[BUG]Optimize and fix the capabilities of 0.5.0 tools (#26)

1. **Unified Naming for CLI Arguments and Environment Variables** 
- All database-related CLI arguments now use the `--doris-*` prefix, and environment variables use `DORIS_*` for consistency and maintainability. 
- Backward compatibility: old `--db-*` arguments are still supported.

2. **Automatic Filtering of System SQL in Slow Query TopN** 
- Slow query analysis now automatically excludes SQL statements involving `__internal_schema`, `information_schema`, and `mysql` system databases, ensuring only business-related slow queries are counted. 
- Filtering is performed at the SQL level using `NOT LIKE` and `state != 'ERR'` for efficiency and safety.

3. **Unified Query Timeout Configuration** 
- If no `timeout` is specified for query execution, the system will use the `config.performance.query_timeout` value as the default, falling back to 30 seconds if not configured.
- This avoids hardcoding and makes timeout management more flexible.

4. **Tool execution optimization**
- Significantly reduce the execution time of some data governance and operation and maintenance tools
- Optimize execution logic and reduce data scanning
- Enable concurrent scanning to speed up retrieval

5. **Log system optimization**
- Fix the Console log printing logic and output the log content correctly
- Add advanced tool execution process log output to facilitate further positioning of error locations

6. **DB Connection optimization**
- Fixed a connection pool acquisition exception caused by deadlock

7. **Other Improvements**
- Help documentation and CLI examples updated to reflect new and legacy parameter compatibility.
- Code comments and documentation further standardized for better team collaboration and open-source community understanding.

doris_mcp_server/main.py

@@ -618,6 +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
         """
     )
 
@@ -641,26 +646,26 @@ Examples:
     )
 
     parser.add_argument(
-        "--db-host",
+        "--doris-host", "--db-host",
         type=str,
-        default=os.getenv("DB_HOST", _default_config.database.host),
+        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=os.getenv("DB_PORT", _default_config.database.port), help=f"Doris database port number (default: {_default_config.database.port})"
+        "--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=os.getenv("DB_USER", _default_config.database.user), help=f"Doris database username (default: {_default_config.database.user})"
+        "--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=os.getenv("DB_DATABASE", _default_config.database.database),
+        default=os.getenv("DORIS_DATABASE", _default_config.database.database),
         help=f"Doris database name (default: {_default_config.database.database})",
     )
 
@@ -684,16 +689,19 @@ async def main():
     config = DorisConfig.from_env()  # First load from .env file and environment variables
     
     # Command line arguments override configuration (if provided)
-    if args.db_host != _default_config.database.host:  # If not default value, use command line argument
-        config.database.host = args.db_host
-    if args.db_port != _default_config.database.port:
-        config.database.port = args.db_port
-    if args.db_user != _default_config.database.user:
-        config.database.user = args.db_user
-    if args.db_password:  # Use password if provided
-        config.database.password = args.db_password
-    if args.db_database != _default_config.database.database:
-        config.database.database = args.db_database
+    # 🔧 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
 

doris_mcp_server/tools/tools_manager.py

@@ -61,7 +61,7 @@ class DorisToolsManager:
         # Initialize v0.5.0 advanced analytics tools
         self.data_governance_tools = DataGovernanceTools(connection_manager)
         self.data_exploration_tools = DataExplorationTools(connection_manager)
-        self.data_quality_tools = DataQualityTools(connection_manager)
+        self.data_quality_tools = DataQualityTools(connection_manager, connection_manager.config)
         self.security_analytics_tools = SecurityAnalyticsTools(connection_manager)
         self.dependency_analysis_tools = DependencyAnalysisTools(connection_manager)
         self.performance_analytics_tools = PerformanceAnalyticsTools(connection_manager)
@@ -464,41 +464,87 @@ class DorisToolsManager:
         
         # 🔄 Unified Data Quality Analysis Tool (New in v0.5.0)
         @mcp.tool(
-            "analyze_data_quality",
-            description="""[Function Description]: Comprehensive data quality analysis combining completeness and distribution analysis.
+            "get_table_basic_info",
+            description="""[Function Description]: Get basic information about a table including row count, column count, partitions, and size.
 
 [Parameter Content]:
 
 - table_name (string) [Required] - Name of the table to analyze
-- analysis_scope (string) [Optional] - Analysis scope, default is "comprehensive"
-  * "completeness": Only completeness analysis (null rates, business rules)
-  * "distribution": Only distribution analysis (statistical patterns)
-  * "comprehensive": Full analysis including both completeness and distribution
+- catalog_name (string) [Optional] - Target catalog name
+- db_name (string) [Optional] - Target database name
+""",
+        )
+        async def get_table_basic_info_tool(
+            table_name: str,
+            catalog_name: str = None,
+            db_name: str = None
+        ) -> str:
+            """Get table basic information"""
+            return await self.call_tool("get_table_basic_info", {
+                "table_name": table_name,
+                "catalog_name": catalog_name,
+                "db_name": db_name
+            })
+
+        @mcp.tool(
+            "analyze_columns",
+            description="""[Function Description]: Analyze completeness and distribution of specified columns in a table.
+
+[Parameter Content]:
+
+- table_name (string) [Required] - Name of the table to analyze
+- columns (array) [Required] - List of column names to analyze
+- analysis_types (array) [Optional] - Types of analysis to perform, default is ["both"]
+  * "completeness": Only completeness analysis (null rates, non-null counts)
+  * "distribution": Only distribution analysis (statistical patterns by data type)
+  * "both": Both completeness and distribution analysis
 - sample_size (integer) [Optional] - Maximum number of rows to sample, default is 100000
-- include_all_columns (boolean) [Optional] - Whether to analyze all columns, default is false
-- business_rules (array) [Optional] - Business rule validations in format [{"rule_name": "email_format", "sql_condition": "email REGEXP '^[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\\.[A-Za-z]{2,}$'"}]
 - catalog_name (string) [Optional] - Target catalog name
 - db_name (string) [Optional] - Target database name
 - detailed_response (boolean) [Optional] - Whether to return detailed response including raw data, default is false
 """,
         )
-        async def analyze_data_quality_tool(
+        async def analyze_columns_tool(
             table_name: str,
-            analysis_scope: str = "comprehensive",
+            columns: List[str],
+            analysis_types: List[str] = None,
             sample_size: int = 100000,
-            include_all_columns: bool = False,
-            business_rules: List[dict] = None,
             catalog_name: str = None,
             db_name: str = None,
             detailed_response: bool = False
         ) -> str:
-            """Unified data quality analysis tool"""
-            return await self.call_tool("analyze_data_quality", {
+            """Analyze table columns"""
+            return await self.call_tool("analyze_columns", {
                 "table_name": table_name,
-                "analysis_scope": analysis_scope,
+                "columns": columns,
+                "analysis_types": analysis_types or ["both"],
                 "sample_size": sample_size,
-                "include_all_columns": include_all_columns,
-                "business_rules": business_rules,
+                "catalog_name": catalog_name,
+                "db_name": db_name,
+                "detailed_response": detailed_response
+            })
+
+        @mcp.tool(
+            "analyze_table_storage",
+            description="""[Function Description]: Analyze table's physical distribution and storage information.
+
+[Parameter Content]:
+
+- table_name (string) [Required] - Name of the table to analyze
+- catalog_name (string) [Optional] - Target catalog name
+- db_name (string) [Optional] - Target database name
+- detailed_response (boolean) [Optional] - Whether to return detailed response including raw data, default is false
+""",
+        )
+        async def analyze_table_storage_tool(
+            table_name: str,
+            catalog_name: str = None,
+            db_name: str = None,
+            detailed_response: bool = False
+        ) -> str:
+            """Analyze table storage"""
+            return await self.call_tool("analyze_table_storage", {
+                "table_name": table_name,
                 "catalog_name": catalog_name,
                 "db_name": db_name,
                 "detailed_response": detailed_response
@@ -721,7 +767,7 @@ No parameters required. Returns connection status, configuration, and diagnostic
             """Get ADBC connection information and status"""
             return await self.call_tool("get_adbc_connection_info", {})
 
-        logger.info("Successfully registered 23 tools to MCP server (14 basic + 7 advanced analytics + 2 ADBC tools)")
+        logger.info("Successfully registered 25 tools to MCP server (14 basic + 9 advanced analytics + 2 ADBC tools)")
 
     async def list_tools(self) -> List[Tool]:
         """List all available query tools (for stdio mode)"""
@@ -1064,20 +1110,14 @@ No parameters required. Returns connection status, configuration, and diagnostic
                 },
             ),
             # ==================== v0.5.0 Advanced Analytics Tools ====================
+            # Atomic Data Quality Analysis Tools
             Tool(
-                name="analyze_data_quality",
-                description="""[Function Description]: Comprehensive data quality analysis combining completeness and distribution analysis.
+                name="get_table_basic_info",
+                description="""[Function Description]: Get basic information about a table including row count, column count, partitions, and size.
 
 [Parameter Content]:
 
 - table_name (string) [Required] - Name of the table to analyze
-- analysis_scope (string) [Optional] - Analysis scope, default is "comprehensive"
-  * "completeness": Only completeness analysis (null rates, business rules)
-  * "distribution": Only distribution analysis (statistical patterns)
-  * "comprehensive": Full analysis including both completeness and distribution
-- sample_size (integer) [Optional] - Maximum number of rows to sample, default is 100000
-- include_all_columns (boolean) [Optional] - Whether to analyze all columns, default is false
-- business_rules (array) [Optional] - Business rule validations in format [{"rule_name": "email_format", "sql_condition": "email REGEXP '^[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\\.[A-Za-z]{2,}$'"}]
 - catalog_name (string) [Optional] - Target catalog name
 - db_name (string) [Optional] - Target database name
 """,
@@ -1085,10 +1125,58 @@ No parameters required. Returns connection status, configuration, and diagnostic
                     "type": "object",
                     "properties": {
                         "table_name": {"type": "string", "description": "Name of the table to analyze"},
-                        "analysis_scope": {"type": "string", "enum": ["completeness", "distribution", "comprehensive"], "description": "Analysis scope", "default": "comprehensive"},
+                        "catalog_name": {"type": "string", "description": "Target catalog name"},
+                        "db_name": {"type": "string", "description": "Target database name"},
+                    },
+                    "required": ["table_name"],
+                },
+            ),
+            Tool(
+                name="analyze_columns",
+                description="""[Function Description]: Analyze completeness and distribution of specified columns in a table.
+
+[Parameter Content]:
+
+- table_name (string) [Required] - Name of the table to analyze
+- columns (array) [Required] - List of column names to analyze
+- analysis_types (array) [Optional] - Types of analysis to perform, default is ["both"]
+  * "completeness": Only completeness analysis (null rates, non-null counts)
+  * "distribution": Only distribution analysis (statistical patterns by data type)
+  * "both": Both completeness and distribution analysis
+- sample_size (integer) [Optional] - Maximum number of rows to sample, default is 100000
+- catalog_name (string) [Optional] - Target catalog name
+- db_name (string) [Optional] - Target database name
+- detailed_response (boolean) [Optional] - Whether to return detailed response including raw data, default is false
+""",
+                inputSchema={
+                    "type": "object",
+                    "properties": {
+                        "table_name": {"type": "string", "description": "Name of the table to analyze"},
+                        "columns": {"type": "array", "items": {"type": "string"}, "description": "List of column names to analyze"},
+                        "analysis_types": {"type": "array", "items": {"type": "string", "enum": ["completeness", "distribution", "both"]}, "description": "Types of analysis to perform", "default": ["both"]},
                         "sample_size": {"type": "integer", "description": "Maximum number of rows to sample", "default": 100000},
-                        "include_all_columns": {"type": "boolean", "description": "Whether to analyze all columns", "default": False},
-                        "business_rules": {"type": "array", "items": {"type": "object"}, "description": "Business rule validations"},
+                        "catalog_name": {"type": "string", "description": "Target catalog name"},
+                        "db_name": {"type": "string", "description": "Target database name"},
+                        "detailed_response": {"type": "boolean", "description": "Whether to return detailed response including raw data", "default": False},
+                    },
+                    "required": ["table_name", "columns"],
+                },
+            ),
+            Tool(
+                name="analyze_table_storage",
+                description="""[Function Description]: Analyze table's physical distribution and storage information.
+
+[Parameter Content]:
+
+- table_name (string) [Required] - Name of the table to analyze
+- catalog_name (string) [Optional] - Target catalog name
+- db_name (string) [Optional] - Target database name
+- detailed_response (boolean) [Optional] - Whether to return detailed response including raw data, default is false
+""",
+                inputSchema={
+                    "type": "object",
+                    "properties": {
+                        "table_name": {"type": "string", "description": "Name of the table to analyze"},
                         "catalog_name": {"type": "string", "description": "Target catalog name"},
                         "db_name": {"type": "string", "description": "Target database name"},
                         "detailed_response": {"type": "boolean", "description": "Whether to return detailed response including raw data", "default": False},
@@ -1096,7 +1184,6 @@ No parameters required. Returns connection status, configuration, and diagnostic
                     "required": ["table_name"],
                 },
             ),
-
             Tool(
                 name="trace_column_lineage",
                 description="""[Function Description]: Trace data lineage for specified columns through SQL analysis and dependency mapping.
@@ -1323,9 +1410,13 @@ No parameters required. Returns connection status, configuration, and diagnostic
             elif name == "get_historical_memory_stats":
                 arguments["data_type"] = "historical"
                 result = await self._get_memory_stats_tool(arguments)
-            # v0.5.0 Advanced Analytics Tools
-            elif name == "analyze_data_quality":
-                result = await self._analyze_data_quality_tool(arguments)
+            # v0.5.0 Advanced Analytics Tools - Atomic Data Quality Tools
+            elif name == "get_table_basic_info":
+                result = await self._get_table_basic_info_tool(arguments)
+            elif name == "analyze_columns":
+                result = await self._analyze_columns_tool(arguments)
+            elif name == "analyze_table_storage":
+                result = await self._analyze_table_storage_tool(arguments)
             elif name == "trace_column_lineage":
                 result = await self._trace_column_lineage_tool(arguments)
             elif name == "monitor_data_freshness":
@@ -1595,26 +1686,46 @@ No parameters required. Returns connection status, configuration, and diagnostic
     
     # ==================== v0.5.0 Advanced Analytics Tools Private Methods ====================
     
-    async def _analyze_data_quality_tool(self, arguments: Dict[str, Any]) -> Dict[str, Any]:
-        """Unified data quality analysis tool routing"""
+    async def _get_table_basic_info_tool(self, arguments: Dict[str, Any]) -> Dict[str, Any]:
+        """Get table basic information tool routing"""
         try:
-            # Extract parameters
             table_name = arguments.get("table_name")
-            analysis_scope = arguments.get("analysis_scope", "comprehensive")
+            catalog_name = arguments.get("catalog_name")
+            db_name = arguments.get("db_name")
+            
+            # Delegate to atomic data quality tools
+            result = await self.data_quality_tools.get_table_basic_info(
+                table_name=table_name,
+                catalog_name=catalog_name,
+                db_name=db_name
+            )
+            
+            return result
+            
+        except Exception as e:
+            return {
+                "error": str(e),
+                "analysis_type": "table_basic_info",
+                "timestamp": datetime.now().isoformat()
+            }
+    
+    async def _analyze_columns_tool(self, arguments: Dict[str, Any]) -> Dict[str, Any]:
+        """Analyze columns tool routing"""
+        try:
+            table_name = arguments.get("table_name")
+            columns = arguments.get("columns")
+            analysis_types = arguments.get("analysis_types", ["both"])
             sample_size = arguments.get("sample_size", 100000)
-            include_all_columns = arguments.get("include_all_columns", False)
-            business_rules = arguments.get("business_rules", [])
             catalog_name = arguments.get("catalog_name")
             db_name = arguments.get("db_name")
             detailed_response = arguments.get("detailed_response", False)
             
-            # Delegate to the unified data quality tools
-            result = await self.data_quality_tools.analyze_data_quality(
+            # Delegate to atomic data quality tools
+            result = await self.data_quality_tools.analyze_columns(
                 table_name=table_name,
-                analysis_scope=analysis_scope,
+                columns=columns,
+                analysis_types=analysis_types,
                 sample_size=sample_size,
-                include_all_columns=include_all_columns,
-                business_rules=business_rules,
                 catalog_name=catalog_name,
                 db_name=db_name,
                 detailed_response=detailed_response
@@ -1625,7 +1736,32 @@ No parameters required. Returns connection status, configuration, and diagnostic
         except Exception as e:
             return {
                 "error": str(e),
-                "analysis_type": "unified_data_quality",
+                "analysis_type": "columns_analysis",
+                "timestamp": datetime.now().isoformat()
+            }
+    
+    async def _analyze_table_storage_tool(self, arguments: Dict[str, Any]) -> Dict[str, Any]:
+        """Analyze table storage tool routing"""
+        try:
+            table_name = arguments.get("table_name")
+            catalog_name = arguments.get("catalog_name")
+            db_name = arguments.get("db_name")
+            detailed_response = arguments.get("detailed_response", False)
+            
+            # Delegate to atomic data quality tools
+            result = await self.data_quality_tools.analyze_table_storage(
+                table_name=table_name,
+                catalog_name=catalog_name,
+                db_name=db_name,
+                detailed_response=detailed_response
+            )
+            
+            return result
+            
+        except Exception as e:
+            return {
+                "error": str(e),
+                "analysis_type": "table_storage_analysis",
                 "timestamp": datetime.now().isoformat()
             }
     

doris_mcp_server/utils/config.py

@@ -137,6 +137,33 @@ class PerformanceConfig:
     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"""
@@ -208,6 +235,7 @@ class DorisConfig:
     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)
@@ -404,6 +432,38 @@ class DorisConfig:
             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)
@@ -443,6 +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"]
@@ -516,6 +583,19 @@ class DorisConfig:
                 "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,
                 "format": self.logging.format,
@@ -602,6 +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")

doris_mcp_server/utils/data_governance_tools.py

@@ -59,29 +59,57 @@ class DataGovernanceTools:
         """
         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}"
             
-            # 1. Verify target column exists
+            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}"}
             
-            # 2. Analyze SQL logs to get lineage relationships
+            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")
             
-            # 3. Analyze downstream usage
+            # 🚀 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")
             
-            # 4. Analyze field transformation rules
+            # 🚀 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
             

doris_mcp_server/utils/db.py

@@ -192,33 +192,35 @@ class DorisConnection:
 
 
 class DorisConnectionManager:
-    """Doris database connection manager - Simplified Strategy
+    """Doris database connection manager - Enhanced Strategy
 
-    Uses direct connection pool management without session-level caching
+    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.security_manager = security_manager
         self.pool: Pool | None = None
         self.logger = get_logger(__name__)
-        self.metrics = ConnectionMetrics()
+        self.security_manager = security_manager
 
-        # Remove session-level connection management
-        # self.session_connections = {}  # REMOVED
-        
-        # Pool health monitoring
-        self.health_check_interval = 30  # seconds
-        self.pool_warmup_size = 3  # connections to maintain
+        # Connection pool state management
+        self.pool_recovering = False
         self.pool_health_check_task = None
         self.pool_cleanup_task = None
         
-        # Pool recovery lock to prevent race conditions
-        self.pool_recovery_lock = asyncio.Lock()
-        self.pool_recovering = False
+        # 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
@@ -231,9 +233,13 @@ class DorisConnectionManager:
         
         # Connection pool parameters - more conservative settings
         self.minsize = config.database.min_connections  # This is always 0
-        self.maxsize = config.database.max_connections or 10
+        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 pool with health monitoring"""
         try:
@@ -506,77 +512,118 @@ class DorisConnectionManager:
             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:
-        """Get database connection - Simplified Strategy with pool validation
+        """🔧 FIX: Simplified connection acquisition without double locking
         
-        Always acquire fresh connection from pool, no session caching
+        Uses only semaphore to prevent too many concurrent acquisitions
         """
-        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)
-                for _ in range(10):
-                    if not self.pool_recovering:
-                        break
-                    await asyncio.sleep(0.5)
-                
+        # 🔧 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.error("Pool recovery is taking too long, proceeding anyway")
-                    # Don't raise error, try to continue
+                    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
                     
-            # Check if pool is available
-            if not self.pool:
-                self.logger.warning("Connection pool is not available, attempting recovery...")
-                await self._recover_pool()
+                    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:
-                    raise RuntimeError("Connection pool is not available and recovery failed")
+                    self.logger.warning("Connection pool is not available, attempting recovery...")
+                    await self._recover_pool_with_lock()
                     
-            # Check if pool is closed
-            if self.pool.closed:
-                self.logger.warning("Connection pool is closed, attempting recovery...")
-                await self._recover_pool()
+                    if not self.pool:
+                        raise RuntimeError("Connection pool is not available and recovery failed")
                 
-                if not self.pool or self.pool.closed:
-                    raise RuntimeError("Connection pool is closed 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()
                     
-            # Simple strategy: always get fresh connection from pool
-            raw_conn = await self.pool.acquire()
+                    if not self.pool or self.pool.closed:
+                        raise RuntimeError("Connection pool is closed and recovery failed")
                 
-            # Wrap in DorisConnection
-            doris_conn = DorisConnection(raw_conn, session_id, self.security_manager)
+                # 🔧 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")
                 
-            # Simple validation - just check if connection is open
-            if raw_conn.closed:
-                raise RuntimeError("Acquired connection is already closed")
+                # Wrap in DorisConnection
+                doris_conn = DorisConnection(raw_conn, session_id, self.security_manager)
                 
-            self.logger.debug(f"✅ Acquired fresh connection for session {session_id}")
-            return doris_conn
+                # 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")
                 
-        except Exception as e:
-            self.logger.error(f"Failed to get connection for session {session_id}: {e}")
-            raise
+                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):
-        """Release connection back to pool - Simplified Strategy"""
+        """🔧 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:
-            if connection and connection.connection:
-                # Simple strategy: always return to pool
-                if not connection.connection.closed:
-                    self.pool.release(connection.connection)
-                    self.logger.debug(f"✅ Released connection for session {session_id}")
-                else:
-                    self.logger.debug(f"Connection already closed for session {session_id}")
+            # 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:
-                if connection and connection.connection:
-                    await connection.connection.ensure_closed()
-            except Exception:
-                pass
+                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"""

doris_mcp_server/utils/security_analytics_tools.py

@@ -56,16 +56,31 @@ class SecurityAnalyticsTools:
         """
         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)
             
-            # 1. Get audit log data
+            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": {
@@ -75,25 +90,49 @@ class SecurityAnalyticsTools:
                     }
                 }
             
-            # 2. Analyze user access patterns
+            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")
             
-            # 3. Analyze role-based access
+            # 🚀 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")
             
-            # 4. Detect security anomalies
+            # 🚀 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")
             
-            # 5. Generate access insights
+            # 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