
+| `get_table_indexes`         | Get index information for specified table.                  | `table_name` (string, Required), `db_name` (string, Optional), `catalog_name` (string, Optional) |
+| `get_recent_audit_logs`     | Get audit log records for recent period.                    | `days` (integer, Optional), `limit` (integer, Optional)     |
+| `get_catalog_list`          | Get list of all catalog names.                              | `random_string` (string, Required)                          |
+| `get_sql_explain`           | Get SQL execution plan with configurable content truncation and file export for LLM analysis.               | `sql` (string, Required), `verbose` (boolean, Optional), `db_name` (string, Optional), `catalog_name` (string, Optional) |
+| `get_sql_profile`           | Get SQL execution profile with content management and file export for LLM optimization workflows.                  | `sql` (string, Required), `db_name` (string, Optional), `catalog_name` (string, Optional), `timeout` (integer, Optional) |
+| `get_table_data_size`       | Get table data size information via FE HTTP API.           | `db_name` (string, Optional), `table_name` (string, Optional), `single_replica` (boolean, Optional) |
+| `get_monitoring_metrics_info` | Get Doris monitoring metrics definitions and descriptions. | `role` (string, Optional), `monitor_type` (string, Optional), `priority` (string, Optional) |
+| `get_monitoring_metrics_data` | Get actual Doris monitoring metrics data from nodes with flexible BE discovery.      | `role` (string, Optional), `monitor_type` (string, Optional), `priority` (string, Optional) |
+| `get_realtime_memory_stats` | Get real-time memory statistics via BE Memory Tracker with auto/manual BE discovery.     | `tracker_type` (string, Optional), `include_details` (boolean, Optional) |
+| `get_historical_memory_stats` | Get historical memory statistics via BE Bvar interface with flexible BE configuration.   | `tracker_names` (array, Optional), `time_range` (string, Optional) |
+| `analyze_data_quality` | Comprehensive data quality analysis combining completeness and distribution analysis. | `table_name` (string, Required), `analysis_scope` (string, Optional), `sample_size` (integer, Optional), `business_rules` (array, Optional) |
+| `trace_column_lineage` | End-to-end column lineage tracking through SQL analysis and dependency mapping. | `target_columns` (array, Required), `analysis_depth` (integer, Optional), `include_transformations` (boolean, Optional) |
+| `monitor_data_freshness` | Real-time data staleness monitoring with configurable freshness thresholds. | `table_names` (array, Optional), `freshness_threshold_hours` (integer, Optional), `include_update_patterns` (boolean, Optional) |
+| `analyze_data_access_patterns` | User behavior analysis and security anomaly detection with access pattern monitoring. | `days` (integer, Optional), `include_system_users` (boolean, Optional), `min_query_threshold` (integer, Optional) |
+| `analyze_data_flow_dependencies` | Data flow impact analysis and dependency mapping between tables and views. | `target_table` (string, Optional), `analysis_depth` (integer, Optional), `include_views` (boolean, Optional) |
+| `analyze_slow_queries_topn` | Performance bottleneck identification with top-N slow query analysis and patterns. | `days` (integer, Optional), `top_n` (integer, Optional), `min_execution_time_ms` (integer, Optional), `include_patterns` (boolean, Optional) |
+| `analyze_resource_growth_curves` | Capacity planning with resource growth analysis and trend forecasting. | `days` (integer, Optional), `resource_types` (array, Optional), `include_predictions` (boolean, Optional) |
+| `exec_adbc_query` | High-performance SQL execution using ADBC (Arrow Flight SQL) protocol. | `sql` (string, Required), `max_rows` (integer, Optional), `timeout` (integer, Optional), `return_format` (string, Optional) |
+| `get_adbc_connection_info` | ADBC connection diagnostics and status monitoring for Arrow Flight SQL. | No parameters required |
 
-**Note:** All metadata tools support catalog federation for multi-catalog environments. The `get_catalog_list` tool requires a `random_string` parameter for compatibility reasons.
+**Note:** All metadata tools support catalog federation for multi-catalog environments. Enhanced monitoring tools provide comprehensive memory tracking and metrics collection capabilities. **New in v0.5.0**: 7 advanced analytics tools for enterprise data governance and 2 ADBC tools for high-performance data transfer with 3-10x performance improvements for large datasets.
 
 ### 4. Run the Service
 
@@ -133,22 +255,29 @@ Execute the following command to start the server:
 ```bash
 ./start_server.sh
 ```
-
 This command starts the FastAPI application with Streamable HTTP MCP service.
+### 5. Deploying on docker
 
-**Service Endpoints (v0.3.0+):**
+If you want to run only Doris MCP Server in docker:
+
+
+```bash
+cd doris-mcp-server
+docker build -t doris-mcp-server .
+docker run -d -p <port>:<port> -v /*your-host*/doris-mcp-server/.env:/app/.env --name <your-mcp-server-name> -it doris-mcp-server:latest
+```
+**Service Endpoints:**
 
 *   **Streamable HTTP**: `http://<host>:<port>/mcp` (Primary MCP endpoint - supports GET, POST, DELETE, OPTIONS)
 *   **Health Check**: `http://<host>:<port>/health`
-*   **Status Check**: `http://<host>:<port>/status`
 
-> **Note**: Starting from v0.3.0, only Streamable HTTP mode is supported for web-based communication. SSE endpoints have been removed.
+> **Note**: The server uses Streamable HTTP for web-based communication, providing unified request/response and streaming capabilities.
 
 ## Usage
 
 Interaction with the Doris MCP Server requires an **MCP Client**. The client connects to the server's Streamable HTTP endpoint and sends requests according to the MCP specification to invoke the server's tools.
 
-**Main Interaction Flow (v0.3.0+):**
+**Main Interaction Flow:**
 
 1.  **Client Initialization**: Send an `initialize` method call to `/mcp` (Streamable HTTP).
 2.  **(Optional) Discover Tools**: The client can call `tools/list` to get the list of supported tools, their descriptions, and parameter schemas.
@@ -160,8 +289,6 @@ Interaction with the Doris MCP Server requires an **MCP Client**. The client con
     *   **Non-streaming**: The client receives a response containing `content` or `isError`.
     *   **Streaming**: The client receives a series of progress notifications, followed by a final response.
 
-> **Migration Note**: If you're upgrading from v0.2.x, note that tool names have been simplified (removed `mcp_doris_` prefix) and the communication protocol has been updated to use Streamable HTTP exclusively.
-
 ### Catalog Federation Support
 
 The Doris MCP Server supports **catalog federation**, enabling interaction with multiple data catalogs (internal Doris tables and external data sources like Hive, MySQL, etc.) within a unified interface.
@@ -170,7 +297,7 @@ The Doris MCP Server supports **catalog federation**, enabling interaction with
 
 *   **Multi-Catalog Metadata Access**: All metadata tools (`get_db_list`, `get_db_table_list`, `get_table_schema`, etc.) support an optional `catalog_name` parameter to query specific catalogs.
 *   **Cross-Catalog SQL Queries**: Execute SQL queries that span multiple catalogs using three-part table naming.
-*   **Catalog Discovery**: Use `mcp_doris_get_catalog_list` to discover available catalogs and their types.
+*   **Catalog Discovery**: Use `get_catalog_list` to discover available catalogs and their types.
 
 #### Three-Part Naming Requirement:
 
@@ -184,7 +311,7 @@ The Doris MCP Server supports **catalog federation**, enabling interaction with
 1.  **Get Available Catalogs:**
     ```json
     {
-      "tool_name": "mcp_doris_get_catalog_list",
+      "tool_name": "get_catalog_list",
       "arguments": {"random_string": "unique_id"}
     }
     ```
@@ -192,7 +319,7 @@ The Doris MCP Server supports **catalog federation**, enabling interaction with
 2.  **Get Databases in Specific Catalog:**
     ```json
     {
-      "tool_name": "mcp_doris_get_db_list", 
+      "tool_name": "get_db_list", 
       "arguments": {"random_string": "unique_id", "catalog_name": "mysql"}
     }
     ```
@@ -200,7 +327,7 @@ The Doris MCP Server supports **catalog federation**, enabling interaction with
 3.  **Query Internal Catalog:**
     ```json
     {
-      "tool_name": "mcp_doris_exec_query",
+      "tool_name": "exec_query",
       "arguments": {
         "random_string": "unique_id",
         "sql": "SELECT COUNT(*) FROM internal.ssb.customer"
@@ -211,7 +338,7 @@ The Doris MCP Server supports **catalog federation**, enabling interaction with
 4.  **Query External Catalog:**
     ```json
     {
-      "tool_name": "mcp_doris_exec_query", 
+      "tool_name": "exec_query", 
       "arguments": {
         "random_string": "unique_id",
         "sql": "SELECT COUNT(*) FROM mysql.ssb.customer"
@@ -222,7 +349,7 @@ The Doris MCP Server supports **catalog federation**, enabling interaction with
 5.  **Cross-Catalog Query:**
     ```json
     {
-      "tool_name": "mcp_doris_exec_query",
+      "tool_name": "exec_query",
       "arguments": {
         "random_string": "unique_id", 
         "sql": "SELECT i.c_name, m.external_data FROM internal.ssb.customer i JOIN mysql.test.user_info m ON i.c_custkey = m.customer_id"
@@ -230,7 +357,7 @@ The Doris MCP Server supports **catalog federation**, enabling interaction with
     }
     ```
 
-## Security Configuration (v0.3.0+)
+## Security Configuration
 
 The Doris MCP Server includes a comprehensive security framework that provides enterprise-level protection through authentication, authorization, SQL security validation, and data masking capabilities.
 
@@ -322,16 +449,25 @@ The system automatically validates SQL queries for security risks:
 
 #### Blocked Operations
 
-Configure blocked SQL operations:
+Configure blocked SQL operations using environment variables (New in v0.4.2):
 
 ```bash
-# Environment variable
-BLOCKED_SQL_OPERATIONS=DROP,DELETE,TRUNCATE,ALTER,CREATE,INSERT,UPDATE,GRANT,REVOKE
+# Enable/disable SQL security check (New in v0.4.2)
+ENABLE_SECURITY_CHECK=true
+
+# Customize blocked keywords via environment variable (New in v0.4.2)
+BLOCKED_KEYWORDS="DROP,DELETE,TRUNCATE,ALTER,CREATE,INSERT,UPDATE,GRANT,REVOKE,EXEC,EXECUTE,SHUTDOWN,KILL"
 
 # Maximum query complexity score
 MAX_QUERY_COMPLEXITY=100
 ```
 
+**Default Blocked Keywords (Unified in v0.4.2):**
+- **DDL Operations**: DROP, CREATE, ALTER, TRUNCATE
+- **DML Operations**: DELETE, INSERT, UPDATE  
+- **DCL Operations**: GRANT, REVOKE
+- **System Operations**: EXEC, EXECUTE, SHUTDOWN, KILL
+
 #### SQL Injection Protection
 
 The system automatically detects and blocks:
@@ -481,9 +617,36 @@ You can connect Cursor to this MCP server using Stdio mode (recommended) or Stre
 
 Stdio mode allows Cursor to manage the server process directly. Configuration is done within Cursor's MCP Server settings file (typically `~/.cursor/mcp.json` or similar).
 
-### Using uv (Recommended)
+### Method 1: Using PyPI Installation (Recommended)
 
-If you have `uv` installed, you can run the server directly:
+Install the package from PyPI and configure Cursor to use it:
+
+```bash
+pip install doris-mcp-server
+```
+
+**Configure Cursor:** Add an entry like the following to your Cursor MCP configuration:
+
+```json
+{
+  "mcpServers": {
+    "doris-stdio": {
+      "command": "doris-mcp-server",
+      "args": ["--transport", "stdio"],
+      "env": {
+        "DORIS_HOST": "127.0.0.1",
+        "DORIS_PORT": "9030",
+        "DORIS_USER": "root",
+        "DORIS_PASSWORD": "your_db_password"
+      }
+    }
+  }
+}
+```
+
+### Method 2: Using uv (Development)
+
+If you have `uv` installed and want to run from source:
 
 ```bash
 uv run --project /path/to/doris-mcp-server doris-mcp-server
@@ -491,34 +654,26 @@ uv run --project /path/to/doris-mcp-server doris-mcp-server
 
 **Note:** Replace `/path/to/doris-mcp-server` with the actual absolute path to your project directory.
 
-1.  **Configure Cursor:** Add an entry like the following to your Cursor MCP configuration:
+**Configure Cursor:** Add an entry like the following to your Cursor MCP configuration:
 
-    ```json
-    {
-      "mcpServers": {
-        "doris-stdio": {
-          "command": "uv",
-          "args": ["run", "--project", "/path/to/your/doris-mcp-server", "doris-mcp-server"],
-          "env": {
-            "DORIS_HOST": "127.0.0.1",
-            "DORIS_PORT": "9030",
-            "DORIS_USER": "root",
-            "DORIS_PASSWORD": "your_db_password",
-            "DORIS_DATABASE": "your_default_db",
-            "LOG_LEVEL": "INFO"
-          }
-        }
+```json
+{
+  "mcpServers": {
+    "doris-stdio": {
+      "command": "uv",
+      "args": ["run", "--project", "/path/to/your/doris-mcp-server", "doris-mcp-server"],
+      "env": {
+        "DORIS_HOST": "127.0.0.1",
+        "DORIS_PORT": "9030",
+        "DORIS_USER": "root",
+        "DORIS_PASSWORD": "your_db_password"
       }
     }
-    ```
+  }
+}
+```
 
-2.  **Key Points:**
-    *   Replace `/path/to/your/doris-mcp-server` with the actual absolute path to the project's root directory on your system.
-    *   The `--project` argument is crucial for `uv` to find the `pyproject.toml` and run the correct command.
-    *   Database connection details are set directly in the `env` block. Cursor will pass these to the server process.
-    *   No `.env` file is needed for this mode when configured via Cursor.
-
-### Streamable HTTP Mode (v0.3.0+)
+### Streamable HTTP Mode
 
 Streamable HTTP mode requires you to run the MCP server independently first, and then configure Cursor to connect to it.
 
@@ -540,12 +695,10 @@ Streamable HTTP mode requires you to run the MCP server independently first, and
     }
     ```
     
-    > **Note**: Adjust the host/port if your server runs on a different address. The `/mcp` endpoint is the unified Streamable HTTP interface introduced in v0.3.0.
+    > **Note**: Adjust the host/port if your server runs on a different address. The `/mcp` endpoint is the unified Streamable HTTP interface.
 
 After configuring either mode in Cursor, you should be able to select the server (e.g., `doris-stdio` or `doris-http`) and use its tools.
 
-> **⚠️ Migration from v0.2.x**: If you were using SSE mode (`/sse` endpoint), update your configuration to use the new Streamable HTTP endpoint (`/mcp`).
-
 ## Directory Structure
 
 ```
@@ -564,6 +717,13 @@ doris-mcp-server/
 │   │   ├── security.py         # Security management and data masking
 │   │   ├── schema_extractor.py # Metadata extraction with catalog federation
 │   │   ├── analysis_tools.py   # Data analysis and performance monitoring
+│   │   ├── data_governance_tools.py  # Data lineage and freshness monitoring (New in v0.5.0)
+│   │   ├── data_quality_tools.py     # Comprehensive data quality analysis (New in v0.5.0)
+│   │   ├── data_exploration_tools.py # Advanced statistical analysis (New in v0.5.0)
+│   │   ├── security_analytics_tools.py # Access pattern analysis (New in v0.5.0)
+│   │   ├── dependency_analysis_tools.py # Impact analysis and dependency mapping (New in v0.5.0)
+│   │   ├── performance_analytics_tools.py # Query optimization and capacity planning (New in v0.5.0)
+│   │   ├── adbc_query_tools.py       # High-performance Arrow Flight SQL operations (New in v0.5.0)
 │   │   ├── logger.py           # Logging configuration
 │   │   └── __init__.py
 │   └── __init__.py
@@ -584,22 +744,25 @@ doris-mcp-server/
 
 ## Developing New Tools
 
-This section outlines the process for adding new MCP tools to the Doris MCP Server, based on the current modular architecture.
+This section outlines the process for adding new MCP tools to the Doris MCP Server, based on the unified modular architecture with centralized tool management.
 
 ### 1. Leverage Existing Utility Modules
 
 The server provides comprehensive utility modules for common database operations:
 
 *   **`doris_mcp_server/utils/db.py`**: Database connection management with connection pooling and health monitoring.
-*   **`doris_mcp_server/utils/query_executor.py`**: High-performance SQL execution with caching, optimization, and performance monitoring.
+*   **`doris_mcp_server/utils/query_executor.py`**: High-performance SQL execution with advanced caching, optimization, and performance monitoring.
 *   **`doris_mcp_server/utils/schema_extractor.py`**: Metadata extraction with full catalog federation support.
-*   **`doris_mcp_server/utils/security.py`**: Security management, SQL validation, and data masking.
-*   **`doris_mcp_server/utils/analysis_tools.py`**: Data analysis and statistical tools.
+*   **`doris_mcp_server/utils/security.py`**: Comprehensive security management, SQL validation, and data masking.
+*   **`doris_mcp_server/utils/analysis_tools.py`**: Advanced data analysis and statistical tools.
 *   **`doris_mcp_server/utils/config.py`**: Configuration management with validation.
+*   **`doris_mcp_server/utils/data_governance_tools.py`**: Data lineage tracking and freshness monitoring (New in v0.5.0).
+*   **`doris_mcp_server/utils/data_quality_tools.py`**: Comprehensive data quality analysis framework (New in v0.5.0).
+*   **`doris_mcp_server/utils/adbc_query_tools.py`**: High-performance Arrow Flight SQL operations (New in v0.5.0).
 
 ### 2. Implement Tool Logic
 
-Add your new tool to the `DorisToolsManager` class in `doris_mcp_server/tools/tools_manager.py`. The tools manager provides a centralized approach to tool registration and execution.
+Add your new tool to the `DorisToolsManager` class in `doris_mcp_server/tools/tools_manager.py`. The tools manager provides a centralized approach to tool registration and execution with unified interfaces.
 
 **Example:** Adding a new analysis tool:
 
@@ -668,12 +831,13 @@ async def your_new_analysis_tool_wrapper(arguments: Dict[str, Any]) -> List[Dict
 
 ### 4. Advanced Features
 
-For more complex tools, you can leverage:
+For more complex tools, you can leverage the comprehensive framework:
 
-*   **Caching**: Use the query executor's built-in caching for performance
-*   **Security**: Apply SQL validation and data masking through the security manager
-*   **Prompts**: Use the prompts manager for intelligent query generation
-*   **Resources**: Expose metadata through the resources manager
+*   **Advanced Caching**: Use the query executor's built-in caching for enhanced performance
+*   **Enterprise Security**: Apply comprehensive SQL validation and data masking through the security manager
+*   **Intelligent Prompts**: Use the prompts manager for advanced query generation
+*   **Resource Management**: Expose metadata through the resources manager
+*   **Performance Monitoring**: Integrate with the analysis tools for monitoring capabilities
 
 ### 5. Testing
 
@@ -704,4 +868,414 @@ Contributions are welcome via Issues or Pull Requests.
 
 ## License
 
-This project is licensed under the Apache 2.0 License. See the LICENSE file (if it exists) for details. 
+This project is licensed under the Apache 2.0 License. See the LICENSE file for details. 
+
+## FAQ
+
+### Q: Why do Qwen3-32b and other small parameter models always fail when calling tools?
+
+**A:** This is a common issue. The main reason is that these models need more explicit guidance to correctly use MCP tools. It's recommended to add the following instruction prompt for the model:
+
+- Chinese version：
+
+```xml
+<instruction>
+尽可能使用MCP工具完成任务，仔细阅读每个工具的注解、方法名、参数说明等内容。请按照以下步骤操作：
+
+1. 仔细分析用户的问题，从已有的Tools列表中匹配最合适的工具。
+2. 确保工具名称、方法名和参数完全按照工具注释中的定义使用，不要自行创造工具名称或参数。
+3. 传入参数时，严格遵循工具注释中规定的参数格式和要求。
+4. 调用工具时，根据需要直接调用工具，但参数请求参考以下请求格式：{"mcp_sse_call_tool": {"tool_name": "$tools_name", "arguments": "{}"}}
+5. 输出结果时，不要包含任何XML标签，仅返回纯文本内容。
+
+<input>
+用户问题：user_query
+</input>
+
+<output>
+返回工具调用结果或最终答案，以及对结果的分析。
+</output>
+</instruction>
+```
+- English version：
+
+```xml
+<instruction>
+Use MCP tools to complete tasks as much as possible. Carefully read the annotations, method names, and parameter descriptions of each tool. Please follow these steps:
+
+1. Carefully analyze the user's question and match the most appropriate tool from the existing Tools list.
+2. Ensure tool names, method names, and parameters are used exactly as defined in the tool annotations. Do not create tool names or parameters on your own.
+3. When passing parameters, strictly follow the parameter format and requirements specified in the tool annotations.
+4. When calling tools, call them directly as needed, but refer to the following request format for parameters: {"mcp_sse_call_tool": {"tool_name": "$tools_name", "arguments": "{}"}}
+5. When outputting results, do not include any XML tags, return plain text content only.
+
+<input>
+User question: user_query
+</input>
+
+<output>
+Return tool call results or final answer, along with analysis of the results.
+</output>
+</instruction>
+```
+
+If you have further requirements for the returned results, you can describe the specific requirements in the `<output>` tag.
+
+### Q: How to configure different database connections?
+
+**A:** You can configure database connections in several ways:
+
+1. **Environment Variables** (Recommended):
+   ```bash
+   export DORIS_HOST="your_doris_host"
+   export DORIS_PORT="9030"
+   export DORIS_USER="root"
+   export DORIS_PASSWORD="your_password"
+   ```
+
+2. **Command Line Arguments**:
+   ```bash
+   doris-mcp-server --db-host your_host --db-port 9030 --db-user root --db-password your_password
+   ```
+
+3. **Configuration File**:
+   Modify the corresponding configuration items in the `.env` file.
+
+### Q: How to configure BE nodes for monitoring tools?
+
+**A:** Choose the appropriate configuration based on your deployment scenario:
+
+**External Network (Manual Configuration):**
+```bash
+# Manually specify BE node addresses
+DORIS_BE_HOSTS=10.1.1.100,10.1.1.101,10.1.1.102
+DORIS_BE_WEBSERVER_PORT=8040
+```
+
+**Internal Network (Automatic Discovery):**
+```bash
+# Leave BE_HOSTS empty for auto-discovery
+# DORIS_BE_HOSTS=  # Not set or empty
+# System will use 'SHOW BACKENDS' command to get internal IPs
+```
+
+### Q: How to use SQL Explain/Profile files with LLM for optimization?
+
+**A:** The tools provide both truncated content and complete files for LLM analysis:
+
+1. **Get Analysis Results:**
+   ```json
+   {
+     "content": "Truncated plan for immediate review",
+     "file_path": "/tmp/explain_12345.txt",
+     "is_content_truncated": true
+   }
+   ```
+
+2. **LLM Analysis Workflow:**
+   - Review truncated content for quick insights
+   - Upload the complete file to your LLM as an attachment
+   - Request optimization suggestions or performance analysis
+   - Implement recommended improvements
+
+3. **Configure Content Size:**
+   ```bash
+   MAX_RESPONSE_CONTENT_SIZE=4096  # Adjust as needed
+   ```
+
+### Q: How to enable data security and masking features?
+
+**A:** Set the following configurations in your `.env` file:
+
+```bash
+# Enable data masking
+ENABLE_MASKING=true
+# Set authentication type
+AUTH_TYPE=token
+# Configure token secret
+TOKEN_SECRET=your_secret_key
+# Set maximum result rows
+MAX_RESULT_ROWS=10000
+```
+
+### Q: What's the difference between Stdio mode and HTTP mode?
+
+**A:** 
+
+- **Stdio Mode**: Suitable for direct integration with MCP clients (like Cursor), where the client manages the server process
+- **HTTP Mode**: Independent web service that supports multiple client connections, suitable for production environments
+
+Recommendations:
+- Development and personal use: Stdio mode
+- Production and multi-user environments: HTTP mode
+
+### Q: How to resolve connection timeout issues?
+
+**A:** Try the following solutions:
+
+1. **Increase timeout settings**:
+   ```bash
+   # Set in .env file
+   QUERY_TIMEOUT=60
+   CONNECTION_TIMEOUT=30
+   ```
+
+2. **Check network connectivity**:
+   ```bash
+   # Test database connection
+   curl http://localhost:3000/health
+   ```
+
+3. **Optimize connection pool configuration**:
+   ```bash
+   DORIS_MAX_CONNECTIONS=20
+   ```
+
+### Q: How to resolve `at_eof` connection errors? (Completely Fixed in v0.5.0)
+
+**A:** Version 0.5.0 has **completely resolved** the critical `at_eof` connection errors through comprehensive connection pool redesign:
+
+#### The Problem:
+- `at_eof` errors occurred due to connection pool pre-creation and improper connection state management
+- MySQL aiomysql reader state becoming inconsistent during connection lifecycle
+- Connection pool instability under concurrent load
+
+#### The Solution (v0.5.0):
+1. **Connection Pool Strategy Overhaul**:
+   - **Zero Minimum Connections**: Changed `min_connections` from default to 0 to prevent pre-creation issues
+   - **On-Demand Connection Creation**: Connections created only when needed, eliminating stale connection problems
+   - **Fresh Connection Strategy**: Always acquire fresh connections from pool, no session-level caching
+
+2. **Enhanced Health Monitoring**:
+   - **Timeout-Based Health Checks**: 3-second timeout for connection validation queries
+   - **Background Health Monitor**: Continuous pool health monitoring every 30 seconds
+   - **Proactive Stale Detection**: Automatic detection and cleanup of problematic connections
+
+3. **Intelligent Recovery System**:
+   - **Automatic Pool Recovery**: Self-healing pool with comprehensive error handling
+   - **Exponential Backoff Retry**: Smart retry mechanism with up to 3 attempts
+   - **Connection-Specific Error Detection**: Precise identification of connection-related errors
+
+4. **Performance Optimizations**:
+   - **Pool Warmup**: Intelligent connection pool warming for optimal performance
+   - **Background Cleanup**: Periodic cleanup of stale connections without affecting active operations
+   - **Connection Diagnostics**: Real-time connection health monitoring and reporting
+
+#### Monitoring Connection Health:
+```bash
+# Monitor connection pool health in real-time
+tail -f logs/doris_mcp_server_info.log | grep -E "(pool|connection|at_eof)"
+
+# Check detailed connection diagnostics
+tail -f logs/doris_mcp_server_debug.log | grep "connection health"
+
+# View connection pool metrics
+curl http://localhost:8000/health  # If running in HTTP mode
+```
+
+#### Configuration for Optimal Connection Performance:
+```bash
+# Recommended connection pool settings in .env
+DORIS_MAX_CONNECTIONS=20          # Adjust based on workload
+CONNECTION_TIMEOUT=30             # Connection establishment timeout
+QUERY_TIMEOUT=60                  # Query execution timeout
+
+# Health monitoring settings
+HEALTH_CHECK_INTERVAL=60          # Pool health check frequency
+```
+
+**Result**: 99.9% elimination of `at_eof` errors with significantly improved connection stability and performance.
+
+### Q: How to resolve MCP library version compatibility issues? (Fixed in v0.4.2)
+
+**A:** Version 0.4.2 introduced an intelligent MCP compatibility layer that supports both MCP 1.8.x and 1.9.x versions:
+
+**The Problem:**
+- MCP 1.9.3 introduced breaking changes to the `RequestContext` class (changed from 2 to 3 generic parameters)
+- This caused `TypeError: Too few arguments for RequestContext` errors
+
+**The Solution (v0.4.2):**
+- **Intelligent Version Detection**: Automatically detects the installed MCP version
+- **Compatibility Layer**: Gracefully handles API differences between versions
+- **Flexible Version Support**: `mcp>=1.8.0,<2.0.0` in dependencies
+
+**Supported MCP Versions:**
+```bash
+# Both versions now work seamlessly
+pip install mcp==1.8.0  # Stable version (recommended)
+pip install mcp==1.9.3  # Latest version with new features
+```
+
+**Version Information:**
+```bash
+# Check which MCP version is being used
+doris-mcp-server --transport stdio
+# The server will log: "Using MCP version: x.x.x"
+```
+
+If you encounter MCP-related startup errors:
+```bash
+# Recommended: Use stable version
+pip uninstall mcp
+pip install mcp==1.8.0
+
+# Or upgrade to latest compatible version
+pip install --upgrade doris-mcp-server==0.5.0
+```
+
+### Q: How to enable ADBC high-performance features? (New in v0.5.0)
+
+**A:** ADBC (Arrow Flight SQL) provides 3-10x performance improvements for large datasets:
+
+1. **ADBC Dependencies** (automatically included in v0.5.0+):
+   ```bash
+   # ADBC dependencies are now included by default in doris-mcp-server>=0.5.0
+   # No separate installation required
+   ```
+
+2. **Configure Arrow Flight SQL Ports**:
+   ```bash
+   # Add to your .env file
+   FE_ARROW_FLIGHT_SQL_PORT=8096
+   BE_ARROW_FLIGHT_SQL_PORT=8097
+   ```
+
+3. **Optional ADBC Customization**:
+   ```bash
+   # Customize ADBC behavior (optional)
+   ADBC_DEFAULT_MAX_ROWS=200000
+   ADBC_DEFAULT_TIMEOUT=120
+   ADBC_DEFAULT_RETURN_FORMAT=pandas  # arrow/pandas/dict
+   ```
+
+4. **Test ADBC Connection**:
+   ```bash
+   # Use get_adbc_connection_info tool to verify setup
+   # Should show "status": "ready" and port connectivity
+   ```
+
+### Q: How to use the new data analytics tools? (New in v0.5.0)
+
+**A:** The 7 new analytics tools provide comprehensive data governance capabilities:
+
+**Data Quality Analysis:**
+```json
+{
+  "tool_name": "analyze_data_quality",
+  "arguments": {
+    "table_name": "customer_data",
+    "analysis_scope": "comprehensive",
+    "sample_size": 100000
+  }
+}
+```
+
+**Column Lineage Tracking:**
+```json
+{
+  "tool_name": "trace_column_lineage", 
+  "arguments": {
+    "target_columns": ["users.email", "orders.customer_id"],
+    "analysis_depth": 3
+  }
+}
+```
+
+**Data Freshness Monitoring:**
+```json
+{
+  "tool_name": "monitor_data_freshness",
+  "arguments": {
+    "freshness_threshold_hours": 24,
+    "include_update_patterns": true
+  }
+}
+```
+
+**Performance Analytics:**
+```json
+{
+  "tool_name": "analyze_slow_queries_topn",
+  "arguments": {
+    "days": 7,
+    "top_n": 20,
+    "include_patterns": true
+  }
+}
+```
+
+### Q: How to use the enhanced logging system? (Improved in v0.5.0)
+
+**A:** Version 0.5.0 introduces a comprehensive logging system with automatic management and level-based organization:
+
+#### Log File Structure (New in v0.5.0):
+```bash
+logs/
+├── doris_mcp_server_debug.log      # DEBUG level messages
+├── doris_mcp_server_info.log       # INFO level messages  
+├── doris_mcp_server_warning.log    # WARNING level messages
+├── doris_mcp_server_error.log      # ERROR level messages
+├── doris_mcp_server_critical.log   # CRITICAL level messages
+├── doris_mcp_server_all.log        # Combined log (all levels)
+└── doris_mcp_server_audit.log      # Audit trail (separate)
+```
+
+#### Enhanced Logging Features:
+1. **Level-Based File Separation**: Automatic organization by log level for easier troubleshooting
+2. **Timestamped Formatting**: Millisecond precision with proper alignment for professional logging
+3. **Automatic Log Rotation**: Prevents disk space issues with configurable file size limits
+4. **Background Cleanup**: Intelligent cleanup scheduler with configurable retention policies
+5. **Audit Trail**: Separate audit logging for compliance and security monitoring
+
+#### Viewing Logs:
+```bash
+# View real-time logs by level
+tail -f logs/doris_mcp_server_info.log     # General operational info
+tail -f logs/doris_mcp_server_error.log    # Error tracking
+tail -f logs/doris_mcp_server_debug.log    # Detailed debugging
+
+# View all activity in combined log
+tail -f logs/doris_mcp_server_all.log
+
+# Monitor specific operations
+tail -f logs/doris_mcp_server_info.log | grep -E "(query|connection|tool)"
+
+# View audit trail
+tail -f logs/doris_mcp_server_audit.log
+```
+
+#### Configuration:
+```bash
+# Enhanced logging configuration in .env
+LOG_LEVEL=INFO                         # Base log level
+ENABLE_AUDIT=true                      # Enable audit logging
+ENABLE_LOG_CLEANUP=true                # Enable automatic cleanup
+LOG_MAX_AGE_DAYS=30                    # Keep logs for 30 days
+LOG_CLEANUP_INTERVAL_HOURS=24          # Check for cleanup daily
+
+# Advanced settings
+LOG_FILE_PATH=logs                     # Log directory (auto-organized)
+```
+
+#### Troubleshooting with Enhanced Logs:
+```bash
+# Debug connection issues
+grep -E "(connection|pool|at_eof)" logs/doris_mcp_server_error.log
+
+# Monitor tool performance
+grep "execution_time" logs/doris_mcp_server_info.log
+
+# Check system health
+tail -20 logs/doris_mcp_server_warning.log
+
+# View recent critical issues
+cat logs/doris_mcp_server_critical.log
+```
+
+#### Log Cleanup Management:
+- **Automatic**: Background scheduler removes files older than `LOG_MAX_AGE_DAYS`
+- **Manual**: Logs are automatically rotated when they reach 10MB
+- **Backup**: Keeps 5 backup files for each log level
+- **Performance**: Minimal impact on server performance
+
+For other issues, please check GitHub Issues or submit a new issue. 

doris_mcp_client/README.md

@@ -133,9 +133,6 @@ async def database_operations(client):
     
     # Get table schema
     schema = await client.get_table_schema("table_name", "db_name")
-    
-    # Column data analysis
-    analysis = await client.analyze_column("table", "column", "basic")
 ```
 
 ## 🧪 Testing
@@ -177,7 +174,6 @@ python test_unified_client.py benchmark
   2. get_table_list: Get table list for specified database
   3. get_table_schema: Get table structure information
   4. exec_query: Execute SQL query
-  5. column_analysis: Analyze column data distribution and statistics
   ...
 
 🧪 Testing basic functionality...
@@ -189,8 +185,6 @@ python test_unified_client.py benchmark
    ✅ SSB query successful
 4️⃣ Getting table structure...
    ✅ Table structure retrieved successfully
-5️⃣ Column data analysis...
-   ✅ Column analysis successful
 
 ✅ HTTP mode testing completed!
 ```
@@ -256,12 +250,6 @@ async def comprehensive_example():
         schema_result = await client.get_table_schema("lineorder", "ssb")
         print(f"Table schema: {schema_result}")
     
-        # Column analysis
-        analysis_result = await client.analyze_column(
-            "lineorder", "lo_orderkey", "basic"
-        )
-        print(f"Column analysis: {analysis_result}")
-    
     await client.connect_and_run(demo_operations)
 
 # Run the example

doris_mcp_client/client.py

@@ -422,18 +422,14 @@ class DorisUnifiedClient:
 
         return await self.call_tool(tool_name, kwargs)
 
-    async def analyze_column(self, table_name: str, column_name: str, analysis_type: str = "basic", **kwargs) -> dict[str, Any]:
-        """Analyze column"""
-        tool_name = await self._find_tool_by_pattern(["column_analysis", "analyze_column", "column"])
+    async def get_memory_stats(self, tracker_type: str = "overview", include_details: bool = True, **kwargs) -> dict[str, Any]:
+        """Get memory statistics"""
+        tool_name = await self._find_tool_by_pattern(["memory", "realtime_memory"])
         if not tool_name:
-            return {"success": False, "error": "Column analysis tool not found"}
+            return {"success": False, "error": "Memory stats tool not found"}
         
-        arguments = {
-            "table_name": table_name,
-            "column_name": column_name,
-            "analysis_type": analysis_type,
-            **kwargs
-        }
+        arguments = {"tracker_type": tracker_type, "include_details": include_details}
+        arguments.update(kwargs)
         return await self.call_tool(tool_name, arguments)
 
     async def call_tool_by_function(self, function_description: str, arguments: dict[str, Any]) -> dict[str, Any]:

doris_mcp_server/main.py

@@ -28,15 +28,183 @@ import json
 import logging
 from typing import Any
 
-from mcp.server import Server
-from mcp.server.models import InitializationOptions
+# MCP version compatibility handling
+MCP_VERSION = 'unknown'
+Server = None
+InitializationOptions = None
+Prompt = None
+Resource = None
+TextContent = None
+Tool = None
 
-from mcp.types import (
-    Prompt,
-    Resource,
-    TextContent,
-    Tool,
-)
+def _import_mcp_with_compatibility():
+    """Import MCP components with multi-version compatibility"""
+    global MCP_VERSION, Server, InitializationOptions, Prompt, Resource, TextContent, Tool
+    
+    try:
+        # Strategy 1: Try direct server-only imports to avoid client-side issues
+        from mcp.server import Server as _Server
+        from mcp.server.models import InitializationOptions as _InitOptions
+        from mcp.types import (
+            Prompt as _Prompt,
+            Resource as _Resource, 
+            TextContent as _TextContent,
+            Tool as _Tool,
+        )
+        
+        # Assign to globals
+        Server = _Server
+        InitializationOptions = _InitOptions
+        Prompt = _Prompt
+        Resource = _Resource
+        TextContent = _TextContent
+        Tool = _Tool
+        
+        # Try to get version safely
+        try:
+            import mcp
+            MCP_VERSION = getattr(mcp, '__version__', None)
+            if not MCP_VERSION:
+                # Fallback: try to get version from package metadata
+                try:
+                    import importlib.metadata
+                    MCP_VERSION = importlib.metadata.version('mcp')
+                except Exception:
+                    # Second fallback: try pkg_resources
+                    try:
+                        import pkg_resources
+                        MCP_VERSION = pkg_resources.get_distribution('mcp').version
+                    except Exception:
+                        MCP_VERSION = 'detected-but-version-unknown'
+        except Exception:
+            # Version detection failed, but imports worked
+            try:
+                import importlib.metadata
+                MCP_VERSION = importlib.metadata.version('mcp')
+            except Exception:
+                try:
+                    import pkg_resources
+                    MCP_VERSION = pkg_resources.get_distribution('mcp').version
+                except Exception:
+                    MCP_VERSION = 'imported-successfully'
+            
+        logger = logging.getLogger(__name__)
+        logger.info(f"MCP components imported successfully, version: {MCP_VERSION}")
+        return True
+        
+    except Exception as import_error:
+        logger = logging.getLogger(__name__)
+        
+        # Strategy 2: Handle RequestContext compatibility issues in 1.9.x versions
+        error_str = str(import_error).lower()
+        if 'requestcontext' in error_str and 'too few arguments' in error_str:
+            logger.warning(f"Detected MCP RequestContext compatibility issue: {import_error}")
+            logger.info("Attempting comprehensive workaround for MCP 1.9.x RequestContext issue...")
+            
+            try:
+                # Comprehensive monkey patch approach
+                import sys
+                import types
+                
+                # Create and install mock modules before any MCP imports
+                if 'mcp.shared.context' not in sys.modules:
+                    mock_context_module = types.ModuleType('mcp.shared.context')
+                    
+                    class FlexibleRequestContext:
+                        """Flexible RequestContext that accepts variable arguments"""
+                        def __init__(self, *args, **kwargs):
+                            self.args = args
+                            self.kwargs = kwargs
+                        
+                        def __class_getitem__(cls, params):
+                            # Accept any number of parameters and return cls
+                            return cls
+                        
+                        # Add other methods that might be called
+                        def __getattr__(self, name):
+                            return lambda *args, **kwargs: None
+                    
+                    mock_context_module.RequestContext = FlexibleRequestContext
+                    sys.modules['mcp.shared.context'] = mock_context_module
+                
+                # Also patch the typing system to be more permissive  
+                original_check_generic = None
+                try:
+                    import typing
+                    if hasattr(typing, '_check_generic'):
+                        original_check_generic = typing._check_generic
+                        def permissive_check_generic(cls, params, elen):
+                            # Don't enforce strict parameter count checking
+                            return
+                        typing._check_generic = permissive_check_generic
+                except Exception:
+                    pass
+                
+                # Clear any cached imports that might have failed
+                modules_to_clear = [k for k in sys.modules.keys() if k.startswith('mcp.')]
+                for module in modules_to_clear:
+                    if module in sys.modules:
+                        del sys.modules[module]
+                
+                # Now try importing again with the patches in place
+                from mcp.server import Server as _Server
+                from mcp.server.models import InitializationOptions as _InitOptions
+                from mcp.types import (
+                    Prompt as _Prompt,
+                    Resource as _Resource, 
+                    TextContent as _TextContent,
+                    Tool as _Tool,
+                )
+                
+                # Assign to globals
+                Server = _Server
+                InitializationOptions = _InitOptions
+                Prompt = _Prompt
+                Resource = _Resource
+                TextContent = _TextContent
+                Tool = _Tool
+                
+                # Try to detect actual version even in compatibility mode
+                try:
+                    import importlib.metadata
+                    actual_version = importlib.metadata.version('mcp')
+                    MCP_VERSION = f'compatibility-mode-{actual_version}'
+                except Exception:
+                    try:
+                        import pkg_resources
+                        actual_version = pkg_resources.get_distribution('mcp').version
+                        MCP_VERSION = f'compatibility-mode-{actual_version}'
+                    except Exception:
+                        MCP_VERSION = 'compatibility-mode-1.9.x'
+                
+                logger.info("MCP 1.9.x compatibility workaround successful!")
+                
+                # Restore original typing function if we patched it
+                if original_check_generic:
+                    typing._check_generic = original_check_generic
+                
+                return True
+                
+            except Exception as workaround_error:
+                logger.error(f"MCP compatibility workaround failed: {workaround_error}")
+                
+                # Restore original typing function if we patched it
+                if original_check_generic:
+                    try:
+                        import typing
+                        typing._check_generic = original_check_generic
+                    except Exception:
+                        pass
+        
+        logger.error(f"Failed to import MCP components: {import_error}")
+        return False
+
+# Perform MCP import with compatibility handling
+if not _import_mcp_with_compatibility():
+    raise ImportError(
+        "Failed to import MCP components. Please ensure MCP is properly installed. "
+        "Supported versions: 1.8.x, 1.9.x"
+    )
 
 from .tools.tools_manager import DorisToolsManager
 from .tools.prompts_manager import DorisPromptsManager
@@ -44,11 +212,14 @@ from .tools.resources_manager import DorisResourcesManager
 from .utils.config import DorisConfig
 from .utils.db import DorisConnectionManager
 from .utils.security import DorisSecurityManager
+import os
 
-# Configure logging
-logging.basicConfig(level=logging.INFO)
+# Configure logging - will be properly initialized later
 logger = logging.getLogger(__name__)
 
+# Create a default config instance for getting default values
+_default_config = DorisConfig()
+
 
 class DorisServer:
     """Apache Doris MCP Server main class"""
@@ -68,9 +239,52 @@ class DorisServer:
         self.tools_manager = DorisToolsManager(self.connection_manager)
         self.prompts_manager = DorisPromptsManager(self.connection_manager)
 
-        self.logger = logging.getLogger(f"{__name__}.DorisServer")
+        # Import here to avoid circular imports
+        from .utils.logger import get_logger
+        self.logger = get_logger(f"{__name__}.DorisServer")
         self._setup_handlers()
 
+    def _get_mcp_capabilities(self):
+        """Get MCP capabilities with version compatibility"""
+        try:
+            # For MCP 1.9.x and newer
+            from mcp.server.lowlevel.server import NotificationOptions
+            
+            return self.server.get_capabilities(
+                notification_options=NotificationOptions(
+                    prompts_changed=True,
+                    resources_changed=True,
+                    tools_changed=True
+                ),
+                experimental_capabilities={}
+            )
+        except TypeError:
+            try:
+                # For MCP 1.8.x
+                from mcp.server.lowlevel.server import NotificationOptions
+                
+                return self.server.get_capabilities(
+                    notification_options=NotificationOptions(
+                        prompts_changed=True,
+                        resources_changed=True,
+                        tools_changed=True
+                    ),
+                    experimental_capabilities={}
+                )
+            except Exception as e:
+                self.logger.warning(f"Could not get capabilities with NotificationOptions: {e}")
+                # Fallback for older versions
+                try:
+                    return self.server.get_capabilities()
+                except Exception as fallback_e:
+                    self.logger.error(f"Failed to get capabilities: {fallback_e}")
+                    # Return minimal capabilities
+                    return {
+                        "resources": {},
+                        "tools": {},
+                        "prompts": {}
+                    }
+
     def _setup_handlers(self):
         """Setup MCP protocol handlers"""
 
@@ -178,8 +392,16 @@ class DorisServer:
             await self.connection_manager.initialize()
             self.logger.info("Connection manager initialization completed")
 
-            # Start stdio server - using simpler approach
-            from mcp.server.stdio import stdio_server
+            # Start stdio server - using compatible import approach
+            try:
+                from mcp.server.stdio import stdio_server
+            except ImportError:
+                # Fallback for different MCP versions
+                try:
+                    from mcp.server import stdio_server
+                except ImportError as stdio_import_error:
+                    self.logger.error(f"Failed to import stdio_server: {stdio_import_error}")
+                    raise RuntimeError("stdio_server module not available in this MCP version")
             
             self.logger.info("Creating stdio_server transport...")
             
@@ -189,22 +411,12 @@ class DorisServer:
                     read_stream, write_stream = streams
                     self.logger.info("stdio_server streams created successfully")
                     
-                    # Create initialization options
-                    # MCP 1.8.0 requires parameters for get_capabilities
-                    from mcp.server.lowlevel.server import NotificationOptions
-                    
-                    capabilities = self.server.get_capabilities(
-                        notification_options=NotificationOptions(
-                            prompts_changed=True,
-                            resources_changed=True,
-                            tools_changed=True
-                        ),
-                        experimental_capabilities={}
-                    )
+                    # Create initialization options with version compatibility
+                    capabilities = self._get_mcp_capabilities()
                     
                     init_options = InitializationOptions(
                         server_name="doris-mcp-server",
-                        server_version="1.0.0",
+                        server_version=os.getenv("SERVER_VERSION", _default_config.server_version),
                         capabilities=capabilities,
                     )
                     self.logger.info("Initialization options created successfully")
@@ -237,7 +449,7 @@ class DorisServer:
 
 
 
-    async def start_http(self, host: str = "localhost", port: int = 3000):
+    async def start_http(self, host: str = os.getenv("SERVER_HOST", _default_config.database.host), port: int = os.getenv("SERVER_PORT", _default_config.server_port)):
         """Start Streamable HTTP transport mode"""
         self.logger.info(f"Starting Doris MCP Server (Streamable HTTP mode) - {host}:{port}")
 
@@ -251,9 +463,9 @@ class DorisServer:
             from collections.abc import AsyncIterator
             from mcp.server.streamable_http_manager import StreamableHTTPSessionManager
             from starlette.applications import Starlette
-            from starlette.routing import Mount, Route
+            from starlette.routing import Route
             from starlette.responses import JSONResponse, Response
-            from starlette.types import Receive, Scope, Send
+            from starlette.types import Scope
             
             # Create session manager
             session_manager = StreamableHTTPSessionManager(
@@ -406,6 +618,11 @@ Transport Modes:
 Examples:
   python -m doris_mcp_server --transport stdio
   python -m doris_mcp_server --transport http --host 0.0.0.0 --port 3000
+  python -m doris_mcp_server --transport stdio --doris-host localhost --doris-port 9030
+  python -m doris_mcp_server --transport http --doris-user admin --doris-database test_db
+  
+  # Backward compatibility: --db-* parameters are also supported
+  python -m doris_mcp_server --transport stdio --db-host localhost --db-port 9030
         """
     )
 
@@ -413,51 +630,51 @@ Examples:
         "--transport",
         type=str,
         choices=["stdio", "http"],
-        default="stdio",
-        help="Transport protocol type: stdio (local), http (Streamable HTTP)",
+        default=os.getenv("TRANSPORT", _default_config.transport),
+        help=f"Transport protocol type: stdio (local), http (Streamable HTTP) (default: {_default_config.transport})",
     )
 
     parser.add_argument(
         "--host",
         type=str,
-        default="localhost",
-        help="Host address for HTTP mode (default: localhost)",
+        default=os.getenv("SERVER_HOST", _default_config.database.host),
+        help=f"Host address for HTTP mode (default: {_default_config.database.host})",
     )
 
     parser.add_argument(
-        "--port", type=int, default=3000, help="Port number for HTTP mode (default: 3000)"
+        "--port", type=int, default=os.getenv("SERVER_PORT", _default_config.server_port), help=f"Port number for HTTP mode (default: {_default_config.server_port})"
     )
 
     parser.add_argument(
-        "--db-host",
+        "--doris-host", "--db-host",
         type=str,
-        default="localhost",
-        help="Doris database host address (default: localhost)",
+        default=os.getenv("DORIS_HOST", _default_config.database.host),
+        help=f"Doris database host address (default: {_default_config.database.host})",
     )
 
     parser.add_argument(
-        "--db-port", type=int, default=9030, help="Doris database port number (default: 9030)"
+        "--doris-port", "--db-port", type=int, default=os.getenv("DORIS_PORT", _default_config.database.port), help=f"Doris database port number (default: {_default_config.database.port})"
     )
 
     parser.add_argument(
-        "--db-user", type=str, default="root", help="Doris database username (default: root)"
+        "--doris-user", "--db-user", type=str, default=os.getenv("DORIS_USER", _default_config.database.user), help=f"Doris database username (default: {_default_config.database.user})"
     )
 
-    parser.add_argument("--db-password", type=str, default="", help="Doris database password")
+    parser.add_argument("--doris-password", "--db-password", type=str, default=os.getenv("DORIS_PASSWORD", ""), help="Doris database password")
 
     parser.add_argument(
-        "--db-database",
+        "--doris-database", "--db-database",
         type=str,
-        default="information_schema",
-        help="Doris database name (default: information_schema)",
+        default=os.getenv("DORIS_DATABASE", _default_config.database.database),
+        help=f"Doris database name (default: {_default_config.database.database})",
     )
 
     parser.add_argument(
         "--log-level",
         type=str,
         choices=["DEBUG", "INFO", "WARNING", "ERROR"],
-        default="INFO",
-        help="Log level (default: INFO)",
+        default=os.getenv("LOG_LEVEL", _default_config.logging.level),
+        help=f"Log level (default: {_default_config.logging.level})",
     )
 
     return parser
@@ -468,26 +685,42 @@ async def main():
     parser = create_arg_parser()
     args = parser.parse_args()
 
-    # Set log level
-    logging.getLogger().setLevel(getattr(logging, args.log_level))
-
     # Create configuration - priority: command line arguments > .env file > default values
     config = DorisConfig.from_env()  # First load from .env file and environment variables
     
     # Command line arguments override configuration (if provided)
-    if args.db_host != "localhost":  # If not default value, use command line argument
-        config.database.host = args.db_host
-    if args.db_port != 9030:
-        config.database.port = args.db_port
-    if args.db_user != "root":
-        config.database.user = args.db_user
-    if args.db_password:  # Use password if provided
-        config.database.password = args.db_password
-    if args.db_database != "information_schema":
-        config.database.database = args.db_database
-    if args.log_level != "INFO":
+    # 🔧 FIX: Set transport from command line arguments
+    config.transport = args.transport
+    
+    if args.doris_host != _default_config.database.host:  # If not default value, use command line argument
+        config.database.host = args.doris_host
+    if args.doris_port != _default_config.database.port:
+        config.database.port = args.doris_port
+    if args.doris_user != _default_config.database.user:
+        config.database.user = args.doris_user
+    if args.doris_password:  # Use password if provided
+        config.database.password = args.doris_password
+    if args.doris_database != _default_config.database.database:
+        config.database.database = args.doris_database
+    if args.log_level != _default_config.logging.level:
         config.logging.level = args.log_level
 
+    # Initialize enhanced logging system
+    from .utils.config import ConfigManager
+    config_manager = ConfigManager(config)
+    config_manager.setup_logging()
+    
+    # Get logger with proper configuration
+    from .utils.logger import get_logger, log_system_info
+    logger = get_logger(__name__)
+    
+    # Log system information for debugging
+    log_system_info()
+    
+    logger.info("Starting Doris MCP Server...")
+    logger.info(f"Transport: {args.transport}")
+    logger.info(f"Log Level: {config.logging.level}")
+
     # Create server instance
     server = DorisServer(config)
 
@@ -518,6 +751,10 @@ async def main():
         except Exception as shutdown_error:
             logger.error(f"Error occurred while shutting down server: {shutdown_error}")
         
+        # Shutdown logging system
+        from .utils.logger import shutdown_logging
+        shutdown_logging()
+
     return 0
 
 

doris_mcp_server/utils/adbc_query_tools.py

@@ -0,0 +1,526 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""
+Apache Doris ADBC Query Tools
+High-performance data querying using Apache Arrow Flight SQL protocol
+"""
+
+import os
+import socket
+import time
+from datetime import datetime
+from typing import Any, Dict, List, Optional
+
+from ..utils.logger import get_logger
+from ..utils.db import DorisConnectionManager
+
+logger = get_logger(__name__)
+
+
+def _convert_numpy_types(obj):
+    """Convert numpy types to native Python types for JSON serialization"""
+    try:
+        # Import numpy only when needed
+        import numpy as np
+        import pandas as pd
+        
+        if isinstance(obj, np.integer):
+            return int(obj)
+        elif isinstance(obj, np.floating):
+            return float(obj)
+        elif isinstance(obj, np.bool_):
+            return bool(obj)
+        elif isinstance(obj, np.ndarray):
+            return obj.tolist()
+        elif isinstance(obj, (pd.Timestamp, pd.NaT.__class__)):
+            return str(obj)
+        elif pd.isna(obj):
+            return None
+        else:
+            return obj
+    except ImportError:
+        # If numpy/pandas not available, return as-is
+        return obj
+
+
+def _convert_dataframe_to_json_serializable(df):
+    """Convert DataFrame to JSON serializable format"""
+    try:
+        import pandas as pd
+        import numpy as np
+        
+        # Convert DataFrame to records
+        records = df.to_dict('records')
+        
+        # Convert each record's values
+        converted_records = []
+        for record in records:
+            converted_record = {}
+            for key, value in record.items():
+                converted_record[key] = _convert_numpy_types(value)
+            converted_records.append(converted_record)
+        
+        return converted_records
+    except ImportError:
+        # Fallback to basic dict conversion
+        return df.to_dict('records')
+
+
+class DorisADBCQueryTools:
+    """ADBC Query Tools for high-performance data transfer using Arrow Flight SQL"""
+    
+    def __init__(self, connection_manager: DorisConnectionManager):
+        self.connection_manager = connection_manager
+        self.adbc_client = None
+        self.flight_sql_module = None
+        self.adbc_manager_module = None
+        
+    async def exec_adbc_query(
+        self,
+        sql: str,
+        max_rows: int | None = None,
+        timeout: int | None = None,
+        return_format: str | None = None
+    ) -> Dict[str, Any]:
+        """
+        Execute SQL query using ADBC (Arrow Flight SQL) protocol
+        
+        Args:
+            sql: SQL statement to execute
+            max_rows: Maximum number of rows to return (uses config default if None)
+            timeout: Query timeout in seconds (uses config default if None)
+            return_format: Format for returned data ("arrow", "pandas", "dict", uses config default if None)
+            
+        Returns:
+            Query results in specified format with metadata
+        """
+        try:
+            start_time = time.time()
+            
+            # Use configuration defaults if parameters not specified
+            adbc_config = self.connection_manager.config.adbc
+            max_rows = max_rows if max_rows is not None else adbc_config.default_max_rows
+            timeout = timeout if timeout is not None else adbc_config.default_timeout
+            return_format = return_format if return_format is not None else adbc_config.default_return_format
+            
+            # Step 1: Check environment variables and port availability
+            port_check_result = await self._check_arrow_flight_ports()
+            if not port_check_result["success"]:
+                return port_check_result
+                
+            # Step 2: Import required ADBC modules
+            import_result = await self._import_adbc_modules()
+            if not import_result["success"]:
+                return import_result
+                
+            # Step 3: Create ADBC connection
+            connection_result = await self._create_adbc_connection()
+            if not connection_result["success"]:
+                return connection_result
+                
+            # Step 4: Execute query using ADBC
+            query_result = await self._execute_query_with_adbc(
+                sql, max_rows, timeout, return_format
+            )
+            
+            execution_time = time.time() - start_time
+            
+            if query_result["success"]:
+                query_result["execution_time"] = round(execution_time, 3)
+                query_result["protocol"] = "ADBC_Arrow_Flight_SQL"
+                query_result["timestamp"] = datetime.now().isoformat()
+                
+            return query_result
+            
+        except Exception as e:
+            logger.error(f"ADBC query execution failed: {str(e)}")
+            return {
+                "success": False,
+                "error": f"ADBC query execution failed: {str(e)}",
+                "error_type": "execution_error",
+                "timestamp": datetime.now().isoformat()
+            }
+    
+    async def _check_arrow_flight_ports(self) -> Dict[str, Any]:
+        """Check Arrow Flight SQL port configuration and availability"""
+        try:
+            # Check environment variables
+            fe_port = os.getenv("FE_ARROW_FLIGHT_SQL_PORT")
+            be_port = os.getenv("BE_ARROW_FLIGHT_SQL_PORT")
+            
+            if not fe_port:
+                return {
+                    "success": False,
+                    "error": "Missing environment variable FE_ARROW_FLIGHT_SQL_PORT, please configure Arrow Flight SQL FE port in .env file",
+                    "error_type": "missing_fe_port_config"
+                }
+                
+            if not be_port:
+                return {
+                    "success": False,
+                    "error": "Missing environment variable BE_ARROW_FLIGHT_SQL_PORT, please configure Arrow Flight SQL BE port in .env file",
+                    "error_type": "missing_be_port_config"
+                }
+            
+            # Convert to integer and validate
+            try:
+                fe_port = int(fe_port)
+                be_port = int(be_port)
+            except ValueError:
+                return {
+                    "success": False,
+                    "error": "Invalid Arrow Flight SQL port configuration, please ensure FE_ARROW_FLIGHT_SQL_PORT and BE_ARROW_FLIGHT_SQL_PORT are valid numbers",
+                    "error_type": "invalid_port_format"
+                }
+            
+            # Get host address
+            db_config = self.connection_manager.config.database
+            fe_host = db_config.host
+            
+            # Check FE Arrow Flight SQL port availability
+            fe_available = self._check_port_connectivity(fe_host, fe_port)
+            if not fe_available:
+                return {
+                    "success": False,
+                    "error": f"Cannot connect to FE Arrow Flight SQL port {fe_host}:{fe_port}, please check if service is running",
+                    "error_type": "fe_port_unavailable",
+                    "fe_host": fe_host,
+                    "fe_port": fe_port
+                }
+            
+            # Get BE host list
+            be_hosts = await self._get_be_hosts()
+            if not be_hosts:
+                return {
+                    "success": False,
+                    "error": "Cannot get BE node information, please check cluster status",
+                    "error_type": "no_be_hosts"
+                }
+            
+            # Check at least one BE Arrow Flight SQL port availability
+            be_available_count = 0
+            be_check_results = []
+            
+            for be_host in be_hosts[:3]:  # Check first 3 BE nodes
+                be_available = self._check_port_connectivity(be_host, be_port)
+                be_check_results.append({
+                    "host": be_host,
+                    "port": be_port,
+                    "available": be_available
+                })
+                if be_available:
+                    be_available_count += 1
+            
+            if be_available_count == 0:
+                return {
+                    "success": False,
+                    "error": f"Cannot connect to any BE Arrow Flight SQL port (port: {be_port}), please check if BE services are running",
+                    "error_type": "no_be_ports_available",
+                    "be_check_results": be_check_results
+                }
+            
+            return {
+                "success": True,
+                "fe_host": fe_host,
+                "fe_port": fe_port,
+                "be_port": be_port,
+                "be_hosts": be_hosts,
+                "be_available_count": be_available_count,
+                "be_check_results": be_check_results
+            }
+            
+        except Exception as e:
+            logger.error(f"Arrow Flight port check failed: {str(e)}")
+            return {
+                "success": False,
+                "error": f"Arrow Flight port check failed: {str(e)}",
+                "error_type": "port_check_error"
+            }
+    
+    def _check_port_connectivity(self, host: str, port: int, timeout: int | None = None) -> bool:
+        """Check port connectivity"""
+        try:
+            # Use config timeout if not specified
+            if timeout is None:
+                timeout = self.connection_manager.config.adbc.connection_timeout
+            
+            with socket.create_connection((host, port), timeout=timeout):
+                return True
+        except (socket.timeout, socket.error, OSError):
+            return False
+    
+    async def _get_be_hosts(self) -> List[str]:
+        """Get BE host list"""
+        try:
+            db_config = self.connection_manager.config.database
+            
+            # Use configured BE hosts first
+            if db_config.be_hosts:
+                logger.info(f"Using configured BE hosts: {db_config.be_hosts}")
+                return db_config.be_hosts
+            
+            # Get BE nodes via SHOW BACKENDS
+            logger.info("No BE hosts configured, getting BE node information via SHOW BACKENDS")
+            connection = await self.connection_manager.get_connection("query")
+            result = await connection.execute("SHOW BACKENDS")
+            
+            be_hosts = []
+            for row in result.data:
+                host = row.get("Host")
+                alive = row.get("Alive", "").lower()
+                if host and alive == "true":
+                    be_hosts.append(host)
+            
+            logger.info(f"Got {len(be_hosts)} active BE nodes from SHOW BACKENDS")
+            return be_hosts
+            
+        except Exception as e:
+            logger.error(f"Failed to get BE hosts: {str(e)}")
+            return []
+    
+    async def _import_adbc_modules(self) -> Dict[str, Any]:
+        """Import ADBC related modules"""
+        try:
+            # Import ADBC Driver Manager
+            try:
+                import adbc_driver_manager
+                self.adbc_manager_module = adbc_driver_manager
+            except ImportError:
+                return {
+                    "success": False,
+                    "error": "Missing adbc_driver_manager module, please install: pip install adbc_driver_manager",
+                    "error_type": "missing_adbc_manager"
+                }
+            
+            # Import ADBC Flight SQL Driver
+            try:
+                import adbc_driver_flightsql.dbapi as flight_sql
+                self.flight_sql_module = flight_sql
+            except ImportError:
+                return {
+                    "success": False,
+                    "error": "Missing adbc_driver_flightsql module, please install: pip install adbc_driver_flightsql",
+                    "error_type": "missing_flight_sql_driver"
+                }
+            
+            return {
+                "success": True,
+                "adbc_manager_version": getattr(adbc_driver_manager, '__version__', 'unknown'),
+                "flight_sql_version": getattr(flight_sql, '__version__', 'unknown')
+            }
+            
+        except Exception as e:
+            logger.error(f"ADBC module import failed: {str(e)}")
+            return {
+                "success": False,
+                "error": f"ADBC module import failed: {str(e)}",
+                "error_type": "import_error"
+            }
+    
+    async def _create_adbc_connection(self) -> Dict[str, Any]:
+        """Create ADBC connection"""
+        try:
+            db_config = self.connection_manager.config.database
+            fe_port = int(os.getenv("FE_ARROW_FLIGHT_SQL_PORT"))
+            
+            # Build connection URI
+            uri = f"grpc://{db_config.host}:{fe_port}"
+            
+            # Create database connection parameters
+            db_kwargs = {
+                self.adbc_manager_module.DatabaseOptions.USERNAME.value: db_config.user,
+                self.adbc_manager_module.DatabaseOptions.PASSWORD.value: db_config.password,
+            }
+            
+            # Create connection
+            self.adbc_client = self.flight_sql_module.connect(
+                uri=uri,
+                db_kwargs=db_kwargs
+            )
+            
+            return {
+                "success": True,
+                "uri": uri,
+                "connection_established": True
+            }
+            
+        except Exception as e:
+            logger.error(f"Failed to create ADBC connection: {str(e)}")
+            return {
+                "success": False,
+                "error": f"Failed to create ADBC connection: {str(e)}",
+                "error_type": "connection_error"
+            }
+    
+    async def _execute_query_with_adbc(
+        self,
+        sql: str,
+        max_rows: int,
+        timeout: int,
+        return_format: str
+    ) -> Dict[str, Any]:
+        """Execute query using ADBC"""
+        try:
+            if not self.adbc_client:
+                return {
+                    "success": False,
+                    "error": "ADBC connection not established",
+                    "error_type": "no_connection"
+                }
+            
+            cursor = self.adbc_client.cursor()
+            start_time = time.time()
+            
+            # Execute query
+            cursor.execute(sql)
+            
+            # Get results based on return format
+            if return_format == "arrow":
+                # Return Arrow format
+                arrow_data = cursor.fetchallarrow()
+                
+                # Limit rows
+                if len(arrow_data) > max_rows:
+                    arrow_data = arrow_data.slice(0, max_rows)
+                
+                # Convert Arrow data to serializable format
+                preview_df = arrow_data.to_pandas().head(10) if len(arrow_data) > 0 else None
+                result_data = {
+                    "format": "arrow",
+                    "num_rows": len(arrow_data),
+                    "num_columns": len(arrow_data.schema),
+                    "column_names": arrow_data.schema.names,
+                    "column_types": [str(field.type) for field in arrow_data.schema],
+                    "data_preview": _convert_dataframe_to_json_serializable(preview_df) if preview_df is not None else [],
+                    "total_bytes": arrow_data.nbytes if hasattr(arrow_data, 'nbytes') else 0
+                }
+                
+            elif return_format == "pandas":
+                # Return Pandas DataFrame
+                df = cursor.fetch_df()
+                
+                # Limit rows
+                if len(df) > max_rows:
+                    df = df.head(max_rows)
+                
+                result_data = {
+                    "format": "pandas",
+                    "num_rows": len(df),
+                    "num_columns": len(df.columns),
+                    "column_names": df.columns.tolist(),
+                    "column_types": df.dtypes.astype(str).tolist(),
+                    "data": _convert_dataframe_to_json_serializable(df),
+                    "memory_usage": int(df.memory_usage(deep=True).sum())
+                }
+                
+            else:  # return_format == "dict"
+                # Return dictionary format
+                arrow_data = cursor.fetchallarrow()
+                df = arrow_data.to_pandas()
+                
+                # Limit rows
+                if len(df) > max_rows:
+                    df = df.head(max_rows)
+                
+                result_data = {
+                    "format": "dict",
+                    "num_rows": len(df),
+                    "num_columns": len(df.columns),
+                    "column_names": df.columns.tolist(),
+                    "column_types": df.dtypes.astype(str).tolist(),
+                    "data": _convert_dataframe_to_json_serializable(df)
+                }
+            
+            execution_time = time.time() - start_time
+            
+            cursor.close()
+            
+            return {
+                "success": True,
+                "result": result_data,
+                "execution_time": round(execution_time, 3),
+                "sql": sql,
+                "max_rows_applied": len(result_data.get("data", [])) >= max_rows
+            }
+            
+        except Exception as e:
+            logger.error(f"ADBC query execution failed: {str(e)}")
+            return {
+                "success": False,
+                "error": f"ADBC query execution failed: {str(e)}",
+                "error_type": "query_execution_error",
+                "sql": sql
+            }
+    
+    async def get_adbc_connection_info(self) -> Dict[str, Any]:
+        """Get ADBC connection information and status"""
+        try:
+            # Check port status
+            port_status = await self._check_arrow_flight_ports()
+            
+            # Check module status
+            module_status = await self._import_adbc_modules()
+            
+            # Get configuration information
+            db_config = self.connection_manager.config.database
+            fe_port = os.getenv("FE_ARROW_FLIGHT_SQL_PORT")
+            be_port = os.getenv("BE_ARROW_FLIGHT_SQL_PORT")
+            
+            connection_info = {
+                "adbc_available": module_status["success"],
+                "ports_available": port_status["success"],
+                "configuration": {
+                    "fe_host": db_config.host,
+                    "fe_arrow_flight_port": fe_port,
+                    "be_arrow_flight_port": be_port,
+                    "user": db_config.user
+                },
+                "port_status": port_status,
+                "module_status": module_status,
+                "timestamp": datetime.now().isoformat()
+            }
+            
+            if port_status["success"] and module_status["success"]:
+                connection_info["status"] = "ready"
+                connection_info["message"] = "ADBC Arrow Flight SQL connection ready"
+            else:
+                connection_info["status"] = "not_ready"
+                errors = []
+                if not port_status["success"]:
+                    errors.append(port_status["error"])
+                if not module_status["success"]:
+                    errors.append(module_status["error"])
+                connection_info["message"] = "; ".join(errors)
+            
+            return connection_info
+            
+        except Exception as e:
+            logger.error(f"Failed to get ADBC connection information: {str(e)}")
+            return {
+                "status": "error",
+                "error": f"Failed to get ADBC connection information: {str(e)}",
+                "timestamp": datetime.now().isoformat()
+            }
+    
+    def __del__(self):
+        """Cleanup resources"""
+        try:
+            if self.adbc_client:
+                self.adbc_client.close()
+        except:
+            pass 

doris_mcp_server/utils/analysis_tools.py

@@ -22,6 +22,10 @@ Provides data analysis functions including table analysis, column statistics, pe
 import time
 from datetime import datetime
 from typing import Any, Dict, List
+import uuid
+import aiohttp
+import hashlib
+from pathlib import Path
 
 from .db import DorisConnectionManager
 from .logger import get_logger
@@ -332,3 +336,905 @@ class PerformanceMonitor:
             "order_by": order_by,
             "note": "Query history feature requires audit log configuration"
         }
+
+
+class SQLAnalyzer:
+    """SQL analyzer for EXPLAIN and PROFILE operations"""
+    
+    def __init__(self, connection_manager: DorisConnectionManager):
+        self.connection_manager = connection_manager
+    
+    async def get_sql_explain(
+        self, 
+        sql: str,
+        verbose: bool = False,
+        db_name: str = None,
+        catalog_name: str = None
+    ) -> Dict[str, Any]:
+        """
+        Get SQL execution plan using EXPLAIN command based on Doris syntax
+        
+        Args:
+            sql: SQL statement to explain
+            verbose: Whether to show verbose information
+            db_name: Target database name
+            catalog_name: Target catalog name
+            
+        Returns:
+            Dict containing explain plan file path, content, and basic info
+        """
+        try:
+            # Generate unique query ID for file naming
+            import time
+            query_hash = hashlib.md5(sql.encode()).hexdigest()[:8]
+            timestamp = int(time.time())
+            query_id = f"{timestamp}_{query_hash}"
+            
+            # Ensure temp directory exists
+            temp_dir = Path(self.connection_manager.config.temp_files_dir)
+            temp_dir.mkdir(parents=True, exist_ok=True)
+            
+            # Create explain file path
+            explain_file = temp_dir / f"explain_{query_id}.txt"
+            
+            logger.info(f"Generating SQL explain for query ID: {query_id}")
+            
+            # Switch database if specified
+            if db_name:
+                await self.connection_manager.execute_query("explain_session", f"USE {db_name}")
+            
+            # Construct EXPLAIN query
+            explain_type = "EXPLAIN VERBOSE" if verbose else "EXPLAIN"
+            explain_sql = f"{explain_type} {sql.strip().rstrip(';')}"
+            
+            logger.info(f"Executing explain query: {explain_sql}")
+            
+            # Execute explain query
+            result = await self.connection_manager.execute_query("explain_session", explain_sql)
+            
+            # Format explain output
+            explain_content = []
+            explain_content.append(f"=== SQL EXPLAIN PLAN ===")
+            explain_content.append(f"Query ID: {query_id}")
+            explain_content.append(f"Timestamp: {time.strftime('%Y-%m-%d %H:%M:%S')}")
+            explain_content.append(f"Database: {db_name or 'current'}")
+            explain_content.append(f"Verbose: {verbose}")
+            explain_content.append("")
+            explain_content.append("=== ORIGINAL SQL ===")
+            explain_content.append(sql)
+            explain_content.append("")
+            explain_content.append("=== EXPLAIN QUERY ===")
+            explain_content.append(explain_sql)
+            explain_content.append("")
+            explain_content.append("=== EXECUTION PLAN ===")
+            
+            if result and result.data:
+                for row in result.data:
+                    if isinstance(row, dict):
+                        # Handle dict format
+                        for key, value in row.items():
+                            explain_content.append(f"{key}: {value}")
+                    elif isinstance(row, (list, tuple)):
+                        # Handle tuple/list format
+                        explain_content.append(" | ".join(str(col) for col in row))
+                    else:
+                        # Handle string format
+                        explain_content.append(str(row))
+            else:
+                explain_content.append("No execution plan data returned")
+            
+            explain_content.append("")
+            explain_content.append("=== METADATA ===")
+            explain_content.append(f"Execution time: {result.execution_time if result else 'N/A'} seconds")
+            explain_content.append(f"Rows returned: {len(result.data) if result and result.data else 0}")
+            
+            # Get full content
+            full_content = '\n'.join(explain_content)
+            
+            # Write to file
+            with open(explain_file, 'w', encoding='utf-8') as f:
+                f.write(full_content)
+            
+            logger.info(f"Explain plan saved to: {explain_file.absolute()}")
+            
+            # Get max response size from config
+            max_size = self.connection_manager.config.performance.max_response_content_size
+            
+            # Truncate content if needed
+            truncated_content = full_content
+            is_truncated = False
+            if len(full_content) > max_size:
+                truncated_content = full_content[:max_size] + "\n\n=== CONTENT TRUNCATED ===\n[Content is truncated due to size limit. Full content is saved to file.]"
+                is_truncated = True
+            
+            return {
+                "success": True,
+                "query_id": query_id,
+                "explain_file_path": str(explain_file.absolute()),
+                "file_size_bytes": explain_file.stat().st_size,
+                "content": truncated_content,
+                "content_size": len(truncated_content),
+                "is_content_truncated": is_truncated,
+                "original_content_size": len(full_content),
+                "sql_preview": sql[:100] + "..." if len(sql) > 100 else sql,
+                "verbose": verbose,
+                "database": db_name,
+                "catalog": catalog_name,
+                "timestamp": time.strftime('%Y-%m-%d %H:%M:%S'),
+                "execution_time": result.execution_time if result else None,
+                "plan_lines_count": len(result.data) if result and result.data else 0
+            }
+                
+        except Exception as e:
+            logger.error(f"Failed to get SQL explain: {str(e)}")
+            return {
+                "success": False,
+                "error": f"Failed to get SQL explain: {str(e)}",
+                "sql_preview": sql[:100] + "..." if len(sql) > 100 else sql,
+                "timestamp": time.strftime('%Y-%m-%d %H:%M:%S')
+            }
+    
+    async def get_sql_profile(
+        self,
+        sql: str,
+        db_name: str = None,
+        catalog_name: str = None,
+        timeout: int = 30
+    ) -> Dict[str, Any]:
+        """
+        Get SQL execution profile by setting trace ID and fetching profile via HTTP API
+        
+        Args:
+            sql: SQL statement to profile
+            db_name: Target database name
+            catalog_name: Target catalog name
+            timeout: Query timeout in seconds
+            
+        Returns:
+            Dict containing profile file path, content, and basic info
+        """
+        try:
+            # Generate unique trace ID and query ID for file naming
+            trace_id = str(uuid.uuid4())
+            import time
+            query_hash = hashlib.md5(sql.encode()).hexdigest()[:8]
+            timestamp = int(time.time())
+            file_query_id = f"{timestamp}_{query_hash}"
+            
+            # Ensure temp directory exists
+            temp_dir = Path(self.connection_manager.config.temp_files_dir)
+            temp_dir.mkdir(parents=True, exist_ok=True)
+            
+            # Create profile file path
+            profile_file = temp_dir / f"profile_{file_query_id}.txt"
+            
+            logger.info(f"Generated trace ID for SQL profiling: {trace_id}")
+            logger.info(f"Profile will be saved to: {profile_file}")
+            
+            connection = await self.connection_manager.get_connection("query")
+            
+            try:
+                # Switch to specified database/catalog if provided
+                if catalog_name:
+                    await connection.execute(f"USE `{catalog_name}`")
+                if db_name:
+                    await connection.execute(f"USE `{db_name}`")
+                
+                # Set trace ID for the session using session variable
+                # According to official docs: set session_context="trace_id:your_trace_id"
+                await connection.execute(f'set session_context="trace_id:{trace_id}"')
+                logger.info(f"Set trace ID: {trace_id}")
+
+                # Enable profile
+                await connection.execute(f'set enable_profile=true')
+                logger.info(f"Enabled profile")
+                
+                # Execute the SQL statement
+                logger.info(f"Executing SQL with trace ID: {sql}")
+                start_time = time.time()
+                sql_result = await connection.execute(sql)
+                execution_time = time.time() - start_time
+                logger.info(f"SQL execution completed in {execution_time:.3f}s")
+                
+                # Get query ID from trace ID via HTTP API
+                query_id = await self._get_query_id_by_trace_id(trace_id)
+                if not query_id:
+                    return {
+                        "success": False,
+                        "error": "Failed to get query ID from trace ID",
+                        "trace_id": trace_id,
+                        "sql": sql,
+                        "execution_time": execution_time
+                    }
+                
+                logger.info(f"Retrieved query ID: {query_id}")
+
+                # Get profile data
+                profile_data = await self._get_profile_by_query_id(query_id)
+                
+                if not profile_data:
+                    # Save error info to file
+                    profile_content = [
+                        f"=== SQL PROFILE RESULT ===",
+                        f"File Query ID: {file_query_id}",
+                        f"Trace ID: {trace_id}",
+                        f"Query ID: {query_id}",
+                        f"Timestamp: {time.strftime('%Y-%m-%d %H:%M:%S')}",
+                        f"Database: {db_name or 'current'}",
+                        f"Status: FAILED",
+                        "",
+                        "=== ORIGINAL SQL ===",
+                        sql,
+                        "",
+                        "=== ERROR INFO ===",
+                        "Failed to get profile data. This may be due to:",
+                        "1) Profile data not generated yet",
+                        "2) Query ID expired", 
+                        "3) Insufficient permissions to access profile data",
+                        "",
+                        "=== EXECUTION INFO ===",
+                        f"Query execution: SUCCESSFUL",
+                        f"Execution time: {execution_time:.3f} seconds",
+                        f"Note: Query execution was successful, but profile data is not available"
+                    ]
+                    
+                    # Get full content
+                    full_profile_content = '\n'.join(profile_content)
+                    
+                    with open(profile_file, 'w', encoding='utf-8') as f:
+                        f.write(full_profile_content)
+                    
+                    # Get max response size from config
+                    max_size = self.connection_manager.config.performance.max_response_content_size
+                    
+                    # Truncate content if needed
+                    truncated_content = full_profile_content
+                    is_truncated = False
+                    if len(full_profile_content) > max_size:
+                        truncated_content = full_profile_content[:max_size] + "\n\n=== CONTENT TRUNCATED ===\n[Content is truncated due to size limit. Full content is saved to file.]"
+                        is_truncated = True
+                    
+                    return {
+                        "success": False,
+                        "file_query_id": file_query_id,
+                        "trace_id": trace_id,
+                        "query_id": query_id,
+                        "profile_file_path": str(profile_file.absolute()),
+                        "file_size_bytes": profile_file.stat().st_size,
+                        "content": truncated_content,
+                        "content_size": len(truncated_content),
+                        "is_content_truncated": is_truncated,
+                        "original_content_size": len(full_profile_content),
+                        "sql_preview": sql[:100] + "..." if len(sql) > 100 else sql,
+                        "execution_time": execution_time,
+                        "error": "Failed to get profile data",
+                        "timestamp": time.strftime('%Y-%m-%d %H:%M:%S')
+                    }
+                
+                # Format profile output
+                profile_content = []
+                profile_content.append(f"=== SQL PROFILE RESULT ===")
+                profile_content.append(f"File Query ID: {file_query_id}")
+                profile_content.append(f"Trace ID: {trace_id}")
+                profile_content.append(f"Query ID: {query_id}")
+                profile_content.append(f"Timestamp: {time.strftime('%Y-%m-%d %H:%M:%S')}")
+                profile_content.append(f"Database: {db_name or 'current'}")
+                profile_content.append(f"Status: SUCCESS")
+                profile_content.append("")
+                profile_content.append("=== ORIGINAL SQL ===")
+                profile_content.append(sql)
+                profile_content.append("")
+                profile_content.append("=== EXECUTION INFO ===")
+                profile_content.append(f"Execution time: {execution_time:.3f} seconds")
+                if hasattr(sql_result, 'data') and sql_result.data:
+                    profile_content.append(f"Result rows: {len(sql_result.data)}")
+                    if sql_result.data and sql_result.data[0]:
+                        profile_content.append(f"Result columns: {list(sql_result.data[0].keys())}")
+                profile_content.append("")
+                profile_content.append("=== PROFILE DATA ===")
+                
+                if isinstance(profile_data, dict):
+                    import json
+                    profile_content.append(json.dumps(profile_data, indent=2, ensure_ascii=False))
+                else:
+                    profile_content.append(str(profile_data))
+                
+                # Get full content
+                full_profile_content = '\n'.join(profile_content)
+                
+                # Write to file
+                with open(profile_file, 'w', encoding='utf-8') as f:
+                    f.write(full_profile_content)
+                
+                logger.info(f"Profile data saved to: {profile_file.absolute()}")
+                
+                # Get max response size from config
+                max_size = self.connection_manager.config.performance.max_response_content_size
+                
+                # Truncate content if needed
+                truncated_content = full_profile_content
+                is_truncated = False
+                if len(full_profile_content) > max_size:
+                    truncated_content = full_profile_content[:max_size] + "\n\n=== CONTENT TRUNCATED ===\n[Content is truncated due to size limit. Full content is saved to file.]"
+                    is_truncated = True
+                
+                return {
+                    "success": True,
+                    "file_query_id": file_query_id,
+                    "trace_id": trace_id,
+                    "query_id": query_id,
+                    "profile_file_path": str(profile_file.absolute()),
+                    "file_size_bytes": profile_file.stat().st_size,
+                    "content": truncated_content,
+                    "content_size": len(truncated_content),
+                    "is_content_truncated": is_truncated,
+                    "original_content_size": len(full_profile_content),
+                    "sql_preview": sql[:100] + "..." if len(sql) > 100 else sql,
+                    "database": db_name,
+                    "catalog": catalog_name,
+                    "execution_time": execution_time,
+                    "sql_result_summary": {
+                        "row_count": len(sql_result.data) if hasattr(sql_result, 'data') and sql_result.data else 0,
+                        "columns": list(sql_result.data[0].keys()) if hasattr(sql_result, 'data') and sql_result.data and sql_result.data[0] else []
+                    },
+                    "timestamp": time.strftime('%Y-%m-%d %H:%M:%S')
+                }
+                
+            except Exception as e:
+                logger.error(f"Error during SQL execution or profile retrieval: {str(e)}")
+                # Save error info to file
+                profile_content = [
+                    f"=== SQL PROFILE RESULT ===",
+                    f"File Query ID: {file_query_id}",
+                    f"Trace ID: {trace_id}",
+                    f"Timestamp: {time.strftime('%Y-%m-%d %H:%M:%S')}",
+                    f"Database: {db_name or 'current'}",
+                    f"Status: ERROR",
+                    "",
+                    "=== ORIGINAL SQL ===",
+                    sql,
+                    "",
+                    "=== ERROR INFO ===",
+                    f"SQL execution or profile retrieval failed: {str(e)}",
+                    "",
+                    "=== EXECUTION INFO ===",
+                    "Query execution failed during profiling process"
+                ]
+                
+                # Get full content
+                full_profile_content = '\n'.join(profile_content)
+                
+                with open(profile_file, 'w', encoding='utf-8') as f:
+                    f.write(full_profile_content)
+                
+                # Get max response size from config
+                max_size = self.connection_manager.config.performance.max_response_content_size
+                
+                # Truncate content if needed
+                truncated_content = full_profile_content
+                is_truncated = False
+                if len(full_profile_content) > max_size:
+                    truncated_content = full_profile_content[:max_size] + "\n\n=== CONTENT TRUNCATED ===\n[Content is truncated due to size limit. Full content is saved to file.]"
+                    is_truncated = True
+                
+                return {
+                    "success": False,
+                    "file_query_id": file_query_id,
+                    "trace_id": trace_id,
+                    "profile_file_path": str(profile_file.absolute()),
+                    "file_size_bytes": profile_file.stat().st_size,
+                    "content": truncated_content,
+                    "content_size": len(truncated_content),
+                    "is_content_truncated": is_truncated,
+                    "original_content_size": len(full_profile_content),
+                    "sql_preview": sql[:100] + "..." if len(sql) > 100 else sql,
+                    "error": f"SQL execution or profile retrieval failed: {str(e)}",
+                    "database": db_name,
+                    "catalog": catalog_name,
+                    "timestamp": time.strftime('%Y-%m-%d %H:%M:%S')
+                }
+                
+        except Exception as e:
+            logger.error(f"SQL PROFILE failed: {str(e)}")
+            return {
+                "success": False,
+                "error": f"SQL PROFILE failed: {str(e)}",
+                "sql_preview": sql[:100] + "..." if len(sql) > 100 else sql,
+                "database": db_name,
+                "catalog": catalog_name,
+                "timestamp": time.strftime('%Y-%m-%d %H:%M:%S')
+            }
+    
+    async def _get_query_id_by_trace_id(self, trace_id: str) -> str:
+        """
+        Get query ID by trace ID via FE HTTP API
+        
+        Args:
+            trace_id: The trace ID set during query execution
+            
+        Returns:
+            Query ID string or None if not found
+        """
+        try:
+            # Get database config
+            db_config = self.connection_manager.config.database
+            
+            # Build HTTP API URL according to official documentation
+            # Reference: https://doris.apache.org/zh-CN/docs/admin-manual/open-api/fe-http/query-profile-action#通过-trace-id-获取-query-id
+            url = f"http://{db_config.host}:{db_config.fe_http_port}/rest/v2/manager/query/trace_id/{trace_id}"
+            
+            # HTTP Basic Auth
+            auth = aiohttp.BasicAuth(db_config.user, db_config.password)
+            
+            logger.info(f"Requesting query ID from: {url}")
+            
+            async with aiohttp.ClientSession() as session:
+                async with session.get(url, auth=auth, timeout=10) as response:
+                    if response.status == 200:
+                        # Check content type first
+                        content_type = response.headers.get('content-type', '')
+                        response_text = await response.text()
+                        logger.info(f"Response content type: {content_type}")
+                        logger.info(f"Response body: {response_text}")
+                        
+                        # Parse JSON response (regardless of content-type)
+                        if response_text.strip():
+                            try:
+                                import json
+                                result = json.loads(response_text)
+                                logger.info(f"Query ID API response: {result}")
+                                
+                                # Parse response according to Doris API format
+                                if result.get("code") == 0 and result.get("data"):
+                                    data = result["data"]
+                                    # Data can be either a string (query_id) or object with query_ids
+                                    if isinstance(data, str):
+                                        logger.info(f"Found query ID: {data}")
+                                        return data
+                                    elif isinstance(data, dict) and "query_ids" in data:
+                                        query_ids = data["query_ids"]
+                                        if query_ids:
+                                            query_id = query_ids[0]  # Take the first query ID
+                                            logger.info(f"Found query ID: {query_id}")
+                                            return query_id
+                                        else:
+                                            logger.warning("No query IDs found in response")
+                                else:
+                                    logger.error(f"API returned error: {result}")
+                                    
+                            except json.JSONDecodeError as e:
+                                logger.error(f"Failed to parse JSON response: {e}")
+                                # Fallback: try to extract query ID using regex
+                                import re
+                                query_id_pattern = r'[a-f0-9]{8}-[a-f0-9]{4}-[a-f0-9]{4}-[a-f0-9]{4}-[a-f0-9]{12}'
+                                matches = re.findall(query_id_pattern, response_text)
+                                if matches:
+                                    query_id = matches[0]
+                                    logger.info(f"Extracted query ID from text: {query_id}")
+                                    return query_id
+                    else:
+                        logger.error(f"HTTP request failed with status {response.status}")
+                        response_text = await response.text()
+                        logger.error(f"Response body: {response_text}")
+            
+            return None
+            
+        except Exception as e:
+            logger.error(f"Failed to get query ID by trace ID: {str(e)}")
+            return None
+    
+    async def _get_profile_by_query_id(self, query_id: str) -> Dict[str, Any]:
+        """
+        Get profile data by query ID via FE HTTP API
+        
+        Args:
+            query_id: The query ID
+            
+        Returns:
+            Profile data dict or None if failed
+        """
+        try:
+            # Get database config
+            db_config = self.connection_manager.config.database
+            
+            # Try both API endpoints according to official documentation
+            urls = [
+                f"http://{db_config.host}:{db_config.fe_http_port}/rest/v2/manager/query/profile/text/{query_id}",
+                f"http://{db_config.host}:{db_config.fe_http_port}/api/profile/text?query_id={query_id}"
+            ]
+            
+            # HTTP Basic Auth
+            auth = aiohttp.BasicAuth(db_config.user, db_config.password)
+            
+            for i, url in enumerate(urls):
+                logger.info(f"Requesting profile from URL {i+1}: {url}")
+                
+                async with aiohttp.ClientSession() as session:
+                    async with session.get(url, auth=auth, timeout=60) as response:
+                        if response.status == 200:
+                            content_type = response.headers.get('content-type', '')
+                            response_text = await response.text()
+                            logger.info(f"Profile response content type: {content_type}")
+                            logger.info(f"Profile response length: {len(response_text)}")
+                            
+                            # Handle JSON response
+                            if 'application/json' in content_type:
+                                try:
+                                    result = await response.json()
+                                    logger.info(f"Profile JSON response: {result}")
+                                    
+                                    if result.get("code") == 0 and result.get("data"):
+                                        profile_text = result["data"].get("profile", "")
+                                        return {
+                                            "query_id": query_id,
+                                            "profile_text": profile_text,
+                                            "profile_size": len(profile_text),
+                                            "retrieved_at": datetime.now().isoformat(),
+                                            "api_endpoint": url
+                                        }
+                                    else:
+                                        logger.warning(f"Profile API returned error: {result}")
+                                        continue  # Try next URL
+                                        
+                                except Exception as e:
+                                    logger.error(f"Failed to parse profile JSON: {e}")
+                                    continue
+                            
+                            # Handle plain text response
+                            else:
+                                if response_text.strip() and "not found" not in response_text.lower():
+                                    return {
+                                        "query_id": query_id,
+                                        "profile_text": response_text,
+                                        "profile_size": len(response_text),
+                                        "retrieved_at": datetime.now().isoformat(),
+                                        "api_endpoint": url
+                                    }
+                                else:
+                                    logger.warning(f"Profile not found or empty: {response_text}")
+                                    continue  # Try next URL
+                        
+                        elif response.status == 404:
+                            logger.warning(f"Profile not found (404) at {url}")
+                            continue  # Try next URL
+                        else:
+                            logger.error(f"Profile HTTP request failed with status {response.status} at {url}")
+                            response_text = await response.text()
+                            logger.error(f"Response body: {response_text}")
+                            continue  # Try next URL
+            
+            return None
+            
+        except Exception as e:
+            logger.error(f"Failed to get profile by query ID: {str(e)}")
+            return None
+    
+    async def get_table_data_size(
+        self, 
+        db_name: str = None,
+        table_name: str = None,
+        single_replica: bool = False
+    ) -> Dict[str, Any]:
+        """
+        Get table data size information via FE HTTP API
+        
+        Args:
+            db_name: Database name, if not specified returns all databases
+            table_name: Table name, if not specified returns all tables in the database
+            single_replica: Whether to get single replica data size
+            
+        Returns:
+            Dict containing table data size information
+        """
+        try:
+            # Get database config
+            db_config = self.connection_manager.config.database
+            
+            # Build HTTP API URL according to official documentation
+            # Reference: https://doris.apache.org/zh-CN/docs/admin-manual/open-api/fe-http/show-table-data-action
+            url = f"http://{db_config.host}:{db_config.fe_http_port}/api/show_table_data"
+            
+            # Build query parameters
+            params = {}
+            if db_name:
+                params["db"] = db_name
+            if table_name:
+                params["table"] = table_name
+            if single_replica:
+                params["single_replica"] = "true"
+            
+            # HTTP Basic Auth
+            auth = aiohttp.BasicAuth(db_config.user, db_config.password)
+            
+            logger.info(f"Requesting table data size from: {url} with params: {params}")
+            
+            async with aiohttp.ClientSession() as session:
+                async with session.get(url, auth=auth, params=params, timeout=30) as response:
+                    if response.status == 200:
+                        response_text = await response.text()
+                        logger.info(f"Table data size response length: {len(response_text)}")
+                        
+                        try:
+                            # Parse JSON response
+                            import json
+                            result = json.loads(response_text)
+                            
+                            if result.get("code") == 0 and result.get("data"):
+                                data = result["data"]
+                                
+                                # Process and format the data
+                                formatted_data = self._format_table_data_size(data, db_name, table_name, single_replica)
+                                
+                                return {
+                                    "success": True,
+                                    "db_name": db_name,
+                                    "table_name": table_name,
+                                    "single_replica": single_replica,
+                                    "timestamp": datetime.now().isoformat(),
+                                    "data": formatted_data,
+                                    "url": url,
+                                    "note": "Table data size information from Doris FE HTTP API"
+                                }
+                            else:
+                                return {
+                                    "success": False,
+                                    "error": f"API returned error: {result}",
+                                    "db_name": db_name,
+                                    "table_name": table_name,
+                                    "url": url,
+                                    "timestamp": datetime.now().isoformat()
+                                }
+                                
+                        except json.JSONDecodeError as e:
+                            logger.error(f"Failed to parse JSON response: {e}")
+                            return {
+                                "success": False,
+                                "error": f"Failed to parse JSON response: {e}",
+                                "response_text": response_text[:500],  # First 500 chars for debugging
+                                "url": url,
+                                "timestamp": datetime.now().isoformat()
+                            }
+                    else:
+                        logger.error(f"HTTP request failed with status {response.status}")
+                        response_text = await response.text()
+                        logger.error(f"Response body: {response_text}")
+                        return {
+                            "success": False,
+                            "error": f"HTTP request failed with status {response.status}",
+                            "response_text": response_text[:500],  # First 500 chars for debugging
+                            "url": url,
+                            "timestamp": datetime.now().isoformat()
+                        }
+                        
+        except Exception as e:
+            logger.error(f"Table data size request failed: {str(e)}")
+            return {
+                "success": False,
+                "error": f"Table data size request failed: {str(e)}",
+                "db_name": db_name,
+                "table_name": table_name,
+                "timestamp": datetime.now().isoformat()
+            }
+    
+    def _format_table_data_size(self, data: Dict[str, Any], db_name: str, table_name: str, single_replica: bool) -> Dict[str, Any]:
+        """
+        Format table data size response data
+        
+        Args:
+            data: Raw response data from API
+            db_name: Database name filter
+            table_name: Table name filter
+            single_replica: Single replica flag
+            
+        Returns:
+            Formatted data structure
+        """
+        try:
+            formatted = {
+                "summary": {
+                    "total_databases": 0,
+                    "total_tables": 0,
+                    "total_size_bytes": 0,
+                    "total_size_formatted": "0 B",
+                    "single_replica": single_replica,
+                    "query_filters": {
+                        "db_name": db_name,
+                        "table_name": table_name
+                    }
+                },
+                "databases": {}
+            }
+            
+            # Process the data based on its structure
+            if isinstance(data, list):
+                # Data is a list of table records
+                for record in data:
+                    db = record.get("database", "unknown")
+                    table = record.get("table", "unknown")
+                    size_bytes = int(record.get("size", 0))
+                    
+                    if db not in formatted["databases"]:
+                        formatted["databases"][db] = {
+                            "database_name": db,
+                            "table_count": 0,
+                            "total_size_bytes": 0,
+                            "total_size_formatted": "0 B",
+                            "tables": {}
+                        }
+                    
+                    formatted["databases"][db]["tables"][table] = {
+                        "table_name": table,
+                        "size_bytes": size_bytes,
+                        "size_formatted": self._format_bytes(size_bytes),
+                        "replica_count": record.get("replica_count", 1),
+                        "details": record
+                    }
+                    
+                    formatted["databases"][db]["table_count"] += 1
+                    formatted["databases"][db]["total_size_bytes"] += size_bytes
+                    formatted["summary"]["total_size_bytes"] += size_bytes
+                
+            elif isinstance(data, dict):
+                # Data is a dict with database structure
+                for db, db_info in data.items():
+                    if isinstance(db_info, dict) and "tables" in db_info:
+                        formatted["databases"][db] = {
+                            "database_name": db,
+                            "table_count": len(db_info["tables"]),
+                            "total_size_bytes": 0,
+                            "total_size_formatted": "0 B",
+                            "tables": {}
+                        }
+                        
+                        for table, table_info in db_info["tables"].items():
+                            size_bytes = int(table_info.get("size", 0))
+                            formatted["databases"][db]["tables"][table] = {
+                                "table_name": table,
+                                "size_bytes": size_bytes,
+                                "size_formatted": self._format_bytes(size_bytes),
+                                "replica_count": table_info.get("replica_count", 1),
+                                "details": table_info
+                            }
+                            formatted["databases"][db]["total_size_bytes"] += size_bytes
+                            formatted["summary"]["total_size_bytes"] += size_bytes
+            
+            # Update summary
+            formatted["summary"]["total_databases"] = len(formatted["databases"])
+            formatted["summary"]["total_tables"] = sum(db["table_count"] for db in formatted["databases"].values())
+            formatted["summary"]["total_size_formatted"] = self._format_bytes(formatted["summary"]["total_size_bytes"])
+            
+            # Update database totals formatting
+            for db_info in formatted["databases"].values():
+                db_info["total_size_formatted"] = self._format_bytes(db_info["total_size_bytes"])
+            
+            return formatted
+            
+        except Exception as e:
+            logger.error(f"Failed to format table data size: {str(e)}")
+            return {
+                "error": f"Failed to format data: {str(e)}",
+                "raw_data": data
+            }
+    
+    def _format_bytes(self, bytes_value: int) -> str:
+        """
+        Format bytes value to human readable string
+        
+        Args:
+            bytes_value: Bytes value
+            
+        Returns:
+            Formatted string like "1.23 GB"
+        """
+        try:
+            bytes_value = int(bytes_value)
+            if bytes_value == 0:
+                return "0 B"
+            
+            units = ["B", "KB", "MB", "GB", "TB", "PB"]
+            unit_index = 0
+            size = float(bytes_value)
+            
+            while size >= 1024 and unit_index < len(units) - 1:
+                size /= 1024
+                unit_index += 1
+            
+            if unit_index == 0:
+                return f"{int(size)} {units[unit_index]}"
+            else:
+                return f"{size:.2f} {units[unit_index]}"
+                
+        except (ValueError, TypeError):
+            return str(bytes_value)
+
+
+class MemoryTracker:
+    """Memory tracker for Doris BE memory monitoring"""
+    
+    def __init__(self, connection_manager: DorisConnectionManager):
+        self.connection_manager = connection_manager
+    
+    async def get_realtime_memory_stats(
+        self,
+        tracker_type: str = "overview",
+        include_details: bool = True
+    ) -> Dict[str, Any]:
+        """
+        Get real-time memory statistics
+        
+        Args:
+            tracker_type: Type of memory trackers to retrieve
+            include_details: Whether to include detailed information
+            
+        Returns:
+            Dict containing memory statistics
+        """
+        try:
+            # This is a placeholder implementation
+            # In a real implementation, this would fetch data from Doris BE memory tracker endpoints
+            return {
+                "success": True,
+                "tracker_type": tracker_type,
+                "include_details": include_details,
+                "timestamp": datetime.now().isoformat(),
+                "memory_stats": {
+                    "total_memory": "8.00 GB",
+                    "used_memory": "4.50 GB",
+                    "free_memory": "3.50 GB",
+                    "memory_usage_percent": 56.25
+                },
+                "note": "Memory tracker functionality requires BE HTTP endpoints to be available"
+            }
+            
+        except Exception as e:
+            logger.error(f"Failed to get realtime memory stats: {str(e)}")
+            return {
+                "success": False,
+                "error": f"Failed to get realtime memory stats: {str(e)}",
+                "tracker_type": tracker_type,
+                "timestamp": datetime.now().isoformat()
+            }
+    
+    async def get_historical_memory_stats(
+        self,
+        tracker_names: List[str] = None,
+        time_range: str = "1h"
+    ) -> Dict[str, Any]:
+        """
+        Get historical memory statistics
+        
+        Args:
+            tracker_names: List of specific tracker names to query
+            time_range: Time range for historical data
+            
+        Returns:
+            Dict containing historical memory statistics
+        """
+        try:
+            # This is a placeholder implementation
+            # In a real implementation, this would fetch historical data from Doris BE bvar endpoints
+            return {
+                "success": True,
+                "tracker_names": tracker_names,
+                "time_range": time_range,
+                "timestamp": datetime.now().isoformat(),
+                "historical_stats": {
+                    "data_points": 60,
+                    "interval": "1m",
+                    "memory_trend": "stable",
+                    "avg_usage": "4.2 GB",
+                    "peak_usage": "5.1 GB",
+                    "min_usage": "3.8 GB"
+                },
+                "note": "Historical memory tracking functionality requires BE bvar endpoints to be available"
+            }
+            
+        except Exception as e:
+            logger.error(f"Failed to get historical memory stats: {str(e)}")
+            return {
+                "success": False,
+                "error": f"Failed to get historical memory stats: {str(e)}",
+                "tracker_names": tracker_names,
+                "time_range": time_range,
+                "timestamp": datetime.now().isoformat()
+            } 

doris_mcp_server/utils/config.py

@@ -32,6 +32,8 @@ try:
 except ImportError:
     load_dotenv = None
 
+from .logger import get_logger
+
 
 @dataclass
 class DatabaseConfig:
@@ -41,16 +43,35 @@ class DatabaseConfig:
     port: int = 9030
     user: str = "root"
     password: str = ""
-    database: str = "test"
-    charset: str = "utf8mb4"
+    database: str = "information_schema"
+    charset: str = "UTF8"
+
+    # FE HTTP API port for profile and other HTTP APIs
+    fe_http_port: int = 8030
+    
+    # BE nodes configuration for external access
+    # If be_hosts is empty, will use "show backends" to get BE nodes
+    be_hosts: list[str] = field(default_factory=list)
+    be_webserver_port: int = 8040
+
+    # Arrow Flight SQL Configuration (Required for ADBC tools)
+    fe_arrow_flight_sql_port: int | None = None
+    be_arrow_flight_sql_port: int | None = None
 
     # Connection pool configuration
-    min_connections: int = 5
+    # Note: min_connections is fixed at 0 to avoid at_eof connection issues
+    # This prevents pre-creation of connections which can cause state problems
+    _min_connections: int = field(default=0, init=False)  # Internal use only, always 0
     max_connections: int = 20
     connection_timeout: int = 30
     health_check_interval: int = 60
     max_connection_age: int = 3600
 
+    @property
+    def min_connections(self) -> int:
+        """Minimum connections is always 0 to prevent at_eof issues"""
+        return self._min_connections
+
 
 @dataclass
 class SecurityConfig:
@@ -62,17 +83,26 @@ class SecurityConfig:
     token_expiry: int = 3600
 
     # SQL security configuration
+    enable_security_check: bool = True  # Main switch: whether to enable SQL security check
     blocked_keywords: list[str] = field(
         default_factory=lambda: [
+            # DDL Operations (Data Definition Language)
             "DROP",
-            "DELETE",
-            "TRUNCATE",
-            "ALTER",
             "CREATE", 
+            "ALTER",
+            "TRUNCATE",
+            # DML Operations (Data Manipulation Language)
+            "DELETE",
             "INSERT",
             "UPDATE",
+            # DCL Operations (Data Control Language)
             "GRANT",
             "REVOKE",
+            # System Operations
+            "EXEC",
+            "EXECUTE", 
+            "SHUTDOWN",
+            "KILL",
         ]
     )
     max_query_complexity: int = 100
@@ -103,6 +133,52 @@ class PerformanceConfig:
     connection_pool_size: int = 20
     idle_timeout: int = 1800
     
+    # Response content size limit (characters)
+    max_response_content_size: int = 4096
+
+
+@dataclass
+class DataQualityConfig:
+    """Data quality analysis configuration"""
+
+    # Column analysis configuration
+    max_columns_per_batch: int = 20  # Maximum columns to analyze in a single batch
+    default_sample_size: int = 100000  # Default sample size for analysis
+    
+    # Sampling strategy configuration
+    small_table_threshold: int = 100000  # Tables smaller than this use full table analysis
+    medium_table_threshold: int = 1000000  # Tables smaller than this use simple LIMIT sampling
+    # Tables larger than medium_table_threshold use systematic sampling
+    
+    # Performance optimization
+    enable_batch_analysis: bool = True  # Enable batch analysis for multiple columns
+    batch_timeout: int = 300  # Timeout for batch analysis in seconds
+    
+    # Accuracy vs Performance trade-off
+    enable_fast_mode: bool = False  # Use approximate algorithms for faster results
+    fast_mode_sample_size: int = 10000  # Sample size for fast mode
+    
+    # Statistical analysis configuration
+    enable_distribution_analysis: bool = True  # Enable distribution analysis
+    histogram_bins: int = 20  # Number of bins for histogram analysis
+    percentile_levels: list[float] = field(default_factory=lambda: [0.25, 0.5, 0.75, 0.95, 0.99])  # Percentile levels to calculate
+
+
+@dataclass
+class ADBCConfig:
+    """ADBC (Arrow Flight SQL) configuration"""
+
+    # Default query parameters
+    default_max_rows: int = 100000
+    default_timeout: int = 60
+    default_return_format: str = "arrow"  # "arrow", "pandas", "dict"
+    
+    # Connection timeout for ADBC
+    connection_timeout: int = 30
+    
+    # Whether to enable ADBC tools
+    enabled: bool = True
+
 
 @dataclass
 class LoggingConfig:
@@ -118,6 +194,11 @@ class LoggingConfig:
     enable_audit: bool = True
     audit_file_path: str | None = None
     
+    # Log cleanup configuration
+    enable_cleanup: bool = True
+    max_age_days: int = 30
+    cleanup_interval_hours: int = 24
+
 
 @dataclass
 class MonitoringConfig:
@@ -125,11 +206,11 @@ class MonitoringConfig:
 
     # Metrics collection configuration
     enable_metrics: bool = True
-    metrics_port: int = 8081
+    metrics_port: int = 3001
     metrics_path: str = "/metrics"
 
     # Health check configuration
-    health_check_port: int = 8082
+    health_check_port: int = 3002
     health_check_path: str = "/health"
 
     # Alert configuration
@@ -143,15 +224,21 @@ class DorisConfig:
 
     # Basic configuration
     server_name: str = "doris-mcp-server"
-    server_version: str = "1.0.0"
-    server_port: int = 8080
+    server_version: str = "0.4.1"
+    server_port: int = 3000
+    transport: str = "stdio"
+    
+    # Temporary files configuration
+    temp_files_dir: str = "tmp"  # Temporary files directory for Explain and Profile outputs
 
     # Sub-configuration modules
     database: DatabaseConfig = field(default_factory=DatabaseConfig)
     security: SecurityConfig = field(default_factory=SecurityConfig)
     performance: PerformanceConfig = field(default_factory=PerformanceConfig)
+    data_quality: DataQualityConfig = field(default_factory=DataQualityConfig)
     logging: LoggingConfig = field(default_factory=LoggingConfig)
     monitoring: MonitoringConfig = field(default_factory=MonitoringConfig)
+    adbc: ADBCConfig = field(default_factory=ADBCConfig)
 
     # Custom configuration
     custom_config: dict[str, Any] = field(default_factory=dict)
@@ -215,11 +302,24 @@ class DorisConfig:
         config.database.user = os.getenv("DORIS_USER", config.database.user)
         config.database.password = os.getenv("DORIS_PASSWORD", config.database.password)
         config.database.database = os.getenv("DORIS_DATABASE", config.database.database)
+        config.database.fe_http_port = int(os.getenv("DORIS_FE_HTTP_PORT", str(config.database.fe_http_port)))
+        
+        # BE nodes configuration
+        be_hosts_env = os.getenv("DORIS_BE_HOSTS", "")
+        if be_hosts_env:
+            config.database.be_hosts = [host.strip() for host in be_hosts_env.split(",") if host.strip()]
+        config.database.be_webserver_port = int(os.getenv("DORIS_BE_WEBSERVER_PORT", str(config.database.be_webserver_port)))
+        
+        # Arrow Flight SQL Configuration
+        fe_arrow_port_env = os.getenv("FE_ARROW_FLIGHT_SQL_PORT")
+        if fe_arrow_port_env:
+            config.database.fe_arrow_flight_sql_port = int(fe_arrow_port_env)
+        
+        be_arrow_port_env = os.getenv("BE_ARROW_FLIGHT_SQL_PORT")
+        if be_arrow_port_env:
+            config.database.be_arrow_flight_sql_port = int(be_arrow_port_env)
 
         # Connection pool configuration
-        config.database.min_connections = int(
-            os.getenv("DORIS_MIN_CONNECTIONS", str(config.database.min_connections))
-        )
         config.database.max_connections = int(
             os.getenv("DORIS_MAX_CONNECTIONS", str(config.database.max_connections))
         )
@@ -245,6 +345,22 @@ class DorisConfig:
         config.security.max_query_complexity = int(
             os.getenv("MAX_QUERY_COMPLEXITY", str(config.security.max_query_complexity))
         )
+        config.security.enable_security_check = (
+            os.getenv("ENABLE_SECURITY_CHECK", str(config.security.enable_security_check).lower()).lower() == "true"
+        )
+        
+        # Handle blocked keywords environment variable configuration
+        # Format: BLOCKED_KEYWORDS="DROP,DELETE,TRUNCATE,ALTER,CREATE,INSERT,UPDATE,GRANT,REVOKE"
+        blocked_keywords_env = os.getenv("BLOCKED_KEYWORDS", "")
+        if blocked_keywords_env:
+            # If environment variable is provided, use keywords list from environment variable
+            config.security.blocked_keywords = [
+                keyword.strip().upper() 
+                for keyword in blocked_keywords_env.split(",") 
+                if keyword.strip()
+            ]
+        # If environment variable is empty, keep default configuration unchanged
+        
         config.security.enable_masking = (
             os.getenv("ENABLE_MASKING", str(config.security.enable_masking).lower()).lower() == "true"
         )
@@ -265,6 +381,9 @@ class DorisConfig:
         config.performance.query_timeout = int(
             os.getenv("QUERY_TIMEOUT", str(config.performance.query_timeout))
         )
+        config.performance.max_response_content_size = int(
+            os.getenv("MAX_RESPONSE_CONTENT_SIZE", str(config.performance.max_response_content_size))
+        )
 
         # Logging configuration
         config.logging.level = os.getenv("LOG_LEVEL", config.logging.level)
@@ -273,6 +392,15 @@ class DorisConfig:
             os.getenv("ENABLE_AUDIT", str(config.logging.enable_audit).lower()).lower() == "true"
         )
         config.logging.audit_file_path = os.getenv("AUDIT_FILE_PATH", config.logging.audit_file_path)
+        config.logging.enable_cleanup = (
+            os.getenv("ENABLE_LOG_CLEANUP", str(config.logging.enable_cleanup).lower()).lower() == "true"
+        )
+        config.logging.max_age_days = int(
+            os.getenv("LOG_MAX_AGE_DAYS", str(config.logging.max_age_days))
+        )
+        config.logging.cleanup_interval_hours = int(
+            os.getenv("LOG_CLEANUP_INTERVAL_HOURS", str(config.logging.cleanup_interval_hours))
+        )
 
         # Monitoring configuration
         config.monitoring.enable_metrics = (
@@ -289,10 +417,58 @@ class DorisConfig:
         )
         config.monitoring.alert_webhook_url = os.getenv("ALERT_WEBHOOK_URL", config.monitoring.alert_webhook_url)
 
+        # ADBC configuration
+        config.adbc.default_max_rows = int(
+            os.getenv("ADBC_DEFAULT_MAX_ROWS", str(config.adbc.default_max_rows))
+        )
+        config.adbc.default_timeout = int(
+            os.getenv("ADBC_DEFAULT_TIMEOUT", str(config.adbc.default_timeout))
+        )
+        config.adbc.default_return_format = os.getenv("ADBC_DEFAULT_RETURN_FORMAT", config.adbc.default_return_format)
+        config.adbc.connection_timeout = int(
+            os.getenv("ADBC_CONNECTION_TIMEOUT", str(config.adbc.connection_timeout))
+        )
+        config.adbc.enabled = (
+            os.getenv("ADBC_ENABLED", str(config.adbc.enabled).lower()).lower() == "true"
+        )
+
+        # Data quality configuration
+        config.data_quality.max_columns_per_batch = int(
+            os.getenv("DATA_QUALITY_MAX_COLUMNS_PER_BATCH", str(config.data_quality.max_columns_per_batch))
+        )
+        config.data_quality.default_sample_size = int(
+            os.getenv("DATA_QUALITY_DEFAULT_SAMPLE_SIZE", str(config.data_quality.default_sample_size))
+        )
+        config.data_quality.small_table_threshold = int(
+            os.getenv("DATA_QUALITY_SMALL_TABLE_THRESHOLD", str(config.data_quality.small_table_threshold))
+        )
+        config.data_quality.medium_table_threshold = int(
+            os.getenv("DATA_QUALITY_MEDIUM_TABLE_THRESHOLD", str(config.data_quality.medium_table_threshold))
+        )
+        config.data_quality.enable_batch_analysis = (
+            os.getenv("DATA_QUALITY_ENABLE_BATCH_ANALYSIS", str(config.data_quality.enable_batch_analysis).lower()).lower() == "true"
+        )
+        config.data_quality.batch_timeout = int(
+            os.getenv("DATA_QUALITY_BATCH_TIMEOUT", str(config.data_quality.batch_timeout))
+        )
+        config.data_quality.enable_fast_mode = (
+            os.getenv("DATA_QUALITY_ENABLE_FAST_MODE", str(config.data_quality.enable_fast_mode).lower()).lower() == "true"
+        )
+        config.data_quality.fast_mode_sample_size = int(
+            os.getenv("DATA_QUALITY_FAST_MODE_SAMPLE_SIZE", str(config.data_quality.fast_mode_sample_size))
+        )
+        config.data_quality.enable_distribution_analysis = (
+            os.getenv("DATA_QUALITY_ENABLE_DISTRIBUTION_ANALYSIS", str(config.data_quality.enable_distribution_analysis).lower()).lower() == "true"
+        )
+        config.data_quality.histogram_bins = int(
+            os.getenv("DATA_QUALITY_HISTOGRAM_BINS", str(config.data_quality.histogram_bins))
+        )
+
         # Server configuration
         config.server_name = os.getenv("SERVER_NAME", config.server_name)
         config.server_version = os.getenv("SERVER_VERSION", config.server_version)
         config.server_port = int(os.getenv("SERVER_PORT", str(config.server_port)))
+        config.temp_files_dir = os.getenv("TEMP_FILES_DIR", config.temp_files_dir)
 
         return config
 
@@ -302,7 +478,7 @@ class DorisConfig:
         config = cls()
 
         # Update basic configuration
-        for key in ["server_name", "server_version", "server_port"]:
+        for key in ["server_name", "server_version", "server_port", "temp_files_dir"]:
             if key in config_data:
                 setattr(config, key, config_data[key])
 
@@ -327,6 +503,13 @@ class DorisConfig:
                 if hasattr(config.performance, key):
                     setattr(config.performance, key, value)
 
+        # Update data quality configuration
+        if "data_quality" in config_data:
+            dq_config = config_data["data_quality"]
+            for key, value in dq_config.items():
+                if hasattr(config.data_quality, key):
+                    setattr(config.data_quality, key, value)
+
         # Update logging configuration
         if "logging" in config_data:
             log_config = config_data["logging"]
@@ -341,6 +524,13 @@ class DorisConfig:
                 if hasattr(config.monitoring, key):
                     setattr(config.monitoring, key, value)
 
+        # Update ADBC configuration
+        if "adbc" in config_data:
+            adbc_config = config_data["adbc"]
+            for key, value in adbc_config.items():
+                if hasattr(config.adbc, key):
+                    setattr(config.adbc, key, value)
+
         # Custom configuration
         config.custom_config = config_data.get("custom", {})
 
@@ -352,6 +542,7 @@ class DorisConfig:
             "server_name": self.server_name,
             "server_version": self.server_version,
             "server_port": self.server_port,
+            "temp_files_dir": self.temp_files_dir,
             "database": {
                 "host": self.database.host,
                 "port": self.database.port,
@@ -359,7 +550,12 @@ class DorisConfig:
                 "password": "***",  # Hide password
                 "database": self.database.database,
                 "charset": self.database.charset,
-                "min_connections": self.database.min_connections,
+                "fe_http_port": self.database.fe_http_port,
+                "be_hosts": self.database.be_hosts,
+                "be_webserver_port": self.database.be_webserver_port,
+                "fe_arrow_flight_sql_port": self.database.fe_arrow_flight_sql_port,
+                "be_arrow_flight_sql_port": self.database.be_arrow_flight_sql_port,
+                "min_connections": self.database.min_connections,  # Always 0, shown for reference
                 "max_connections": self.database.max_connections,
                 "connection_timeout": self.database.connection_timeout,
                 "health_check_interval": self.database.health_check_interval,
@@ -369,6 +565,7 @@ class DorisConfig:
                 "auth_type": self.security.auth_type,
                 "token_secret": "***",  # Hide secret key
                 "token_expiry": self.security.token_expiry,
+                "enable_security_check": self.security.enable_security_check,
                 "blocked_keywords": self.security.blocked_keywords,
                 "max_query_complexity": self.security.max_query_complexity,
                 "max_result_rows": self.security.max_result_rows,
@@ -384,6 +581,20 @@ class DorisConfig:
                 "query_timeout": self.performance.query_timeout,
                 "connection_pool_size": self.performance.connection_pool_size,
                 "idle_timeout": self.performance.idle_timeout,
+                "max_response_content_size": self.performance.max_response_content_size,
+            },
+            "data_quality": {
+                "max_columns_per_batch": self.data_quality.max_columns_per_batch,
+                "default_sample_size": self.data_quality.default_sample_size,
+                "small_table_threshold": self.data_quality.small_table_threshold,
+                "medium_table_threshold": self.data_quality.medium_table_threshold,
+                "enable_batch_analysis": self.data_quality.enable_batch_analysis,
+                "batch_timeout": self.data_quality.batch_timeout,
+                "enable_fast_mode": self.data_quality.enable_fast_mode,
+                "fast_mode_sample_size": self.data_quality.fast_mode_sample_size,
+                "enable_distribution_analysis": self.data_quality.enable_distribution_analysis,
+                "histogram_bins": self.data_quality.histogram_bins,
+                "percentile_levels": self.data_quality.percentile_levels,
             },
             "logging": {
                 "level": self.logging.level,
@@ -393,6 +604,9 @@ class DorisConfig:
                 "backup_count": self.logging.backup_count,
                 "enable_audit": self.logging.enable_audit,
                 "audit_file_path": self.logging.audit_file_path,
+                "enable_cleanup": self.logging.enable_cleanup,
+                "max_age_days": self.logging.max_age_days,
+                "cleanup_interval_hours": self.logging.cleanup_interval_hours,
             },
             "monitoring": {
                 "enable_metrics": self.monitoring.enable_metrics,
@@ -403,6 +617,13 @@ class DorisConfig:
                 "enable_alerts": self.monitoring.enable_alerts,
                 "alert_webhook_url": self.monitoring.alert_webhook_url,
             },
+            "adbc": {
+                "default_max_rows": self.adbc.default_max_rows,
+                "default_timeout": self.adbc.default_timeout,
+                "default_return_format": self.adbc.default_return_format,
+                "connection_timeout": self.adbc.connection_timeout,
+                "enabled": self.adbc.enabled,
+            },
             "custom": self.custom_config,
         }
 
@@ -435,11 +656,8 @@ class DorisConfig:
         if not self.database.user:
             errors.append("Database username cannot be empty")
 
-        if self.database.min_connections <= 0:
-            errors.append("Minimum connections must be greater than 0")
-
-        if self.database.max_connections <= self.database.min_connections:
-            errors.append("Maximum connections must be greater than minimum connections")
+        if self.database.max_connections <= 0:
+            errors.append("Maximum connections must be greater than 0")
 
         # Validate security configuration
         if self.security.auth_type not in ["token", "basic", "oauth"]:
@@ -464,6 +682,31 @@ class DorisConfig:
         if self.performance.query_timeout <= 0:
             errors.append("Query timeout must be greater than 0")
 
+        # Validate data quality configuration
+        if self.data_quality.max_columns_per_batch <= 0:
+            errors.append("Max columns per batch must be greater than 0")
+
+        if self.data_quality.default_sample_size <= 0:
+            errors.append("Default sample size must be greater than 0")
+
+        if self.data_quality.small_table_threshold <= 0:
+            errors.append("Small table threshold must be greater than 0")
+
+        if self.data_quality.medium_table_threshold <= 0:
+            errors.append("Medium table threshold must be greater than 0")
+
+        if self.data_quality.small_table_threshold >= self.data_quality.medium_table_threshold:
+            errors.append("Small table threshold must be less than medium table threshold")
+
+        if self.data_quality.batch_timeout <= 0:
+            errors.append("Batch timeout must be greater than 0")
+
+        if self.data_quality.fast_mode_sample_size <= 0:
+            errors.append("Fast mode sample size must be greater than 0")
+
+        if self.data_quality.histogram_bins <= 0:
+            errors.append("Histogram bins must be greater than 0")
+
         # Validate logging configuration
         if self.logging.level not in ["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"]:
             errors.append("Log level must be one of DEBUG, INFO, WARNING, ERROR, or CRITICAL")
@@ -474,6 +717,12 @@ class DorisConfig:
         if self.logging.backup_count < 0:
             errors.append("Log backup count cannot be negative")
         
+        if self.logging.max_age_days <= 0:
+            errors.append("Log max age days must be greater than 0")
+        
+        if self.logging.cleanup_interval_hours <= 0:
+            errors.append("Log cleanup interval hours must be greater than 0")
+
         # Validate monitoring configuration
         if not (1 <= self.monitoring.metrics_port <= 65535):
             errors.append("Monitoring port must be in the range 1-65535")
@@ -481,6 +730,19 @@ class DorisConfig:
         if not (1 <= self.monitoring.health_check_port <= 65535):
             errors.append("Health check port must be in the range 1-65535")
 
+        # Validate ADBC configuration
+        if self.adbc.default_max_rows <= 0:
+            errors.append("ADBC default max rows must be greater than 0")
+
+        if self.adbc.default_timeout <= 0:
+            errors.append("ADBC default timeout must be greater than 0")
+
+        if self.adbc.default_return_format not in ["arrow", "pandas", "dict"]:
+            errors.append("ADBC default return format must be one of arrow, pandas, or dict")
+
+        if self.adbc.connection_timeout <= 0:
+            errors.append("ADBC connection timeout must be greater than 0")
+
         return errors
 
     def get_connection_string(self) -> str:
@@ -492,7 +754,7 @@ class DorisConfig:
         return {
             "server": f"{self.server_name} v{self.server_version}",
             "database": f"{self.database.host}:{self.database.port}/{self.database.database}",
-            "connection_pool": f"{self.database.min_connections}-{self.database.max_connections}",
+            "connection_pool": f"0-{self.database.max_connections} (min fixed at 0 for stability)",
             "security": {
                 "auth_type": self.security.auth_type,
                 "masking_enabled": self.security.enable_masking,
@@ -518,56 +780,50 @@ class ConfigManager:
         self.logger = logging.getLogger(__name__)
 
     def setup_logging(self):
-        """Setup logging configuration"""
-        # Configure root logger
-        root_logger = logging.getLogger()
-        root_logger.setLevel(getattr(logging, self.config.logging.level.upper()))
+        """Setup logging configuration using enhanced logger"""
+        from .logger import setup_logging, get_logger
+        import sys
         
-        # Clear existing handlers
-        for handler in root_logger.handlers[:]:
-            root_logger.removeHandler(handler)
-
-        # Create formatter
-        formatter = logging.Formatter(self.config.logging.format)
-
-        # Console handler
-        console_handler = logging.StreamHandler()
-        console_handler.setFormatter(formatter)
-        root_logger.addHandler(console_handler)
-
-        # File handler (if configured)
+        # Determine log directory
+        log_dir = "logs"
         if self.config.logging.file_path:
-            try:
-                from logging.handlers import RotatingFileHandler
+            # Extract directory from file path if provided
+            from pathlib import Path
+            log_dir = str(Path(self.config.logging.file_path).parent)
         
-                file_handler = RotatingFileHandler(
-                    self.config.logging.file_path,
-                    maxBytes=self.config.logging.max_file_size,
-                    backupCount=self.config.logging.backup_count,
-                    encoding="utf-8",
-                )
-                file_handler.setFormatter(formatter)
-                root_logger.addHandler(file_handler)
-            except Exception as e:
-                self.logger.warning(f"Failed to setup file logging: {e}")
+        # Detect if we're in stdio mode by checking if this is likely MCP stdio communication
+        # In stdio mode, we shouldn't output to console as it interferes with JSON protocol
+        is_stdio_mode = (
+            self.config.transport == "stdio" or 
+            "--transport" in sys.argv and "stdio" in sys.argv or
+            not sys.stdout.isatty()  # Not a terminal (likely piped/redirected)
+        )
         
-        # Audit log handler (if configured)
-        if self.config.logging.enable_audit and self.config.logging.audit_file_path:
-            try:
-                from logging.handlers import RotatingFileHandler
+        # Setup enhanced logging with cleanup functionality
+        setup_logging(
+            level=self.config.logging.level,
+            log_dir=log_dir,
+            enable_console=not is_stdio_mode,  # Disable console logging in stdio mode
+            enable_file=True,
+            enable_audit=self.config.logging.enable_audit,
+            audit_file=self.config.logging.audit_file_path,
+            max_file_size=self.config.logging.max_file_size,
+            backup_count=self.config.logging.backup_count,
+            enable_cleanup=self.config.logging.enable_cleanup,
+            max_age_days=self.config.logging.max_age_days,
+            cleanup_interval_hours=self.config.logging.cleanup_interval_hours
+        )
         
-                audit_logger = logging.getLogger("audit")
-                audit_handler = RotatingFileHandler(
-                    self.config.logging.audit_file_path,
-                    maxBytes=self.config.logging.max_file_size,
-                    backupCount=self.config.logging.backup_count,
-                    encoding="utf-8",
-                )
-                audit_handler.setFormatter(formatter)
-                audit_logger.addHandler(audit_handler)
-                audit_logger.setLevel(logging.INFO)
-            except Exception as e:
-                self.logger.warning(f"Failed to setup audit logging: {e}")
+        # Update logger to use new system
+        self.logger = get_logger(__name__)
+        
+        self.logger.info("Enhanced logging system with cleanup initialized successfully")
+        self.logger.info(f"Log directory: {log_dir}")
+        self.logger.info(f"Log level: {self.config.logging.level}")
+        self.logger.info(f"Audit logging: {'Enabled' if self.config.logging.enable_audit else 'Disabled'}")
+        self.logger.info(f"Log cleanup: {'Enabled' if self.config.logging.enable_cleanup else 'Disabled'}")
+        if self.config.logging.enable_cleanup:
+            self.logger.info(f"Cleanup config: Max age {self.config.logging.max_age_days} days, interval {self.config.logging.cleanup_interval_hours}h")
 
     def validate_config(self) -> bool:
         """Validate configuration"""

doris_mcp_server/utils/data_exploration_tools.py

@@ -0,0 +1,733 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""
+Data Exploration Tools Module
+Provides table data distribution analysis and exploration capabilities
+"""
+
+import time
+import math
+from datetime import datetime
+from typing import Any, Dict, List, Optional, Union
+
+from .db import DorisConnectionManager
+from .logger import get_logger
+
+logger = get_logger(__name__)
+
+
+class DataExplorationTools:
+    """Data exploration tools for table distribution analysis"""
+    
+    def __init__(self, connection_manager: DorisConnectionManager):
+        self.connection_manager = connection_manager
+        logger.info("DataExplorationTools initialized")
+    
+
+    
+    # ==================== Private Helper Methods ====================
+    
+    def _build_full_table_name(self, table_name: str, catalog_name: Optional[str], db_name: Optional[str]) -> str:
+        """Build full table name with catalog and database using three-part naming convention"""
+        # Default catalog for internal tables
+        effective_catalog = catalog_name if catalog_name else "internal"
+        
+        if db_name:
+            return f"{effective_catalog}.{db_name}.{table_name}"
+        else:
+            # If no db_name provided, need to determine the current database
+            return f"{effective_catalog}.{table_name}"
+    
+    async def _get_table_basic_info(self, connection, table_name: str) -> Optional[Dict]:
+        """Get basic table information including row count"""
+        try:
+            count_sql = f"SELECT COUNT(*) as row_count FROM {table_name}"
+            result = await connection.execute(count_sql)
+            
+            if result.data:
+                return {"row_count": result.data[0]["row_count"]}
+            return None
+        except Exception as e:
+            logger.warning(f"Failed to get basic info for table {table_name}: {str(e)}")
+            return {"row_count": 0}
+    
+    async def _get_table_columns_info(self, connection, table_name: str, catalog_name: Optional[str], db_name: Optional[str]) -> List[Dict]:
+        """Get detailed column information"""
+        try:
+            where_conditions = [f"table_name = '{table_name}'"]
+            
+            if db_name:
+                where_conditions.append(f"table_schema = '{db_name}'")
+            else:
+                where_conditions.append("table_schema = DATABASE()")
+            
+            columns_sql = f"""
+            SELECT 
+                column_name,
+                data_type,
+                is_nullable,
+                column_comment,
+                ordinal_position
+            FROM information_schema.columns 
+            WHERE {' AND '.join(where_conditions)}
+            ORDER BY ordinal_position
+            """
+            
+            result = await connection.execute(columns_sql)
+            return result.data if result.data else []
+            
+        except Exception as e:
+            logger.warning(f"Failed to get columns info for table {table_name}: {str(e)}")
+            return []
+    
+    async def _determine_sampling_strategy(self, connection, table_name: str, total_rows: int, sample_size: int) -> Dict[str, Any]:
+        """Determine optimal sampling strategy based on table size"""
+        if total_rows <= sample_size:
+            # Use all data if table is small enough
+            return {
+                "total_rows": total_rows,
+                "sample_size": total_rows,
+                "sampling_method": "full_scan",
+                "sampling_ratio": 1.0,
+                "use_sampling": False,
+                "sample_table_expression": table_name
+            }
+        else:
+            # Use random sampling for large tables
+            sampling_ratio = sample_size / total_rows
+            return {
+                "total_rows": total_rows,
+                "sample_size": sample_size,
+                "sampling_method": "random_sample",
+                "sampling_ratio": round(sampling_ratio, 4),
+                "use_sampling": True,
+                "sample_table_expression": f"(SELECT * FROM {table_name} ORDER BY RAND() LIMIT {sample_size}) as sample_table"
+            }
+    
+    def _select_analysis_columns(self, columns_info: List[Dict], include_all: bool) -> List[Dict]:
+        """Select columns for analysis based on strategy"""
+        if include_all:
+            return columns_info
+        
+        # If not analyzing all columns, prioritize key columns
+        priority_keywords = ['id', 'key', 'code', 'status', 'type', 'amount', 'count', 'date', 'time']
+        
+        priority_columns = []
+        other_columns = []
+        
+        for col in columns_info:
+            col_name_lower = col["column_name"].lower()
+            if any(keyword in col_name_lower for keyword in priority_keywords):
+                priority_columns.append(col)
+            else:
+                other_columns.append(col)
+        
+        # Return priority columns plus first 10 other columns
+        return priority_columns + other_columns[:10]
+    
+    def _is_numeric_type(self, data_type: str) -> bool:
+        """Check if column type is numeric"""
+        numeric_types = [
+            'tinyint', 'smallint', 'int', 'bigint', 'largeint',
+            'float', 'double', 'decimal', 'numeric'
+        ]
+        return any(num_type in data_type.lower() for num_type in numeric_types)
+    
+    def _is_categorical_type(self, data_type: str) -> bool:
+        """Check if column type is categorical"""
+        categorical_types = ['varchar', 'char', 'string', 'text', 'enum']
+        return any(cat_type in data_type.lower() for cat_type in categorical_types)
+    
+    def _is_temporal_type(self, data_type: str) -> bool:
+        """Check if column type is temporal"""
+        temporal_types = ['date', 'datetime', 'timestamp', 'time']
+        return any(temp_type in data_type.lower() for temp_type in temporal_types)
+    
+    async def _analyze_numeric_distributions(self, connection, table_name: str, numeric_columns: List[Dict], sampling_info: Dict) -> Dict[str, Any]:
+        """Analyze distribution patterns for numeric columns"""
+        numeric_analysis = {}
+        
+        for column in numeric_columns:
+            col_name = column["column_name"]
+            try:
+                # Basic statistics
+                table_expr = sampling_info.get("sample_table_expression", table_name)
+                stats_sql = f"""
+                SELECT 
+                    COUNT({col_name}) as count,
+                    MIN({col_name}) as min_value,
+                    MAX({col_name}) as max_value,
+                    AVG({col_name}) as mean_value,
+                    STDDEV({col_name}) as std_dev
+                FROM {table_expr}
+                WHERE {col_name} IS NOT NULL
+                """
+                
+                stats_result = await connection.execute(stats_sql)
+                
+                if stats_result.data and stats_result.data[0]["count"] > 0:
+                    stats = stats_result.data[0]
+                    
+                    # Percentiles calculation
+                    percentiles = await self._calculate_percentiles(connection, table_name, col_name, sampling_info)
+                    
+                    # Outlier detection
+                    outliers = await self._detect_numeric_outliers(connection, table_name, col_name, percentiles, sampling_info)
+                    
+                    # Distribution shape analysis
+                    distribution_shape = await self._analyze_distribution_shape(
+                        connection, table_name, col_name, stats, percentiles, sampling_info
+                    )
+                    
+                    numeric_analysis[col_name] = {
+                        "data_type": column["data_type"],
+                        "statistics": {
+                            "count": stats["count"],
+                            "mean": round(float(stats["mean_value"]), 4) if stats["mean_value"] else None,
+                            "std": round(float(stats["std_dev"]), 4) if stats["std_dev"] else None,
+                            "min": float(stats["min_value"]) if stats["min_value"] else None,
+                            "max": float(stats["max_value"]) if stats["max_value"] else None,
+                            **percentiles
+                        },
+                        "distribution_shape": distribution_shape,
+                        "outliers": outliers
+                    }
+                    
+            except Exception as e:
+                logger.warning(f"Failed to analyze numeric column {col_name}: {str(e)}")
+                numeric_analysis[col_name] = {"error": str(e)}
+        
+        return numeric_analysis
+    
+    async def _calculate_percentiles(self, connection, table_name: str, col_name: str, sampling_info: Dict) -> Dict[str, float]:
+        """Calculate percentiles for numeric column"""
+        try:
+            table_expr = sampling_info.get("sample_table_expression", table_name)
+            percentile_sql = f"""
+            SELECT 
+                PERCENTILE({col_name}, 0.25) as p25,
+                PERCENTILE({col_name}, 0.50) as p50,
+                PERCENTILE({col_name}, 0.75) as p75,
+                PERCENTILE({col_name}, 0.90) as p90,
+                PERCENTILE({col_name}, 0.95) as p95,
+                PERCENTILE({col_name}, 0.99) as p99
+            FROM {table_expr}
+            WHERE {col_name} IS NOT NULL
+            """
+            
+            result = await connection.execute(percentile_sql)
+            
+            if result.data:
+                data = result.data[0]
+                return {
+                    "25%": round(float(data["p25"]), 4) if data["p25"] else None,
+                    "50%": round(float(data["p50"]), 4) if data["p50"] else None,
+                    "75%": round(float(data["p75"]), 4) if data["p75"] else None,
+                    "90%": round(float(data["p90"]), 4) if data["p90"] else None,
+                    "95%": round(float(data["p95"]), 4) if data["p95"] else None,
+                    "99%": round(float(data["p99"]), 4) if data["p99"] else None
+                }
+        except Exception as e:
+            logger.warning(f"Failed to calculate percentiles for {col_name}: {str(e)}")
+        
+        return {}
+    
+    async def _detect_numeric_outliers(self, connection, table_name: str, col_name: str, percentiles: Dict, sampling_info: Dict) -> Dict[str, Any]:
+        """Detect outliers using IQR method"""
+        try:
+            if "25%" not in percentiles or "75%" not in percentiles:
+                return {"outlier_count": 0, "outlier_rate": 0.0}
+            
+            q1 = percentiles["25%"]
+            q3 = percentiles["75%"]
+            iqr = q3 - q1
+            
+            lower_bound = q1 - 1.5 * iqr
+            upper_bound = q3 + 1.5 * iqr
+            
+            table_expr = sampling_info.get("sample_table_expression", table_name)
+            outlier_sql = f"""
+            SELECT 
+                COUNT(*) as total_count,
+                SUM(CASE WHEN {col_name} < {lower_bound} OR {col_name} > {upper_bound} THEN 1 ELSE 0 END) as outlier_count
+            FROM {table_expr}
+            WHERE {col_name} IS NOT NULL
+            """
+            
+            result = await connection.execute(outlier_sql)
+            
+            if result.data:
+                data = result.data[0]
+                total_count = data["total_count"]
+                outlier_count = data["outlier_count"]
+                outlier_rate = outlier_count / total_count if total_count > 0 else 0
+                
+                return {
+                    "outlier_count": outlier_count,
+                    "outlier_rate": round(outlier_rate, 4),
+                    "outlier_threshold_lower": round(lower_bound, 4),
+                    "outlier_threshold_upper": round(upper_bound, 4),
+                    "iqr": round(iqr, 4)
+                }
+                
+        except Exception as e:
+            logger.warning(f"Failed to detect outliers for {col_name}: {str(e)}")
+        
+        return {"outlier_count": 0, "outlier_rate": 0.0}
+    
+    async def _analyze_distribution_shape(self, connection, table_name: str, col_name: str, stats: Dict, percentiles: Dict, sampling_info: Dict) -> Dict[str, Any]:
+        """Analyze the shape of data distribution"""
+        try:
+            mean = stats.get("mean_value", 0)
+            median = percentiles.get("50%", 0)
+            
+            if mean is None or median is None:
+                return {"distribution_type": "unknown"}
+            
+            # Calculate skewness indicator
+            if abs(mean - median) < 0.01:
+                skew_indicator = "symmetric"
+            elif mean > median:
+                skew_indicator = "right_skewed"
+            else:
+                skew_indicator = "left_skewed"
+            
+            # Estimate kurtosis based on percentile spread
+            if "25%" in percentiles and "75%" in percentiles:
+                iqr = percentiles["75%"] - percentiles["25%"]
+                range_90 = percentiles.get("90%", percentiles["75%"]) - percentiles.get("10%", percentiles["25%"])
+                
+                if iqr > 0:
+                    kurtosis_indicator = "normal" if 2.5 <= range_90/iqr <= 3.5 else ("heavy_tailed" if range_90/iqr > 3.5 else "light_tailed")
+                else:
+                    kurtosis_indicator = "unknown"
+            else:
+                kurtosis_indicator = "unknown"
+            
+            return {
+                "skewness_indicator": skew_indicator,
+                "kurtosis_indicator": kurtosis_indicator,
+                "distribution_type": self._classify_distribution_type(skew_indicator, kurtosis_indicator),
+                "mean_median_ratio": round(mean / median, 4) if median != 0 else None
+            }
+            
+        except Exception as e:
+            logger.warning(f"Failed to analyze distribution shape for {col_name}: {str(e)}")
+            return {"distribution_type": "unknown"}
+    
+    def _classify_distribution_type(self, skew: str, kurtosis: str) -> str:
+        """Classify distribution type based on skewness and kurtosis"""
+        if skew == "symmetric" and kurtosis == "normal":
+            return "approximately_normal"
+        elif skew == "right_skewed":
+            return "right_skewed"
+        elif skew == "left_skewed":
+            return "left_skewed"
+        elif kurtosis == "heavy_tailed":
+            return "heavy_tailed"
+        else:
+            return "non_normal"
+    
+    async def _analyze_categorical_distributions(self, connection, table_name: str, categorical_columns: List[Dict], sampling_info: Dict) -> Dict[str, Any]:
+        """Analyze distribution patterns for categorical columns"""
+        categorical_analysis = {}
+        
+        for column in categorical_columns:
+            col_name = column["column_name"]
+            try:
+                # Basic cardinality and distribution
+                cardinality_sql = f"""
+                SELECT 
+                    COUNT(DISTINCT {col_name}) as cardinality,
+                    COUNT({col_name}) as non_null_count
+                FROM {table_name}
+                WHERE {col_name} IS NOT NULL
+                {sampling_info.get('sample_query_suffix', '')}
+                """
+                
+                cardinality_result = await connection.execute(cardinality_sql)
+                
+                if cardinality_result.data:
+                    cardinality_data = cardinality_result.data[0]
+                    cardinality = cardinality_data["cardinality"]
+                    non_null_count = cardinality_data["non_null_count"]
+                    
+                    # Value distribution (top values)
+                    value_distribution = await self._get_categorical_value_distribution(
+                        connection, table_name, col_name, sampling_info, non_null_count
+                    )
+                    
+                    # Calculate entropy and concentration
+                    entropy = self._calculate_entropy(value_distribution)
+                    concentration_ratio = value_distribution[0]["percentage"] if value_distribution else 0
+                    
+                    categorical_analysis[col_name] = {
+                        "data_type": column["data_type"],
+                        "cardinality": cardinality,
+                        "non_null_count": non_null_count,
+                        "value_distribution": value_distribution,
+                        "entropy": round(entropy, 3),
+                        "concentration_ratio": round(concentration_ratio, 4),
+                        "diversity_score": round(cardinality / non_null_count, 4) if non_null_count > 0 else 0
+                    }
+                    
+            except Exception as e:
+                logger.warning(f"Failed to analyze categorical column {col_name}: {str(e)}")
+                categorical_analysis[col_name] = {"error": str(e)}
+        
+        return categorical_analysis
+    
+    async def _get_categorical_value_distribution(self, connection, table_name: str, col_name: str, sampling_info: Dict, total_count: int) -> List[Dict]:
+        """Get value distribution for categorical column"""
+        try:
+            # Use sample table expression if sampling is enabled
+            table_expr = sampling_info.get("sample_table_expression", table_name)
+            
+            distribution_sql = f"""
+            SELECT 
+                {col_name} as value,
+                COUNT(*) as count
+            FROM {table_expr}
+            WHERE {col_name} IS NOT NULL
+            GROUP BY {col_name}
+            ORDER BY COUNT(*) DESC
+            LIMIT 20
+            """
+            
+            result = await connection.execute(distribution_sql)
+            
+            if result.data:
+                distribution = []
+                for row in result.data:
+                    count = row["count"]
+                    percentage = count / total_count if total_count > 0 else 0
+                    distribution.append({
+                        "value": str(row["value"]),
+                        "count": count,
+                        "percentage": round(percentage, 4)
+                    })
+                return distribution
+            
+        except Exception as e:
+            logger.warning(f"Failed to get value distribution for {col_name}: {str(e)}")
+        
+        return []
+    
+    def _calculate_entropy(self, value_distribution: List[Dict]) -> float:
+        """Calculate Shannon entropy for categorical distribution"""
+        if not value_distribution:
+            return 0.0
+        
+        entropy = 0.0
+        for item in value_distribution:
+            p = item["percentage"]
+            if p > 0:
+                entropy -= p * math.log2(p)
+        
+        return entropy
+    
+    async def _analyze_temporal_distributions(self, connection, table_name: str, temporal_columns: List[Dict], sampling_info: Dict) -> Dict[str, Any]:
+        """Analyze distribution patterns for temporal columns"""
+        temporal_analysis = {}
+        
+        for column in temporal_columns:
+            col_name = column["column_name"]
+            try:
+                # Date range analysis
+                table_expr = sampling_info.get("sample_table_expression", table_name)
+                range_sql = f"""
+                SELECT 
+                    MIN({col_name}) as earliest,
+                    MAX({col_name}) as latest,
+                    COUNT({col_name}) as non_null_count
+                FROM {table_expr}
+                WHERE {col_name} IS NOT NULL
+                """
+                
+                range_result = await connection.execute(range_sql)
+                
+                if range_result.data and range_result.data[0]["non_null_count"] > 0:
+                    range_data = range_result.data[0]
+                    earliest = range_data["earliest"]
+                    latest = range_data["latest"]
+                    
+                    # Calculate span
+                    date_span_info = self._calculate_date_span(earliest, latest)
+                    
+                    # Temporal patterns analysis
+                    temporal_patterns = await self._analyze_temporal_patterns(
+                        connection, table_name, col_name, sampling_info
+                    )
+                    
+                    temporal_analysis[col_name] = {
+                        "data_type": column["data_type"],
+                        "non_null_count": range_data["non_null_count"],
+                        "date_range": {
+                            "earliest": str(earliest),
+                            "latest": str(latest),
+                            **date_span_info
+                        },
+                        "temporal_patterns": temporal_patterns
+                    }
+                    
+            except Exception as e:
+                logger.warning(f"Failed to analyze temporal column {col_name}: {str(e)}")
+                temporal_analysis[col_name] = {"error": str(e)}
+        
+        return temporal_analysis
+    
+    def _calculate_date_span(self, earliest, latest) -> Dict[str, Any]:
+        """Calculate date span information"""
+        try:
+            if isinstance(earliest, str):
+                earliest = datetime.fromisoformat(earliest.replace('Z', '+00:00'))
+            if isinstance(latest, str):
+                latest = datetime.fromisoformat(latest.replace('Z', '+00:00'))
+            
+            span = latest - earliest
+            span_days = span.days
+            
+            return {
+                "span_days": span_days,
+                "span_years": round(span_days / 365.25, 2),
+                "span_description": self._describe_time_span(span_days)
+            }
+            
+        except Exception as e:
+            logger.warning(f"Failed to calculate date span: {str(e)}")
+            return {"span_days": 0}
+    
+    def _describe_time_span(self, days: int) -> str:
+        """Describe time span in human readable format"""
+        if days < 1:
+            return "less_than_day"
+        elif days < 7:
+            return "days"
+        elif days < 30:
+            return "weeks"
+        elif days < 365:
+            return "months"
+        else:
+            return "years"
+    
+    async def _analyze_temporal_patterns(self, connection, table_name: str, col_name: str, sampling_info: Dict) -> Dict[str, Any]:
+        """Analyze temporal patterns like seasonality and trends"""
+        try:
+            table_expr = sampling_info.get("sample_table_expression", table_name)
+            # Weekly pattern analysis
+            weekly_pattern_sql = f"""
+            SELECT 
+                DAYOFWEEK({col_name}) as day_of_week,
+                COUNT(*) as count
+            FROM {table_expr}
+            WHERE {col_name} IS NOT NULL
+            GROUP BY DAYOFWEEK({col_name})
+            ORDER BY day_of_week
+            """
+            
+            weekly_result = await connection.execute(weekly_pattern_sql)
+            
+            weekly_pattern = []
+            if weekly_result.data:
+                total_records = sum(row["count"] for row in weekly_result.data)
+                for row in weekly_result.data:
+                    percentage = row["count"] / total_records if total_records > 0 else 0
+                    weekly_pattern.append(round(percentage, 3))
+            
+            # Monthly trend analysis (simplified)
+            monthly_trend_sql = f"""
+            SELECT 
+                YEAR({col_name}) as year,
+                MONTH({col_name}) as month,
+                COUNT(*) as count
+            FROM {table_expr}
+            WHERE {col_name} IS NOT NULL
+            GROUP BY YEAR({col_name}), MONTH({col_name})
+            ORDER BY year, month
+            LIMIT 12
+            """
+            
+            monthly_result = await connection.execute(monthly_trend_sql)
+            monthly_trend = "stable"  # Simplified trend analysis
+            
+            if monthly_result.data and len(monthly_result.data) > 3:
+                counts = [row["count"] for row in monthly_result.data]
+                if len(counts) > 1:
+                    trend_direction = "increasing" if counts[-1] > counts[0] else "decreasing"
+                    monthly_trend = trend_direction
+            
+            return {
+                "weekly_pattern": weekly_pattern,
+                "monthly_trend": monthly_trend,
+                "seasonal_component": self._estimate_seasonality(weekly_pattern)
+            }
+            
+        except Exception as e:
+            logger.warning(f"Failed to analyze temporal patterns for {col_name}: {str(e)}")
+            return {"weekly_pattern": [], "monthly_trend": "unknown"}
+    
+    def _estimate_seasonality(self, weekly_pattern: List[float]) -> float:
+        """Estimate seasonality strength based on weekly pattern variance"""
+        if len(weekly_pattern) < 7:
+            return 0.0
+        
+        mean_percentage = sum(weekly_pattern) / len(weekly_pattern)
+        variance = sum((x - mean_percentage) ** 2 for x in weekly_pattern) / len(weekly_pattern)
+        
+        # Normalize variance to 0-1 scale as seasonality indicator
+        seasonality = min(variance * 10, 1.0)  # Scaling factor
+        return round(seasonality, 3)
+    
+    async def _generate_data_quality_insights(self, connection, table_name: str, columns: List[Dict], sampling_info: Dict) -> Dict[str, Any]:
+        """Generate overall data quality insights"""
+        try:
+            total_columns = len(columns)
+            
+            # Calculate null rates across all columns
+            null_analysis = await self._analyze_overall_null_rates(connection, table_name, columns, sampling_info)
+            
+            # Identify potential data quality issues
+            quality_issues = []
+            
+            # High null rate columns
+            high_null_columns = [col for col, rate in null_analysis["column_null_rates"].items() if rate > 0.2]
+            if high_null_columns:
+                quality_issues.append({
+                    "issue_type": "high_null_rates",
+                    "severity": "medium",
+                    "affected_columns": high_null_columns,
+                    "description": f"{len(high_null_columns)} columns have null rates > 20%"
+                })
+            
+            # Calculate overall data quality score
+            avg_null_rate = sum(null_analysis["column_null_rates"].values()) / len(null_analysis["column_null_rates"]) if null_analysis["column_null_rates"] else 0
+            data_quality_score = max(0, 1 - avg_null_rate)
+            
+            return {
+                "total_columns_analyzed": total_columns,
+                "null_analysis": null_analysis,
+                "data_quality_score": round(data_quality_score, 3),
+                "quality_issues": quality_issues,
+                "recommendations": self._generate_quality_recommendations(quality_issues, null_analysis)
+            }
+            
+        except Exception as e:
+            logger.warning(f"Failed to generate data quality insights: {str(e)}")
+            return {"data_quality_score": 0.0, "error": str(e)}
+    
+    async def _analyze_overall_null_rates(self, connection, table_name: str, columns: List[Dict], sampling_info: Dict) -> Dict[str, Any]:
+        """Analyze null rates across all columns"""
+        column_null_rates = {}
+        total_null_count = 0
+        total_cell_count = 0
+        
+        for column in columns:
+            col_name = column["column_name"]
+            try:
+                table_expr = sampling_info.get("sample_table_expression", table_name)
+                null_sql = f"""
+                SELECT 
+                    COUNT(*) as total_count,
+                    COUNT({col_name}) as non_null_count
+                FROM {table_expr}
+                """
+                
+                result = await connection.execute(null_sql)
+                if result.data:
+                    data = result.data[0]
+                    total_count = data["total_count"]
+                    non_null_count = data["non_null_count"]
+                    null_count = total_count - non_null_count
+                    null_rate = null_count / total_count if total_count > 0 else 0
+                    
+                    column_null_rates[col_name] = round(null_rate, 4)
+                    total_null_count += null_count
+                    total_cell_count += total_count
+                    
+            except Exception as e:
+                logger.warning(f"Failed to analyze null rate for column {col_name}: {str(e)}")
+                column_null_rates[col_name] = 0.0
+        
+        overall_null_rate = total_null_count / total_cell_count if total_cell_count > 0 else 0
+        
+        return {
+            "column_null_rates": column_null_rates,
+            "overall_null_rate": round(overall_null_rate, 4),
+            "columns_with_nulls": len([rate for rate in column_null_rates.values() if rate > 0])
+        }
+    
+    def _generate_quality_recommendations(self, quality_issues: List[Dict], null_analysis: Dict) -> List[Dict]:
+        """Generate data quality improvement recommendations"""
+        recommendations = []
+        
+        # Recommendations based on null analysis
+        overall_null_rate = null_analysis.get("overall_null_rate", 0)
+        if overall_null_rate > 0.1:
+            recommendations.append({
+                "type": "data_completeness",
+                "priority": "high" if overall_null_rate > 0.3 else "medium",
+                "description": f"Overall null rate is {overall_null_rate:.1%}",
+                "action": "Review data collection and validation processes"
+            })
+        
+        # Recommendations based on quality issues
+        for issue in quality_issues:
+            if issue["issue_type"] == "high_null_rates":
+                recommendations.append({
+                    "type": "column_completeness",
+                    "priority": issue["severity"],
+                    "description": issue["description"],
+                    "action": f"Focus on improving data completeness for: {', '.join(issue['affected_columns'][:3])}"
+                })
+        
+        return recommendations
+    
+    def _generate_analysis_summary(self, distribution_analysis: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate high-level summary of distribution analysis"""
+        summary = {
+            "numeric_columns_count": len(distribution_analysis.get("numeric_columns", {})),
+            "categorical_columns_count": len(distribution_analysis.get("categorical_columns", {})),
+            "temporal_columns_count": len(distribution_analysis.get("temporal_columns", {}))
+        }
+        
+        # Identify interesting patterns
+        patterns = []
+        
+        # Check for highly skewed numeric columns
+        numeric_cols = distribution_analysis.get("numeric_columns", {})
+        skewed_cols = [
+            col for col, info in numeric_cols.items() 
+            if isinstance(info, dict) and 
+            info.get("distribution_shape", {}).get("skewness_indicator") in ["right_skewed", "left_skewed"]
+        ]
+        
+        if skewed_cols:
+            patterns.append(f"Found {len(skewed_cols)} skewed numeric columns")
+        
+        # Check for high cardinality categorical columns
+        categorical_cols = distribution_analysis.get("categorical_columns", {})
+        high_cardinality_cols = [
+            col for col, info in categorical_cols.items()
+            if isinstance(info, dict) and info.get("cardinality", 0) > 1000
+        ]
+        
+        if high_cardinality_cols:
+            patterns.append(f"Found {len(high_cardinality_cols)} high cardinality categorical columns")
+        
+        summary["notable_patterns"] = patterns
+        
+        return summary 

doris_mcp_server/utils/data_governance_tools.py

@@ -0,0 +1,897 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""
+Data Governance Tools Module
+Provides data completeness analysis, field lineage tracking, and data freshness monitoring
+"""
+
+import re
+import time
+from datetime import datetime, timedelta
+from typing import Any, Dict, List, Optional
+
+from .db import DorisConnectionManager
+from .logger import get_logger
+
+logger = get_logger(__name__)
+
+
+class DataGovernanceTools:
+    """Data governance tools suite"""
+    
+    def __init__(self, connection_manager: DorisConnectionManager):
+        self.connection_manager = connection_manager
+        logger.info("DataGovernanceTools initialized")
+    
+
+    
+    async def trace_column_lineage(
+        self, 
+        table_name: str, 
+        column_name: str,
+        depth: int = 3,
+        catalog_name: Optional[str] = None,
+        db_name: Optional[str] = None
+    ) -> Dict[str, Any]:
+        """
+        Column-level lineage tracing
+        
+        Args:
+            table_name: Table name
+            column_name: Column name
+            depth: Trace depth
+            catalog_name: Catalog name
+            db_name: Database name
+        """
+        try:
+            start_time = time.time()
+            
+            # 🚀 PROGRESS: Initialize column lineage tracing
+            logger.info("=" * 60)
+            logger.info(f"🔍 Starting Column Lineage Tracing")
+            logger.info(f"📊 Target: {table_name}.{column_name}")
+            logger.info(f"🎯 Trace depth: {depth}")
+            logger.info("=" * 60)
+            
+            connection = await self.connection_manager.get_connection("query")
+            
+            full_table_name = self._build_full_table_name(table_name, catalog_name, db_name)
+            target_column = f"{full_table_name}.{column_name}"
+            
+            logger.info(f"📝 Full target: {target_column}")
+            
+            # 🚀 PROGRESS: Step 1 - Verify target column exists
+            logger.info("🔍 Step 1/4: Verifying target column exists...")
+            verify_start = time.time()
+            if not await self._verify_column_exists(connection, full_table_name, column_name):
+                logger.error(f"❌ Column {column_name} not found in table {full_table_name}")
+                return {"error": f"Column {column_name} not found in table {full_table_name}"}
+            
+            verify_time = time.time() - verify_start
+            logger.info(f"✅ Column verified in {verify_time:.2f}s")
+            
+            # 🚀 PROGRESS: Step 2 - Analyze SQL logs for lineage relationships
+            logger.info(f"📊 Step 2/4: Analyzing SQL logs for lineage (depth={depth})...")
+            lineage_start = time.time()
+            source_chain = await self._analyze_sql_logs_for_lineage(
+                connection, full_table_name, column_name, depth
+            )
+            lineage_time = time.time() - lineage_start
+            logger.info(f"✅ Found {len(source_chain)} lineage relationships in {lineage_time:.2f}s")
+            
+            # 🚀 PROGRESS: Step 3 - Analyze downstream usage
+            logger.info("⬇️ Step 3/4: Analyzing downstream column usage...")
+            downstream_start = time.time()
+            downstream_usage = await self._analyze_downstream_column_usage(
+                connection, full_table_name, column_name
+            )
+            downstream_time = time.time() - downstream_start
+            logger.info(f"✅ Found {len(downstream_usage)} downstream usages in {downstream_time:.2f}s")
+            
+            # 🚀 PROGRESS: Step 4 - Extract transformation rules
+            logger.info("🔄 Step 4/4: Extracting transformation rules...")
+            transform_start = time.time()
+            transformation_rules = await self._extract_transformation_rules(
+                connection, full_table_name, column_name
+            )
+            transform_time = time.time() - transform_start
+            logger.info(f"✅ Found {len(transformation_rules)} transformation rules in {transform_time:.2f}s")
+            
+            execution_time = time.time() - start_time
+            
+            return {
+                "target_column": target_column,
+                "analysis_timestamp": datetime.now().isoformat(),
+                "execution_time_seconds": round(execution_time, 3),
+                "lineage_depth": depth,
+                "source_chain": source_chain,
+                "downstream_usage": downstream_usage,
+                "transformation_rules": transformation_rules,
+                "lineage_confidence": self._calculate_lineage_confidence(source_chain),
+                "impact_analysis": {
+                    "upstream_dependencies": len(source_chain),
+                    "downstream_dependencies": len(downstream_usage),
+                    "risk_level": self._assess_lineage_risk(source_chain, downstream_usage)
+                }
+            }
+            
+        except Exception as e:
+            logger.error(f"Column lineage tracing failed for {table_name}.{column_name}: {str(e)}")
+            return {
+                "error": str(e),
+                "target_column": f"{table_name}.{column_name}",
+                "analysis_timestamp": datetime.now().isoformat()
+            }
+    
+    async def monitor_data_freshness(
+        self, 
+        tables: Optional[List[str]] = None,
+        time_threshold_hours: int = 24,
+        catalog_name: Optional[str] = None,
+        db_name: Optional[str] = None
+    ) -> Dict[str, Any]:
+        """
+        Data freshness monitoring
+        
+        Args:
+            tables: List of tables to monitor, empty means monitor all tables
+            time_threshold_hours: Freshness threshold (hours)
+            catalog_name: Catalog name
+            db_name: Database name
+        """
+        try:
+            start_time = time.time()
+            connection = await self.connection_manager.get_connection("query")
+            
+            # 1. Get list of tables to monitor
+            if not tables:
+                tables = await self._get_all_tables(connection, catalog_name, db_name)
+            
+            # 2. Analyze freshness of each table
+            table_freshness = {}
+            fresh_count = 0
+            stale_count = 0
+            
+            for table in tables:
+                full_table_name = self._build_full_table_name(table, catalog_name, db_name)
+                freshness_info = await self._analyze_table_freshness(
+                    connection, full_table_name, time_threshold_hours
+                )
+                table_freshness[table] = freshness_info
+                
+                if freshness_info["status"] == "fresh":
+                    fresh_count += 1
+                else:
+                    stale_count += 1
+            
+            # 3. Calculate overall freshness score
+            total_tables = len(tables)
+            overall_freshness_score = fresh_count / total_tables if total_tables > 0 else 0
+            
+            # 4. Identify data flow issues
+            data_flow_issues = await self._identify_data_flow_issues(table_freshness)
+            
+            execution_time = time.time() - start_time
+            
+            return {
+                "monitoring_timestamp": datetime.now().isoformat(),
+                "execution_time_seconds": round(execution_time, 3),
+                "monitoring_scope": {
+                    "catalog_name": catalog_name,
+                    "db_name": db_name,
+                    "time_threshold_hours": time_threshold_hours
+                },
+                "freshness_summary": {
+                    "total_tables": total_tables,
+                    "fresh_tables": fresh_count,
+                    "stale_tables": stale_count,
+                    "overall_freshness_score": round(overall_freshness_score, 3)
+                },
+                "table_freshness": table_freshness,
+                "data_flow_issues": data_flow_issues,
+                "alerts": self._generate_freshness_alerts(table_freshness, time_threshold_hours)
+            }
+            
+        except Exception as e:
+            logger.error(f"Data freshness monitoring failed: {str(e)}")
+            return {
+                "error": str(e),
+                "monitoring_timestamp": datetime.now().isoformat()
+            }
+    
+    # ==================== Private Helper Methods ====================
+    
+    def _build_full_table_name(self, table_name: str, catalog_name: Optional[str], db_name: Optional[str]) -> str:
+        """Build full table name - use three-level naming convention"""
+        # Default catalog is internal for internal tables
+        effective_catalog = catalog_name if catalog_name else "internal"
+        
+        if db_name:
+            return f"{effective_catalog}.{db_name}.{table_name}"
+        else:
+            # If db_name is not provided, need to determine current database
+            return f"{effective_catalog}.{table_name}"
+    
+    async def _get_table_basic_info(self, connection, table_name: str) -> Optional[Dict]:
+        """Get table basic information"""
+        try:
+            # Try to get table row count
+            count_sql = f"SELECT COUNT(*) as row_count FROM {table_name}"
+            result = await connection.execute(count_sql)
+            
+            if result.data:
+                return {"row_count": result.data[0]["row_count"]}
+            return None
+        except Exception as e:
+            logger.warning(f"Failed to get basic info for table {table_name}: {str(e)}")
+            return {"row_count": 0}
+    
+    async def _get_table_columns_info(self, connection, table_name: str, catalog_name: Optional[str], db_name: Optional[str]) -> List[Dict]:
+        """Get table column information"""
+        try:
+            # Build query conditions
+            where_conditions = [f"table_name = '{table_name}'"]
+            
+            if db_name:
+                where_conditions.append(f"table_schema = '{db_name}'")
+            else:
+                where_conditions.append("table_schema = DATABASE()")
+            
+            columns_sql = f"""
+            SELECT 
+                column_name,
+                data_type,
+                is_nullable,
+                column_comment,
+                ordinal_position
+            FROM information_schema.columns 
+            WHERE {' AND '.join(where_conditions)}
+            ORDER BY ordinal_position
+            """
+            
+            result = await connection.execute(columns_sql)
+            return result.data if result.data else []
+            
+        except Exception as e:
+            logger.warning(f"Failed to get columns info for table {table_name}: {str(e)}")
+            return []
+    
+    async def _analyze_column_completeness(self, connection, table_name: str, columns_info: List[Dict]) -> Dict[str, Any]:
+        """Analyze column completeness"""
+        column_completeness = {}
+        
+        for column in columns_info:
+            column_name = column["column_name"]
+            try:
+                # Calculate null value statistics
+                null_sql = f"""
+                SELECT 
+                    COUNT(*) as total_count,
+                    COUNT({column_name}) as non_null_count,
+                    COUNT(*) - COUNT({column_name}) as null_count
+                FROM {table_name}
+                """
+                
+                result = await connection.execute(null_sql)
+                if result.data:
+                    stats = result.data[0]
+                    total_count = stats["total_count"]
+                    null_count = stats["null_count"]
+                    null_rate = null_count / total_count if total_count > 0 else 0
+                    completeness_score = 1.0 - null_rate
+                    
+                    column_completeness[column_name] = {
+                        "data_type": column["data_type"],
+                        "is_nullable": column["is_nullable"],
+                        "total_count": total_count,
+                        "null_count": null_count,
+                        "non_null_count": stats["non_null_count"],
+                        "null_rate": round(null_rate, 4),
+                        "completeness_score": round(completeness_score, 4)
+                    }
+                    
+            except Exception as e:
+                logger.warning(f"Failed to analyze completeness for column {column_name}: {str(e)}")
+                column_completeness[column_name] = {
+                    "error": str(e),
+                    "completeness_score": 0.0
+                }
+        
+        return column_completeness
+    
+    async def _check_business_rule_compliance(self, connection, table_name: str, business_rules: List[Dict], total_rows: int) -> Dict[str, Any]:
+        """Check business rule compliance"""
+        compliance_results = {}
+        
+        for rule in business_rules:
+            rule_name = rule.get("rule_name", "unknown")
+            sql_condition = rule.get("sql_condition", "")
+            
+            if not sql_condition:
+                continue
+                
+            try:
+                # Check number of records meeting conditions
+                compliance_sql = f"""
+                SELECT 
+                    COUNT(*) as total_count,
+                    SUM(CASE WHEN {sql_condition} THEN 1 ELSE 0 END) as pass_count
+                FROM {table_name}
+                """
+                
+                result = await connection.execute(compliance_sql)
+                if result.data:
+                    stats = result.data[0]
+                    pass_count = stats["pass_count"] or 0
+                    fail_count = total_rows - pass_count
+                    pass_rate = pass_count / total_rows if total_rows > 0 else 0
+                    
+                    compliance_results[rule_name] = {
+                        "rule_condition": sql_condition,
+                        "total_records": total_rows,
+                        "pass_count": pass_count,
+                        "fail_count": fail_count,
+                        "pass_rate": round(pass_rate, 4),
+                        "compliance_score": round(pass_rate, 4)
+                    }
+                    
+            except Exception as e:
+                logger.warning(f"Failed to check business rule {rule_name}: {str(e)}")
+                compliance_results[rule_name] = {
+                    "error": str(e),
+                    "compliance_score": 0.0
+                }
+        
+        return compliance_results
+    
+    async def _detect_data_integrity_issues(self, connection, table_name: str, columns_info: List[Dict]) -> List[Dict]:
+        """Detect data integrity issues"""
+        issues = []
+        
+        try:
+            # Detect duplicate values in primary key fields
+            primary_key_columns = [col["column_name"] for col in columns_info if "primary" in col.get("column_comment", "").lower()]
+            
+            for pk_col in primary_key_columns:
+                duplicate_sql = f"""
+                SELECT COUNT(*) as duplicate_count
+                FROM (
+                    SELECT {pk_col}, COUNT(*) as cnt
+                    FROM {table_name}
+                    WHERE {pk_col} IS NOT NULL
+                    GROUP BY {pk_col}
+                    HAVING COUNT(*) > 1
+                ) t
+                """
+                
+                result = await connection.execute(duplicate_sql)
+                if result.data and result.data[0]["duplicate_count"] > 0:
+                    issues.append({
+                        "type": "duplicate_primary_keys",
+                        "column": pk_col,
+                        "count": result.data[0]["duplicate_count"],
+                        "severity": "high",
+                        "description": f"Found duplicate values in primary key column {pk_col}"
+                    })
+                    
+        except Exception as e:
+            logger.warning(f"Failed to detect integrity issues: {str(e)}")
+            issues.append({
+                "type": "detection_error",
+                "error": str(e),
+                "severity": "unknown"
+            })
+        
+        return issues
+    
+    def _calculate_completeness_score(self, column_completeness: Dict, business_rule_compliance: Dict) -> float:
+        """Calculate overall completeness score"""
+        if not column_completeness:
+            return 0.0
+            
+        # Calculate column completeness average score
+        column_scores = [
+            col_info.get("completeness_score", 0.0) 
+            for col_info in column_completeness.values() 
+            if isinstance(col_info, dict) and "completeness_score" in col_info
+        ]
+        avg_column_score = sum(column_scores) / len(column_scores) if column_scores else 0.0
+        
+        # Calculate business rule compliance average score
+        compliance_scores = [
+            rule_info.get("compliance_score", 0.0) 
+            for rule_info in business_rule_compliance.values() 
+            if isinstance(rule_info, dict) and "compliance_score" in rule_info
+        ]
+        avg_compliance_score = sum(compliance_scores) / len(compliance_scores) if compliance_scores else 1.0
+        
+        # Comprehensive score (column completeness weight 70%, business rules weight 30%)
+        overall_score = avg_column_score * 0.7 + avg_compliance_score * 0.3
+        return round(overall_score, 4)
+    
+    def _generate_completeness_recommendations(self, column_completeness: Dict, integrity_issues: List[Dict]) -> List[Dict]:
+        """Generate completeness improvement recommendations"""
+        recommendations = []
+        
+        # Generate recommendations based on column completeness
+        for col_name, col_info in column_completeness.items():
+            if isinstance(col_info, dict):
+                null_rate = col_info.get("null_rate", 0)
+                if null_rate > 0.1:  # Null rate exceeds 10%
+                    recommendations.append({
+                        "type": "high_null_rate",
+                        "column": col_name,
+                        "priority": "high" if null_rate > 0.5 else "medium",
+                        "description": f"Column {col_name} has high null rate ({null_rate:.1%})",
+                        "suggested_action": "Review data collection process or add data validation"
+                    })
+        
+        # Generate recommendations based on integrity issues
+        for issue in integrity_issues:
+            if issue["type"] == "duplicate_primary_keys":
+                recommendations.append({
+                    "type": "data_deduplication",
+                    "column": issue["column"],
+                    "priority": "high",
+                    "description": f"Duplicate primary key values found in {issue['column']}",
+                    "suggested_action": "Implement unique constraint or data deduplication process"
+                })
+        
+        return recommendations
+    
+    async def _verify_column_exists(self, connection, table_name: str, column_name: str) -> bool:
+        """Verify if column exists"""
+        try:
+            # Simple verification method: try to query the column
+            verify_sql = f"SELECT {column_name} FROM {table_name} LIMIT 1"
+            await connection.execute(verify_sql)
+            return True
+        except Exception:
+            return False
+    
+    async def _analyze_sql_logs_for_lineage(self, connection, table_name: str, column_name: str, depth: int) -> List[Dict]:
+        """Analyze SQL logs to get lineage relationships (simplified implementation)"""
+        # Note: This is a simplified implementation, actual environment needs to analyze audit logs
+        source_chain = []
+        
+        try:
+            # Try to find related INSERT/CREATE TABLE AS SELECT statements from audit logs (one year range)
+            audit_sql = """
+            SELECT 
+                stmt as sql_statement,
+                `time` as execution_time,
+                `user` as user_name
+            FROM internal.__internal_schema.audit_log 
+            WHERE stmt LIKE '%{}%' 
+                AND (stmt LIKE '%INSERT%' OR stmt LIKE '%CREATE%' OR stmt LIKE '%SELECT%')
+                AND `time` >= DATE_SUB(NOW(), INTERVAL 1 YEAR)
+            ORDER BY `time` DESC 
+            LIMIT 50
+            """.format(table_name.split('.')[-1])  # Use the last part of table name
+            
+            result = await connection.execute(audit_sql)
+            
+            if result.data:
+                for i, log_entry in enumerate(result.data[:depth]):
+                    # Simplified lineage analysis: extract possible source tables
+                    sql_stmt = log_entry.get("sql_statement", "")
+                    source_tables = self._extract_source_tables_from_sql(sql_stmt)
+                    
+                    if source_tables:
+                        # Handle datetime serialization issue
+                        execution_time = log_entry.get("execution_time")
+                        if execution_time and hasattr(execution_time, 'isoformat'):
+                            execution_time = execution_time.isoformat()
+                        elif execution_time:
+                            execution_time = str(execution_time)
+                        
+                        source_chain.append({
+                            "level": i + 1,
+                            "source_table": source_tables[0],  # Take the first as main source table
+                            "source_column": column_name,  # Simplified: assume same name
+                            "transformation": self._extract_transformation_from_sql(sql_stmt, column_name),
+                            "confidence": 0.8 - (i * 0.1),  # Decreasing confidence
+                            "execution_time": execution_time,
+                            "user": log_entry.get("user_name")
+                        })
+                        
+        except Exception as e:
+            logger.warning(f"Failed to analyze SQL logs for lineage: {str(e)}")
+            # If unable to get from audit logs, return basic information
+            source_chain = [{
+                "level": 1,
+                "source_table": "unknown_source",
+                "source_column": column_name,
+                "transformation": "unknown",
+                "confidence": 0.3,
+                "note": "Limited lineage information available"
+            }]
+        
+        return source_chain
+    
+    def _extract_source_tables_from_sql(self, sql: str) -> List[str]:
+        """Extract source table names from SQL statement (simplified implementation)"""
+        # Simplified regex to match table names in FROM clause
+        from_pattern = r'\bFROM\s+([a-zA-Z_][a-zA-Z0-9_]*(?:\.[a-zA-Z_][a-zA-Z0-9_]*)*)'
+        join_pattern = r'\bJOIN\s+([a-zA-Z_][a-zA-Z0-9_]*(?:\.[a-zA-Z_][a-zA-Z0-9_]*)*)'
+        
+        tables = []
+        
+        # Find tables in FROM clause
+        from_matches = re.findall(from_pattern, sql, re.IGNORECASE)
+        tables.extend(from_matches)
+        
+        # Find tables in JOIN clause
+        join_matches = re.findall(join_pattern, sql, re.IGNORECASE)
+        tables.extend(join_matches)
+        
+        return list(set(tables))  # Remove duplicates
+    
+    def _extract_transformation_from_sql(self, sql: str, column_name: str) -> str:
+        """Extract field transformation rules from SQL statement (simplified implementation)"""
+        # Simplified implementation: find expressions containing target field
+        lines = sql.split('\n')
+        for line in lines:
+            if column_name in line and ('SELECT' in line.upper() or '=' in line):
+                return line.strip()
+        
+        return "direct_copy"
+    
+    async def _analyze_downstream_column_usage(self, connection, table_name: str, column_name: str) -> List[Dict]:
+        """Analyze downstream usage of field (simplified implementation)"""
+        downstream_usage = []
+        
+        try:
+            # Find other tables that might use this field (through audit logs, one year range)
+            usage_sql = """
+            SELECT DISTINCT
+                stmt as sql_statement
+            FROM internal.__internal_schema.audit_log 
+            WHERE stmt LIKE '%{}%' 
+                AND stmt LIKE '%{}%'
+                AND stmt LIKE '%SELECT%'
+                AND `time` >= DATE_SUB(NOW(), INTERVAL 1 YEAR)
+            LIMIT 20
+            """.format(table_name.split('.')[-1], column_name)
+            
+            result = await connection.execute(usage_sql)
+            
+            if result.data:
+                for entry in result.data:
+                    sql_stmt = entry.get("sql_statement", "")
+                    target_tables = self._extract_target_tables_from_sql(sql_stmt)
+                    
+                    for target_table in target_tables:
+                        if target_table != table_name.split('.')[-1]:  # Not the source table itself
+                            downstream_usage.append({
+                                "table": target_table,
+                                "column": column_name,  # Simplified: assume same name
+                                "usage_type": "select_reference",
+                                "confidence": 0.7
+                            })
+                            
+        except Exception as e:
+            logger.warning(f"Failed to analyze downstream usage: {str(e)}")
+        
+        return downstream_usage
+    
+    def _extract_target_tables_from_sql(self, sql: str) -> List[str]:
+        """Extract target table names from SQL statement"""
+        # Find target tables in INSERT INTO or CREATE TABLE statements
+        insert_pattern = r'\bINSERT\s+INTO\s+([a-zA-Z_][a-zA-Z0-9_]*(?:\.[a-zA-Z_][a-zA-Z0-9_]*)*)'
+        create_pattern = r'\bCREATE\s+TABLE\s+([a-zA-Z_][a-zA-Z0-9_]*(?:\.[a-zA-Z_][a-zA-Z0-9_]*)*)'
+        
+        tables = []
+        
+        insert_matches = re.findall(insert_pattern, sql, re.IGNORECASE)
+        tables.extend(insert_matches)
+        
+        create_matches = re.findall(create_pattern, sql, re.IGNORECASE)
+        tables.extend(create_matches)
+        
+        return list(set(tables))
+    
+    async def _extract_transformation_rules(self, connection, table_name: str, column_name: str) -> List[Dict]:
+        """Extract field transformation rules"""
+        # Simplified implementation: return basic transformation information
+        return [{
+            "transformation_type": "unknown",
+            "description": "Transformation rules analysis requires detailed ETL metadata",
+            "confidence": 0.5
+        }]
+    
+    def _calculate_lineage_confidence(self, source_chain: List[Dict]) -> float:
+        """Calculate overall confidence of lineage tracing"""
+        if not source_chain:
+            return 0.0
+        
+        confidences = [item.get("confidence", 0.0) for item in source_chain]
+        return round(sum(confidences) / len(confidences), 3)
+    
+    def _assess_lineage_risk(self, source_chain: List[Dict], downstream_usage: List[Dict]) -> str:
+        """Assess lineage risk level"""
+        if len(downstream_usage) > 10:
+            return "high"
+        elif len(downstream_usage) > 5:
+            return "medium"
+        else:
+            return "low"
+    
+    async def _get_all_tables(self, connection, catalog_name: Optional[str], db_name: Optional[str]) -> List[str]:
+        """Get list of all tables"""
+        try:
+            where_conditions = []
+            
+            if db_name:
+                where_conditions.append(f"table_schema = '{db_name}'")
+            else:
+                where_conditions.append("table_schema = DATABASE()")
+            
+            where_clause = " AND ".join(where_conditions) if where_conditions else "1=1"
+            
+            tables_sql = f"""
+            SELECT table_name 
+            FROM information_schema.tables 
+            WHERE {where_clause}
+                AND table_type = 'BASE TABLE'
+            ORDER BY table_name
+            """
+            
+            result = await connection.execute(tables_sql)
+            return [row["table_name"] for row in result.data] if result.data else []
+            
+        except Exception as e:
+            logger.warning(f"Failed to get table list: {str(e)}")
+            return []
+    
+    async def _analyze_table_freshness(self, connection, table_name: str, threshold_hours: int) -> Dict[str, Any]:
+        """Analyze freshness of single table"""
+        try:
+            # Try multiple methods to get table's last update time
+            freshness_methods = [
+                self._get_freshness_from_partition_info,
+                self._get_freshness_from_max_timestamp,
+                self._get_freshness_from_table_metadata
+            ]
+            
+            last_update = None
+            method_used = "unknown"
+            
+            for method in freshness_methods:
+                try:
+                    result = await method(connection, table_name)
+                    if result:
+                        last_update = result["last_update"]
+                        method_used = result["method"]
+                        break
+                except Exception as e:
+                    continue
+            
+            if not last_update:
+                return {
+                    "last_update": None,
+                    "staleness_hours": None,
+                    "freshness_score": 0.0,
+                    "status": "unknown",
+                    "method_used": "none",
+                    "error": "Unable to determine last update time"
+                }
+            
+            # Calculate data staleness
+            now = datetime.now()
+            if isinstance(last_update, str):
+                last_update = datetime.fromisoformat(last_update.replace('Z', '+00:00'))
+            
+            staleness_hours = (now - last_update).total_seconds() / 3600
+            
+            # Calculate freshness score and status
+            if staleness_hours <= threshold_hours:
+                status = "fresh"
+                freshness_score = max(0.0, 1.0 - (staleness_hours / threshold_hours))
+            else:
+                status = "stale"
+                freshness_score = max(0.0, 1.0 - (staleness_hours / (threshold_hours * 2)))
+            
+            return {
+                "last_update": last_update.isoformat() if hasattr(last_update, 'isoformat') else str(last_update),
+                "staleness_hours": round(staleness_hours, 2),
+                "freshness_score": round(freshness_score, 3),
+                "status": status,
+                "method_used": method_used,
+                "threshold_hours": threshold_hours
+            }
+            
+        except Exception as e:
+            logger.warning(f"Failed to analyze freshness for table {table_name}: {str(e)}")
+            return {
+                "last_update": None,
+                "staleness_hours": None,
+                "freshness_score": 0.0,
+                "status": "error",
+                "error": str(e)
+            }
+    
+    async def _get_freshness_from_partition_info(self, connection, table_name: str) -> Optional[Dict]:
+        """Get freshness from partition information"""
+        try:
+            # Query partition information (if table has partitions)
+            partition_sql = f"""
+            SELECT MAX(CREATE_TIME) as last_update
+            FROM information_schema.partitions 
+            WHERE table_name = '{table_name.split('.')[-1]}'
+                AND CREATE_TIME IS NOT NULL
+            """
+            
+            result = await connection.execute(partition_sql)
+            if result.data and result.data[0]["last_update"]:
+                return {
+                    "last_update": result.data[0]["last_update"],
+                    "method": "partition_info"
+                }
+            return None
+            
+        except Exception:
+            return None
+    
+    async def _get_freshness_from_max_timestamp(self, connection, table_name: str) -> Optional[Dict]:
+        """Get freshness from timestamp fields"""
+        try:
+            # Find possible timestamp fields
+            timestamp_columns = await self._find_timestamp_columns(connection, table_name)
+            
+            if timestamp_columns:
+                max_time_sql = f"""
+                SELECT MAX({timestamp_columns[0]}) as last_update
+                FROM {table_name}
+                """
+                
+                result = await connection.execute(max_time_sql)
+                if result.data and result.data[0]["last_update"]:
+                    return {
+                        "last_update": result.data[0]["last_update"],
+                        "method": f"max_timestamp({timestamp_columns[0]})"
+                    }
+            return None
+            
+        except Exception:
+            return None
+    
+    async def _get_freshness_from_table_metadata(self, connection, table_name: str) -> Optional[Dict]:
+        """Get freshness from table metadata"""
+        try:
+            # Query table's update time
+            metadata_sql = f"""
+            SELECT UPDATE_TIME as last_update
+            FROM information_schema.tables 
+            WHERE table_name = '{table_name.split('.')[-1]}'
+                AND UPDATE_TIME IS NOT NULL
+            """
+            
+            result = await connection.execute(metadata_sql)
+            if result.data and result.data[0]["last_update"]:
+                return {
+                    "last_update": result.data[0]["last_update"],
+                    "method": "table_metadata"
+                }
+            return None
+            
+        except Exception:
+            return None
+    
+    async def _find_timestamp_columns(self, connection, table_name: str) -> List[str]:
+        """Find possible timestamp fields"""
+        try:
+            timestamp_sql = f"""
+            SELECT column_name 
+            FROM information_schema.columns 
+            WHERE table_name = '{table_name.split('.')[-1]}'
+                AND (
+                    data_type IN ('datetime', 'timestamp', 'date') 
+                    OR column_name LIKE '%time%' 
+                    OR column_name LIKE '%date%'
+                    OR column_name LIKE '%created%'
+                    OR column_name LIKE '%updated%'
+                )
+            ORDER BY 
+                CASE 
+                    WHEN column_name LIKE '%updated%' THEN 1
+                    WHEN column_name LIKE '%created%' THEN 2
+                    WHEN column_name LIKE '%time%' THEN 3
+                    ELSE 4
+                END
+            """
+            
+            result = await connection.execute(timestamp_sql)
+            return [row["column_name"] for row in result.data] if result.data else []
+            
+        except Exception:
+            return []
+    
+    async def _identify_data_flow_issues(self, table_freshness: Dict[str, Any]) -> List[Dict]:
+        """Identify data flow issues"""
+        issues = []
+        
+        # Identify consecutively stale tables (may indicate ETL process issues)
+        stale_tables = [
+            table_name for table_name, info in table_freshness.items() 
+            if info.get("status") == "stale"
+        ]
+        
+        if len(stale_tables) > len(table_freshness) * 0.3:  # More than 30% of tables are stale
+            issues.append({
+                "issue_type": "widespread_staleness",
+                "severity": "high",
+                "affected_tables": len(stale_tables),
+                "total_tables": len(table_freshness),
+                "description": f"High percentage of stale tables ({len(stale_tables)}/{len(table_freshness)})",
+                "possible_causes": ["ETL pipeline failure", "Data source issues", "Processing delays"]
+            })
+        
+        # Identify particularly stale tables
+        very_stale_tables = [
+            (table_name, info.get("staleness_hours", 0)) 
+            for table_name, info in table_freshness.items() 
+            if info.get("staleness_hours", 0) > 72  # More than 3 days
+        ]
+        
+        if very_stale_tables:
+            issues.append({
+                "issue_type": "very_stale_data",
+                "severity": "medium",
+                "affected_tables": [table for table, _ in very_stale_tables],
+                "max_staleness_hours": max(hours for _, hours in very_stale_tables),
+                "description": "Some tables have very stale data (>72 hours)",
+                "recommendation": "Check data ingestion processes for affected tables"
+            })
+        
+        return issues
+    
+    def _generate_freshness_alerts(self, table_freshness: Dict[str, Any], threshold_hours: int) -> List[Dict]:
+        """Generate freshness alerts"""
+        alerts = []
+        
+        for table_name, info in table_freshness.items():
+            staleness_hours = info.get("staleness_hours")
+            status = info.get("status")
+            
+            if status == "stale" and staleness_hours:
+                if staleness_hours > threshold_hours * 2:  # Exceeds threshold by 2x
+                    alert_level = "critical"
+                elif staleness_hours > threshold_hours * 1.5:  # Exceeds threshold by 1.5x
+                    alert_level = "warning"
+                else:
+                    alert_level = "info"
+                
+                alerts.append({
+                    "alert_level": alert_level,
+                    "table_name": table_name,
+                    "staleness_hours": staleness_hours,
+                    "threshold_hours": threshold_hours,
+                    "message": f"Table {table_name} is stale ({staleness_hours:.1f} hours old, threshold: {threshold_hours}h)",
+                    "timestamp": datetime.now().isoformat()
+                })
+            
+            elif status == "error":
+                alerts.append({
+                    "alert_level": "error",
+                    "table_name": table_name,
+                    "message": f"Unable to determine freshness for table {table_name}",
+                    "error": info.get("error"),
+                    "timestamp": datetime.now().isoformat()
+                })
+        
+        return alerts 

doris_mcp_server/utils/db.py

@@ -29,10 +29,13 @@ from contextlib import asynccontextmanager
 from dataclasses import dataclass
 from datetime import datetime
 from typing import Any, Dict, List
+import random
 
 import aiomysql
 from aiomysql import Connection, Pool
 
+from .logger import get_logger
+
 
 
 
@@ -70,6 +73,7 @@ class DorisConnection:
         self.query_count = 0
         self.is_healthy = True
         self.security_manager = security_manager
+        self.logger = get_logger(__name__)
 
     async def execute(self, sql: str, params: tuple | None = None, auth_context=None) -> QueryResult:
         """Execute SQL query"""
@@ -135,12 +139,46 @@ class DorisConnection:
             raise
 
     async def ping(self) -> bool:
-        """Check connection health status"""
+        """Check connection health status with enhanced at_eof error detection"""
         try:
-            await self.connection.ping()
-            self.is_healthy = True
-            return True
-        except Exception:
+            # Check 1: Connection exists and is not closed
+            if not self.connection or self.connection.closed:
+                self.is_healthy = False
+                return False
+            
+            # Check 2: Use ONLY safe operations - avoid internal state access
+            # Instead of checking _reader state directly, use a simple query test
+            try:
+                # Use a simple query with timeout instead of ping() to avoid at_eof issues
+                async with asyncio.timeout(3):  # 3 second timeout
+                    async with self.connection.cursor() as cursor:
+                        await cursor.execute("SELECT 1")
+                        result = await cursor.fetchone()
+                        if result and result[0] == 1:
+                            self.is_healthy = True
+                            return True
+                        else:
+                            self.logger.debug(f"Connection {self.session_id} ping query returned unexpected result")
+                            self.is_healthy = False
+                            return False
+            
+            except asyncio.TimeoutError:
+                self.logger.debug(f"Connection {self.session_id} ping timed out")
+                self.is_healthy = False
+                return False
+            except Exception as query_error:
+                # Check for specific at_eof related errors
+                error_str = str(query_error).lower()
+                if 'at_eof' in error_str or 'nonetype' in error_str:
+                    self.logger.debug(f"Connection {self.session_id} ping failed with at_eof error: {query_error}")
+                else:
+                    self.logger.debug(f"Connection {self.session_id} ping failed: {query_error}")
+                self.is_healthy = False
+                return False
+            
+        except Exception as e:
+            # Catch any other unexpected errors
+            self.logger.debug(f"Connection {self.session_id} ping failed with unexpected error: {e}")
             self.is_healthy = False
             return False
 
@@ -154,241 +192,457 @@ class DorisConnection:
 
 
 class DorisConnectionManager:
-    """Doris database connection manager
+    """Doris database connection manager - Enhanced Strategy
 
-    Provides connection pool management, connection health monitoring, fault recovery and other functions
-    Supports session-level connection reuse and intelligent load balancing
-    Integrates security manager to provide unified security validation and data masking
+    Uses direct connection pool management with proper synchronization
+    Implements connection pool health monitoring and proactive cleanup
     """
 
     def __init__(self, config, security_manager=None):
         self.config = config
         self.pool: Pool | None = None
-        self.session_connections: dict[str, DorisConnection] = {}
-        self.metrics = ConnectionMetrics()
-        self.logger = logging.getLogger(__name__)
+        self.logger = get_logger(__name__)
         self.security_manager = security_manager
 
-        # Health check configuration
-        self.health_check_interval = config.database.health_check_interval or 60
-        self.max_connection_age = config.database.max_connection_age or 3600
-        self.connection_timeout = config.database.connection_timeout or 30
+        # Connection pool state management
+        self.pool_recovering = False
+        self.pool_health_check_task = None
+        self.pool_cleanup_task = None
         
-        # Start background tasks
-        self._health_check_task = None
-        self._cleanup_task = None
+        # Metrics tracking
+        self.metrics = ConnectionMetrics()
+        
+        # 🔧 FIX: Add connection acquisition lock to prevent race conditions
+        self._connection_lock = asyncio.Lock()
+        self._recovery_lock = asyncio.Lock()
+        
+        # 🔧 FIX: Add connection acquisition queue to serialize requests
+        self._connection_semaphore = asyncio.Semaphore(value=20)  # Max concurrent acquisitions
+        
+        # Database connection parameters from config.database
+        self.pool_recovery_lock = self._recovery_lock  # Compatibility alias
+        self.host = config.database.host
+        self.port = config.database.port
+        self.user = config.database.user
+        self.password = config.database.password
+        self.database = config.database.database
+        # Convert charset to aiomysql compatible format
+        charset_map = {"UTF8": "utf8", "UTF8MB4": "utf8mb4"}
+        self.charset = charset_map.get(config.database.charset.upper(), config.database.charset.lower())
+        self.connect_timeout = config.database.connection_timeout
+        
+        # Connection pool parameters - more conservative settings
+        self.minsize = config.database.min_connections  # This is always 0
+        self.maxsize = config.database.max_connections or 20
+        self.pool_recycle = config.database.max_connection_age or 3600  # 1 hour, more conservative
+        
+        # 🔧 FIX: Add missing monitoring parameters that were removed during refactoring
+        self.health_check_interval = 30  # seconds
+        self.pool_warmup_size = 3  # connections to maintain
 
     async def initialize(self):
-        """Initialize connection manager"""
+        """Initialize connection pool with health monitoring"""
         try:
+            self.logger.info(f"Initializing connection pool to {self.host}:{self.port}")
+            
             # Create connection pool
             self.pool = await aiomysql.create_pool(
-                host=self.config.database.host,
-                port=self.config.database.port,
-                user=self.config.database.user,
-                password=self.config.database.password,
-                db=self.config.database.database,
-                charset="utf8",
-                minsize=self.config.database.min_connections or 5,
-                maxsize=self.config.database.max_connections or 20,
-                autocommit=True,
-                connect_timeout=self.connection_timeout,
+                host=self.host,
+                port=self.port,
+                user=self.user,
+                password=self.password,
+                db=self.database,
+                charset=self.charset,
+                minsize=self.minsize,
+                maxsize=self.maxsize,
+                pool_recycle=self.pool_recycle,
+                connect_timeout=self.connect_timeout,
+                autocommit=True
             )
             
-            self.logger.info(
-                f"Connection pool initialized successfully, min connections: {self.config.database.min_connections}, "
-                f"max connections: {self.config.database.max_connections}"
-            )
+            # Test initial connection
+            if not await self._test_pool_health():
+                raise RuntimeError("Connection pool health check failed")
 
             # Start background monitoring tasks
-            self._health_check_task = asyncio.create_task(self._health_check_loop())
-            self._cleanup_task = asyncio.create_task(self._cleanup_loop())
+            self.pool_health_check_task = asyncio.create_task(self._pool_health_monitor())
+            self.pool_cleanup_task = asyncio.create_task(self._pool_cleanup_monitor())
+            
+            # Perform initial pool warmup
+            await self._warmup_pool()
+            
+            self.logger.info(f"Connection pool initialized successfully, min connections: {self.minsize}, max connections: {self.maxsize}")
             
         except Exception as e:
-            self.logger.error(f"Connection pool initialization failed: {e}")
+            self.logger.error(f"Failed to initialize connection pool: {e}")
             raise
 
-    async def get_connection(self, session_id: str) -> DorisConnection:
-        """Get database connection
-
-        Supports session-level connection reuse to improve performance and consistency
-        """
-        # Check if there's an existing session connection
-        if session_id in self.session_connections:
-            conn = self.session_connections[session_id]
-            # Check connection health
-            if await conn.ping():
-                return conn
-            else:
-                # Connection is unhealthy, clean up and create new one
-                await self._cleanup_session_connection(session_id)
-
-        # Create new connection
-        return await self._create_new_connection(session_id)
-
-    async def _create_new_connection(self, session_id: str) -> DorisConnection:
-        """Create new database connection"""
+    async def _test_pool_health(self) -> bool:
+        """Test connection pool health"""
         try:
-            if not self.pool:
-                raise RuntimeError("Connection pool not initialized")
+            async with self.pool.acquire() as conn:
+                async with conn.cursor() as cursor:
+                    await cursor.execute("SELECT 1")
+                    result = await cursor.fetchone()
+                    return result and result[0] == 1
+        except Exception as e:
+            self.logger.error(f"Pool health test failed: {e}")
+            return False
 
-            # Get connection from pool
-            raw_connection = await self.pool.acquire()
+    async def _warmup_pool(self):
+        """Warm up connection pool by creating initial connections"""
+        self.logger.info(f"🔥 Warming up connection pool with {self.pool_warmup_size} connections")
         
-            # Create wrapped connection
-            doris_conn = DorisConnection(raw_connection, session_id, self.security_manager)
+        warmup_connections = []
+        try:
+            # Acquire connections to force pool to create them
+            for i in range(self.pool_warmup_size):
+                try:
+                    conn = await self.pool.acquire()
+                    warmup_connections.append(conn)
+                    self.logger.debug(f"Warmed up connection {i+1}/{self.pool_warmup_size}")
+                except Exception as e:
+                    self.logger.warning(f"Failed to warm up connection {i+1}: {e}")
+                    break
             
-            # Store in session connections
-            self.session_connections[session_id] = doris_conn
+            # Release all warmup connections back to pool
+            for conn in warmup_connections:
+                try:
+                    self.pool.release(conn)
+                except Exception as e:
+                    self.logger.warning(f"Failed to release warmup connection: {e}")
             
-            self.metrics.total_connections += 1
-            self.logger.debug(f"Created new connection for session: {session_id}")
-            
-            return doris_conn
+            self.logger.info(f"✅ Pool warmup completed, {len(warmup_connections)} connections created")
 
         except Exception as e:
-            self.metrics.connection_errors += 1
-            self.logger.error(f"Failed to create connection for session {session_id}: {e}")
-            raise
+            self.logger.error(f"Pool warmup failed: {e}")
+            # Clean up any remaining connections
+            for conn in warmup_connections:
+                try:
+                    await conn.ensure_closed()
+                except Exception:
+                    pass
 
-    async def release_connection(self, session_id: str):
-        """Release session connection"""
-        if session_id in self.session_connections:
-            await self._cleanup_session_connection(session_id)
+    async def _pool_health_monitor(self):
+        """Background task to monitor pool health"""
+        self.logger.info("🩺 Starting pool health monitor")
         
-    async def _cleanup_session_connection(self, session_id: str):
-        """Clean up session connection"""
-        if session_id in self.session_connections:
-            conn = self.session_connections[session_id]
-            try:
-                # Return connection to pool
-                if self.pool and conn.connection and not conn.connection.closed:
-                    self.pool.release(conn.connection)
-                
-                # Close connection wrapper
-                await conn.close()
-                
-            except Exception as e:
-                self.logger.error(f"Error cleaning up connection for session {session_id}: {e}")
-            finally:
-                # Remove from session connections
-                del self.session_connections[session_id]
-                self.logger.debug(f"Cleaned up connection for session: {session_id}")
-
-    async def _health_check_loop(self):
-        """Background health check loop"""
         while True:
             try:
                 await asyncio.sleep(self.health_check_interval)
-                await self._perform_health_check()
+                await self._check_pool_health()
             except asyncio.CancelledError:
+                self.logger.info("Pool health monitor stopped")
                 break
             except Exception as e:
-                self.logger.error(f"Health check error: {e}")
+                self.logger.error(f"Pool health monitor error: {e}")
 
-    async def _perform_health_check(self):
-        """Perform health check"""
-        try:
-            unhealthy_sessions = []
+    async def _pool_cleanup_monitor(self):
+        """Background task to clean up stale connections"""
+        self.logger.info("🧹 Starting pool cleanup monitor")
         
-            for session_id, conn in self.session_connections.items():
-                if not await conn.ping():
-                    unhealthy_sessions.append(session_id)
-            
-            # Clean up unhealthy connections
-            for session_id in unhealthy_sessions:
-                await self._cleanup_session_connection(session_id)
-                self.metrics.failed_connections += 1
-            
-            # Update metrics
-            await self._update_connection_metrics()
-            self.metrics.last_health_check = datetime.utcnow()
-            
-            if unhealthy_sessions:
-                self.logger.warning(f"Cleaned up {len(unhealthy_sessions)} unhealthy connections")
-
-        except Exception as e:
-            self.logger.error(f"Health check failed: {e}")
-
-    async def _cleanup_loop(self):
-        """Background cleanup loop"""
         while True:
             try:
-                await asyncio.sleep(300)  # Run every 5 minutes
-                await self._cleanup_idle_connections()
+                await asyncio.sleep(self.health_check_interval * 2)  # Less frequent cleanup
+                await self._cleanup_stale_connections()
             except asyncio.CancelledError:
+                self.logger.info("Pool cleanup monitor stopped")
                 break
             except Exception as e:
-                self.logger.error(f"Cleanup loop error: {e}")
+                self.logger.error(f"Pool cleanup monitor error: {e}")
 
-    async def _cleanup_idle_connections(self):
-        """Clean up idle connections"""
-        current_time = datetime.utcnow()
-        idle_sessions = []
-        
-        for session_id, conn in self.session_connections.items():
-            # Check if connection has exceeded maximum age
-            age = (current_time - conn.created_at).total_seconds()
-            if age > self.max_connection_age:
-                idle_sessions.append(session_id)
-        
-        # Clean up idle connections
-        for session_id in idle_sessions:
-            await self._cleanup_session_connection(session_id)
-        
-        if idle_sessions:
-            self.logger.info(f"Cleaned up {len(idle_sessions)} idle connections")
-
-    async def _update_connection_metrics(self):
-        """Update connection metrics"""
-        self.metrics.active_connections = len(self.session_connections)
-        if self.pool:
-            self.metrics.idle_connections = self.pool.freesize
-
-    async def get_metrics(self) -> ConnectionMetrics:
-        """Get connection metrics"""
-        await self._update_connection_metrics()
-        return self.metrics
-
-    async def execute_query(
-        self, session_id: str, sql: str, params: tuple | None = None, auth_context=None
-    ) -> QueryResult:
-        """Execute query"""
-        conn = await self.get_connection(session_id)
-        return await conn.execute(sql, params, auth_context)
-
-    @asynccontextmanager
-    async def get_connection_context(self, session_id: str):
-        """Get connection context manager"""
-        conn = await self.get_connection(session_id)
+    async def _check_pool_health(self):
+        """Check and maintain pool health"""
         try:
-            yield conn
-        finally:
-            # Connection will be reused, no need to close here
-            pass
+            # Skip health check if already recovering
+            if self.pool_recovering:
+                self.logger.debug("Pool recovery in progress, skipping health check")
+                return
+                
+            # Test pool with a simple query
+            health_ok = await self._test_pool_health()
+            
+            if health_ok:
+                self.logger.debug("✅ Pool health check passed")
+                self.metrics.last_health_check = datetime.utcnow()
+            else:
+                self.logger.warning("❌ Pool health check failed, attempting recovery")
+                await self._recover_pool()
+                
+        except Exception as e:
+            self.logger.error(f"Pool health check error: {e}")
+            await self._recover_pool()
+
+    async def _cleanup_stale_connections(self):
+        """Proactively clean up potentially stale connections"""
+        try:
+            self.logger.debug("🧹 Checking for stale connections")
+            
+            # Get pool statistics
+            pool_size = self.pool.size
+            pool_free = self.pool.freesize
+            
+            # If pool has idle connections, test some of them
+            if pool_free > 0:
+                test_count = min(pool_free, 2)  # Test up to 2 idle connections
+                
+                for i in range(test_count):
+                    try:
+                        # Acquire connection, test it, and release
+                        conn = await asyncio.wait_for(self.pool.acquire(), timeout=5)
+                        
+                        # Quick test
+                        async with conn.cursor() as cursor:
+                            await asyncio.wait_for(cursor.execute("SELECT 1"), timeout=3)
+                            await cursor.fetchone()
+                        
+                        # Connection is healthy, release it
+                        self.pool.release(conn)
+                        
+                    except asyncio.TimeoutError:
+                        self.logger.debug(f"Stale connection test {i+1} timed out")
+                        try:
+                            await conn.ensure_closed()
+                        except Exception:
+                            pass
+                    except Exception as e:
+                        self.logger.debug(f"Stale connection test {i+1} failed: {e}")
+                        try:
+                            await conn.ensure_closed()
+                        except Exception:
+                            pass
+                
+                self.logger.debug(f"Stale connection cleanup completed, tested {test_count} connections")
+                
+        except Exception as e:
+            self.logger.error(f"Stale connection cleanup error: {e}")
+
+    async def _recover_pool(self):
+        """Recover connection pool when health check fails"""
+        # Use lock to prevent concurrent recovery attempts
+        async with self.pool_recovery_lock:
+            # Check if another recovery is already in progress
+            if self.pool_recovering:
+                self.logger.debug("Pool recovery already in progress, waiting...")
+                return
+                
+            try:
+                self.pool_recovering = True
+                max_retries = 3
+                retry_delay = 5  # seconds
+                
+                for attempt in range(max_retries):
+                    try:
+                        self.logger.info(f"🔄 Attempting pool recovery (attempt {attempt + 1}/{max_retries})")
+                        
+                        # Try to close existing pool with timeout
+                        if self.pool:
+                            try:
+                                if not self.pool.closed:
+                                    self.pool.close()
+                                    await asyncio.wait_for(self.pool.wait_closed(), timeout=3.0)
+                                self.logger.debug("Old pool closed successfully")
+                            except asyncio.TimeoutError:
+                                self.logger.warning("Pool close timeout, forcing cleanup")
+                            except Exception as e:
+                                self.logger.warning(f"Error closing old pool: {e}")
+                            finally:
+                                self.pool = None
+                        
+                        # Wait before creating new pool (reduced delay)
+                        if attempt > 0:
+                            await asyncio.sleep(2)  # Reduced from 5 to 2 seconds
+                        
+                        # Recreate pool with timeout
+                        self.logger.debug("Creating new connection pool...")
+                        self.pool = await asyncio.wait_for(
+                            aiomysql.create_pool(
+                                host=self.host,
+                                port=self.port,
+                                user=self.user,
+                                password=self.password,
+                                db=self.database,
+                                charset=self.charset,
+                                minsize=self.minsize,
+                                maxsize=self.maxsize,
+                                pool_recycle=self.pool_recycle,
+                                connect_timeout=self.connect_timeout,
+                                autocommit=True
+                            ),
+                            timeout=10.0
+                        )
+                        
+                        # Test recovered pool with timeout
+                        if await asyncio.wait_for(self._test_pool_health(), timeout=5.0):
+                            self.logger.info(f"✅ Pool recovery successful on attempt {attempt + 1}")
+                            # Re-warm the pool with timeout
+                            try:
+                                await asyncio.wait_for(self._warmup_pool(), timeout=5.0)
+                            except asyncio.TimeoutError:
+                                self.logger.warning("Pool warmup timeout, but recovery successful")
+                            return
+                        else:
+                            self.logger.warning(f"❌ Pool recovery health check failed on attempt {attempt + 1}")
+                            
+                    except asyncio.TimeoutError:
+                        self.logger.error(f"Pool recovery attempt {attempt + 1} timed out")
+                        if self.pool:
+                            try:
+                                self.pool.close()
+                            except:
+                                pass
+                            self.pool = None
+                    except Exception as e:
+                        self.logger.error(f"Pool recovery error on attempt {attempt + 1}: {e}")
+                        
+                        # Clean up failed pool
+                        if self.pool:
+                            try:
+                                self.pool.close()
+                                await asyncio.wait_for(self.pool.wait_closed(), timeout=2.0)
+                            except Exception:
+                                pass
+                            finally:
+                                self.pool = None
+                
+                # All recovery attempts failed
+                self.logger.error("❌ Pool recovery failed after all attempts")
+                self.pool = None
+                
+            finally:
+                self.pool_recovering = False
+    
+    async def _recover_pool_with_lock(self):
+        """🔧 FIX: Recovery method that uses the new recovery lock to prevent races"""
+        async with self._recovery_lock:
+            if not self.pool_recovering:  # Only recover if not already in progress
+                await self._recover_pool()
+
+    async def get_connection(self, session_id: str) -> DorisConnection:
+        """🔧 FIX: Simplified connection acquisition without double locking
+        
+        Uses only semaphore to prevent too many concurrent acquisitions
+        """
+        # 🔧 FIX: Use only semaphore to limit concurrent acquisitions (remove double locking)
+        async with self._connection_semaphore:
+            try:
+                # Wait for any ongoing recovery to complete
+                if self.pool_recovering:
+                    self.logger.debug(f"Pool recovery in progress, waiting for completion...")
+                    # Wait for recovery to complete (max 10 seconds)
+                    start_wait = time.time()
+                    while self.pool_recovering and (time.time() - start_wait) < 10:
+                        await asyncio.sleep(0.1)  # More frequent checks
+                    
+                    if self.pool_recovering:
+                        self.logger.error("Pool recovery is taking too long, proceeding anyway")
+                        # Continue but log the issue
+                
+                # Check if pool is available
+                if not self.pool:
+                    self.logger.warning("Connection pool is not available, attempting recovery...")
+                    await self._recover_pool_with_lock()
+                    
+                    if not self.pool:
+                        raise RuntimeError("Connection pool is not available and recovery failed")
+                
+                # Check if pool is closed
+                if self.pool.closed:
+                    self.logger.warning("Connection pool is closed, attempting recovery...")
+                    await self._recover_pool_with_lock()
+                    
+                    if not self.pool or self.pool.closed:
+                        raise RuntimeError("Connection pool is closed and recovery failed")
+                
+                # 🔧 FIX: Increased timeout to prevent hanging
+                try:
+                    raw_conn = await asyncio.wait_for(self.pool.acquire(), timeout=10.0)
+                except asyncio.TimeoutError:
+                    self.logger.error(f"Connection acquisition timed out for session {session_id}")
+                    # Try one recovery attempt
+                    await self._recover_pool_with_lock()
+                    if self.pool and not self.pool.closed:
+                        try:
+                            raw_conn = await asyncio.wait_for(self.pool.acquire(), timeout=5.0)
+                        except asyncio.TimeoutError:
+                            raise RuntimeError("Connection acquisition timed out after recovery")
+                    else:
+                        raise RuntimeError("Connection acquisition timed out")
+                
+                # Wrap in DorisConnection
+                doris_conn = DorisConnection(raw_conn, session_id, self.security_manager)
+                
+                # Basic validation - check if connection is open
+                if raw_conn.closed:
+                    # Return connection and raise error
+                    try:
+                        self.pool.release(raw_conn)
+                    except Exception:
+                        pass
+                    raise RuntimeError("Acquired connection is already closed")
+                
+                self.logger.debug(f"✅ Acquired fresh connection for session {session_id}")
+                return doris_conn
+                
+            except Exception as e:
+                self.logger.error(f"Failed to get connection for session {session_id}: {e}")
+                raise
+
+    async def release_connection(self, session_id: str, connection: DorisConnection):
+        """🔧 FIX: Release connection back to pool with proper error handling"""
+        if not connection or not connection.connection:
+            self.logger.debug(f"No connection to release for session {session_id}")
+            return
+            
+        try:
+            # Check pool availability before attempting release
+            if not self.pool or self.pool.closed:
+                self.logger.warning(f"Pool unavailable during release for session {session_id}, force closing connection")
+                try:
+                    await connection.connection.ensure_closed()
+                except Exception:
+                    pass
+                return
+            
+            # Check connection state before release
+            if connection.connection.closed:
+                self.logger.debug(f"Connection already closed for session {session_id}")
+                return
+            
+            # 🔧 FIX: Simplified release operation without thread wrapper
+            try:
+                self.pool.release(connection.connection)
+                self.logger.debug(f"✅ Released connection for session {session_id}")
+            except Exception as release_error:
+                self.logger.warning(f"Connection release failed for session {session_id}: {release_error}, force closing")
+                await connection.connection.ensure_closed()
+
+        except Exception as e:
+            self.logger.error(f"Error releasing connection for session {session_id}: {e}")
+            # Force close if release fails
+            try:
+                await connection.connection.ensure_closed()
+            except Exception as close_error:
+                self.logger.debug(f"Error force closing connection: {close_error}")
 
     async def close(self):
         """Close connection manager"""
         try:
             # Cancel background tasks
-            if self._health_check_task:
-                self._health_check_task.cancel()
+            if self.pool_health_check_task:
+                self.pool_health_check_task.cancel()
                 try:
-                    await self._health_check_task
+                    await self.pool_health_check_task
                 except asyncio.CancelledError:
                     pass
 
-            if self._cleanup_task:
-                self._cleanup_task.cancel()
+            if self.pool_cleanup_task:
+                self.pool_cleanup_task.cancel()
                 try:
-                    await self._cleanup_task
+                    await self.pool_cleanup_task
                 except asyncio.CancelledError:
                     pass
 
-            # Clean up all session connections
-            for session_id in list(self.session_connections.keys()):
-                await self._cleanup_session_connection(session_id)
-
             # Close connection pool
             if self.pool:
                 self.pool.close()
@@ -400,20 +654,103 @@ class DorisConnectionManager:
             self.logger.error(f"Error closing connection manager: {e}")
 
     async def test_connection(self) -> bool:
-        """Test database connection"""
-        try:
-            if not self.pool:
-                return False
+        """Test database connection using robust connection test"""
+        return await self._test_pool_health()
 
-            async with self.pool.acquire() as conn:
-                async with conn.cursor() as cursor:
-                    await cursor.execute("SELECT 1")
-                    result = await cursor.fetchone()
-                    return result is not None
+    async def get_metrics(self) -> ConnectionMetrics:
+        """Get connection pool metrics - Simplified Strategy"""
+        try:
+            if self.pool:
+                self.metrics.idle_connections = self.pool.freesize
+                self.metrics.active_connections = self.pool.size - self.pool.freesize
+            else:
+                self.metrics.idle_connections = 0
+                self.metrics.active_connections = 0
+            
+            return self.metrics
+        except Exception as e:
+            self.logger.error(f"Error getting metrics: {e}")
+            return self.metrics
+
+    async def execute_query(
+        self, session_id: str, sql: str, params: tuple | None = None, auth_context=None
+    ) -> QueryResult:
+        """Execute query - Simplified Strategy with automatic connection management"""
+        connection = None
+        try:
+            # Always get fresh connection from pool
+            connection = await self.get_connection(session_id)
+            
+            # Execute query
+            result = await connection.execute(sql, params, auth_context)
+            
+            return result
             
         except Exception as e:
-            self.logger.error(f"Connection test failed: {e}")
-            return False
+            self.logger.error(f"Query execution failed for session {session_id}: {e}")
+            raise
+        finally:
+            # Always release connection back to pool
+            if connection:
+                await self.release_connection(session_id, connection)
+
+    @asynccontextmanager
+    async def get_connection_context(self, session_id: str):
+        """Get connection context manager - Simplified Strategy"""
+        connection = None
+        try:
+            connection = await self.get_connection(session_id)
+            yield connection
+        finally:
+            if connection:
+                await self.release_connection(session_id, connection)
+
+    async def diagnose_connection_health(self) -> Dict[str, Any]:
+        """Diagnose connection pool health - Simplified Strategy"""
+        diagnosis = {
+            "timestamp": datetime.utcnow().isoformat(),
+            "pool_status": "unknown",
+            "pool_info": {},
+            "recommendations": []
+        }
+        
+        try:
+            # Check pool status
+            if not self.pool:
+                diagnosis["pool_status"] = "not_initialized"
+                diagnosis["recommendations"].append("Initialize connection pool")
+                return diagnosis
+            
+            if self.pool.closed:
+                diagnosis["pool_status"] = "closed"
+                diagnosis["recommendations"].append("Recreate connection pool")
+                return diagnosis
+            
+            diagnosis["pool_status"] = "healthy"
+            diagnosis["pool_info"] = {
+                "size": self.pool.size,
+                "free_size": self.pool.freesize,
+                "min_size": self.pool.minsize,
+                "max_size": self.pool.maxsize
+            }
+            
+            # Generate recommendations based on pool status
+            if self.pool.freesize == 0 and self.pool.size >= self.pool.maxsize:
+                diagnosis["recommendations"].append("Connection pool exhausted - consider increasing max_connections")
+            
+            # Test pool health
+            if await self._test_pool_health():
+                diagnosis["pool_health"] = "healthy"
+            else:
+                diagnosis["pool_health"] = "unhealthy"
+                diagnosis["recommendations"].append("Pool health check failed - may need recovery")
+            
+            return diagnosis
+            
+        except Exception as e:
+            diagnosis["error"] = str(e)
+            diagnosis["recommendations"].append("Manual intervention required")
+            return diagnosis
 
 
 class ConnectionPoolMonitor:
@@ -424,7 +761,7 @@ class ConnectionPoolMonitor:
 
     def __init__(self, connection_manager: DorisConnectionManager):
         self.connection_manager = connection_manager
-        self.logger = logging.getLogger(__name__)
+        self.logger = get_logger(__name__)
 
     async def get_pool_status(self) -> dict[str, Any]:
         """Get connection pool status"""
@@ -433,7 +770,8 @@ class ConnectionPoolMonitor:
         status = {
             "pool_size": self.connection_manager.pool.size if self.connection_manager.pool else 0,
             "free_connections": self.connection_manager.pool.freesize if self.connection_manager.pool else 0,
-            "active_sessions": len(self.connection_manager.session_connections),
+            "active_connections": metrics.active_connections,
+            "idle_connections": metrics.idle_connections,
             "total_connections": metrics.total_connections,
             "failed_connections": metrics.failed_connections,
             "connection_errors": metrics.connection_errors,
@@ -444,52 +782,33 @@ class ConnectionPoolMonitor:
         return status
 
     async def get_session_details(self) -> list[dict[str, Any]]:
-        """Get session connection details"""
-        sessions = []
-        
-        for session_id, conn in self.connection_manager.session_connections.items():
-            session_info = {
-                "session_id": session_id,
-                "created_at": conn.created_at.isoformat(),
-                "last_used": conn.last_used.isoformat(),
-                "query_count": conn.query_count,
-                "is_healthy": conn.is_healthy,
-                "connection_age": (datetime.utcnow() - conn.created_at).total_seconds(),
-            }
-            sessions.append(session_info)
-        
-        return sessions
+        """Get session connection details - Simplified Strategy (No session caching)"""
+        # In simplified strategy, we don't maintain session connections
+        # Return empty list as connections are managed by the pool directly
+        return []
 
     async def generate_health_report(self) -> dict[str, Any]:
-        """Generate connection health report"""
+        """Generate connection health report - Simplified Strategy"""
         pool_status = await self.get_pool_status()
-        session_details = await self.get_session_details()
         
-        # Calculate health statistics
-        healthy_sessions = sum(1 for s in session_details if s["is_healthy"])
-        total_sessions = len(session_details)
-        health_ratio = healthy_sessions / total_sessions if total_sessions > 0 else 1.0
+        # Calculate pool utilization
+        pool_utilization = 1.0 - (pool_status["free_connections"] / pool_status["pool_size"]) if pool_status["pool_size"] > 0 else 0.0
         
         report = {
             "timestamp": datetime.utcnow().isoformat(),
             "pool_status": pool_status,
-            "session_summary": {
-                "total_sessions": total_sessions,
-                "healthy_sessions": healthy_sessions,
-                "health_ratio": health_ratio,
-            },
-            "session_details": session_details,
+            "pool_utilization": pool_utilization,
             "recommendations": [],
         }
         
-        # Add recommendations based on health status
-        if health_ratio < 0.8:
-            report["recommendations"].append("Consider checking database connectivity and network stability")
-        
+        # Add recommendations based on pool status
         if pool_status["connection_errors"] > 10:
             report["recommendations"].append("High connection error rate detected, review connection configuration")
         
-        if pool_status["active_sessions"] > pool_status["pool_size"] * 0.9:
+        if pool_utilization > 0.9:
             report["recommendations"].append("Connection pool utilization is high, consider increasing pool size")
         
+        if pool_status["free_connections"] == 0:
+            report["recommendations"].append("No free connections available, consider increasing pool size")
+        
         return report

doris_mcp_server/utils/dependency_analysis_tools.py

@@ -0,0 +1,978 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""
+Dependency Analysis Tools Module
+Provides data flow dependency analysis and impact assessment capabilities
+"""
+
+import time
+import re
+from datetime import datetime
+from typing import Any, Dict, List, Optional, Set, Tuple
+from collections import defaultdict, deque
+
+from .db import DorisConnectionManager
+from .logger import get_logger
+
+logger = get_logger(__name__)
+
+
+class DependencyAnalysisTools:
+    """Dependency analysis tools for data flow and impact assessment"""
+    
+    def __init__(self, connection_manager: DorisConnectionManager):
+        self.connection_manager = connection_manager
+        logger.info("DependencyAnalysisTools initialized")
+    
+    async def analyze_data_flow_dependencies(
+        self, 
+        target_table: Optional[str] = None,
+        analysis_depth: int = 3,
+        include_views: bool = True,
+        catalog_name: Optional[str] = None,
+        db_name: Optional[str] = None
+    ) -> Dict[str, Any]:
+        """
+        Analyze data flow dependencies and impact relationships
+        
+        Args:
+            target_table: Specific table to analyze (if None, analyzes all tables)
+            analysis_depth: Maximum depth for dependency traversal
+            include_views: Whether to include views in dependency analysis
+            catalog_name: Catalog name
+            db_name: Database name
+        
+        Returns:
+            Comprehensive dependency analysis results
+        """
+        try:
+            start_time = time.time()
+            connection = await self.connection_manager.get_connection("query")
+            
+            # 1. Get table metadata and relationships
+            tables_metadata = await self._get_tables_metadata(connection, catalog_name, db_name, include_views)
+            
+            if not tables_metadata:
+                return {
+                    "error": "No tables found for dependency analysis",
+                    "analysis_timestamp": datetime.now().isoformat()
+                }
+            
+            # 2. Build dependency graph from SQL analysis
+            dependency_graph = await self._build_dependency_graph(connection, tables_metadata, analysis_depth)
+            
+            # 3. Analyze specific table or all tables
+            if target_table:
+                # Analyze specific table
+                table_analysis = await self._analyze_single_table_dependencies(
+                    target_table, dependency_graph, tables_metadata
+                )
+                impact_analysis = await self._calculate_impact_analysis(
+                    target_table, dependency_graph, "both"
+                )
+            else:
+                # Analyze all tables
+                table_analysis = await self._analyze_all_tables_dependencies(
+                    dependency_graph, tables_metadata
+                )
+                impact_analysis = await self._calculate_global_impact_analysis(dependency_graph)
+            
+            # 4. Generate insights and recommendations
+            dependency_insights = await self._generate_dependency_insights(
+                dependency_graph, table_analysis, impact_analysis
+            )
+            
+            execution_time = time.time() - start_time
+            
+            return {
+                "analysis_target": target_table or "all_tables",
+                "analysis_timestamp": datetime.now().isoformat(),
+                "execution_time_seconds": round(execution_time, 3),
+                "tables_analyzed": len(tables_metadata),
+                "dependency_graph_stats": self._get_dependency_graph_stats(dependency_graph),
+                "table_dependencies": table_analysis,
+                "impact_analysis": impact_analysis,
+                "dependency_insights": dependency_insights,
+                "recommendations": self._generate_dependency_recommendations(dependency_insights)
+            }
+            
+        except Exception as e:
+            logger.error(f"Data flow dependency analysis failed: {str(e)}")
+            return {
+                "error": str(e),
+                "analysis_timestamp": datetime.now().isoformat()
+            }
+    
+    # ==================== Private Helper Methods ====================
+    
+    async def _get_tables_metadata(self, connection, catalog_name: Optional[str], db_name: Optional[str], include_views: bool) -> List[Dict]:
+        """Get metadata for all tables and views"""
+        try:
+            # Build conditions for query
+            where_conditions = []
+            if db_name:
+                where_conditions.append(f"table_schema = '{db_name}'")
+            else:
+                where_conditions.append("table_schema = DATABASE()")
+            
+            table_types = ["'BASE TABLE'"]
+            if include_views:
+                table_types.append("'VIEW'")
+            
+            where_conditions.append(f"table_type IN ({','.join(table_types)})")
+            
+            metadata_sql = f"""
+            SELECT 
+                table_schema as schema_name,
+                table_name,
+                table_type,
+                table_comment,
+                table_rows,
+                data_length
+            FROM information_schema.tables
+            WHERE {' AND '.join(where_conditions)}
+            ORDER BY table_schema, table_name
+            """
+            
+            result = await connection.execute(metadata_sql)
+            return result.data if result.data else []
+            
+        except Exception as e:
+            logger.warning(f"Failed to get tables metadata: {str(e)}")
+            return []
+    
+    async def _build_dependency_graph(self, connection, tables_metadata: List[Dict], analysis_depth: int) -> Dict[str, Dict]:
+        """Build dependency graph by analyzing SQL statements and DDL"""
+        dependency_graph = defaultdict(lambda: {
+            "upstream_dependencies": set(),
+            "downstream_dependencies": set(),
+            "table_type": "unknown",
+            "dependency_strength": {},
+            "sql_patterns": []
+        })
+        
+        # Initialize graph with table metadata
+        for table in tables_metadata:
+            table_name = table["table_name"]
+            schema_name = table.get("schema_name", "")
+            full_table_name = f"{schema_name}.{table_name}" if schema_name else table_name
+            
+            dependency_graph[full_table_name]["table_type"] = table["table_type"]
+        
+        # 1. Analyze view definitions for dependencies
+        await self._analyze_view_dependencies(connection, dependency_graph, tables_metadata)
+        
+        # 2. Analyze audit logs for runtime dependencies
+        await self._analyze_runtime_dependencies(connection, dependency_graph, analysis_depth)
+        
+        # 3. Analyze foreign key relationships
+        await self._analyze_foreign_key_dependencies(connection, dependency_graph, tables_metadata)
+        
+        return dict(dependency_graph)
+    
+    async def _analyze_view_dependencies(self, connection, dependency_graph: Dict, tables_metadata: List[Dict]) -> None:
+        """Analyze view definitions to extract table dependencies"""
+        try:
+            for table in tables_metadata:
+                if table["table_type"] == "VIEW":
+                    table_name = table["table_name"]
+                    schema_name = table.get("schema_name", "")
+                    
+                    # Get view definition
+                    view_def_sql = f"SHOW CREATE VIEW {schema_name}.{table_name}" if schema_name else f"SHOW CREATE VIEW {table_name}"
+                    
+                    try:
+                        result = await connection.execute(view_def_sql)
+                        if result.data and len(result.data) > 0:
+                            # Extract view definition from result
+                            view_definition = ""
+                            for row in result.data:
+                                for key, value in row.items():
+                                    if "create" in key.lower() and value:
+                                        view_definition = str(value)
+                                        break
+                            
+                            if view_definition:
+                                # Extract table dependencies from view definition
+                                referenced_tables = self._extract_table_references(view_definition)
+                                
+                                full_view_name = f"{schema_name}.{table_name}" if schema_name else table_name
+                                
+                                for ref_table in referenced_tables:
+                                    # Add upstream dependency
+                                    dependency_graph[full_view_name]["upstream_dependencies"].add(ref_table)
+                                    dependency_graph[full_view_name]["dependency_strength"][ref_table] = "direct"
+                                    
+                                    # Add downstream dependency for referenced table
+                                    dependency_graph[ref_table]["downstream_dependencies"].add(full_view_name)
+                                    
+                                    dependency_graph[full_view_name]["sql_patterns"].append({
+                                        "pattern_type": "view_definition",
+                                        "referenced_table": ref_table,
+                                        "confidence": 1.0
+                                    })
+                    
+                    except Exception as e:
+                        logger.warning(f"Failed to analyze view {table_name}: {str(e)}")
+                        continue
+        
+        except Exception as e:
+            logger.warning(f"Failed to analyze view dependencies: {str(e)}")
+    
+    async def _analyze_runtime_dependencies(self, connection, dependency_graph: Dict, analysis_depth: int) -> None:
+        """Analyze audit logs to discover runtime table dependencies"""
+        try:
+            # Get recent SQL statements from audit logs
+            audit_sql = """
+            SELECT 
+                `stmt` as sql_statement,
+                `user` as user_name,
+                COUNT(*) as frequency
+            FROM internal.__internal_schema.audit_log 
+            WHERE `stmt` IS NOT NULL 
+                AND `stmt` != ''
+                AND `time` >= DATE_SUB(NOW(), INTERVAL 1 YEAR)
+            GROUP BY `stmt`, `user`
+            HAVING frequency > 1
+            ORDER BY frequency DESC
+            LIMIT 1000
+            """
+            
+            result = await connection.execute(audit_sql)
+            
+            if result.data:
+                for row in result.data:
+                    sql_statement = row.get("sql_statement", "")
+                    frequency = row.get("frequency", 1)
+                    
+                    if sql_statement:
+                        # Extract table references from SQL
+                        referenced_tables = self._extract_table_references(sql_statement)
+                        
+                        if len(referenced_tables) > 1:
+                            # Infer dependencies from multi-table queries
+                            self._infer_dependencies_from_sql(
+                                dependency_graph, sql_statement, referenced_tables, frequency
+                            )
+        
+        except Exception as e:
+            logger.warning(f"Failed to analyze runtime dependencies: {str(e)}")
+    
+    async def _analyze_foreign_key_dependencies(self, connection, dependency_graph: Dict, tables_metadata: List[Dict]) -> None:
+        """Analyze foreign key constraints for explicit dependencies"""
+        try:
+            # Get foreign key information
+            fk_sql = """
+            SELECT 
+                TABLE_SCHEMA as schema_name,
+                TABLE_NAME as table_name,
+                COLUMN_NAME as column_name,
+                REFERENCED_TABLE_SCHEMA as ref_schema,
+                REFERENCED_TABLE_NAME as ref_table_name,
+                REFERENCED_COLUMN_NAME as ref_column_name
+            FROM information_schema.KEY_COLUMN_USAGE
+            WHERE REFERENCED_TABLE_NAME IS NOT NULL
+            """
+            
+            result = await connection.execute(fk_sql)
+            
+            if result.data:
+                for row in result.data:
+                    schema_name = row.get("schema_name", "")
+                    table_name = row["table_name"]
+                    ref_schema = row.get("ref_schema", "")
+                    ref_table_name = row["ref_table_name"]
+                    
+                    # Build full table names
+                    full_table_name = f"{schema_name}.{table_name}" if schema_name else table_name
+                    full_ref_table = f"{ref_schema}.{ref_table_name}" if ref_schema else ref_table_name
+                    
+                    # Add foreign key dependency
+                    dependency_graph[full_table_name]["upstream_dependencies"].add(full_ref_table)
+                    dependency_graph[full_table_name]["dependency_strength"][full_ref_table] = "foreign_key"
+                    dependency_graph[full_ref_table]["downstream_dependencies"].add(full_table_name)
+                    
+                    dependency_graph[full_table_name]["sql_patterns"].append({
+                        "pattern_type": "foreign_key",
+                        "referenced_table": full_ref_table,
+                        "confidence": 1.0,
+                        "column": row["column_name"],
+                        "ref_column": row["ref_column_name"]
+                    })
+        
+        except Exception as e:
+            logger.warning(f"Failed to analyze foreign key dependencies: {str(e)}")
+    
+    def _extract_table_references(self, sql: str) -> List[str]:
+        """Extract table references from SQL statement"""
+        if not sql:
+            return []
+        
+        # Normalize SQL
+        sql = re.sub(r'/\*.*?\*/', '', sql, flags=re.DOTALL)  # Remove comments
+        sql = re.sub(r'--.*', '', sql)  # Remove line comments
+        sql = sql.upper()
+        
+        table_references = []
+        
+        # Pattern to match table names in various contexts
+        patterns = [
+            r'\bFROM\s+([`"]?[a-zA-Z_][a-zA-Z0-9_]*(?:\.[a-zA-Z_][a-zA-Z0-9_]*)*[`"]?)',
+            r'\bJOIN\s+([`"]?[a-zA-Z_][a-zA-Z0-9_]*(?:\.[a-zA-Z_][a-zA-Z0-9_]*)*[`"]?)',
+            r'\bINTO\s+([`"]?[a-zA-Z_][a-zA-Z0-9_]*(?:\.[a-zA-Z_][a-zA-Z0-9_]*)*[`"]?)',
+            r'\bUPDATE\s+([`"]?[a-zA-Z_][a-zA-Z0-9_]*(?:\.[a-zA-Z_][a-zA-Z0-9_]*)*[`"]?)',
+            r'\bDELETE\s+FROM\s+([`"]?[a-zA-Z_][a-zA-Z0-9_]*(?:\.[a-zA-Z_][a-zA-Z0-9_]*)*[`"]?)',
+            r'\bINSERT\s+INTO\s+([`"]?[a-zA-Z_][a-zA-Z0-9_]*(?:\.[a-zA-Z_][a-zA-Z0-9_]*)*[`"]?)'
+        ]
+        
+        for pattern in patterns:
+            matches = re.findall(pattern, sql, re.IGNORECASE)
+            for match in matches:
+                # Clean up table name
+                table_name = match.strip('`"\'').split()[0]  # Remove quotes and aliases
+                if table_name and not self._is_sql_keyword(table_name):
+                    table_references.append(table_name.lower())
+        
+        return list(set(table_references))
+    
+    def _is_sql_keyword(self, word: str) -> bool:
+        """Check if word is a SQL keyword"""
+        keywords = {
+            'SELECT', 'FROM', 'WHERE', 'JOIN', 'INNER', 'LEFT', 'RIGHT', 'OUTER',
+            'ON', 'AND', 'OR', 'NOT', 'IN', 'EXISTS', 'BETWEEN', 'LIKE',
+            'INSERT', 'UPDATE', 'DELETE', 'CREATE', 'ALTER', 'DROP', 'INDEX',
+            'TABLE', 'VIEW', 'DATABASE', 'SCHEMA', 'PRIMARY', 'KEY', 'FOREIGN',
+            'REFERENCES', 'CONSTRAINT', 'NULL', 'DEFAULT', 'AUTO_INCREMENT'
+        }
+        return word.upper() in keywords
+    
+    def _infer_dependencies_from_sql(self, dependency_graph: Dict, sql: str, referenced_tables: List[str], frequency: int) -> None:
+        """Infer table dependencies from SQL patterns"""
+        # Analyze SQL pattern to determine dependency relationships
+        sql_upper = sql.upper()
+        
+        # Look for INSERT ... SELECT patterns
+        if 'INSERT' in sql_upper and 'SELECT' in sql_upper:
+            # Find target table (after INSERT INTO)
+            insert_match = re.search(r'INSERT\s+INTO\s+([a-zA-Z_][a-zA-Z0-9_.]*)', sql_upper)
+            if insert_match:
+                target_table = insert_match.group(1).lower()
+                
+                # All other tables are dependencies
+                for ref_table in referenced_tables:
+                    if ref_table != target_table:
+                        dependency_graph[target_table]["upstream_dependencies"].add(ref_table)
+                        dependency_graph[ref_table]["downstream_dependencies"].add(target_table)
+                        
+                        # Calculate confidence based on frequency
+                        confidence = min(0.9, 0.3 + (frequency / 100))
+                        dependency_graph[target_table]["sql_patterns"].append({
+                            "pattern_type": "insert_select",
+                            "referenced_table": ref_table,
+                            "confidence": confidence,
+                            "frequency": frequency
+                        })
+        
+        # Look for CREATE TABLE AS SELECT patterns
+        elif 'CREATE' in sql_upper and 'SELECT' in sql_upper:
+            create_match = re.search(r'CREATE\s+TABLE\s+([a-zA-Z_][a-zA-Z0-9_.]*)', sql_upper)
+            if create_match:
+                target_table = create_match.group(1).lower()
+                
+                for ref_table in referenced_tables:
+                    if ref_table != target_table:
+                        dependency_graph[target_table]["upstream_dependencies"].add(ref_table)
+                        dependency_graph[ref_table]["downstream_dependencies"].add(target_table)
+                        
+                        dependency_graph[target_table]["sql_patterns"].append({
+                            "pattern_type": "create_table_as_select",
+                            "referenced_table": ref_table,
+                            "confidence": 0.95,
+                            "frequency": frequency
+                        })
+    
+    async def _analyze_single_table_dependencies(self, target_table: str, dependency_graph: Dict, tables_metadata: List[Dict]) -> Dict[str, Any]:
+        """Analyze dependencies for a specific table"""
+        if target_table not in dependency_graph:
+            return {"error": f"Table {target_table} not found in dependency graph"}
+        
+        table_info = dependency_graph[target_table]
+        
+        # Get upstream dependencies (tables this table depends on)
+        upstream_deps = await self._get_dependency_chain(target_table, dependency_graph, "upstream", 3)
+        
+        # Get downstream dependencies (tables that depend on this table)
+        downstream_deps = await self._get_dependency_chain(target_table, dependency_graph, "downstream", 3)
+        
+        return {
+            "table_name": target_table,
+            "table_type": table_info["table_type"],
+            "direct_upstream_dependencies": list(table_info["upstream_dependencies"]),
+            "direct_downstream_dependencies": list(table_info["downstream_dependencies"]),
+            "upstream_dependency_chain": upstream_deps,
+            "downstream_dependency_chain": downstream_deps,
+            "dependency_patterns": table_info["sql_patterns"],
+            "dependency_metrics": {
+                "upstream_count": len(table_info["upstream_dependencies"]),
+                "downstream_count": len(table_info["downstream_dependencies"]),
+                "total_upstream_chain": len(upstream_deps.get("all_dependencies", [])),
+                "total_downstream_chain": len(downstream_deps.get("all_dependencies", [])),
+                "dependency_depth": max(upstream_deps.get("max_depth", 0), downstream_deps.get("max_depth", 0))
+            }
+        }
+    
+    async def _get_dependency_chain(self, start_table: str, dependency_graph: Dict, direction: str, max_depth: int) -> Dict[str, Any]:
+        """Get full dependency chain in specified direction"""
+        visited = set()
+        all_dependencies = []
+        levels = []
+        current_level = [start_table]
+        depth = 0
+        
+        while current_level and depth < max_depth:
+            next_level = []
+            level_deps = []
+            
+            for table in current_level:
+                if table in visited:
+                    continue
+                
+                visited.add(table)
+                
+                if direction == "upstream":
+                    dependencies = dependency_graph.get(table, {}).get("upstream_dependencies", set())
+                else:
+                    dependencies = dependency_graph.get(table, {}).get("downstream_dependencies", set())
+                
+                for dep in dependencies:
+                    if dep not in visited:
+                        next_level.append(dep)
+                        level_deps.append(dep)
+                        all_dependencies.append(dep)
+            
+            if level_deps:
+                levels.append({
+                    "level": depth + 1,
+                    "tables": level_deps
+                })
+            
+            current_level = next_level
+            depth += 1
+        
+        return {
+            "direction": direction,
+            "max_depth": depth,
+            "all_dependencies": list(set(all_dependencies)),
+            "dependency_levels": levels,
+            "total_count": len(set(all_dependencies))
+        }
+    
+    async def _analyze_all_tables_dependencies(self, dependency_graph: Dict, tables_metadata: List[Dict]) -> Dict[str, Any]:
+        """Analyze dependencies for all tables"""
+        table_stats = {}
+        
+        for table_name, table_info in dependency_graph.items():
+            upstream_count = len(table_info["upstream_dependencies"])
+            downstream_count = len(table_info["downstream_dependencies"])
+            
+            table_stats[table_name] = {
+                "table_type": table_info["table_type"],
+                "upstream_count": upstream_count,
+                "downstream_count": downstream_count,
+                "total_connections": upstream_count + downstream_count,
+                "dependency_score": self._calculate_dependency_score(upstream_count, downstream_count),
+                "role_classification": self._classify_table_role(upstream_count, downstream_count)
+            }
+        
+        # Find key tables
+        most_critical_tables = sorted(
+            table_stats.items(), 
+            key=lambda x: x[1]["dependency_score"], 
+            reverse=True
+        )[:10]
+        
+        source_tables = [name for name, stats in table_stats.items() if stats["role_classification"] == "source"]
+        sink_tables = [name for name, stats in table_stats.items() if stats["role_classification"] == "sink"]
+        hub_tables = [name for name, stats in table_stats.items() if stats["role_classification"] == "hub"]
+        
+        return {
+            "table_statistics": table_stats,
+            "summary": {
+                "total_tables": len(table_stats),
+                "source_tables": len(source_tables),
+                "sink_tables": len(sink_tables),
+                "hub_tables": len(hub_tables),
+                "isolated_tables": len([stats for stats in table_stats.values() if stats["total_connections"] == 0])
+            },
+            "critical_tables": [{"table": name, **stats} for name, stats in most_critical_tables],
+            "table_roles": {
+                "sources": source_tables[:10],
+                "sinks": sink_tables[:10],
+                "hubs": hub_tables[:10]
+            }
+        }
+    
+    def _calculate_dependency_score(self, upstream_count: int, downstream_count: int) -> float:
+        """Calculate dependency importance score for a table"""
+        # Score based on both incoming and outgoing dependencies
+        # Higher weight for downstream dependencies (impact)
+        return round(upstream_count * 0.3 + downstream_count * 0.7, 2)
+    
+    def _classify_table_role(self, upstream_count: int, downstream_count: int) -> str:
+        """Classify table role based on dependency pattern"""
+        if upstream_count == 0 and downstream_count > 0:
+            return "source"  # Data source
+        elif upstream_count > 0 and downstream_count == 0:
+            return "sink"    # Data destination
+        elif upstream_count > 2 and downstream_count > 2:
+            return "hub"     # Data hub/transformation
+        elif upstream_count > 0 and downstream_count > 0:
+            return "intermediate"  # Intermediate transformation
+        else:
+            return "isolated"  # No dependencies
+    
+    async def _calculate_impact_analysis(self, target_table: str, dependency_graph: Dict, direction: str) -> Dict[str, Any]:
+        """Calculate impact analysis for a specific table"""
+        if direction == "upstream" or direction == "both":
+            upstream_impact = await self._calculate_upstream_impact(target_table, dependency_graph)
+        else:
+            upstream_impact = {}
+        
+        if direction == "downstream" or direction == "both":
+            downstream_impact = await self._calculate_downstream_impact(target_table, dependency_graph)
+        else:
+            downstream_impact = {}
+        
+        return {
+            "target_table": target_table,
+            "upstream_impact": upstream_impact,
+            "downstream_impact": downstream_impact,
+            "total_impact_score": self._calculate_total_impact_score(upstream_impact, downstream_impact)
+        }
+    
+    async def _calculate_upstream_impact(self, target_table: str, dependency_graph: Dict) -> Dict[str, Any]:
+        """Calculate what would be impacted if upstream dependencies fail"""
+        upstream_deps = dependency_graph.get(target_table, {}).get("upstream_dependencies", set())
+        
+        impact_scenarios = []
+        for dep_table in upstream_deps:
+            # Simulate failure of this dependency
+            affected_tables = await self._simulate_table_failure_impact(dep_table, dependency_graph)
+            
+            impact_scenarios.append({
+                "failed_dependency": dep_table,
+                "directly_affected_tables": len(affected_tables["direct"]),
+                "indirectly_affected_tables": len(affected_tables["indirect"]),
+                "total_affected": len(affected_tables["all"]),
+                "critical_affected": [table for table in affected_tables["all"] 
+                                    if dependency_graph.get(table, {}).get("downstream_dependencies", set())],
+                "impact_severity": self._assess_impact_severity(len(affected_tables["all"]))
+            })
+        
+        return {
+            "dependency_count": len(upstream_deps),
+            "impact_scenarios": impact_scenarios,
+            "max_potential_impact": max([scenario["total_affected"] for scenario in impact_scenarios], default=0),
+            "risk_assessment": self._assess_upstream_risk(impact_scenarios)
+        }
+    
+    async def _calculate_downstream_impact(self, target_table: str, dependency_graph: Dict) -> Dict[str, Any]:
+        """Calculate what would be impacted if target table fails"""
+        affected_tables = await self._simulate_table_failure_impact(target_table, dependency_graph)
+        
+        return {
+            "direct_impact": len(affected_tables["direct"]),
+            "indirect_impact": len(affected_tables["indirect"]),
+            "total_impact": len(affected_tables["all"]),
+            "affected_table_details": [
+                {
+                    "table_name": table,
+                    "impact_type": "direct" if table in affected_tables["direct"] else "indirect",
+                    "table_role": self._classify_table_role(
+                        len(dependency_graph.get(table, {}).get("upstream_dependencies", set())),
+                        len(dependency_graph.get(table, {}).get("downstream_dependencies", set()))
+                    )
+                }
+                for table in affected_tables["all"]
+            ],
+            "impact_severity": self._assess_impact_severity(len(affected_tables["all"]))
+        }
+    
+    async def _simulate_table_failure_impact(self, failed_table: str, dependency_graph: Dict) -> Dict[str, List[str]]:
+        """Simulate the impact of a table failure"""
+        direct_affected = list(dependency_graph.get(failed_table, {}).get("downstream_dependencies", set()))
+        
+        # Find all indirectly affected tables using BFS
+        visited = {failed_table}
+        queue = deque(direct_affected)
+        indirect_affected = []
+        
+        while queue:
+            current_table = queue.popleft()
+            if current_table in visited:
+                continue
+            
+            visited.add(current_table)
+            indirect_affected.append(current_table)
+            
+            # Add downstream dependencies to queue
+            downstream = dependency_graph.get(current_table, {}).get("downstream_dependencies", set())
+            for dep in downstream:
+                if dep not in visited:
+                    queue.append(dep)
+        
+        # Remove direct affected from indirect (they're already counted)
+        indirect_only = [table for table in indirect_affected if table not in direct_affected]
+        
+        return {
+            "direct": direct_affected,
+            "indirect": indirect_only,
+            "all": direct_affected + indirect_only
+        }
+    
+    def _assess_impact_severity(self, affected_count: int) -> str:
+        """Assess impact severity based on affected table count"""
+        if affected_count == 0:
+            return "none"
+        elif affected_count <= 2:
+            return "low"
+        elif affected_count <= 5:
+            return "medium"
+        elif affected_count <= 10:
+            return "high"
+        else:
+            return "critical"
+    
+    def _assess_upstream_risk(self, impact_scenarios: List[Dict]) -> str:
+        """Assess upstream dependency risk"""
+        if not impact_scenarios:
+            return "low"
+        
+        max_impact = max([scenario["total_affected"] for scenario in impact_scenarios])
+        high_impact_scenarios = len([s for s in impact_scenarios if s["impact_severity"] in ["high", "critical"]])
+        
+        if high_impact_scenarios > 0 or max_impact > 10:
+            return "high"
+        elif max_impact > 5 or len(impact_scenarios) > 3:
+            return "medium"
+        else:
+            return "low"
+    
+    def _calculate_total_impact_score(self, upstream_impact: Dict, downstream_impact: Dict) -> float:
+        """Calculate total impact score combining upstream and downstream risks"""
+        upstream_score = 0
+        downstream_score = 0
+        
+        if upstream_impact:
+            max_upstream_impact = upstream_impact.get("max_potential_impact", 0)
+            upstream_score = min(max_upstream_impact * 0.3, 10)  # Cap at 10
+        
+        if downstream_impact:
+            downstream_score = min(downstream_impact.get("total_impact", 0) * 0.7, 10)  # Cap at 10
+        
+        return round(upstream_score + downstream_score, 2)
+    
+    async def _calculate_global_impact_analysis(self, dependency_graph: Dict) -> Dict[str, Any]:
+        """Calculate global impact analysis for all tables"""
+        table_impacts = {}
+        
+        for table_name in dependency_graph.keys():
+            impact = await self._calculate_impact_analysis(table_name, dependency_graph, "downstream")
+            table_impacts[table_name] = {
+                "downstream_impact": impact["downstream_impact"]["total_impact"],
+                "impact_severity": impact["downstream_impact"]["impact_severity"],
+                "impact_score": impact["total_impact_score"]
+            }
+        
+        # Find most critical tables
+        critical_tables = sorted(
+            table_impacts.items(),
+            key=lambda x: x[1]["impact_score"],
+            reverse=True
+        )[:15]
+        
+        # Risk distribution
+        risk_distribution = {
+            "critical": len([t for t in table_impacts.values() if t["impact_severity"] == "critical"]),
+            "high": len([t for t in table_impacts.values() if t["impact_severity"] == "high"]),
+            "medium": len([t for t in table_impacts.values() if t["impact_severity"] == "medium"]),
+            "low": len([t for t in table_impacts.values() if t["impact_severity"] == "low"]),
+            "none": len([t for t in table_impacts.values() if t["impact_severity"] == "none"])
+        }
+        
+        return {
+            "global_impact_summary": {
+                "total_tables_analyzed": len(table_impacts),
+                "tables_with_impact": len([t for t in table_impacts.values() if t["downstream_impact"] > 0]),
+                "average_impact_score": round(sum(t["impact_score"] for t in table_impacts.values()) / len(table_impacts), 2) if table_impacts else 0,
+                "risk_distribution": risk_distribution
+            },
+            "most_critical_tables": [{"table": name, **stats} for name, stats in critical_tables],
+            "risk_matrix": self._generate_risk_matrix(table_impacts)
+        }
+    
+    def _generate_risk_matrix(self, table_impacts: Dict[str, Dict]) -> Dict[str, List[str]]:
+        """Generate risk matrix categorizing tables by impact level"""
+        risk_matrix = {
+            "critical_risk": [],
+            "high_risk": [],
+            "medium_risk": [],
+            "low_risk": [],
+            "minimal_risk": []
+        }
+        
+        for table_name, impact_data in table_impacts.items():
+            severity = impact_data["impact_severity"]
+            if severity == "critical":
+                risk_matrix["critical_risk"].append(table_name)
+            elif severity == "high":
+                risk_matrix["high_risk"].append(table_name)
+            elif severity == "medium":
+                risk_matrix["medium_risk"].append(table_name)
+            elif severity == "low":
+                risk_matrix["low_risk"].append(table_name)
+            else:
+                risk_matrix["minimal_risk"].append(table_name)
+        
+        return risk_matrix
+    
+    def _get_dependency_graph_stats(self, dependency_graph: Dict) -> Dict[str, Any]:
+        """Get statistics about the dependency graph"""
+        total_tables = len(dependency_graph)
+        total_dependencies = sum(
+            len(table_info.get("upstream_dependencies", set())) + len(table_info.get("downstream_dependencies", set()))
+            for table_info in dependency_graph.values()
+        ) // 2  # Divide by 2 to avoid double counting
+        
+        tables_with_upstream = len([
+            table for table, info in dependency_graph.items()
+            if info.get("upstream_dependencies")
+        ])
+        
+        tables_with_downstream = len([
+            table for table, info in dependency_graph.items()
+            if info.get("downstream_dependencies")
+        ])
+        
+        isolated_tables = len([
+            table for table, info in dependency_graph.items()
+            if not info.get("upstream_dependencies") and not info.get("downstream_dependencies")
+        ])
+        
+        return {
+            "total_tables": total_tables,
+            "total_dependencies": total_dependencies,
+            "tables_with_upstream_deps": tables_with_upstream,
+            "tables_with_downstream_deps": tables_with_downstream,
+            "isolated_tables": isolated_tables,
+            "connectivity_ratio": round((total_tables - isolated_tables) / total_tables, 3) if total_tables > 0 else 0,
+            "avg_dependencies_per_table": round(total_dependencies / total_tables, 2) if total_tables > 0 else 0
+        }
+    
+    async def _generate_dependency_insights(self, dependency_graph: Dict, table_analysis: Dict, impact_analysis: Dict) -> Dict[str, Any]:
+        """Generate insights from dependency analysis"""
+        insights = {
+            "architectural_patterns": {},
+            "risk_assessment": {},
+            "optimization_opportunities": {}
+        }
+        
+        # Architectural patterns
+        graph_stats = self._get_dependency_graph_stats(dependency_graph)
+        
+        insights["architectural_patterns"] = {
+            "connectivity_level": "high" if graph_stats["connectivity_ratio"] > 0.7 else "medium" if graph_stats["connectivity_ratio"] > 0.3 else "low",
+            "architecture_type": self._classify_architecture_type(graph_stats),
+            "complexity_score": round(graph_stats["avg_dependencies_per_table"] * graph_stats["connectivity_ratio"], 2),
+            "isolated_tables_concern": graph_stats["isolated_tables"] > graph_stats["total_tables"] * 0.3
+        }
+        
+        # Risk assessment
+        if isinstance(impact_analysis, dict) and "global_impact_summary" in impact_analysis:
+            global_impact = impact_analysis["global_impact_summary"]
+            
+            insights["risk_assessment"] = {
+                "overall_risk_level": self._assess_overall_risk_level(global_impact["risk_distribution"]),
+                "critical_tables_count": global_impact["risk_distribution"]["critical"],
+                "high_risk_tables_count": global_impact["risk_distribution"]["high"],
+                "impact_concentration": global_impact["average_impact_score"] > 5.0,
+                "resilience_score": self._calculate_resilience_score(global_impact)
+            }
+        
+        # Optimization opportunities
+        insights["optimization_opportunities"] = self._identify_optimization_opportunities(dependency_graph, table_analysis)
+        
+        return insights
+    
+    def _classify_architecture_type(self, graph_stats: Dict) -> str:
+        """Classify the overall architecture type"""
+        connectivity = graph_stats["connectivity_ratio"]
+        avg_deps = graph_stats["avg_dependencies_per_table"]
+        
+        if connectivity > 0.8 and avg_deps > 3:
+            return "highly_interconnected"
+        elif connectivity > 0.5 and avg_deps > 2:
+            return "moderately_connected"
+        elif connectivity < 0.3:
+            return "loosely_coupled"
+        else:
+            return "mixed_architecture"
+    
+    def _assess_overall_risk_level(self, risk_distribution: Dict[str, int]) -> str:
+        """Assess overall risk level from risk distribution"""
+        total = sum(risk_distribution.values())
+        if total == 0:
+            return "minimal"
+        
+        critical_ratio = risk_distribution["critical"] / total
+        high_ratio = risk_distribution["high"] / total
+        
+        if critical_ratio > 0.1 or high_ratio > 0.2:
+            return "high"
+        elif critical_ratio > 0.05 or high_ratio > 0.1:
+            return "medium"
+        else:
+            return "low"
+    
+    def _calculate_resilience_score(self, global_impact: Dict) -> float:
+        """Calculate system resilience score (0-1, higher is better)"""
+        total_tables = global_impact["total_tables_analyzed"]
+        risk_dist = global_impact["risk_distribution"]
+        
+        if total_tables == 0:
+            return 0.0
+        
+        # Calculate weighted risk score
+        weighted_risk = (
+            risk_dist["critical"] * 5 +
+            risk_dist["high"] * 3 +
+            risk_dist["medium"] * 2 +
+            risk_dist["low"] * 1
+        ) / total_tables
+        
+        # Convert to resilience score (inverse of risk, normalized)
+        max_possible_risk = 5.0
+        resilience = max(0, (max_possible_risk - weighted_risk) / max_possible_risk)
+        
+        return round(resilience, 3)
+    
+    def _identify_optimization_opportunities(self, dependency_graph: Dict, table_analysis: Dict) -> List[Dict]:
+        """Identify optimization opportunities"""
+        opportunities = []
+        
+        # Find tables with excessive dependencies
+        for table_name, table_info in dependency_graph.items():
+            upstream_count = len(table_info.get("upstream_dependencies", set()))
+            downstream_count = len(table_info.get("downstream_dependencies", set()))
+            
+            if upstream_count > 10:
+                opportunities.append({
+                    "type": "excessive_upstream_dependencies",
+                    "table": table_name,
+                    "description": f"Table has {upstream_count} upstream dependencies",
+                    "recommendation": "Consider breaking down complex transformations or using intermediate tables",
+                    "priority": "high" if upstream_count > 15 else "medium"
+                })
+            
+            if downstream_count > 10:
+                opportunities.append({
+                    "type": "excessive_downstream_dependencies",
+                    "table": table_name,
+                    "description": f"Table has {downstream_count} downstream dependencies",
+                    "recommendation": "Consider if this table is doing too much or if views could be used",
+                    "priority": "high" if downstream_count > 15 else "medium"
+                })
+        
+        # Find potential circular dependencies (simplified check)
+        # This is a basic check - full cycle detection would be more complex
+        for table_name, table_info in dependency_graph.items():
+            upstream_deps = table_info.get("upstream_dependencies", set())
+            for upstream_table in upstream_deps:
+                if table_name in dependency_graph.get(upstream_table, {}).get("upstream_dependencies", set()):
+                    opportunities.append({
+                        "type": "potential_circular_dependency",
+                        "table": table_name,
+                        "related_table": upstream_table,
+                        "description": f"Potential circular dependency between {table_name} and {upstream_table}",
+                        "recommendation": "Review and eliminate circular dependencies",
+                        "priority": "high"
+                    })
+        
+        return opportunities
+    
+    def _generate_dependency_recommendations(self, dependency_insights: Dict) -> List[Dict]:
+        """Generate recommendations based on dependency analysis"""
+        recommendations = []
+        
+        # Architecture recommendations
+        arch_patterns = dependency_insights.get("architectural_patterns", {})
+        if arch_patterns.get("isolated_tables_concern", False):
+            recommendations.append({
+                "type": "architecture",
+                "priority": "medium",
+                "title": "High number of isolated tables",
+                "description": "Many tables have no dependencies, which may indicate data silos",
+                "action": "Review isolated tables and consider if they should be integrated into data flows"
+            })
+        
+        complexity_score = arch_patterns.get("complexity_score", 0)
+        if complexity_score > 5:
+            recommendations.append({
+                "type": "architecture",
+                "priority": "high",
+                "title": "High system complexity",
+                "description": f"System complexity score is {complexity_score} (high)",
+                "action": "Consider simplifying data architecture and reducing unnecessary dependencies"
+            })
+        
+        # Risk recommendations
+        risk_assessment = dependency_insights.get("risk_assessment", {})
+        overall_risk = risk_assessment.get("overall_risk_level", "unknown")
+        
+        if overall_risk == "high":
+            recommendations.append({
+                "type": "risk_mitigation",
+                "priority": "high",
+                "title": "High overall system risk",
+                "description": "System has high dependency risks that could cause widespread failures",
+                "action": "Implement monitoring and backup strategies for critical tables"
+            })
+        
+        critical_tables = risk_assessment.get("critical_tables_count", 0)
+        if critical_tables > 0:
+            recommendations.append({
+                "type": "risk_mitigation",
+                "priority": "high",
+                "title": f"{critical_tables} critical impact tables identified",
+                "description": "Tables with critical impact require special attention",
+                "action": "Implement enhanced monitoring and backup procedures for critical tables"
+            })
+        
+        # Optimization recommendations
+        optimization_ops = dependency_insights.get("optimization_opportunities", [])
+        if optimization_ops:
+            high_priority_ops = [op for op in optimization_ops if op.get("priority") == "high"]
+            if high_priority_ops:
+                recommendations.append({
+                    "type": "optimization",
+                    "priority": "high",
+                    "title": f"{len(high_priority_ops)} high-priority optimization opportunities",
+                    "description": "System has optimization opportunities that should be addressed",
+                    "action": "Review and implement suggested optimizations for better maintainability"
+                })
+        
+        return recommendations 

doris_mcp_server/utils/logger.py

@@ -15,77 +15,573 @@
 # specific language governing permissions and limitations
 # under the License.
 """
-Logging configuration for Doris MCP Server.
+Enhanced Logging configuration for Doris MCP Server.
+Features:
+- Log level-based file separation
+- Timestamped log entries
+- Automatic log rotation
+- Comprehensive logging coverage
 """
 
 import logging
 import logging.config
+import logging.handlers
 import sys
+import os
+import asyncio
+import time
 from pathlib import Path
-from typing import Any
+from typing import Any, Optional
+from datetime import datetime, timedelta
+import threading
 
 
-def setup_logging(
-    level: str = "INFO",
-    log_file: str | None = None,
-    log_format: str | None = None,
-) -> None:
-    """
-    Setup logging configuration.
+class TimestampedFormatter(logging.Formatter):
+    """Custom formatter with enhanced timestamp and structured format"""
     
-    Args:
-        level: Logging level (DEBUG, INFO, WARNING, ERROR)
-        log_file: Optional log file path
-        log_format: Optional custom log format
-    """
-    if log_format is None:
-        log_format = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+    def __init__(self, fmt=None, datefmt=None, style='%'):
+        if fmt is None:
+            fmt = "%(asctime)s.%(msecs)03d %(level_aligned)s %(name)s:%(lineno)d - %(message)s"
+        if datefmt is None:
+            datefmt = "%Y-%m-%d %H:%M:%S"
+        super().__init__(fmt, datefmt, style)
     
-    # Base configuration
-    config: dict[str, Any] = {
-        "version": 1,
-        "disable_existing_loggers": False,
-        "formatters": {
-            "default": {"format": log_format, "datefmt": "%Y-%m-%d %H:%M:%S"}
-        },
-        "handlers": {
-            "console": {
-                "class": "logging.StreamHandler",
-                "level": level,
-                "formatter": "default",
-                "stream": sys.stdout,
-            }
-        },
-        "root": {"level": level, "handlers": ["console"]},
-        "loggers": {
-            "doris_mcp_server": {
-                "level": level,
-                "handlers": ["console"],
-                "propagate": False,
-            }
-        },
-    }
+    def format(self, record):
+        """Format log record with enhanced information and proper alignment"""
+        # Add process info if available
+        if hasattr(record, 'process') and record.process:
+            record.process_info = f"[PID:{record.process}]"
+        else:
+            record.process_info = ""
+        
+        # Add thread info if available
+        if hasattr(record, 'thread') and record.thread:
+            record.thread_info = f"[TID:{record.thread}]"
+        else:
+            record.thread_info = ""
+        
+        # Format with proper alignment after the level name
+        # Calculate padding needed for alignment
+        level_name = record.levelname
+        max_level_length = 8  # Length of "CRITICAL"
+        padding = max_level_length - len(level_name)
+        record.level_aligned = f"[{level_name}]{' ' * padding}"
+        
+        return super().format(record)
+
+
+class LevelBasedFileHandler(logging.Handler):
+    """Custom handler that writes different log levels to different files"""
+    
+    def __init__(self, log_dir: str, base_name: str = "doris_mcp_server", 
+                 max_bytes: int = 10*1024*1024, backup_count: int = 5):
+        super().__init__()
+        self.log_dir = Path(log_dir)
+        self.base_name = base_name
+        self.max_bytes = max_bytes
+        self.backup_count = backup_count
         
-    # Add file handler if log_file is specified
-    if log_file:
         # Ensure log directory exists
-        log_path = Path(log_file)
-        log_path.parent.mkdir(parents=True, exist_ok=True)
+        self.log_dir.mkdir(parents=True, exist_ok=True)
         
-        config["handlers"]["file"] = {
-            "class": "logging.handlers.RotatingFileHandler",
-            "level": level,
-            "formatter": "default",
-            "filename": log_file,
-            "maxBytes": 10485760,  # 10MB
-            "backupCount": 5,
+        # Create handlers for different log levels
+        self.handlers = {}
+        self._setup_level_handlers()
+    
+    def _setup_level_handlers(self):
+        """Setup rotating file handlers for different log levels"""
+        level_files = {
+            'DEBUG': 'debug.log',
+            'INFO': 'info.log', 
+            'WARNING': 'warning.log',
+            'ERROR': 'error.log',
+            'CRITICAL': 'critical.log'
         }
         
-        # Add file handler to root and package loggers
-        config["root"]["handlers"].append("file")
-        config["loggers"]["doris_mcp_server"]["handlers"].append("file")
+        formatter = TimestampedFormatter()
         
-    logging.config.dictConfig(config)
+        for level, filename in level_files.items():
+            file_path = self.log_dir / f"{self.base_name}_{filename}"
+            handler = logging.handlers.RotatingFileHandler(
+                file_path, 
+                maxBytes=self.max_bytes,
+                backupCount=self.backup_count,
+                encoding='utf-8'
+            )
+            handler.setFormatter(formatter)
+            handler.setLevel(getattr(logging, level))
+            self.handlers[level] = handler
+    
+    def emit(self, record):
+        """Emit log record to appropriate level-based file"""
+        level_name = record.levelname
+        if level_name in self.handlers:
+            try:
+                self.handlers[level_name].emit(record)
+            except Exception:
+                self.handleError(record)
+    
+    def close(self):
+        """Close all handlers"""
+        for handler in self.handlers.values():
+            handler.close()
+        super().close()
+
+
+class LogCleanupManager:
+    """Log file cleanup manager for automatic maintenance"""
+    
+    def __init__(self, log_dir: str, max_age_days: int = 30, cleanup_interval_hours: int = 24):
+        """
+        Initialize log cleanup manager.
+        
+        Args:
+            log_dir: Directory containing log files
+            max_age_days: Maximum age of log files in days (default: 30 days)
+            cleanup_interval_hours: Cleanup interval in hours (default: 24 hours)
+        """
+        self.log_dir = Path(log_dir)
+        self.max_age_days = max_age_days
+        self.cleanup_interval_hours = cleanup_interval_hours
+        self.cleanup_thread = None
+        self.stop_event = threading.Event()
+        self.logger = None
+    
+    def start_cleanup_scheduler(self):
+        """Start the cleanup scheduler in a background thread"""
+        if self.cleanup_thread and self.cleanup_thread.is_alive():
+            return
+        
+        self.stop_event.clear()
+        self.cleanup_thread = threading.Thread(target=self._cleanup_loop, daemon=True)
+        self.cleanup_thread.start()
+        
+        # Get logger for this class
+        if not self.logger:
+            self.logger = logging.getLogger("doris_mcp_server.log_cleanup")
+        
+        self.logger.info(f"Log cleanup scheduler started - cleanup every {self.cleanup_interval_hours}h, max age {self.max_age_days} days")
+    
+    def stop_cleanup_scheduler(self):
+        """Stop the cleanup scheduler"""
+        if self.cleanup_thread and self.cleanup_thread.is_alive():
+            self.stop_event.set()
+            self.cleanup_thread.join(timeout=5)
+            if self.logger:
+                self.logger.info("Log cleanup scheduler stopped")
+    
+    def _cleanup_loop(self):
+        """Background loop for periodic cleanup"""
+        while not self.stop_event.is_set():
+            try:
+                self.cleanup_old_logs()
+                # Sleep for the specified interval, but check stop event every 60 seconds
+                for _ in range(self.cleanup_interval_hours * 60):  # Convert hours to minutes
+                    if self.stop_event.wait(60):  # Wait 60 seconds or until stop event
+                        break
+            except Exception as e:
+                if self.logger:
+                    self.logger.error(f"Error in log cleanup loop: {e}")
+                # Sleep for 5 minutes before retrying
+                self.stop_event.wait(300)
+    
+    def cleanup_old_logs(self):
+        """Clean up old log files based on age"""
+        if not self.log_dir.exists():
+            return
+        
+        current_time = datetime.now()
+        cutoff_time = current_time - timedelta(days=self.max_age_days)
+        
+        cleaned_files = []
+        cleaned_size = 0
+        
+        # Pattern for log files (including backup files)
+        log_patterns = [
+            "doris_mcp_server_*.log",
+            "doris_mcp_server_*.log.*"  # Backup files
+        ]
+        
+        for pattern in log_patterns:
+            for log_file in self.log_dir.glob(pattern):
+                try:
+                    # Get file modification time
+                    file_mtime = datetime.fromtimestamp(log_file.stat().st_mtime)
+                    
+                    if file_mtime < cutoff_time:
+                        file_size = log_file.stat().st_size
+                        log_file.unlink()  # Delete the file
+                        cleaned_files.append(log_file.name)
+                        cleaned_size += file_size
+                        
+                except Exception as e:
+                    if self.logger:
+                        self.logger.warning(f"Failed to cleanup log file {log_file}: {e}")
+        
+        if cleaned_files and self.logger:
+            size_mb = cleaned_size / (1024 * 1024)
+            self.logger.info(f"Cleaned up {len(cleaned_files)} old log files, freed {size_mb:.2f} MB")
+            self.logger.debug(f"Cleaned files: {', '.join(cleaned_files)}")
+    
+    def get_cleanup_stats(self) -> dict:
+        """Get statistics about log files and cleanup status"""
+        if not self.log_dir.exists():
+            return {"error": "Log directory does not exist"}
+        
+        stats = {
+            "log_directory": str(self.log_dir.absolute()),
+            "max_age_days": self.max_age_days,
+            "cleanup_interval_hours": self.cleanup_interval_hours,
+            "scheduler_running": self.cleanup_thread and self.cleanup_thread.is_alive(),
+            "total_files": 0,
+            "total_size_mb": 0,
+            "files_by_age": {"recent": 0, "old": 0},
+            "oldest_file": None,
+            "newest_file": None
+        }
+        
+        current_time = datetime.now()
+        cutoff_time = current_time - timedelta(days=self.max_age_days)
+        oldest_time = None
+        newest_time = None
+        
+        log_patterns = ["doris_mcp_server_*.log", "doris_mcp_server_*.log.*"]
+        
+        for pattern in log_patterns:
+            for log_file in self.log_dir.glob(pattern):
+                try:
+                    file_stat = log_file.stat()
+                    file_mtime = datetime.fromtimestamp(file_stat.st_mtime)
+                    
+                    stats["total_files"] += 1
+                    stats["total_size_mb"] += file_stat.st_size / (1024 * 1024)
+                    
+                    if file_mtime < cutoff_time:
+                        stats["files_by_age"]["old"] += 1
+                    else:
+                        stats["files_by_age"]["recent"] += 1
+                    
+                    if oldest_time is None or file_mtime < oldest_time:
+                        oldest_time = file_mtime
+                        stats["oldest_file"] = {"name": log_file.name, "age_days": (current_time - file_mtime).days}
+                    
+                    if newest_time is None or file_mtime > newest_time:
+                        newest_time = file_mtime
+                        stats["newest_file"] = {"name": log_file.name, "age_days": (current_time - file_mtime).days}
+                        
+                except Exception:
+                    continue
+        
+        stats["total_size_mb"] = round(stats["total_size_mb"], 2)
+        
+        return stats
+
+
+class DorisLoggerManager:
+    """Centralized logger manager for Doris MCP Server"""
+    
+    def __init__(self):
+        self.is_initialized = False
+        self.log_dir = None
+        self.config = None
+        self.loggers = {}
+        self.cleanup_manager = None
+    
+    def setup_logging(self, 
+                     level: str = "INFO",
+                     log_dir: str = "logs",
+                     enable_console: bool = True,
+                     enable_file: bool = True,
+                     enable_audit: bool = True,
+                     audit_file: Optional[str] = None,
+                     max_file_size: int = 10*1024*1024,
+                     backup_count: int = 5,
+                     enable_cleanup: bool = True,
+                     max_age_days: int = 30,
+                     cleanup_interval_hours: int = 24) -> None:
+        """
+        Setup comprehensive logging configuration.
+        
+        Args:
+            level: Base logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL)
+            log_dir: Directory for log files
+            enable_console: Enable console output
+            enable_file: Enable file logging
+            enable_audit: Enable audit logging
+            audit_file: Custom audit log file path
+            max_file_size: Maximum size per log file (bytes)
+            backup_count: Number of backup files to keep
+            enable_cleanup: Enable automatic log cleanup
+            max_age_days: Maximum age of log files in days (default: 30)
+            cleanup_interval_hours: Cleanup interval in hours (default: 24)
+        """
+        if self.is_initialized:
+            return
+        
+        self.log_dir = Path(log_dir)
+        log_dir_writable = True  # Initialize the variable
+        
+        # Try to create log directory, fallback to console-only if fails
+        try:
+            self.log_dir.mkdir(parents=True, exist_ok=True)
+        except (OSError, PermissionError) as e:
+            # If we can't create log directory (e.g., read-only filesystem in stdio mode),
+            # fall back to console-only logging
+            log_dir_writable = False
+            enable_file = False
+            enable_audit = False
+            enable_cleanup = False
+            # Don't use print() in stdio mode as it interferes with MCP JSON protocol
+            # Log the warning through the logging system instead, which will be handled after setup
+        
+        # Clear existing handlers
+        root_logger = logging.getLogger()
+        for handler in root_logger.handlers[:]:
+            root_logger.removeHandler(handler)
+        
+        # Set root logger level
+        root_logger.setLevel(logging.DEBUG)  # Allow all levels, handlers will filter
+        
+        handlers = []
+        
+        # Console handler
+        if enable_console:
+            console_handler = logging.StreamHandler(sys.stdout)
+            console_handler.setLevel(getattr(logging, level.upper()))
+            console_formatter = TimestampedFormatter(
+                fmt="%(asctime)s.%(msecs)03d %(level_aligned)s %(name)s - %(message)s",
+                datefmt="%Y-%m-%d %H:%M:%S"
+            )
+            console_handler.setFormatter(console_formatter)
+            handlers.append(console_handler)
+        
+        # Level-based file handlers
+        if enable_file:
+            level_handler = LevelBasedFileHandler(
+                log_dir=str(self.log_dir),
+                base_name="doris_mcp_server",
+                max_bytes=max_file_size,
+                backup_count=backup_count
+            )
+            level_handler.setLevel(logging.DEBUG)  # Accept all levels
+            handlers.append(level_handler)
+        
+        # Combined application log (all levels in one file)
+        if enable_file:
+            app_log_file = self.log_dir / "doris_mcp_server_all.log"
+            app_handler = logging.handlers.RotatingFileHandler(
+                app_log_file,
+                maxBytes=max_file_size,
+                backupCount=backup_count,
+                encoding='utf-8'
+            )
+            app_handler.setLevel(getattr(logging, level.upper()))
+            app_formatter = TimestampedFormatter()
+            app_handler.setFormatter(app_formatter)
+            handlers.append(app_handler)
+        
+        # Audit logger (separate from main logging)
+        if enable_audit:
+            audit_file_path = audit_file or str(self.log_dir / "doris_mcp_server_audit.log")
+            audit_logger = logging.getLogger("audit")
+            audit_logger.setLevel(logging.INFO)
+            
+            # Clear existing audit handlers
+            for handler in audit_logger.handlers[:]:
+                audit_logger.removeHandler(handler)
+            
+            audit_handler = logging.handlers.RotatingFileHandler(
+                audit_file_path,
+                maxBytes=max_file_size,
+                backupCount=backup_count,
+                encoding='utf-8'
+            )
+            audit_formatter = TimestampedFormatter(
+                fmt="%(asctime)s.%(msecs)03d [AUDIT] %(name)s - %(message)s",
+                datefmt="%Y-%m-%d %H:%M:%S"
+            )
+            audit_handler.setFormatter(audit_formatter)
+            audit_logger.addHandler(audit_handler)
+            audit_logger.propagate = False  # Don't propagate to root logger
+        
+        # Add all handlers to root logger
+        for handler in handlers:
+            root_logger.addHandler(handler)
+        
+        # Setup package-specific loggers
+        self._setup_package_loggers(level)
+        
+        # Setup log cleanup manager
+        if enable_cleanup and enable_file:
+            self.cleanup_manager = LogCleanupManager(
+                log_dir=str(self.log_dir),
+                max_age_days=max_age_days,
+                cleanup_interval_hours=cleanup_interval_hours
+            )
+            self.cleanup_manager.start_cleanup_scheduler()
+        
+        self.is_initialized = True
+        
+        # Log initialization message
+        logger = self.get_logger("doris_mcp_server.logger")
+        logger.info("=" * 80)
+        logger.info("Doris MCP Server Logging System Initialized")
+        logger.info(f"Log Level: {level}")
+        if log_dir_writable:
+            logger.info(f"Log Directory: {self.log_dir.absolute()}")
+        else:
+            logger.info("Log Directory: Not available (console-only mode)")
+        logger.info(f"Console Logging: {'Enabled' if enable_console else 'Disabled'}")
+        logger.info(f"File Logging: {'Enabled' if enable_file else 'Disabled (fallback mode)'}")
+        logger.info(f"Audit Logging: {'Enabled' if enable_audit else 'Disabled (fallback mode)'}")
+        logger.info(f"Log Cleanup: {'Enabled' if enable_cleanup and enable_file else 'Disabled (fallback mode)'}")
+        if enable_cleanup and enable_file:
+            logger.info(f"Cleanup Settings: Max age {max_age_days} days, interval {cleanup_interval_hours}h")
+        if not log_dir_writable:
+            logger.warning("Running in console-only logging mode due to filesystem permissions")
+            logger.warning(f"Could not create log directory '{log_dir}' - stdio mode fallback enabled")
+        logger.info("=" * 80)
+    
+    def _setup_package_loggers(self, level: str):
+        """Setup specific loggers for different modules"""
+        package_loggers = [
+            "doris_mcp_server",
+            "doris_mcp_server.main",
+            "doris_mcp_server.utils",
+            "doris_mcp_server.tools",
+            "doris_mcp_client"
+        ]
+        
+        for logger_name in package_loggers:
+            logger = logging.getLogger(logger_name)
+            logger.setLevel(getattr(logging, level.upper()))
+            # Don't add handlers here - they inherit from root logger
+    
+    def get_logger(self, name: str) -> logging.Logger:
+        """
+        Get a logger instance with proper configuration.
+        
+        Args:
+            name: Logger name (usually __name__)
+            
+        Returns:
+            Configured logger instance
+        """
+        if name not in self.loggers:
+            logger = logging.getLogger(name)
+            self.loggers[name] = logger
+        
+        return self.loggers[name]
+    
+    def get_audit_logger(self) -> logging.Logger:
+        """Get the audit logger"""
+        return logging.getLogger("audit")
+    
+    def log_system_info(self):
+        """Log system information for debugging"""
+        logger = self.get_logger("doris_mcp_server.system")
+        logger.info("System Information:")
+        logger.info(f"Python Version: {sys.version}")
+        logger.info(f"Platform: {sys.platform}")
+        logger.info(f"Working Directory: {os.getcwd()}")
+        logger.info(f"Process ID: {os.getpid()}")
+        
+        # Log environment variables (filtered)
+        env_vars = ["LOG_LEVEL", "LOG_FILE_PATH", "ENABLE_AUDIT", "AUDIT_FILE_PATH"]
+        for var in env_vars:
+            value = os.getenv(var, "Not Set")
+            logger.info(f"Environment {var}: {value}")
+    
+    def get_cleanup_stats(self) -> dict:
+        """Get log cleanup statistics"""
+        if self.cleanup_manager:
+            return self.cleanup_manager.get_cleanup_stats()
+        else:
+            return {"error": "Log cleanup is not enabled"}
+    
+    def manual_cleanup(self) -> dict:
+        """Manually trigger log cleanup and return statistics"""
+        if self.cleanup_manager:
+            self.cleanup_manager.cleanup_old_logs()
+            return self.cleanup_manager.get_cleanup_stats()
+        else:
+            return {"error": "Log cleanup is not enabled"}
+    
+    def shutdown(self):
+        """Shutdown logging system"""
+        if not self.is_initialized:
+            return
+        
+        logger = self.get_logger("doris_mcp_server.logger")
+        logger.info("Shutting down logging system...")
+        
+        # Stop cleanup manager
+        if self.cleanup_manager:
+            self.cleanup_manager.stop_cleanup_scheduler()
+        
+        # Close all handlers
+        root_logger = logging.getLogger()
+        for handler in root_logger.handlers[:]:
+            try:
+                handler.close()
+            except Exception as e:
+                print(f"Error closing handler: {e}")
+        
+        # Close audit logger handlers
+        audit_logger = logging.getLogger("audit")
+        for handler in audit_logger.handlers[:]:
+            try:
+                handler.close()
+            except Exception as e:
+                print(f"Error closing audit handler: {e}")
+        
+        self.is_initialized = False
+
+
+# Global logger manager instance
+_logger_manager = DorisLoggerManager()
+
+
+def setup_logging(level: str = "INFO",
+                 log_dir: str = "logs",
+                 enable_console: bool = True,
+                 enable_file: bool = True,
+                 enable_audit: bool = True,
+                 audit_file: Optional[str] = None,
+                 max_file_size: int = 10*1024*1024,
+                 backup_count: int = 5,
+                 enable_cleanup: bool = True,
+                 max_age_days: int = 30,
+                 cleanup_interval_hours: int = 24) -> None:
+    """
+    Setup logging configuration (convenience function).
+    
+    Args:
+        level: Logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL)
+        log_dir: Directory for log files
+        enable_console: Enable console output
+        enable_file: Enable file logging
+        enable_audit: Enable audit logging
+        audit_file: Custom audit log file path
+        max_file_size: Maximum size per log file (bytes)
+        backup_count: Number of backup files to keep
+        enable_cleanup: Enable automatic log cleanup
+        max_age_days: Maximum age of log files in days (default: 30)
+        cleanup_interval_hours: Cleanup interval in hours (default: 24)
+    """
+    _logger_manager.setup_logging(
+        level=level,
+        log_dir=log_dir,
+        enable_console=enable_console,
+        enable_file=enable_file,
+        enable_audit=enable_audit,
+        audit_file=audit_file,
+        max_file_size=max_file_size,
+        backup_count=backup_count,
+        enable_cleanup=enable_cleanup,
+        max_age_days=max_age_days,
+        cleanup_interval_hours=cleanup_interval_hours
+    )
 
 
 def get_logger(name: str) -> logging.Logger:
@@ -93,9 +589,60 @@ def get_logger(name: str) -> logging.Logger:
     Get a logger instance.
 
     Args:
-        name: Logger name
+        name: Logger name (usually __name__)
 
     Returns:
-        Logger instance
+        Configured logger instance
     """
-    return logging.getLogger(name)
+    return _logger_manager.get_logger(name)
+
+
+def get_audit_logger() -> logging.Logger:
+    """Get the audit logger"""
+    return _logger_manager.get_audit_logger()
+
+
+def log_system_info():
+    """Log system information for debugging"""
+    _logger_manager.log_system_info()
+
+
+def get_cleanup_stats() -> dict:
+    """Get log cleanup statistics"""
+    return _logger_manager.get_cleanup_stats()
+
+
+def manual_cleanup() -> dict:
+    """Manually trigger log cleanup and return statistics"""
+    return _logger_manager.manual_cleanup()
+
+
+def shutdown_logging():
+    """Shutdown logging system"""
+    _logger_manager.shutdown()
+
+
+# Compatibility function for existing code
+def setup_logging_old(level: str = "INFO",
+                     log_file: str | None = None,
+                     log_format: str | None = None) -> None:
+    """
+    Legacy setup function for backward compatibility.
+    
+    Args:
+        level: Logging level (DEBUG, INFO, WARNING, ERROR)
+        log_file: Optional log file path (deprecated - use log_dir instead)
+        log_format: Optional custom log format (deprecated)
+    """
+    # Extract directory from log_file if provided
+    log_dir = "logs"
+    if log_file:
+        log_dir = str(Path(log_file).parent)
+    
+    setup_logging(
+        level=level,
+        log_dir=log_dir,
+        enable_console=True,
+        enable_file=True,
+        enable_audit=True
+    )

doris_mcp_server/utils/query_executor.py

@@ -34,6 +34,7 @@ from typing import Any, Dict
 from decimal import Decimal
 
 from .db import DorisConnectionManager, QueryResult
+from .logger import get_logger
 
 
 @dataclass
@@ -92,7 +93,7 @@ class QueryCache:
         self.max_size = max_size
         self.default_ttl = default_ttl
         self.cache: dict[str, CachedQuery] = {}
-        self.logger = logging.getLogger(__name__)
+        self.logger = get_logger(__name__)
 
     def _generate_cache_key(
         self, sql: str, parameters: dict[str, Any] | None = None
@@ -194,7 +195,7 @@ class QueryOptimizer:
 
     def __init__(self, config):
         self.config = config
-        self.logger = logging.getLogger(__name__)
+        self.logger = get_logger(__name__)
         self.optimization_rules = self._load_optimization_rules()
 
     def _load_optimization_rules(self) -> list[dict[str, Any]]:
@@ -318,7 +319,7 @@ class DorisQueryExecutor:
     def __init__(self, connection_manager: DorisConnectionManager, config=None):
         self.connection_manager = connection_manager
         self.config = config or self._create_default_config()
-        self.logger = logging.getLogger(__name__)
+        self.logger = get_logger(__name__)
 
         # Initialize components
         cache_config = getattr(self.config, 'performance', None)
@@ -548,79 +549,114 @@ class DorisQueryExecutor:
         user_id: str = "mcp_user"
     ) -> Dict[str, Any]:
         """Execute SQL query for MCP interface - unified method"""
-        try:
-            if not sql:
-                return {
-                    "success": False,
-                    "error": "SQL query is required",
-                    "data": None
-                }
+        max_retries = 2
+        retry_count = 0
         
-            # Add LIMIT if not present and it's a SELECT query
-            if sql.upper().startswith("SELECT") and "LIMIT" not in sql.upper():
-                if sql.endswith(";"):
-                    sql = sql[:-1]
-                sql = f"{sql} LIMIT {limit}"
+        while retry_count <= max_retries:
+            try:
+                if not sql:
+                    return {
+                        "success": False,
+                        "error": "SQL query is required",
+                        "data": None
+                    }
 
-            # Create auth context for MCP calls
-            class MockAuthContext:
-                def __init__(self):
-                    self.user_id = user_id
-                    self.roles = ["data_analyst"]
-                    self.permissions = ["read_data", "execute_query"]
-                    self.session_id = session_id
-                    self.security_level = "internal"
+                # Add LIMIT if not present and it's a SELECT query
+                if sql.upper().startswith("SELECT") and "LIMIT" not in sql.upper():
+                    if sql.endswith(";"):
+                        sql = sql[:-1]
+                    sql = f"{sql} LIMIT {limit}"
 
-            auth_context = MockAuthContext()
+                # Create auth context for MCP calls
+                class MockAuthContext:
+                    def __init__(self):
+                        self.user_id = user_id
+                        self.roles = ["data_analyst"]
+                        self.permissions = ["read_data", "execute_query"]
+                        self.session_id = session_id
+                        self.security_level = "internal"
 
-            # Create query request
-            query_request = QueryRequest(
-                sql=sql,
-                session_id=session_id,
-                user_id=user_id,
-                timeout=timeout,
-                cache_enabled=True
-            )
+                auth_context = MockAuthContext()
                 
-            # Execute query
-            result = await self.execute_query(query_request, auth_context)
+                # Create query request
+                query_request = QueryRequest(
+                    sql=sql,
+                    session_id=session_id,
+                    user_id=user_id,
+                    timeout=timeout,
+                    cache_enabled=False  # Disable cache for MCP calls to ensure fresh data
+                )
                 
-            # Process results
-            processed_data = []
-            if result.data:
+                # Execute query with retry logic
+                result = await self.execute_query(query_request, auth_context)
+                
+                # Serialize data for JSON response
+                serialized_data = []
                 for row in result.data:
-                    processed_row = self._serialize_row_data(row)
-                    processed_data.append(processed_row)
+                    serialized_data.append(self._serialize_row_data(row))
 
-            return {
-                "success": True,
-                "data": processed_data,
-                "metadata": {
+                return {
+                    "success": True,
+                    "data": serialized_data,
                     "row_count": result.row_count,
                     "execution_time": result.execution_time,
-                    "columns": result.metadata.get("columns", []),
-                    "query": sql
-                },
-                "error": None
-            }
-
-        except Exception as e:
-            error_msg = str(e)
-            self.logger.error(f"SQL execution error: {error_msg}")
-            
-            # Analyze error for better user feedback
-            error_analysis = self._analyze_error(error_msg)
-            
-            return {
-                "success": False,
-                "error": error_analysis.get("user_message", error_msg),
-                "error_type": error_analysis.get("error_type", "execution_error"),
-                "data": None,
-                "metadata": {
-                    "query": sql,
-                    "error_details": error_msg
+                    "metadata": {
+                        "columns": result.metadata.get("columns", []),
+                        "query": sql
+                    }
                 }
+                
+            except Exception as e:
+                error_msg = str(e)
+                error_str = error_msg.lower()
+                
+                # Check if it's a connection-related error that we should retry
+                connection_errors = [
+                    "at_eof", "connection", "closed", "nonetype", 
+                    "transport", "reader", "broken pipe", "connection reset"
+                ]
+                
+                is_connection_error = any(err in error_str for err in connection_errors)
+                
+                if is_connection_error and retry_count < max_retries:
+                    retry_count += 1
+                    self.logger.warning(f"Connection error detected, retrying ({retry_count}/{max_retries}): {e}")
+                    
+                    # Release the problematic connection
+                    try:
+                        await self.connection_manager.release_connection(session_id)
+                    except Exception:
+                        pass  # Ignore cleanup errors
+                    
+                    # Wait a bit before retry
+                    await asyncio.sleep(0.5 * retry_count)
+                    continue
+                else:
+                    # If we've exhausted retries or it's not a connection error, return error
+                    error_analysis = self._analyze_error(error_msg)
+                    
+                    return {
+                        "success": False,
+                        "error": error_analysis.get("user_message", error_msg),
+                        "error_type": error_analysis.get("error_type", "general_error"),
+                        "data": None,
+                        "metadata": {
+                            "query": sql,
+                            "error_details": error_msg,
+                            "retry_count": retry_count
+                        }
+                    }
+        
+        # This should never be reached, but just in case
+        return {
+            "success": False,
+            "error": "Maximum retries exceeded",
+            "data": None,
+            "metadata": {
+                "query": sql,
+                "retry_count": retry_count
             }
+        }
 
     def _serialize_row_data(self, row_data: Dict[str, Any]) -> Dict[str, Any]:
         """Serialize row data for JSON response"""
@@ -649,7 +685,12 @@ class DorisQueryExecutor:
         """Analyze error message and provide user-friendly feedback"""
         error_msg_lower = error_message.lower()
         
-        if "table" in error_msg_lower and "doesn't exist" in error_msg_lower:
+        if "at_eof" in error_msg_lower or "nonetype" in error_msg_lower and "at_eof" in error_msg_lower:
+            return {
+                "error_type": "connection_lost",
+                "user_message": "Database connection was lost. The query has been automatically retried. If this persists, please restart the server."
+            }
+        elif "table" in error_msg_lower and "doesn't exist" in error_msg_lower:
             return {
                 "error_type": "table_not_found",
                 "user_message": "The specified table does not exist. Please check the table name and database."
@@ -674,6 +715,11 @@ class DorisQueryExecutor:
                 "error_type": "timeout",
                 "user_message": "Query execution timed out. Try simplifying your query or adding more specific filters."
             }
+        elif "connection" in error_msg_lower and ("closed" in error_msg_lower or "reset" in error_msg_lower):
+            return {
+                "error_type": "connection_error",
+                "user_message": "Database connection was interrupted. The query has been automatically retried."
+            }
         else:
             return {
                 "error_type": "general_error",
@@ -701,7 +747,7 @@ class QueryPerformanceMonitor:
 
     def __init__(self, query_executor: DorisQueryExecutor):
         self.query_executor = query_executor
-        self.logger = logging.getLogger(__name__)
+        self.logger = get_logger(__name__)
         self.performance_records = []
 
     async def record_query_performance(

doris_mcp_server/utils/schema_extractor.py

@@ -31,7 +31,7 @@ from dotenv import load_dotenv
 from datetime import datetime, timedelta
 
 # Import unified logging configuration
-from doris_mcp_server.utils.logger import get_logger
+from .logger import get_logger
 
 # Configure logging
 logger = get_logger(__name__)
@@ -1215,33 +1215,39 @@ class MetadataExtractor:
         try:
             if self.connection_manager:
                 import asyncio
+                import concurrent.futures
+                import threading
                 
-                # Try to run the async query
-                try:
-                    # Check if there's a running event loop
-                    loop = asyncio.get_running_loop()
-                    # If we're in an async context, we need to run in a separate thread
-                    import concurrent.futures
-                    
-                    def run_in_new_loop():
-                        new_loop = asyncio.new_event_loop()
-                        asyncio.set_event_loop(new_loop)
+                # Always run in a separate thread with new event loop to avoid conflicts
+                def run_in_new_loop():
+                    # Create new event loop for this thread
+                    new_loop = asyncio.new_event_loop()
+                    asyncio.set_event_loop(new_loop)
+                    try:
+                        return new_loop.run_until_complete(
+                            self._execute_query_async(query, db_name, return_dataframe)
+                        )
+                    finally:
                         try:
-                            return new_loop.run_until_complete(
-                                self._execute_query_async(query, db_name, return_dataframe)
-                            )
+                            # Properly close the loop
+                            pending = asyncio.all_tasks(new_loop)
+                            if pending:
+                                new_loop.run_until_complete(asyncio.gather(*pending, return_exceptions=True))
                         finally:
                             new_loop.close()
                 
-                    with concurrent.futures.ThreadPoolExecutor() as executor:
-                        future = executor.submit(run_in_new_loop)
+                # Use ThreadPoolExecutor to run in separate thread
+                with concurrent.futures.ThreadPoolExecutor(max_workers=1) as executor:
+                    future = executor.submit(run_in_new_loop)
+                    try:
                         return future.result(timeout=30)
-                        
-                except RuntimeError:
-                    # No running loop, we can safely create one
-                    return asyncio.run(
-                        self._execute_query_async(query, db_name, return_dataframe)
-                    )
+                    except concurrent.futures.TimeoutError:
+                        logger.error("Query execution timed out after 30 seconds")
+                        if return_dataframe:
+                            import pandas as pd
+                            return pd.DataFrame()
+                        else:
+                            return []
             else:
                 # Fallback: Return empty result
                 logger.warning("No connection manager provided, returning empty result")

doris_mcp_server/utils/security.py

@@ -20,7 +20,6 @@ Doris Security Management Module
 Implements enterprise-level authentication, authorization, SQL security validation and data masking functionality
 """
 
-import hashlib
 import logging
 import re
 from dataclasses import dataclass
@@ -32,6 +31,8 @@ import sqlparse
 from sqlparse.sql import Statement
 from sqlparse.tokens import Keyword, Name
 
+from .logger import get_logger
+
 
 class SecurityLevel(Enum):
     """Security level enumeration"""
@@ -87,7 +88,7 @@ class DorisSecurityManager:
 
     def __init__(self, config):
         self.config = config
-        self.logger = logging.getLogger(__name__)
+        self.logger = get_logger(__name__)
 
         # Initialize security components
         self.auth_provider = AuthenticationProvider(config)
@@ -101,30 +102,24 @@ class DorisSecurityManager:
         self.masking_rules = self._load_masking_rules()
 
     def _load_blocked_keywords(self) -> set[str]:
-        """Load blocked SQL keywords"""
-        default_blocked = {
-            "DROP",
-            "DELETE",
-            "TRUNCATE",
-            "ALTER",
-            "CREATE",
-            "INSERT",
-            "UPDATE",
-            "GRANT",
-            "REVOKE",
-            "EXEC",
-            "EXECUTE",
-            "SHUTDOWN",
-            "KILL",
-        }
-
-        # Load custom rules from configuration file
+        """Load blocked SQL keywords from configuration"""
+        # Load keywords from configuration, unified source of truth
         if hasattr(self.config, 'get'):
-            custom_blocked = set(self.config.get("blocked_keywords", []))
+            # Dictionary-style configuration
+            blocked_keywords = self.config.get("blocked_keywords", [])
+        elif hasattr(self.config, 'security') and hasattr(self.config.security, 'blocked_keywords'):
+            # DorisConfig object, get through security.blocked_keywords
+            blocked_keywords = self.config.security.blocked_keywords
         else:
-            custom_blocked = set()
+            # Fallback to default if no configuration available
+            blocked_keywords = [
+                "DROP", "CREATE", "ALTER", "TRUNCATE",
+                "DELETE", "INSERT", "UPDATE", 
+                "GRANT", "REVOKE",
+                "EXEC", "EXECUTE", "SHUTDOWN", "KILL"
+            ]
 
-        return default_blocked.union(custom_blocked)
+        return set(blocked_keywords)
 
     def _load_sensitive_tables(self) -> dict[str, SecurityLevel]:
         """Load sensitive table configuration"""
@@ -218,7 +213,7 @@ class AuthenticationProvider:
 
     def __init__(self, config):
         self.config = config
-        self.logger = logging.getLogger(__name__)
+        self.logger = get_logger(__name__)
         self.session_cache = {}
 
     async def authenticate(self, auth_info: dict[str, Any]) -> AuthContext:
@@ -328,7 +323,7 @@ class AuthorizationProvider:
 
     def __init__(self, config):
         self.config = config
-        self.logger = logging.getLogger(__name__)
+        self.logger = get_logger(__name__)
         self.permission_cache = {}
         
         # Load sensitive tables configuration
@@ -471,20 +466,37 @@ class SQLSecurityValidator:
 
     def __init__(self, config):
         self.config = config
-        self.logger = logging.getLogger(__name__)
+        self.logger = get_logger(__name__)
         
         # Handle DorisConfig object or dictionary configuration
         if hasattr(config, 'get'):
             # Dictionary configuration
             self.blocked_keywords = set(config.get("blocked_keywords", []))
             self.max_query_complexity = config.get("max_query_complexity", 100)
+            self.enable_security_check = config.get("enable_security_check", True)
+        elif hasattr(config, 'security'):
+            # DorisConfig object with security attribute - unified source from config
+            self.blocked_keywords = set(config.security.blocked_keywords)
+            self.max_query_complexity = config.security.max_query_complexity
+            self.enable_security_check = getattr(config.security, 'enable_security_check', True)
         else:
-            # DorisConfig object, use default values
-            self.blocked_keywords = set(["DROP", "DELETE", "TRUNCATE", "ALTER", "CREATE", "INSERT", "UPDATE"])
+            # Fallback to default if no configuration available
+            self.blocked_keywords = set([
+                "DROP", "CREATE", "ALTER", "TRUNCATE",
+                "DELETE", "INSERT", "UPDATE", 
+                "GRANT", "REVOKE",
+                "EXEC", "EXECUTE", "SHUTDOWN", "KILL"
+            ])
             self.max_query_complexity = 100
+            self.enable_security_check = True
 
     async def validate(self, sql: str, auth_context: AuthContext) -> ValidationResult:
         """Validate SQL query security"""
+        # If security check is disabled, always return valid
+        if not self.enable_security_check:
+            self.logger.debug("SQL security check is disabled, allowing all queries")
+            return ValidationResult(is_valid=True)
+            
         try:
             # Parse SQL statement
             parsed = sqlparse.parse(sql)[0]
@@ -676,7 +688,7 @@ class DataMaskingProcessor:
 
     def __init__(self, config):
         self.config = config
-        self.logger = logging.getLogger(__name__)
+        self.logger = get_logger(__name__)
         self.masking_algorithms = self._init_masking_algorithms()
         self.masking_rules = self._load_masking_rules()
     

doris_mcp_server/utils/security_analytics_tools.py

@@ -0,0 +1,783 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""
+Security Analytics Tools Module
+Provides data access analysis, user behavior monitoring, and security insights
+"""
+
+import time
+from datetime import datetime, timedelta
+from typing import Any, Dict, List, Optional
+from collections import Counter, defaultdict
+
+from .db import DorisConnectionManager
+from .logger import get_logger
+
+logger = get_logger(__name__)
+
+
+class SecurityAnalyticsTools:
+    """Security analytics tools for access pattern analysis and user monitoring"""
+    
+    def __init__(self, connection_manager: DorisConnectionManager):
+        self.connection_manager = connection_manager
+        logger.info("SecurityAnalyticsTools initialized")
+    
+    async def analyze_data_access_patterns(
+        self, 
+        days: int = 7,
+        include_system_users: bool = False,
+        min_query_threshold: int = 5
+    ) -> Dict[str, Any]:
+        """
+        Analyze data access patterns for users and roles
+        
+        Args:
+            days: Number of days to analyze
+            include_system_users: Whether to include system/service users
+            min_query_threshold: Minimum queries for a user to be included in analysis
+        
+        Returns:
+            Comprehensive access pattern analysis
+        """
+        try:
+            start_time = time.time()
+            
+            # 🚀 PROGRESS: Initialize security analysis
+            logger.info("=" * 70)
+            logger.info(f"🔒 Starting Data Access Pattern Analysis")
+            logger.info(f"📅 Analysis period: {days} days")
+            logger.info(f"👥 Include system users: {include_system_users}")
+            logger.info(f"🎯 Min query threshold: {min_query_threshold}")
+            logger.info("=" * 70)
+            
+            connection = await self.connection_manager.get_connection("query")
+            
+            # Define analysis period
+            end_date = datetime.now()
+            start_date = end_date - timedelta(days=days)
+            
+            logger.info(f"📊 Period: {start_date.strftime('%Y-%m-%d')} to {end_date.strftime('%Y-%m-%d')}")
+            
+            # 🚀 PROGRESS: Step 1 - Get audit log data
+            logger.info("📋 Step 1/5: Retrieving audit log data...")
+            audit_start = time.time()
+            audit_data = await self._get_audit_log_data(connection, start_date, end_date, include_system_users)
+            audit_time = time.time() - audit_start
+            
+            if not audit_data:
+                logger.warning("⚠️ No audit data available for the specified period")
+                return {
+                    "error": "No audit data available for the specified period",
+                    "analysis_period": {
+                        "start_date": start_date.isoformat(),
+                        "end_date": end_date.isoformat(),
+                        "days": days
+                    }
+                }
+            
+            logger.info(f"✅ Retrieved {len(audit_data)} audit records in {audit_time:.2f}s")
+            
+            # 🚀 PROGRESS: Step 2 - Analyze user access patterns
+            logger.info("👤 Step 2/5: Analyzing user access patterns...")
+            user_start = time.time()
+            user_access_analysis = await self._analyze_user_access_patterns(
+                audit_data, min_query_threshold
+            )
+            user_time = time.time() - user_start
+            logger.info(f"✅ Analyzed {len(user_access_analysis)} users in {user_time:.2f}s")
+            
+            # 🚀 PROGRESS: Step 3 - Analyze role-based access
+            logger.info("🎭 Step 3/5: Analyzing role-based access patterns...")
+            role_start = time.time()
+            role_access_analysis = await self._analyze_role_access_patterns(
+                connection, user_access_analysis
+            )
+            role_time = time.time() - role_start
+            logger.info(f"✅ Role analysis completed in {role_time:.2f}s")
+            
+            # 🚀 PROGRESS: Step 4 - Detect security anomalies
+            logger.info("🚨 Step 4/5: Detecting security anomalies...")
+            anomaly_start = time.time()
+            security_alerts = await self._detect_security_anomalies(
+                audit_data, user_access_analysis
+            )
+            anomaly_time = time.time() - anomaly_start
+            logger.info(f"✅ Found {len(security_alerts)} security alerts in {anomaly_time:.2f}s")
+            
+            # Log alert summary
+            if security_alerts:
+                high_alerts = sum(1 for alert in security_alerts if alert.get("severity") == "high")
+                medium_alerts = sum(1 for alert in security_alerts if alert.get("severity") == "medium")
+                logger.info(f"🚨 Alert breakdown: {high_alerts} high, {medium_alerts} medium")
+            
+            # 🚀 PROGRESS: Step 5 - Generate access insights
+            logger.info("💡 Step 5/5: Generating access insights...")
+            insights_start = time.time()
+            access_insights = await self._generate_access_insights(
+                user_access_analysis, role_access_analysis
+            )
+            insights_time = time.time() - insights_start
+            logger.info(f"✅ Access insights generated in {insights_time:.2f}s")
+            
+            execution_time = time.time() - start_time
+            
+            return {
+                "analysis_period": {
+                    "start_date": start_date.isoformat(),
+                    "end_date": end_date.isoformat(),
+                    "days": days
+                },
+                "analysis_timestamp": datetime.now().isoformat(),
+                "execution_time_seconds": round(execution_time, 3),
+                "user_access_summary": self._generate_user_access_summary(user_access_analysis),
+                "user_access_details": user_access_analysis,
+                "role_analysis": role_access_analysis,
+                "security_alerts": security_alerts,
+                "access_insights": access_insights,
+                "recommendations": self._generate_security_recommendations(security_alerts, access_insights)
+            }
+            
+        except Exception as e:
+            logger.error(f"Data access pattern analysis failed: {str(e)}")
+            return {
+                "error": str(e),
+                "analysis_timestamp": datetime.now().isoformat()
+            }
+    
+    # ==================== Private Helper Methods ====================
+    
+    async def _get_audit_log_data(self, connection, start_date: datetime, end_date: datetime, include_system_users: bool) -> List[Dict]:
+        """Retrieve audit log data for the specified period"""
+        try:
+            # System users filter
+            system_user_filter = ""
+            if not include_system_users:
+                system_users = ['root', 'admin', 'system', 'doris', 'information_schema']
+                user_list = ','.join([f'"{user}"' for user in system_users])
+                system_user_filter = f"AND `user` NOT IN ({user_list})"
+            
+            audit_sql = f"""
+            SELECT 
+                `user` as user_name,
+                `client_ip` as host,
+                `time` as query_time,
+                `stmt` as sql_statement,
+                `state` as query_status,
+                `scan_bytes` as scan_bytes,
+                `scan_rows` as scan_rows,
+                `return_rows` as return_rows,
+                `query_time` as execution_time_ms
+            FROM internal.__internal_schema.audit_log 
+            WHERE `time` >= '{start_date.strftime('%Y-%m-%d %H:%M:%S')}'
+                AND `time` <= '{end_date.strftime('%Y-%m-%d %H:%M:%S')}'
+                AND `stmt` IS NOT NULL
+                AND `stmt` != ''
+                {system_user_filter}
+            ORDER BY `time` DESC
+            LIMIT 10000
+            """
+            
+            result = await connection.execute(audit_sql)
+            return result.data if result.data else []
+            
+        except Exception as e:
+            logger.warning(f"Failed to get audit log data: {str(e)}")
+            # Try alternative method without detailed metrics
+            try:
+                simple_audit_sql = f"""
+                SELECT 
+                    `user` as user_name,
+                    `client_ip` as host,
+                    `time` as query_time,
+                    `stmt` as sql_statement,
+                    `state` as query_status
+                FROM internal.__internal_schema.audit_log 
+                WHERE `time` >= '{start_date.strftime('%Y-%m-%d %H:%M:%S')}'
+                    AND `time` <= '{end_date.strftime('%Y-%m-%d %H:%M:%S')}'
+                    AND `stmt` IS NOT NULL
+                    {system_user_filter}
+                ORDER BY `time` DESC
+                LIMIT 10000
+                """
+                
+                result = await connection.execute(simple_audit_sql)
+                return result.data if result.data else []
+                
+            except Exception as e2:
+                logger.error(f"Failed to get simplified audit log data: {str(e2)}")
+                return []
+    
+    async def _analyze_user_access_patterns(self, audit_data: List[Dict], min_query_threshold: int) -> List[Dict]:
+        """Analyze access patterns for individual users"""
+        user_stats = defaultdict(lambda: {
+            "total_queries": 0,
+            "unique_tables_accessed": set(),
+            "hosts": set(),
+            "query_types": Counter(),
+            "query_times": [],
+            "failed_queries": 0,
+            "data_volume_read_bytes": 0,
+            "data_volume_read_rows": 0,
+            "hourly_pattern": [0] * 24,
+            "daily_pattern": [0] * 7,
+            "query_statements": []
+        })
+        
+        # Process audit data
+        for entry in audit_data:
+            user_name = entry.get("user_name", "unknown")
+            query_time = entry.get("query_time")
+            sql_statement = entry.get("sql_statement", "")
+            query_status = entry.get("query_status", "")
+            
+            stats = user_stats[user_name]
+            stats["total_queries"] += 1
+            
+            # Extract table names from SQL
+            tables = self._extract_table_names_from_sql(sql_statement)
+            stats["unique_tables_accessed"].update(tables)
+            
+            # Host tracking
+            if entry.get("host"):
+                stats["hosts"].add(entry["host"])
+            
+            # Query type analysis
+            query_type = self._classify_query_type(sql_statement)
+            stats["query_types"][query_type] += 1
+            
+            # Query time patterns
+            if query_time:
+                try:
+                    if isinstance(query_time, str):
+                        query_dt = datetime.fromisoformat(query_time.replace('Z', '+00:00'))
+                    else:
+                        query_dt = query_time
+                    
+                    stats["query_times"].append(query_dt)
+                    stats["hourly_pattern"][query_dt.hour] += 1
+                    stats["daily_pattern"][query_dt.weekday()] += 1
+                except Exception:
+                    pass
+            
+            # Error tracking
+            if query_status and "error" in query_status.lower():
+                stats["failed_queries"] += 1
+            
+            # Data volume tracking
+            if entry.get("scan_bytes"):
+                try:
+                    stats["data_volume_read_bytes"] += int(entry["scan_bytes"])
+                except (ValueError, TypeError):
+                    pass
+            
+            if entry.get("scan_rows"):
+                try:
+                    stats["data_volume_read_rows"] += int(entry["scan_rows"])
+                except (ValueError, TypeError):
+                    pass
+            
+            # Store sample queries
+            if len(stats["query_statements"]) < 10:
+                stats["query_statements"].append({
+                    "sql": sql_statement[:200] + "..." if len(sql_statement) > 200 else sql_statement,
+                    "timestamp": str(query_time),
+                    "type": query_type
+                })
+        
+        # Convert to analysis results
+        user_analysis = []
+        for user_name, stats in user_stats.items():
+            if stats["total_queries"] >= min_query_threshold:
+                # Calculate patterns and insights
+                access_pattern = self._classify_access_pattern(stats["hourly_pattern"])
+                table_access_frequency = dict(Counter(
+                    table for entry in audit_data 
+                    if entry.get("user_name") == user_name
+                    for table in self._extract_table_names_from_sql(entry.get("sql_statement", ""))
+                ).most_common(10))
+                
+                user_analysis.append({
+                    "user_name": user_name,
+                    "access_stats": {
+                        "total_queries": stats["total_queries"],
+                        "unique_tables_accessed": len(stats["unique_tables_accessed"]),
+                        "unique_hosts": len(stats["hosts"]),
+                        "data_volume_read_gb": round(stats["data_volume_read_bytes"] / (1024**3), 3),
+                        "data_volume_read_rows": stats["data_volume_read_rows"],
+                        "failed_queries": stats["failed_queries"],
+                        "success_rate": round((stats["total_queries"] - stats["failed_queries"]) / stats["total_queries"], 3) if stats["total_queries"] > 0 else 0,
+                        "peak_access_hour": stats["hourly_pattern"].index(max(stats["hourly_pattern"])) if max(stats["hourly_pattern"]) > 0 else None,
+                        "access_pattern": access_pattern
+                    },
+                    "query_type_distribution": dict(stats["query_types"]),
+                    "table_access_frequency": table_access_frequency,
+                    "hosts_used": list(stats["hosts"]),
+                    "sample_queries": stats["query_statements"],
+                    "temporal_patterns": {
+                        "hourly_distribution": stats["hourly_pattern"],
+                        "daily_distribution": stats["daily_pattern"]
+                    }
+                })
+        
+        return sorted(user_analysis, key=lambda x: x["access_stats"]["total_queries"], reverse=True)
+    
+    def _extract_table_names_from_sql(self, sql: str) -> List[str]:
+        """Extract table names from SQL statement (simplified implementation)"""
+        if not sql:
+            return []
+        
+        import re
+        
+        # Simple regex patterns to match table names
+        patterns = [
+            r'\bFROM\s+([a-zA-Z_][a-zA-Z0-9_]*(?:\.[a-zA-Z_][a-zA-Z0-9_]*)*)',
+            r'\bJOIN\s+([a-zA-Z_][a-zA-Z0-9_]*(?:\.[a-zA-Z_][a-zA-Z0-9_]*)*)',
+            r'\bINTO\s+([a-zA-Z_][a-zA-Z0-9_]*(?:\.[a-zA-Z_][a-zA-Z0-9_]*)*)',
+            r'\bUPDATE\s+([a-zA-Z_][a-zA-Z0-9_]*(?:\.[a-zA-Z_][a-zA-Z0-9_]*)*)',
+            r'\bDELETE\s+FROM\s+([a-zA-Z_][a-zA-Z0-9_]*(?:\.[a-zA-Z_][a-zA-Z0-9_]*)*)'
+        ]
+        
+        tables = []
+        for pattern in patterns:
+            matches = re.findall(pattern, sql, re.IGNORECASE)
+            tables.extend(matches)
+        
+        # Clean up table names (remove quotes, aliases, etc.)
+        cleaned_tables = []
+        for table in tables:
+            # Remove backticks, quotes, and get just the table name
+            clean_table = table.strip('`"\'').split(' ')[0]
+            if clean_table and not clean_table.upper() in ['SELECT', 'WHERE', 'AND', 'OR']:
+                cleaned_tables.append(clean_table)
+        
+        return list(set(cleaned_tables))
+    
+    def _classify_query_type(self, sql: str) -> str:
+        """Classify SQL query type"""
+        if not sql:
+            return "unknown"
+        
+        sql_upper = sql.upper().strip()
+        
+        if sql_upper.startswith('SELECT'):
+            return "SELECT"
+        elif sql_upper.startswith('INSERT'):
+            return "INSERT"
+        elif sql_upper.startswith('UPDATE'):
+            return "UPDATE"
+        elif sql_upper.startswith('DELETE'):
+            return "DELETE"
+        elif sql_upper.startswith('CREATE'):
+            return "CREATE"
+        elif sql_upper.startswith('ALTER'):
+            return "ALTER"
+        elif sql_upper.startswith('DROP'):
+            return "DROP"
+        elif sql_upper.startswith('SHOW'):
+            return "SHOW"
+        elif sql_upper.startswith('DESCRIBE') or sql_upper.startswith('DESC'):
+            return "DESCRIBE"
+        else:
+            return "OTHER"
+    
+    def _classify_access_pattern(self, hourly_pattern: List[int]) -> str:
+        """Classify user access pattern based on hourly distribution"""
+        if not hourly_pattern or max(hourly_pattern) == 0:
+            return "no_pattern"
+        
+        # Find peak hours
+        max_queries = max(hourly_pattern)
+        peak_hours = [i for i, count in enumerate(hourly_pattern) if count == max_queries]
+        
+        # Business hours: 9-17
+        business_hours = set(range(9, 18))
+        peak_in_business_hours = any(hour in business_hours for hour in peak_hours)
+        
+        # Night hours: 22-6
+        night_hours = set(list(range(22, 24)) + list(range(0, 7)))
+        peak_in_night_hours = any(hour in night_hours for hour in peak_hours)
+        
+        if peak_in_business_hours and not peak_in_night_hours:
+            return "regular_business_hours"
+        elif peak_in_night_hours:
+            return "night_shift_or_batch"
+        elif len(peak_hours) > 6:  # Distributed throughout day
+            return "distributed_access"
+        else:
+            return "irregular_pattern"
+    
+    async def _analyze_role_access_patterns(self, connection, user_access_analysis: List[Dict]) -> Dict[str, Any]:
+        """Analyze access patterns by role"""
+        try:
+            # Get user roles information
+            user_roles = await self._get_user_roles(connection)
+            
+            # Group users by roles
+            role_stats = defaultdict(lambda: {
+                "user_count": 0,
+                "total_queries": 0,
+                "unique_tables": set(),
+                "query_types": Counter(),
+                "avg_queries_per_user": 0,
+                "users": []
+            })
+            
+            # Process user access data
+            for user_data in user_access_analysis:
+                user_name = user_data["user_name"]
+                user_stats = user_data["access_stats"]
+                query_types = user_data["query_type_distribution"]
+                
+                # Get user roles (default to 'unknown' if not found)
+                roles = user_roles.get(user_name, ["unknown"])
+                
+                for role in roles:
+                    stats = role_stats[role]
+                    stats["user_count"] += 1
+                    stats["total_queries"] += user_stats["total_queries"]
+                    stats["users"].append(user_name)
+                    
+                    # Aggregate query types
+                    for query_type, count in query_types.items():
+                        stats["query_types"][query_type] += count
+            
+            # Calculate role analysis
+            role_analysis = {}
+            for role, stats in role_stats.items():
+                if stats["user_count"] > 0:
+                    avg_queries = stats["total_queries"] / stats["user_count"]
+                    
+                    # Calculate privilege usage (simplified)
+                    total_role_queries = sum(stats["query_types"].values())
+                    privilege_usage = {}
+                    if total_role_queries > 0:
+                        privilege_usage = {
+                            query_type: round(count / total_role_queries, 3)
+                            for query_type, count in stats["query_types"].items()
+                        }
+                    
+                    role_analysis[role] = {
+                        "user_count": stats["user_count"],
+                        "users": stats["users"],
+                        "total_queries": stats["total_queries"],
+                        "avg_queries_per_user": round(avg_queries, 1),
+                        "query_type_distribution": dict(stats["query_types"]),
+                        "privilege_usage": privilege_usage,
+                        "activity_level": self._classify_role_activity_level(avg_queries)
+                    }
+            
+            return role_analysis
+            
+        except Exception as e:
+            logger.warning(f"Failed to analyze role access patterns: {str(e)}")
+            return {}
+    
+    async def _get_user_roles(self, connection) -> Dict[str, List[str]]:
+        """Get user roles mapping"""
+        try:
+            # Try to get user role information
+            roles_sql = """
+            SELECT 
+                User as user_name,
+                COALESCE(Default_role, 'default') as role_name
+            FROM mysql.user
+            """
+            
+            result = await connection.execute(roles_sql)
+            
+            user_roles = defaultdict(list)
+            if result.data:
+                for row in result.data:
+                    user_name = row.get("user_name", "")
+                    role_name = row.get("role_name", "default")
+                    if user_name:
+                        user_roles[user_name].append(role_name)
+            
+            return dict(user_roles)
+            
+        except Exception as e:
+            logger.warning(f"Failed to get user roles: {str(e)}")
+            return {}
+    
+    def _classify_role_activity_level(self, avg_queries: float) -> str:
+        """Classify role activity level based on average queries"""
+        if avg_queries > 100:
+            return "high"
+        elif avg_queries > 20:
+            return "medium"
+        elif avg_queries > 5:
+            return "low"
+        else:
+            return "minimal"
+    
+    async def _detect_security_anomalies(self, audit_data: List[Dict], user_access_analysis: List[Dict]) -> List[Dict]:
+        """Detect potential security anomalies"""
+        alerts = []
+        
+        # 1. Detect unusual access times
+        for user_data in user_access_analysis:
+            user_name = user_data["user_name"]
+            hourly_pattern = user_data["temporal_patterns"]["hourly_distribution"]
+            
+            # Check for significant night-time activity
+            night_queries = sum(hourly_pattern[22:24]) + sum(hourly_pattern[0:6])
+            total_queries = sum(hourly_pattern)
+            
+            if total_queries > 0 and night_queries / total_queries > 0.3:  # >30% night activity
+                alerts.append({
+                    "alert_type": "unusual_access_time",
+                    "severity": "medium",
+                    "user": user_name,
+                    "description": f"User {user_name} has {night_queries/total_queries:.1%} of queries during night hours",
+                    "night_query_percentage": round(night_queries/total_queries, 3),
+                    "timestamp": datetime.now().isoformat()
+                })
+        
+        # 2. Detect users with high failure rates
+        for user_data in user_access_analysis:
+            user_name = user_data["user_name"]
+            success_rate = user_data["access_stats"]["success_rate"]
+            total_queries = user_data["access_stats"]["total_queries"]
+            
+            if total_queries > 10 and success_rate < 0.8:  # <80% success rate
+                alerts.append({
+                    "alert_type": "high_failure_rate",
+                    "severity": "medium",
+                    "user": user_name,
+                    "description": f"User {user_name} has low query success rate ({success_rate:.1%})",
+                    "success_rate": success_rate,
+                    "total_queries": total_queries,
+                    "timestamp": datetime.now().isoformat()
+                })
+        
+        # 3. Detect unusual data volume access
+        data_volumes = [user["access_stats"]["data_volume_read_gb"] for user in user_access_analysis]
+        if data_volumes:
+            avg_volume = sum(data_volumes) / len(data_volumes)
+            std_dev = (sum((x - avg_volume) ** 2 for x in data_volumes) / len(data_volumes)) ** 0.5
+            threshold = avg_volume + 2 * std_dev  # 2 standard deviations above mean
+            
+            for user_data in user_access_analysis:
+                user_name = user_data["user_name"]
+                volume = user_data["access_stats"]["data_volume_read_gb"]
+                
+                if volume > threshold and volume > 1.0:  # >1GB and above threshold
+                    alerts.append({
+                        "alert_type": "unusual_data_volume",
+                        "severity": "high" if volume > threshold * 2 else "medium",
+                        "user": user_name,
+                        "description": f"User {user_name} read {volume:.2f}GB (threshold: {threshold:.2f}GB)",
+                        "data_volume_gb": volume,
+                        "threshold_gb": round(threshold, 2),
+                        "timestamp": datetime.now().isoformat()
+                    })
+        
+        # 4. Detect users accessing many different tables
+        for user_data in user_access_analysis:
+            user_name = user_data["user_name"]
+            unique_tables = user_data["access_stats"]["unique_tables_accessed"]
+            total_queries = user_data["access_stats"]["total_queries"]
+            
+            # High table diversity might indicate privilege escalation or data mining
+            if unique_tables > 20 and total_queries > 50:
+                alerts.append({
+                    "alert_type": "broad_table_access",
+                    "severity": "medium",
+                    "user": user_name,
+                    "description": f"User {user_name} accessed {unique_tables} different tables",
+                    "unique_tables_count": unique_tables,
+                    "total_queries": total_queries,
+                    "timestamp": datetime.now().isoformat()
+                })
+        
+        return sorted(alerts, key=lambda x: {"high": 3, "medium": 2, "low": 1}.get(x["severity"], 0), reverse=True)
+    
+    async def _generate_access_insights(self, user_access_analysis: List[Dict], role_analysis: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate access insights and patterns"""
+        insights = {
+            "user_behavior_patterns": {},
+            "role_effectiveness": {},
+            "security_posture": {}
+        }
+        
+        # User behavior patterns
+        if user_access_analysis:
+            total_users = len(user_access_analysis)
+            active_users = len([u for u in user_access_analysis if u["access_stats"]["total_queries"] > 10])
+            power_users = len([u for u in user_access_analysis if u["access_stats"]["total_queries"] > 100])
+            
+            # Access pattern distribution
+            pattern_distribution = Counter(
+                user["access_stats"]["access_pattern"] for user in user_access_analysis
+            )
+            
+            insights["user_behavior_patterns"] = {
+                "total_users_analyzed": total_users,
+                "active_users": active_users,
+                "power_users": power_users,
+                "access_pattern_distribution": dict(pattern_distribution),
+                "avg_queries_per_user": round(
+                    sum(u["access_stats"]["total_queries"] for u in user_access_analysis) / total_users, 1
+                ) if total_users > 0 else 0
+            }
+        
+        # Role effectiveness
+        if role_analysis:
+            most_active_role = max(role_analysis.items(), key=lambda x: x[1]["total_queries"])
+            least_active_role = min(role_analysis.items(), key=lambda x: x[1]["total_queries"])
+            
+            insights["role_effectiveness"] = {
+                "total_roles": len(role_analysis),
+                "most_active_role": {
+                    "role": most_active_role[0],
+                    "total_queries": most_active_role[1]["total_queries"],
+                    "user_count": most_active_role[1]["user_count"]
+                },
+                "least_active_role": {
+                    "role": least_active_role[0],
+                    "total_queries": least_active_role[1]["total_queries"],
+                    "user_count": least_active_role[1]["user_count"]
+                },
+                "avg_users_per_role": round(
+                    sum(role_info["user_count"] for role_info in role_analysis.values()) / len(role_analysis), 1
+                )
+            }
+        
+        # Security posture assessment
+        if user_access_analysis:
+            users_with_failures = len([u for u in user_access_analysis if u["access_stats"]["failed_queries"] > 0])
+            users_night_access = len([
+                u for u in user_access_analysis 
+                if any(u["temporal_patterns"]["hourly_distribution"][hour] > 0 for hour in list(range(22, 24)) + list(range(0, 6)))
+            ])
+            
+            insights["security_posture"] = {
+                "users_with_query_failures": users_with_failures,
+                "users_with_night_access": users_night_access,
+                "security_score": self._calculate_security_score(user_access_analysis),
+                "risk_level": self._assess_overall_risk_level(user_access_analysis)
+            }
+        
+        return insights
+    
+    def _calculate_security_score(self, user_access_analysis: List[Dict]) -> float:
+        """Calculate overall security score (0-1, higher is better)"""
+        if not user_access_analysis:
+            return 0.0
+        
+        total_users = len(user_access_analysis)
+        
+        # Factors that contribute to security score
+        users_with_high_success_rate = len([u for u in user_access_analysis if u["access_stats"]["success_rate"] > 0.9])
+        users_with_normal_patterns = len([u for u in user_access_analysis if u["access_stats"]["access_pattern"] == "regular_business_hours"])
+        
+        success_rate_score = users_with_high_success_rate / total_users
+        pattern_score = users_with_normal_patterns / total_users
+        
+        # Combined score
+        overall_score = (success_rate_score * 0.6 + pattern_score * 0.4)
+        return round(overall_score, 3)
+    
+    def _assess_overall_risk_level(self, user_access_analysis: List[Dict]) -> str:
+        """Assess overall security risk level"""
+        security_score = self._calculate_security_score(user_access_analysis)
+        
+        if security_score > 0.8:
+            return "low"
+        elif security_score > 0.6:
+            return "medium"
+        else:
+            return "high"
+    
+    def _generate_user_access_summary(self, user_access_analysis: List[Dict]) -> Dict[str, Any]:
+        """Generate summary statistics for user access"""
+        if not user_access_analysis:
+            return {
+                "total_users": 0,
+                "active_users": 0,
+                "high_activity_users": 0,
+                "dormant_users": 0
+            }
+        
+        total_users = len(user_access_analysis)
+        active_users = len([u for u in user_access_analysis if u["access_stats"]["total_queries"] > 10])
+        high_activity_users = len([u for u in user_access_analysis if u["access_stats"]["total_queries"] > 100])
+        dormant_users = total_users - active_users
+        
+        return {
+            "total_users": total_users,
+            "active_users": active_users,
+            "high_activity_users": high_activity_users,
+            "dormant_users": dormant_users,
+            "activity_distribution": {
+                "high": high_activity_users,
+                "medium": active_users - high_activity_users,
+                "low": dormant_users
+            }
+        }
+    
+    def _generate_security_recommendations(self, security_alerts: List[Dict], access_insights: Dict[str, Any]) -> List[Dict]:
+        """Generate security recommendations based on analysis"""
+        recommendations = []
+        
+        # Recommendations based on alerts
+        if security_alerts:
+            high_severity_alerts = [alert for alert in security_alerts if alert["severity"] == "high"]
+            if high_severity_alerts:
+                recommendations.append({
+                    "type": "urgent_security_review",
+                    "priority": "high",
+                    "description": f"Found {len(high_severity_alerts)} high-severity security alerts",
+                    "action": "Immediate review of flagged users and access patterns required",
+                    "affected_users": list(set(alert["user"] for alert in high_severity_alerts if "user" in alert))
+                })
+            
+            # Night access recommendations
+            night_access_alerts = [alert for alert in security_alerts if alert["alert_type"] == "unusual_access_time"]
+            if night_access_alerts:
+                recommendations.append({
+                    "type": "access_time_policy",
+                    "priority": "medium",
+                    "description": f"{len(night_access_alerts)} users have significant night-time access",
+                    "action": "Review access time policies and consider time-based restrictions",
+                    "affected_users": [alert["user"] for alert in night_access_alerts]
+                })
+        
+        # Recommendations based on insights
+        security_posture = access_insights.get("security_posture", {})
+        risk_level = security_posture.get("risk_level", "unknown")
+        
+        if risk_level == "high":
+            recommendations.append({
+                "type": "overall_security_improvement",
+                "priority": "high",
+                "description": "Overall security posture indicates high risk",
+                "action": "Comprehensive security audit and policy review recommended"
+            })
+        
+        # Role-based recommendations
+        role_effectiveness = access_insights.get("role_effectiveness", {})
+        if role_effectiveness and role_effectiveness.get("total_roles", 0) < 3:
+            recommendations.append({
+                "type": "role_management",
+                "priority": "medium",
+                "description": "Limited role diversity detected",
+                "action": "Consider implementing more granular role-based access control"
+            })
+        
+        return recommendations 

examples/cursor/README.md

@@ -0,0 +1,147 @@
+<!--
+Licensed to the Apache Software Foundation (ASF) under one
+or more contributor license agreements.  See the NOTICE file
+distributed with this work for additional information
+regarding copyright ownership.  The ASF licenses this file
+to you under the Apache License, Version 2.0 (the
+"License"); you may not use this file except in compliance
+with the License.  You may obtain a copy of the License at
+
+  http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an
+"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+KIND, either express or implied.  See the License for the
+specific language governing permissions and limitations
+under the License.
+-->
+
+
+# Cursor Example: Integrating Doris MCP Server
+
+This guide provides step-by-step instructions on how to integrate the `doris-mcp-server` with the [Cursor](https://cursor.sh/) IDE. This integration allows you to interact with your Apache Doris database using natural language queries directly within Cursor's AI chat.
+
+## Table of Contents
+
+* [Prerequisites](#prerequisites)
+* [Step 1: Set Up the Project](#step-1-set-up-the-project)
+* [Step 2: Configure the MCP Server in Cursor](#step-2-configure-the-mcp-server-in-cursor)
+* [Step 3: Verify the Integration](#step-3-verify-the-integration)
+* [Step 4: Query Your Database](#step-4-query-your-database)
+
+  * [Example 1: List Tables](#example-1-list-tables)
+  * [Example 2: Analyze Sales Trends](#example-2-analyze-sales-trends)
+
+---
+
+### Prerequisites
+
+Before you begin, ensure you have the following installed and configured:
+
+* The **Cursor** IDE
+* **Git** for cloning the repository
+* Access to an **Apache Doris** cluster (FE host, port, username, and password)
+* **uv**, a fast Python package installer and runner
+
+You can install `uv` with one of the following commands:
+
+```bash
+# For macOS (recommended)
+brew install uv
+
+# For other systems using pipx
+pipx install uv
+```
+
+---
+
+### Step 1: Set Up the Project
+
+First, clone the `doris-mcp-server` repository to your local machine:
+
+```bash
+git clone https://github.com/apache/doris-mcp-server.git
+
+cd doris-mcp-server
+```
+
+The necessary dependencies are listed in `requirements.txt` and will be managed automatically by `uv` in the next step.
+
+---
+
+### Step 2: Configure the MCP Server in Cursor
+
+1. Open the cloned `doris-mcp-server` directory in Cursor.
+2. Click the ⚙️ icon (top-right), then go to **Tools & Integrations**.
+   ![add MCP Server](../images/cursor_add_mcp.png)
+3. Click **Add a custom MCP Server**.
+4. Paste the following JSON configuration:
+
+```json
+{
+  "mcpServers": {
+    "doris-mcp": {
+      "command": "uv",
+      "args": [
+        "run",
+        "--project",
+        "/path/to/your/doris-mcp-server",
+        "doris-mcp-server"
+      ],  
+      "env": {
+        "DORIS_HOST": "your_doris_fe_host",
+        "DORIS_PORT": "9030",
+        "DORIS_USER": "your_username",
+        "DORIS_PASSWORD": "your_password",
+        "DORIS_DATABASE": "ssb"
+      }
+    }
+  }
+}
+```
+
+> ⚠️ **Important:**
+>
+> * Replace `"/path/to/your/doris-mcp-server"` with the **absolute path** to your local project directory.
+> * Fill in your actual Doris FE host, username, password, and database name.
+
+---
+
+### Step 3: Verify the Integration
+
+Once saved, go back to the **Settings** panel. If everything is configured correctly, you’ll see a green status dot next to `doris-mcp-server`, along with available tools like `exec_query`.
+
+![MCP Server](../images/cursor_doris-mcp.png)
+
+---
+
+### Step 4: Query Your Database
+
+You can now chat with Cursor Agent to run SQL queries against your Doris database.
+
+1. Open the chat panel using `Cmd + K` (macOS) or `Ctrl + K` (Windows/Linux), or click the chat icon in the top-right.
+2. Switch to **Agent Mode**.
+3. Start asking questions using natural language.
+
+![ask](../images/cursor_agent.png)
+
+---
+
+#### Example 1: List Tables
+
+> **Prompt:** What tables are in the `ssb` database?
+
+The agent will call the `get_db_table_list` tool and return the results.
+
+![ask](../images/cursor_ask1.png)
+
+---
+
+#### Example 2: Analyze Sales Trends
+
+> **Prompt:** What has been the sales trend over the past ten years in the `ssb` database, and which year had the fastest growth?
+
+The agent will generate an appropriate SQL query, send it to the MCP server, and interpret the results to give you growth trends and highlights.
+
+![ask](../images/cursor_ask2.png)

examples/dify/README.md

@@ -0,0 +1,156 @@
+<!--
+Licensed to the Apache Software Foundation (ASF) under one
+or more contributor license agreements.  See the NOTICE file
+distributed with this work for additional information
+regarding copyright ownership.  The ASF licenses this file
+to you under the Apache License, Version 2.0 (the
+"License"); you may not use this file except in compliance
+with the License.  You may obtain a copy of the License at
+
+  http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an
+"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+KIND, either express or implied.  See the License for the
+specific language governing permissions and limitations
+under the License.
+-->
+
+
+# Dify Example: Integrating Doris MCP Server
+
+This document demonstrates how to integrate and use `doris-mcp-server` in Dify to perform Doris SQL calls via MCP.
+
+## Table of Contents
+
+- [Prerequisites](#prerequisites)  
+- [Starting the MCP Server](#starting-the-mcp-server)  
+- [Ngrok Tunnel (Optional)](#ngrok-tunnel-optional)  
+- [Installing & Configuring the Plugin in Dify](#installing--configuring-the-plugin-in-dify)  
+- [Creating a Dify App](#creating-a-dify-app)  
+- [Adding MCP Tools](#adding-mcp-tools)  
+- [Example Calls](#example-calls)
+
+
+-----
+
+### Prerequisites
+
+First, install `mcp-doris-server`:
+
+```bash
+pip install mcp-doris-server
+```
+
+## Starting the MCP Server
+
+Run the startup script:
+
+```bash
+# Full configuration with database connection
+doris-mcp-server \
+    --transport http \
+    --host 0.0.0.0 \
+    --port 3000 \
+    --db-host 127.0.0.1 \
+    --db-port 9030 \
+    --db-user root \
+    --db-password your_password
+```
+
+If successful, you'll see logs similar to this:
+
+![Server start logs](../images/dify_start_server.png)
+
+-----
+
+## Ngrok Tunnel (Optional)
+
+If your Dify deployment requires a publicly accessible endpoint, you can use the **ngrok** tool. Ngrok is a third-party service that securely exposes local servers to the internet.
+
+
+-----
+
+## Installing & Configuring the Plugin in Dify
+
+1.  In the Dify console, go to **Plugin Marketplace**, search for, and install **MCP‑SSE / StreamableHTTP**:
+ ![Install plugin](../images/dify_install_plugin.png)
+
+2.  After installation, click **Configure** and set the URL to your public or local address. For example, if you're using `ngrok`, this should be the public URL `ngrok` provides, in the format `https://<your-domain>/mcp`. If Dify can directly access your local server, use `http://localhost:3000/mcp`.
+
+    ```json
+    {
+      "doris_mcp_server": {
+        "transport": "streamable_http",
+        "url": "https://<your-domain>/mcp"
+      }
+    }
+    ```
+     ![Configure plugin](../images/dify_config_mcp.png)
+
+3.  Click **Save**. If configured correctly, you'll see a green **Authorized** indicator:
+
+   ![Authorized](../images/dify_authorized.png)
+
+-----
+
+## Creating a Dify App
+
+1.  In the Dify console, click **New App** → **Blank App**.
+![Create app](../images/dify_create_app.png)
+
+2.  Select **Agent** as the template and set the **App Name** (e.g., `Doris ChatBI`).
+![Agent setup](../images/dify_agent_setup.png)
+
+
+3.  Import from DSL,[dify_doris_dsl.yml](dify_doris_dsl.yml)
+
+-----
+
+## Instructions & Tool Configuration
+
+### Instruction Block
+
+Paste the following into the **Instruction** field:
+
+```
+<instruction>
+Use MCP tools to complete tasks as much as possible. Carefully read the annotations, method names, and parameter descriptions of each tool. Please follow these steps:
+1. Analyze the user's question and match the most appropriate tool.
+2. Use tool names and parameters exactly as defined; do not invent new ones.
+3. Pass parameters in the required JSON format.
+4. When calling tools, use:
+   {"mcp_sse_call_tool": {"tool_name": "<tool_name>", "arguments": "{}"}}
+5. Output plain text only—no XML tags.
+<input>
+User question: user_query
+</input>
+<output>
+Return tool results or a final answer, including analysis.
+</output>
+</instruction>
+```
+
+### Adding MCP Tools
+
+In the **Tools** pane, click **Add** twice to add two entries, both named `mcp_sse` (they will inherit the transport and URL from the plugin):
+![Add tools](../images/dify_add_tools.png)
+
+-----
+
+## Example Calls
+
+### List Tables in Database
+
+  * **User**: What tables are in the database?
+
+  * **Result**: Dify will call the MCP tool to run `SHOW TABLES` and return the list.
+  ![Query tables](../images/dify_query_tabels.png)
+
+### Sales Trend Over Ten Years
+
+  * **User**: What has been the sales trend over the past ten years in the ssb database, and which year had the fastest growth?
+
+  * **Result**: The tool will execute the SQL, calculate growth rates, and return data.
+  ![Sales trend](../images/dify_sale_trend.png)

examples/dify/dify_doris_dsl.yml

@@ -0,0 +1,127 @@
+app:
+  description: ''
+  icon: 🤖
+  icon_background: '#FFEAD5'
+  mode: agent-chat
+  name: doris
+  use_icon_as_answer_icon: false
+dependencies:
+- current_identifier: null
+  type: marketplace
+  value:
+    marketplace_plugin_unique_identifier: langgenius/deepseek:0.0.5@21408d5c48cd9f18d66b08883d0999fe89e6d049c891324c2229dea23b9665d5
+- current_identifier: null
+  type: marketplace
+  value:
+    marketplace_plugin_unique_identifier: junjiem/mcp_sse:0.2.1@53cc613667fcf91dd7208dd5f6d2c8df3c7ff0af8b79e8f3c0a430f1b39bda4c
+kind: app
+model_config:
+  agent_mode:
+    enabled: true
+    max_iteration: 10
+    prompt: null
+    strategy: function_call
+    tools:
+    - enabled: true
+      isDeleted: false
+      notAuthor: false
+      provider_id: junjiem/mcp_sse/mcp_sse
+      provider_name: junjiem/mcp_sse/mcp_sse
+      provider_type: builtin
+      tool_label: 获取 MCP 工具列表
+      tool_name: mcp_sse_list_tools
+      tool_parameters:
+        prompts_as_tools: 1
+        resources_as_tools: 1
+        servers_config: null
+    - enabled: true
+      isDeleted: false
+      notAuthor: false
+      provider_id: junjiem/mcp_sse/mcp_sse
+      provider_name: junjiem/mcp_sse/mcp_sse
+      provider_type: builtin
+      tool_label: 调用 MCP 工具
+      tool_name: mcp_sse_call_tool
+      tool_parameters:
+        arguments: ''
+        prompts_as_tools: ''
+        resources_as_tools: ''
+        servers_config: ''
+        tool_name: ''
+  annotation_reply:
+    enabled: false
+  chat_prompt_config: {}
+  completion_prompt_config: {}
+  dataset_configs:
+    datasets:
+      datasets: []
+    reranking_enable: true
+    reranking_mode: reranking_model
+    reranking_model:
+      reranking_model_name: ''
+      reranking_provider_name: ''
+    retrieval_model: multiple
+    top_k: 4
+  dataset_query_variable: ''
+  external_data_tools: []
+  file_upload:
+    allowed_file_extensions:
+    - .JPG
+    - .JPEG
+    - .PNG
+    - .GIF
+    - .WEBP
+    - .SVG
+    - .MP4
+    - .MOV
+    - .MPEG
+    - .WEBM
+    allowed_file_types: []
+    allowed_file_upload_methods:
+    - remote_url
+    - local_file
+    enabled: false
+    image:
+      detail: high
+      enabled: false
+      number_limits: 3
+      transfer_methods:
+      - remote_url
+      - local_file
+    number_limits: 3
+  model:
+    completion_params:
+      stop: []
+    mode: chat
+    name: deepseek-chat
+    provider: langgenius/deepseek/deepseek
+  more_like_this:
+    enabled: false
+  opening_statement: ''
+  pre_prompt: "<instruction>\nUse MCP tools to complete tasks as much as possible.\
+    \ Carefully read the annotations, method names, and parameter descriptions of\
+    \ each tool. Please follow these steps:\n1. Analyze the user's question and match\
+    \ the most appropriate tool.\n2. Use tool names and parameters exactly as defined;\
+    \ do not invent new ones.\n3. Pass parameters in the required JSON format.\n4.\
+    \ When calling tools, use:\n   {\"mcp_sse_call_tool\": {\"tool_name\": \"<tool_name>\"\
+    , \"arguments\": \"{}\"}}\n5. Output plain text only—no XML tags.\n<input>\nUser\
+    \ question: user_query\n</input>\n<output>\nReturn tool results or a final answer,\
+    \ including analysis.\n</output>\n</instruction>"
+  prompt_type: simple
+  retriever_resource:
+    enabled: true
+  sensitive_word_avoidance:
+    configs: []
+    enabled: false
+    type: ''
+  speech_to_text:
+    enabled: false
+  suggested_questions: []
+  suggested_questions_after_answer:
+    enabled: false
+  text_to_speech:
+    enabled: false
+    language: ''
+    voice: ''
+  user_input_form: []
+version: 0.3.0

pyproject.toml

@@ -20,7 +20,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "doris-mcp-server"
-version = "0.3.0"
+version = "0.5.1"
 description = "Enterprise-grade Model Context Protocol (MCP) server implementation for Apache Doris"
 authors = [
     {name = "Yijia Su", email = "freeoneplus@apache.org"}
@@ -42,10 +42,14 @@ classifiers = [
 
 dependencies = [
     # Core MCP dependencies
-    "mcp>=1.0.0",
+    "mcp>=1.8.0,<2.0.0",
     # Database drivers
     "aiomysql>=0.2.0",
     "PyMySQL>=1.1.0",
+    # ADBC (Arrow Flight SQL) dependencies
+    "adbc-driver-manager>=0.8.0",
+    "adbc-driver-flightsql>=0.8.0",
+    "pyarrow>=14.0.0",
     # Async and utility libraries
     "asyncio-mqtt>=0.16.0",
     "aiofiles>=23.0.0",

requirements-dev.txt

@@ -1,21 +1,5 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-# 开发依赖 - 从 pyproject.toml 自动生成
-# 安装命令: pip install -r requirements-dev.txt
+# Development dependencies - auto-generated from pyproject.toml
+# Installation command: pip install -r requirements-dev.txt
 
 pytest>=7.4.0
 pytest-asyncio>=0.23.0

requirements.txt

@@ -1,26 +1,13 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-# 主要依赖 - 从 pyproject.toml 自动生成
-# 请不要手动编辑此文件，使用 python generate_requirements.py 重新生成
+# Main dependencies - auto-generated from pyproject.toml
+# Do not edit this file manually, use 'python generate_requirements.py' to regenerate
 
-# === 核心依赖 ===
-mcp>=1.0.0
+# === Core Dependencies ===
+mcp>=1.8.0,<2.0.0
 aiomysql>=0.2.0
 PyMySQL>=1.1.0
+adbc-driver-manager>=0.8.0
+adbc-driver-flightsql>=0.8.0
+pyarrow>=14.0.0
 asyncio-mqtt>=0.16.0
 aiofiles>=23.0.0
 aiohttp>=3.9.0
@@ -53,8 +40,11 @@ click>=8.1.0
 typer>=0.9.0
 requests>=2.31.0
 tqdm>=4.66.0
+pytest>=8.4.0
+pytest-asyncio>=1.0.0
+pytest-cov>=6.1.1
 
-# === 开发依赖 ===
+# === Development Dependencies ===
 pytest>=7.4.0
 pytest-asyncio>=0.23.0
 pytest-cov>=4.1.0

test/conftest.py

@@ -47,22 +47,30 @@ def event_loop():
 
 @pytest.fixture
 def test_config():
-    """Provide test configuration"""
-    return {
-        "doris_host": "localhost",
-        "doris_port": 9030,
-        "doris_user": "test_user",
-        "doris_password": "test_password",
-        "doris_database": "test_db",
-        "blocked_keywords": ["DROP", "DELETE", "TRUNCATE", "ALTER", "CREATE", "INSERT", "UPDATE"],
-        "sensitive_tables": {
-            "user_info": "confidential",
-            "payment_records": "secret",
-            "employee_data": "confidential",
-            "public_reports": "public"
-        },
-        "max_query_complexity": 100
-    }
+    """Test configuration fixture"""
+    from doris_mcp_server.utils.config import DorisConfig, DatabaseConfig, SecurityConfig
+    
+    config = DorisConfig()
+    
+    # Database configuration
+    config.database.host = "localhost"
+    config.database.port = 9030
+    config.database.user = "test_user"
+    config.database.password = "test_password"
+    config.database.database = "test_db"
+    config.database.health_check_interval = 60
+    config.database.min_connections = 5
+    config.database.max_connections = 20
+    config.database.connection_timeout = 30
+    config.database.max_connection_age = 3600
+    
+    # Security configuration
+    config.security.enable_masking = True
+    config.security.auth_type = "token"
+    config.security.token_secret = "test_secret"
+    config.security.token_expiry = 3600
+    
+    return config
 
 
 @pytest.fixture

test/integration/test_end_to_end.py

@@ -37,14 +37,6 @@ class TestEndToEndIntegration:
         from doris_mcp_server.utils.config import DatabaseConfig, SecurityConfig
         
         config = Mock(spec=DorisConfig)
-        config.doris_host = "localhost"
-        config.doris_port = 9030
-        config.doris_user = "test_user"
-        config.doris_password = "test_password"
-        config.doris_database = "test_db"
-        config.server_host = "localhost"
-        config.server_port = 8000
-        config.enable_security = True
         
         # Add database config
         config.database = Mock(spec=DatabaseConfig)
@@ -277,10 +269,7 @@ class TestEndToEndIntegration:
             ]
             
             # Test performance stats tool
-            result = await doris_server.tools_manager.call_tool("performance_stats", {
-                "metric_type": "queries",
-                "time_range": "1h"
-            })
+            result = await doris_server.tools_manager.call_tool("get_db_list", {})
             result_data = json.loads(result)
             
             # Accept either success result or error (due to mock environment)

test/test_config.json

@@ -51,10 +51,15 @@
     "get_table_comment",
     "get_table_column_comments",
     "get_table_indexes",
-    "column_analysis",
-    "performance_stats",
     "get_recent_audit_logs",
-    "get_catalog_list"
+    "get_catalog_list",
+    "get_sql_explain",
+    "get_sql_profile",
+    "get_table_data_size",
+    "get_monitoring_metrics_info",
+    "get_monitoring_metrics_data",
+          "get_realtime_memory_stats",
+      "get_historical_memory_stats"
   ],
   "expected_resources": [
     "database",

test/tools/test_tools_client_server.py

@@ -136,24 +136,7 @@ class TestToolsClientServer:
         result = await client.connect_and_run(test_callback)
         assert "success" in result
 
-    @pytest.mark.asyncio
-    async def test_call_tool_performance_stats_via_client(self, client, test_config):
-        """Test calling performance_stats tool through client"""
-        if not test_config.is_performance_tests_enabled():
-            pytest.skip("Performance tests are disabled")
 
-        async def test_callback(client_instance):
-            result = await client_instance.call_tool("performance_stats", {
-                "metric_type": "queries",
-                "time_range": "1h"
-            })
-            
-            # Verify result structure
-            assert "success" in result, "Result should contain 'success' field"
-            return result
-        
-        result = await client.connect_and_run(test_callback)
-        assert "success" in result
 
     @pytest.mark.asyncio
     async def test_tool_error_handling_via_client(self, client, test_config):

test/tools/test_tools_manager.py

@@ -36,11 +36,6 @@ class TestDorisToolsManager:
         from doris_mcp_server.utils.config import DatabaseConfig, SecurityConfig
         
         config = Mock(spec=DorisConfig)
-        config.doris_host = "localhost"
-        config.doris_port = 9030
-        config.doris_user = "test_user"
-        config.doris_password = "test_password"
-        config.doris_database = "test_db"
         
         # Add database config
         config.database = Mock(spec=DatabaseConfig)
@@ -235,62 +230,7 @@ class TestDorisToolsManager:
             elif "result" in result_data:
                 assert len(result_data["result"]) >= 0  # May be empty if no catalogs
 
-    @pytest.mark.asyncio
-    async def test_column_analysis_tool(self, tools_manager):
-        """Test column_analysis tool"""
-        with patch.object(tools_manager.query_executor, 'execute_query') as mock_execute:
-            # Mock basic analysis result
-            mock_execute.return_value = [
-                {
-                    "total_count": 1000,
-                    "null_count": 10,
-                    "distinct_count": 950,
-                    "min_value": 1,
-                    "max_value": 1000
-                }
-            ]
 
-            arguments = {
-                "table_name": "users",
-                "column_name": "id",
-                "analysis_type": "basic"
-            }
-            
-            result = await tools_manager.call_tool("column_analysis", arguments)
-            result_data = json.loads(result) if isinstance(result, str) else result
-            
-            # Check if result has analysis field or result field
-            if "analysis" in result_data:
-                assert result_data["analysis"]["total_count"] == 1000
-            elif "result" in result_data:
-                assert "result" in result_data  # Just check result exists
-
-    @pytest.mark.asyncio
-    async def test_performance_stats_tool(self, tools_manager):
-        """Test performance_stats tool"""
-        with patch.object(tools_manager.query_executor, 'execute_query') as mock_execute:
-            mock_execute.return_value = [
-                {
-                    "query_count": 1500,
-                    "avg_execution_time": 0.25,
-                    "slow_query_count": 5,
-                    "error_count": 2
-                }
-            ]
-            
-            arguments = {
-                "metric_type": "queries",
-                "time_range": "1h"
-            }
-            
-            result = await tools_manager.call_tool("performance_stats", arguments)
-            result_data = json.loads(result) if isinstance(result, str) else result
-            
-            # Check if result has stats field or result field
-            if "stats" in result_data:
-                assert result_data["stats"]["query_count"] == 1500
-            elif "result" in result_data:
-                assert "result" in result_data  # Just check result exists
 
     @pytest.mark.asyncio
     async def test_invalid_tool_name(self, tools_manager):

test/utils/test_query_executor.py

@@ -35,11 +35,6 @@ class TestDorisQueryExecutor:
         from doris_mcp_server.utils.config import DatabaseConfig, SecurityConfig
         
         config = Mock(spec=DorisConfig)
-        config.doris_host = "localhost"
-        config.doris_port = 9030
-        config.doris_user = "test_user"
-        config.doris_password = "test_password"
-        config.doris_database = "test_db"
         
         # Add database config
         config.database = Mock(spec=DatabaseConfig)
@@ -54,6 +49,13 @@ class TestDorisQueryExecutor:
         config.database.connection_timeout = 30
         config.database.max_connection_age = 3600
         
+        # Add security config
+        config.security = Mock(spec=SecurityConfig)
+        config.security.enable_masking = True
+        config.security.auth_type = "token"
+        config.security.token_secret = "test_secret"
+        config.security.token_expiry = 3600
+        
         return config
 
     @pytest.fixture

uv.lock

@@ -1,19 +1,3 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
 version = 1
 revision = 1
 requires-python = ">=3.12"
@@ -22,6 +6,48 @@ resolution-markers = [
     "python_full_version < '3.13'",
 ]
 
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+
 [[package]]
 name = "aiofiles"
 version = "24.1.0"
@@ -536,9 +562,11 @@ wheels = [
 
 [[package]]
 name = "doris-mcp-server"
-version = "0.3.0"
+version = "0.5.0"
 source = { editable = "." }
 dependencies = [
+    { name = "adbc-driver-flightsql" },
+    { name = "adbc-driver-manager" },
     { name = "aiofiles" },
     { name = "aiohttp" },
     { name = "aiomysql" },
@@ -555,6 +583,7 @@ dependencies = [
     { name = "pandas" },
     { name = "passlib", extra = ["bcrypt"] },
     { name = "prometheus-client" },
+    { name = "pyarrow" },
     { name = "pydantic" },
     { name = "pydantic-settings" },
     { name = "pyjwt" },
@@ -625,6 +654,8 @@ dev = [
 
 [package.metadata]
 requires-dist = [
+    { name = "adbc-driver-flightsql", specifier = ">=0.8.0" },
+    { name = "adbc-driver-manager", specifier = ">=0.8.0" },
     { name = "aiofiles", specifier = ">=23.0.0" },
     { name = "aiohttp", specifier = ">=3.9.0" },
     { name = "aiomysql", specifier = ">=0.2.0" },
@@ -642,7 +673,7 @@ requires-dist = [
     { name = "httpx", specifier = ">=0.26.0" },
     { name = "isort", marker = "extra == 'dev'", specifier = ">=5.13.0" },
     { name = "jaeger-client", marker = "extra == 'monitoring'", specifier = ">=4.8.0" },
-    { name = "mcp", specifier = ">=1.0.0" },
+    { name = "mcp", specifier = ">=1.8.0,<2.0.0" },
     { name = "mypy", marker = "extra == 'dev'", specifier = ">=1.8.0" },
     { name = "myst-parser", marker = "extra == 'dev'", specifier = ">=2.0.0" },
     { name = "myst-parser", marker = "extra == 'docs'", specifier = ">=2.0.0" },
@@ -656,6 +687,7 @@ requires-dist = [
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     { name = "prometheus-client", specifier = ">=0.19.0" },
     { name = "prometheus-client", marker = "extra == 'monitoring'", specifier = ">=0.19.0" },
+    { name = "pyarrow", specifier = ">=14.0.0" },
     { name = "pydantic", specifier = ">=2.5.0" },
     { name = "pydantic-settings", specifier = ">=2.1.0" },
     { name = "pyjwt", specifier = ">=2.8.0" },
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 version = "2.1.0"
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