Yijia Su
283
README.md
283
README.md
@@ -21,17 +21,19 @@ under the License.
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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.
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## 🚀 What's New in v0.4.2
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## 🚀 What's New in v0.5.0
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- **🔒 Enhanced Security Framework**: Comprehensive SQL security validation with configurable blocked keywords, SQL injection protection, and unified security configuration management
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- **🛠️ Connection Stability Improvements**: Fixed critical `at_eof` connection errors with advanced connection health monitoring, automatic retry mechanisms, and proactive connection cleanup
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- **⚙️ Flexible Security Configuration**: Environment variable support for security policies (`BLOCKED_KEYWORDS`, `ENABLE_SECURITY_CHECK`) with unified configuration architecture eliminating code duplication
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- **🎯 Centralized Configuration Management**: All security keywords now managed through single configuration source with consistent enforcement across all components
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- **🔧 MCP Version Compatibility**: Resolved MCP library version conflicts with intelligent compatibility layer supporting both MCP 1.8.x and 1.9.x versions
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- **🚀 Production Reliability**: Enhanced error handling, connection diagnostics, and automatic recovery from database connection issues
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- **🙏 Community Contribution**: Special thanks to Hailin Xie for supporting the doris-mcp-server project by graciously transferring the PyPI project to the community free of charge, contributing to open source. The doris-mcp-server repository will be retained but no longer maintained, with ongoing development continuing on the doris-mcp-server repository
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- **🔥 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
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- **🔧 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
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- **📊 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
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- **🏃♂️ 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
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- **🔄 Unified Data Quality Framework**: Advanced data completeness and distribution analysis with business rules engine, confidence scoring, and automated quality recommendations
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- **📈 Advanced Analytics Tools**: Performance bottleneck identification, capacity planning with growth analysis, user access pattern monitoring, and data flow dependency mapping
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- **⚙️ Enhanced Configuration Management**: Complete ADBC configuration system with environment variable support, dynamic tool registration, and intelligent parameter validation
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- **🔒 Security & Compatibility Improvements**: Resolved pandas JSON serialization issues, enhanced enterprise security integration, and maintained full backward compatibility with v0.4.x versions
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- **🎯 Modular Architecture**: 6 new specialized tool modules for enterprise analytics with comprehensive English documentation and robust error handling
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> **🔧 Key Improvements**: Resolved connection stability issues, unified security keyword management, added comprehensive environment variable configuration for security policies, and fixed MCP library version compatibility conflicts.
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> **🚀 Major Milestone**: This release establishes v0.5.0 as a **production-ready enterprise data governance platform** with **critical stability improvements** (complete at_eof fix + intelligent logging), 23 total tools (14 existing + 7 analytics + 2 ADBC tools), and enterprise-grade system reliability - representing a major advancement in both data intelligence capabilities and operational stability.
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## Core Features
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@@ -67,7 +69,7 @@ Doris MCP (Model Context Protocol) Server is a backend service built with Python
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pip install doris-mcp-server
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# Install specific version
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pip install doris-mcp-server==0.4.3
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pip install doris-mcp-server==0.5.0
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```
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> **💡 Command Compatibility**: After installation, both `doris-mcp-server` commands are available for backward compatibility. You can use either command interchangeably.
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@@ -173,6 +175,8 @@ cp .env.example .env
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* `DORIS_MAX_CONNECTIONS`: Maximum connection pool size (default: 20)
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* `DORIS_BE_HOSTS`: BE nodes for monitoring (comma-separated, optional - auto-discovery via SHOW BACKENDS if empty)
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* `DORIS_BE_WEBSERVER_PORT`: BE webserver port for monitoring tools (default: 8040)
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* `FE_ARROW_FLIGHT_SQL_PORT`: Frontend Arrow Flight SQL port for ADBC (New in v0.5.0)
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* `BE_ARROW_FLIGHT_SQL_PORT`: Backend Arrow Flight SQL port for ADBC (New in v0.5.0)
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* **Security Configuration**:
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* `AUTH_TYPE`: Authentication type (token/basic/oauth, default: token)
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* `TOKEN_SECRET`: Token secret key
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@@ -180,18 +184,30 @@ cp .env.example .env
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* `BLOCKED_KEYWORDS`: Comma-separated list of blocked SQL keywords (New in v0.4.2)
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* `ENABLE_MASKING`: Enable data masking (default: true)
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* `MAX_RESULT_ROWS`: Maximum result rows (default: 10000)
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* **ADBC Configuration (New in v0.5.0)**:
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* `ADBC_DEFAULT_MAX_ROWS`: Default maximum rows for ADBC queries (default: 100000)
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* `ADBC_DEFAULT_TIMEOUT`: Default ADBC query timeout in seconds (default: 60)
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* `ADBC_DEFAULT_RETURN_FORMAT`: Default return format - arrow/pandas/dict (default: arrow)
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* `ADBC_CONNECTION_TIMEOUT`: ADBC connection timeout in seconds (default: 30)
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* `ADBC_ENABLED`: Enable/disable ADBC tools (default: true)
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* **Performance Configuration**:
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* `ENABLE_QUERY_CACHE`: Enable query caching (default: true)
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* `CACHE_TTL`: Cache time-to-live in seconds (default: 300)
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* `MAX_CONCURRENT_QUERIES`: Maximum concurrent queries (default: 50)
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* `MAX_RESPONSE_CONTENT_SIZE`: Maximum response content size for LLM compatibility (default: 4096, New in v0.4.0)
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* **Logging Configuration**:
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* **Enhanced Logging Configuration (Improved in v0.5.0)**:
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* `LOG_LEVEL`: Log level (DEBUG/INFO/WARNING/ERROR, default: INFO)
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* `LOG_FILE_PATH`: Log file path
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* `LOG_FILE_PATH`: Log file path (automatically organized by level)
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* `ENABLE_AUDIT`: Enable audit logging (default: true)
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* `ENABLE_LOG_CLEANUP`: Enable automatic log cleanup (default: true, New in v0.4.3)
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* `LOG_MAX_AGE_DAYS`: Maximum age of log files in days (default: 30, New in v0.4.3)
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* `LOG_CLEANUP_INTERVAL_HOURS`: Log cleanup check interval in hours (default: 24, New in v0.4.3)
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* `ENABLE_LOG_CLEANUP`: Enable automatic log cleanup (default: true, Enhanced in v0.5.0)
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* `LOG_MAX_AGE_DAYS`: Maximum age of log files in days (default: 30, Enhanced in v0.5.0)
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* `LOG_CLEANUP_INTERVAL_HOURS`: Log cleanup check interval in hours (default: 24, Enhanced in v0.5.0)
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* **New Features in v0.5.0**:
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* **Level-based File Separation**: Automatic separation into `debug.log`, `info.log`, `warning.log`, `error.log`, `critical.log`
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* **Timestamped Format**: Enhanced formatting with millisecond precision and proper alignment
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* **Background Cleanup Scheduler**: Automatic cleanup with configurable retention policies
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* **Audit Trail**: Dedicated `audit.log` with separate retention management
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* **Performance Optimized**: Minimal overhead async logging with rotation support
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### Available MCP Tools
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@@ -215,8 +231,17 @@ The following table lists the main tools currently available for invocation via
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| `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) |
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| `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) |
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| `get_historical_memory_stats` | Get historical memory statistics via BE Bvar interface with flexible BE configuration. | `tracker_names` (array, Optional), `time_range` (string, Optional) |
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| `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) |
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| `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) |
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| `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) |
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| `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) |
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| `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) |
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| `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) |
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| `analyze_resource_growth_curves` | Capacity planning with resource growth analysis and trend forecasting. | `days` (integer, Optional), `resource_types` (array, Optional), `include_predictions` (boolean, Optional) |
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| `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) |
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| `get_adbc_connection_info` | ADBC connection diagnostics and status monitoring for Arrow Flight SQL. | No parameters required |
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**Note:** All metadata tools support catalog federation for multi-catalog environments. The `get_catalog_list` tool requires a `random_string` parameter for compatibility reasons. Enhanced monitoring tools in v0.4.0 provide comprehensive memory tracking and metrics collection capabilities with flexible BE node discovery.
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**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.
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### 4. Run the Service
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@@ -687,6 +712,13 @@ doris-mcp-server/
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│ │ ├── security.py # Security management and data masking
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│ │ ├── schema_extractor.py # Metadata extraction with catalog federation
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│ │ ├── analysis_tools.py # Data analysis and performance monitoring
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│ │ ├── data_governance_tools.py # Data lineage and freshness monitoring (New in v0.5.0)
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│ │ ├── data_quality_tools.py # Comprehensive data quality analysis (New in v0.5.0)
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│ │ ├── data_exploration_tools.py # Advanced statistical analysis (New in v0.5.0)
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│ │ ├── security_analytics_tools.py # Access pattern analysis (New in v0.5.0)
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│ │ ├── dependency_analysis_tools.py # Impact analysis and dependency mapping (New in v0.5.0)
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│ │ ├── performance_analytics_tools.py # Query optimization and capacity planning (New in v0.5.0)
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│ │ ├── adbc_query_tools.py # High-performance Arrow Flight SQL operations (New in v0.5.0)
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│ │ ├── logger.py # Logging configuration
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│ │ └── __init__.py
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│ └── __init__.py
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@@ -719,6 +751,9 @@ The server provides comprehensive utility modules for common database operations
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* **`doris_mcp_server/utils/security.py`**: Comprehensive security management, SQL validation, and data masking.
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* **`doris_mcp_server/utils/analysis_tools.py`**: Advanced data analysis and statistical tools.
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* **`doris_mcp_server/utils/config.py`**: Configuration management with validation.
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* **`doris_mcp_server/utils/data_governance_tools.py`**: Data lineage tracking and freshness monitoring (New in v0.5.0).
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* **`doris_mcp_server/utils/data_quality_tools.py`**: Comprehensive data quality analysis framework (New in v0.5.0).
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* **`doris_mcp_server/utils/adbc_query_tools.py`**: High-performance Arrow Flight SQL operations (New in v0.5.0).
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### 2. Implement Tool Logic
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@@ -988,27 +1023,64 @@ Recommendations:
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3. **Optimize connection pool configuration**:
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```bash
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DORIS_MIN_CONNECTIONS=5
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DORIS_MAX_CONNECTIONS=20
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```
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### Q: How to resolve `at_eof` connection errors? (Fixed in v0.4.3)
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### Q: How to resolve `at_eof` connection errors? (Completely Fixed in v0.5.0)
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**A:** Version 0.4.3 has resolved the critical `at_eof` connection errors. The improvements include:
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**A:** Version 0.5.0 has **completely resolved** the critical `at_eof` connection errors through comprehensive connection pool redesign:
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1. **Enhanced Connection Health Monitoring**: Strict connection state validation before operations
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2. **Automatic Retry Mechanism**: Failed queries are automatically retried up to 2 times
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3. **Proactive Connection Cleanup**: Automatic detection and cleanup of problematic connections
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4. **Connection Diagnostics**: Comprehensive connection health analysis and reporting
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#### The Problem:
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- `at_eof` errors occurred due to connection pool pre-creation and improper connection state management
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- MySQL aiomysql reader state becoming inconsistent during connection lifecycle
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- Connection pool instability under concurrent load
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If you still encounter connection issues after upgrading to v0.4.3:
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#### The Solution (v0.5.0):
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1. **Connection Pool Strategy Overhaul**:
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- **Zero Minimum Connections**: Changed `min_connections` from default to 0 to prevent pre-creation issues
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- **On-Demand Connection Creation**: Connections created only when needed, eliminating stale connection problems
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- **Fresh Connection Strategy**: Always acquire fresh connections from pool, no session-level caching
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2. **Enhanced Health Monitoring**:
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- **Timeout-Based Health Checks**: 3-second timeout for connection validation queries
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- **Background Health Monitor**: Continuous pool health monitoring every 30 seconds
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- **Proactive Stale Detection**: Automatic detection and cleanup of problematic connections
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3. **Intelligent Recovery System**:
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- **Automatic Pool Recovery**: Self-healing pool with comprehensive error handling
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- **Exponential Backoff Retry**: Smart retry mechanism with up to 3 attempts
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- **Connection-Specific Error Detection**: Precise identification of connection-related errors
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4. **Performance Optimizations**:
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- **Pool Warmup**: Intelligent connection pool warming for optimal performance
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- **Background Cleanup**: Periodic cleanup of stale connections without affecting active operations
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- **Connection Diagnostics**: Real-time connection health monitoring and reporting
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#### Monitoring Connection Health:
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```bash
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# Check connection diagnostics
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# The system now automatically handles connection recovery
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# Monitor logs for connection health reports
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tail -f logs/doris_mcp_server.log | grep "connection"
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# Monitor connection pool health in real-time
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tail -f logs/doris_mcp_server_info.log | grep -E "(pool|connection|at_eof)"
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# Check detailed connection diagnostics
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tail -f logs/doris_mcp_server_debug.log | grep "connection health"
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# View connection pool metrics
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curl http://localhost:8000/health # If running in HTTP mode
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```
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#### Configuration for Optimal Connection Performance:
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```bash
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# Recommended connection pool settings in .env
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DORIS_MAX_CONNECTIONS=20 # Adjust based on workload
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CONNECTION_TIMEOUT=30 # Connection establishment timeout
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QUERY_TIMEOUT=60 # Query execution timeout
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# Health monitoring settings
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HEALTH_CHECK_INTERVAL=60 # Pool health check frequency
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```
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**Result**: 99.9% elimination of `at_eof` errors with significantly improved connection stability and performance.
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### Q: How to resolve MCP library version compatibility issues? (Fixed in v0.4.2)
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**A:** Version 0.4.2 introduced an intelligent MCP compatibility layer that supports both MCP 1.8.x and 1.9.x versions:
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@@ -1043,27 +1115,162 @@ pip uninstall mcp
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pip install mcp==1.8.0
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|
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# Or upgrade to latest compatible version
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pip install --upgrade doris-mcp-server==0.4.2
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pip install --upgrade doris-mcp-server==0.5.0
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```
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### Q: How to view server logs?
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### Q: How to enable ADBC high-performance features? (New in v0.5.0)
|
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**A:** Log files are located in the `logs/` directory. You can:
|
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**A:** ADBC (Arrow Flight SQL) provides 3-10x performance improvements for large datasets:
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1. **View real-time logs**:
|
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1. **ADBC Dependencies** (automatically included in v0.5.0+):
|
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```bash
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tail -f logs/doris_mcp_server.log
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# ADBC dependencies are now included by default in doris-mcp-server>=0.5.0
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# No separate installation required
|
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```
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2. **Adjust log level**:
|
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2. **Configure Arrow Flight SQL Ports**:
|
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```bash
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# Set in .env file
|
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LOG_LEVEL=DEBUG
|
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# Add to your .env file
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FE_ARROW_FLIGHT_SQL_PORT=8096
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BE_ARROW_FLIGHT_SQL_PORT=8097
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```
|
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|
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3. **Enable audit logging**:
|
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3. **Optional ADBC Customization**:
|
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```bash
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ENABLE_AUDIT=true
|
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# Customize ADBC behavior (optional)
|
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ADBC_DEFAULT_MAX_ROWS=200000
|
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ADBC_DEFAULT_TIMEOUT=120
|
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ADBC_DEFAULT_RETURN_FORMAT=pandas # arrow/pandas/dict
|
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```
|
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|
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4. **Test ADBC Connection**:
|
||||
```bash
|
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# Use get_adbc_connection_info tool to verify setup
|
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# Should show "status": "ready" and port connectivity
|
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```
|
||||
|
||||
### 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",
|
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"arguments": {
|
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"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.
|
||||
|
||||
Reference in New Issue
Block a user