byted-bytehouse-slow-query

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Original

English

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ByteHouse 慢查询分析 Skill

ByteHouse Slow Query Analysis Skill

🔵 ByteHouse 品牌标识

🔵 ByteHouse Brand Identity

「ByteHouse」—— 火山引擎云原生数据仓库，极速、稳定、安全、易用

本Skill基于ByteHouse MCP Server，提供完整的慢查询分析和性能优化能力

"ByteHouse" - Volcano Engine Cloud-Native Data Warehouse: Fast, Stable, Secure, Easy to Use

This Skill is based on ByteHouse MCP Server and provides comprehensive slow query analysis and performance optimization capabilities

描述

Description

ByteHouse慢查询分析和性能优化工具。

当以下情况时使用此 Skill: (1) 需要识别和分析慢查询 (2) 需要查询性能优化建议 (3) 需要查看查询执行计划 (4) 需要分析查询历史趋势 (5) 用户提到"慢查询"、"查询优化"、"性能分析"、"执行计划"

ByteHouse Slow Query Analysis and Performance Optimization Tool.

Use this Skill when: (1) You need to identify and analyze slow queries (2) You need query performance optimization suggestions (3) You need to view query execution plans (4) You need to analyze query historical trends (5) You mention terms like "slow query", "query optimization", "performance analysis", "execution plan"

前置条件

Prerequisites

Python 3.8+
uv (已安装在
```
/root/.local/bin/uv
```
)
ByteHouse MCP Server Skill - 本skill依赖
```
bytehouse-mcp
```
skill提供的ByteHouse访问能力

Python 3.8+
uv (installed at
```
/root/.local/bin/uv
```
)
ByteHouse MCP Server Skill - This skill depends on the ByteHouse access capability provided by the
```
bytehouse-mcp
```
skill

依赖关系

Dependencies

本skill依赖

bytehouse-mcp

skill，使用其提供的MCP Server访问ByteHouse。

确保

bytehouse-mcp

skill已正确配置并可以正常使用。

This skill depends on the

bytehouse-mcp

skill and uses its MCP Server to access ByteHouse.

Ensure the

bytehouse-mcp

skill is properly configured and functioning normally.

📁 文件说明

📁 File Description

SKILL.md - 本文件，技能主文档
slow_query_analyzer.py - 慢查询分析主程序
README.md - 快速入门指南

SKILL.md - This file, the main skill documentation
slow_query_analyzer.py - Main program for slow query analysis
README.md - Quick start guide

配置信息

Configuration Information

ByteHouse连接配置

ByteHouse Connection Configuration

本skill复用

bytehouse-mcp

skill的配置。请确保已在

bytehouse-mcp

skill中配置好：

bash

export BYTEHOUSE_HOST="<ByteHouse-host>"
export BYTEHOUSE_PORT="<ByteHouse-port>"
export BYTEHOUSE_USER="<ByteHouse-user>"
export BYTEHOUSE_PASSWORD="<ByteHouse-password>"
export BYTEHOUSE_SECURE="true"
export BYTEHOUSE_VERIFY="true"

This skill reuses the configuration of the

bytehouse-mcp

skill. Please ensure the following configurations are set in the

bytehouse-mcp

skill:

bash

export BYTEHOUSE_HOST="<ByteHouse-host>"
export BYTEHOUSE_PORT="<ByteHouse-port>"
export BYTEHOUSE_USER="<ByteHouse-user>"
export BYTEHOUSE_PASSWORD="<ByteHouse-password>"
export BYTEHOUSE_SECURE="true"
export BYTEHOUSE_VERIFY="true"

🎯 功能特性

🎯 Feature Highlights

1. 慢查询识别

1. Slow Query Identification

从query_log表获取慢查询
按执行时间排序
识别Top N慢查询
分析慢查询模式

Retrieve slow queries from the query_log table
Sort by execution time
Identify Top N slow queries
Analyze slow query patterns

2. 查询性能分析

2. Query Performance Analysis

查询执行时间分布
查询类型统计
查询频率分析
性能趋势分析

Query execution time distribution
Query type statistics
Query frequency analysis
Performance trend analysis

3. 执行计划分析

3. Execution Plan Analysis

获取查询执行计划
分析执行计划节点
识别性能瓶颈
提供优化建议

Retrieve query execution plans
Analyze execution plan nodes
Identify performance bottlenecks
Provide optimization suggestions

4. 优化建议生成

4. Optimization Suggestion Generation

索引优化建议
查询重写建议
表引擎建议
配置参数调优

Index optimization suggestions
Query rewrite suggestions
Table engine suggestions
Configuration parameter tuning

🚀 快速开始

🚀 Quick Start

方法1: 运行慢查询分析

Method 1: Run Slow Query Analysis

bash

cd /root/.openclaw/workspace/skills/bytehouse-slow-query

bash

cd /root/.openclaw/workspace/skills/bytehouse-slow-query

先设置环境变量（复用bytehouse-mcp的配置）

Set environment variables first (reuse bytehouse-mcp configuration)

export BYTEHOUSE_HOST="<ByteHouse-host>" export BYTEHOUSE_PORT="<ByteHouse-port>" export BYTEHOUSE_USER="<ByteHouse-user>" export BYTEHOUSE_PASSWORD="<ByteHouse-password>" export BYTEHOUSE_SECURE="true" export BYTEHOUSE_VERIFY="true"

运行慢查询分析

Run slow query analysis

uv run slow_query_analyzer.py


**分析内容包括：**
- Top 20慢查询
- 查询性能统计
- 执行时间分布
- 优化建议生成

**输出文件（保存在 `output/` 目录）：**
1. **`slow_queries_{timestamp}.json`** - 慢查询列表
2. **`query_stats_{timestamp}.json`** - 查询统计报告
3. **`optimization_suggestions_{timestamp}.json`** - 优化建议

uv run slow_query_analyzer.py


**Analysis content includes:**
- Top 20 slow queries
- Query performance statistics
- Execution time distribution
- Optimization suggestion generation

**Output files (saved in `output/` directory):**
1. **`slow_queries_{timestamp}.json`** - Slow query list
2. **`query_stats_{timestamp}.json`** - Query statistics report
3. **`optimization_suggestions_{timestamp}.json`** - Optimization suggestions

💻 慢查询分析维度

💻 Slow Query Analysis Dimensions

时间维度分析

Time Dimension Analysis

按小时: 每小时慢查询数量
按天: 每天慢查询趋势
按周: 每周慢查询模式
按月: 每月慢查询统计

By Hour: Number of slow queries per hour
By Day: Daily slow query trend
By Week: Weekly slow query pattern
By Month: Monthly slow query statistics

查询类型分析

Query Type Analysis

SELECT查询: 查询语句分析
INSERT查询: 写入性能分析
UPDATE查询: 更新性能分析
DELETE查询: 删除性能分析
DDL查询: 建表/改表性能分析

SELECT Queries: Query statement analysis
INSERT Queries: Write performance analysis
UPDATE Queries: Update performance analysis
DELETE Queries: Delete performance analysis
DDL Queries: Table creation/alteration performance analysis

性能指标

Performance Metrics

平均执行时间: 所有查询平均耗时
P50执行时间: 中位数执行时间
P95执行时间: 95分位执行时间
P99执行时间: 99分位执行时间
最大执行时间: 最慢查询耗时

Average Execution Time: Average time taken by all queries
P50 Execution Time: Median execution time
P95 Execution Time: 95th percentile execution time
P99 Execution Time: 99th percentile execution time
Maximum Execution Time: Time taken by the slowest query

📊 慢查询报告示例

📊 Slow Query Report Example

慢查询列表

Slow Query List

json

{
  "analysis_time": "2026-03-12T21:00:00",
  "total_queries": 10000,
  "slow_queries": 150,
  "top_slow_queries": [
    {
      "query_id": "query-12345",
      "query_text": "SELECT * FROM large_table WHERE ...",
      "duration_ms": 15000,
      "start_time": "2026-03-12T20:55:00",
      "read_rows": 1000000,
      "read_bytes": 104857600
    }
  ]
}

json

{
  "analysis_time": "2026-03-12T21:00:00",
  "total_queries": 10000,
  "slow_queries": 150,
  "top_slow_queries": [
    {
      "query_id": "query-12345",
      "query_text": "SELECT * FROM large_table WHERE ...",
      "duration_ms": 15000,
      "start_time": "2026-03-12T20:55:00",
      "read_rows": 1000000,
      "read_bytes": 104857600
    }
  ]
}

📚 更多信息

📚 More Information

详细使用说明请参考 bytehouse-mcp skill

最后更新: 2026-03-12

For detailed usage instructions, please refer to bytehouse-mcp skill

Last Updated: 2026-03-12