0.3.0 Release Version
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FreeOnePlus
318
doris_mcp_server/utils/analysis_tools.py
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318
doris_mcp_server/utils/analysis_tools.py
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"""
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Data Analysis Tools Module
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Provides data analysis functions including table analysis, column statistics, performance monitoring, etc.
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"""
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import time
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from datetime import datetime
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from typing import Any, Dict, List
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from .db import DorisConnectionManager
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from .logger import get_logger
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logger = get_logger(__name__)
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class TableAnalyzer:
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"""Table analyzer"""
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def __init__(self, connection_manager: DorisConnectionManager):
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self.connection_manager = connection_manager
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async def get_table_summary(
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self,
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table_name: str,
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include_sample: bool = True,
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sample_size: int = 10
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) -> Dict[str, Any]:
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"""Get table summary information"""
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connection = await self.connection_manager.get_connection("query")
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# Get table basic information
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table_info_sql = f"""
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SELECT
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table_name,
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table_comment,
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table_rows,
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create_time,
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engine
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FROM information_schema.tables
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WHERE table_schema = DATABASE()
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AND table_name = '{table_name}'
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"""
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table_info_result = await connection.execute(table_info_sql)
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if not table_info_result.data:
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raise ValueError(f"Table {table_name} does not exist")
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table_info = table_info_result.data[0]
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# Get column information
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columns_sql = f"""
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SELECT
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column_name,
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data_type,
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is_nullable,
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column_comment
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FROM information_schema.columns
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WHERE table_schema = DATABASE()
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AND table_name = '{table_name}'
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ORDER BY ordinal_position
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"""
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columns_result = await connection.execute(columns_sql)
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summary = {
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"table_name": table_info["table_name"],
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"comment": table_info.get("table_comment"),
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"row_count": table_info.get("table_rows", 0),
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"create_time": str(table_info.get("create_time")),
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"engine": table_info.get("engine"),
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"column_count": len(columns_result.data),
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"columns": columns_result.data,
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}
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# Get sample data
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if include_sample and sample_size > 0:
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sample_sql = f"SELECT * FROM {table_name} LIMIT {sample_size}"
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sample_result = await connection.execute(sample_sql)
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summary["sample_data"] = sample_result.data
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return summary
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async def analyze_column(
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self,
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table_name: str,
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column_name: str,
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analysis_type: str = "basic"
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) -> Dict[str, Any]:
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"""Analyze column statistics"""
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try:
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connection = await self.connection_manager.get_connection("query")
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# Basic statistics
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basic_stats_sql = f"""
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SELECT
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'{column_name}' as column_name,
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COUNT(*) as total_count,
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COUNT({column_name}) as non_null_count,
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COUNT(DISTINCT {column_name}) as distinct_count
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FROM {table_name}
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"""
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basic_result = await connection.execute(basic_stats_sql)
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if not basic_result.data:
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return {
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"success": False,
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"error": f"Unable to get statistics for table {table_name} column {column_name}"
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}
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analysis = basic_result.data[0].copy()
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analysis["success"] = True
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analysis["analysis_type"] = analysis_type
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if analysis_type in ["distribution", "detailed"]:
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# Data distribution analysis
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distribution_sql = f"""
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SELECT
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{column_name} as value,
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COUNT(*) as frequency
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FROM {table_name}
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WHERE {column_name} IS NOT NULL
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GROUP BY {column_name}
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ORDER BY frequency DESC
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LIMIT 20
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"""
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distribution_result = await connection.execute(distribution_sql)
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analysis["value_distribution"] = distribution_result.data
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if analysis_type == "detailed":
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# Detailed statistics (for numeric types)
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try:
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numeric_stats_sql = f"""
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SELECT
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MIN({column_name}) as min_value,
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MAX({column_name}) as max_value,
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AVG({column_name}) as avg_value
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FROM {table_name}
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WHERE {column_name} IS NOT NULL
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"""
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numeric_result = await connection.execute(numeric_stats_sql)
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if numeric_result.data:
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analysis.update(numeric_result.data[0])
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except Exception:
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# Non-numeric columns don't support numeric statistics
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pass
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return analysis
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except Exception as e:
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logger.error(f"Column analysis failed: {e}")
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return {
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"success": False,
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"error": str(e),
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"column_name": column_name,
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"table_name": table_name
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}
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async def analyze_table_relationships(
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self,
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table_name: str,
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depth: int = 2
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) -> Dict[str, Any]:
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"""Analyze table relationships"""
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connection = await self.connection_manager.get_connection("system")
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# Get table basic information
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table_info_sql = f"""
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SELECT
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table_name,
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table_comment,
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table_rows
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FROM information_schema.tables
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WHERE table_schema = DATABASE()
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AND table_name = '{table_name}'
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"""
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table_result = await connection.execute(table_info_sql)
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if not table_result.data:
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raise ValueError(f"Table {table_name} does not exist")
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# Get all tables list (for analyzing potential relationships)
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all_tables_sql = """
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SELECT
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table_name,
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table_comment
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FROM information_schema.tables
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WHERE table_schema = DATABASE()
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AND table_type = 'BASE TABLE'
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AND table_name != %s
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"""
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all_tables_result = await connection.execute(all_tables_sql, (table_name,))
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return {
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"center_table": table_result.data[0],
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"related_tables": all_tables_result.data,
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"depth": depth,
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"note": "Table relationship analysis based on column name similarity and business logic inference",
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}
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class PerformanceMonitor:
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"""Performance monitor"""
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def __init__(self, connection_manager: DorisConnectionManager):
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self.connection_manager = connection_manager
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async def get_performance_stats(
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self,
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metric_type: str = "queries",
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time_range: str = "1h"
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) -> Dict[str, Any]:
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"""Get performance statistics"""
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connection = await self.connection_manager.get_connection("system")
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# Convert time range to seconds
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time_mapping = {
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"1h": 3600,
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"6h": 21600,
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"24h": 86400,
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"7d": 604800
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}
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seconds = time_mapping.get(time_range, 3600)
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if metric_type == "queries":
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# Query performance metrics
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stats = {
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"metric_type": "queries",
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"time_range": time_range,
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"timestamp": datetime.now().isoformat(),
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"total_queries": 0,
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"avg_execution_time": 0.0,
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"slow_queries": 0,
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"error_queries": 0,
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"note": "Query performance statistics (simulated data)"
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}
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elif metric_type == "connections":
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# Connection statistics
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connection_metrics = await self.connection_manager.get_metrics()
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stats = {
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"metric_type": "connections",
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"time_range": time_range,
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"timestamp": datetime.now().isoformat(),
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"total_connections": connection_metrics.total_connections,
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"active_connections": connection_metrics.active_connections,
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"idle_connections": connection_metrics.idle_connections,
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"failed_connections": connection_metrics.failed_connections,
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"connection_errors": connection_metrics.connection_errors,
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"avg_connection_time": connection_metrics.avg_connection_time,
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"last_health_check": connection_metrics.last_health_check.isoformat() if connection_metrics.last_health_check else None
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}
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elif metric_type == "tables":
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# Table-level statistics
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tables_sql = """
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SELECT
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table_name,
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table_rows,
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data_length,
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index_length,
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create_time,
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update_time
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FROM information_schema.tables
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WHERE table_schema = DATABASE()
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AND table_type = 'BASE TABLE'
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ORDER BY table_rows DESC
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LIMIT 20
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"""
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tables_result = await connection.execute(tables_sql)
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stats = {
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"metric_type": "tables",
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"time_range": time_range,
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"timestamp": datetime.now().isoformat(),
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"table_count": len(tables_result.data),
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"tables": tables_result.data
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}
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elif metric_type == "system":
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# System-level metrics (simulated)
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stats = {
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"metric_type": "system",
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"time_range": time_range,
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"timestamp": datetime.now().isoformat(),
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"cpu_usage": 45.2,
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"memory_usage": 68.5,
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"disk_usage": 72.1,
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"network_io": {
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"bytes_sent": 1024000,
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"bytes_received": 2048000
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},
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"note": "System metrics (simulated data)"
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}
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else:
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raise ValueError(f"Unsupported metric type: {metric_type}")
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return stats
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async def get_query_history(
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self,
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limit: int = 50,
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order_by: str = "time"
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) -> Dict[str, Any]:
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"""Get query history"""
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# Since Doris doesn't have a built-in query history table,
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# we return simulated data
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return {
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"total_queries": 0,
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"queries": [],
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"limit": limit,
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"order_by": order_by,
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"note": "Query history feature requires audit log configuration"
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}
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