byted-bytehouse-load-analyzer

ByteHouse Load Analysis Skill

🔵 ByteHouse brand logo

"ByteHouse" — Volcano Engine cloud-native data warehouse, extremely fast, stable, secure, and easy to use

This Skill is based on ByteHouse MCP Server and provides complete cluster load analysis and performance monitoring capabilities

Description

ByteHouse cluster load analysis and performance monitoring tool.

Use this Skill when the following situations occur: (1) Need to analyze cluster load conditions (2) Need to monitor resource usage (3) Need to analyze query throughput (4) Need to identify performance bottlenecks (5) The user mentions "load analysis", "performance monitoring", "resource usage", "throughput"

Prerequisites

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

Dependencies

This skill depends on the bytehouse-mcp skill and uses the MCP Server it provides to access ByteHouse.

Ensure that the bytehouse-mcp skill is correctly configured and can be used normally.

📁 File description

SKILL.md - This file, the main skill document
load_analyzer.py - Main load analysis program
README.md - Quick start guide

Configuration information

ByteHouse connection configuration

This skill reuses the configuration of the bytehouse-mcp skill. Please ensure that the following has been configured in the bytehouse-mcp skill:

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"

🎯 Features

1. Resource usage analysis

CPU usage monitoring
Memory usage analysis
Disk space monitoring
Network traffic statistics

2. Query load analysis

QPS (queries per second) statistics
Query concurrency analysis
Query type distribution
Peak period identification

3. Table load analysis

Table access popularity ranking
Table read/write ratio analysis
Table size growth trend
Partition load distribution

4. Performance bottleneck identification

Resource bottleneck identification
Query queue analysis
Lock wait statistics
Optimization suggestion generation

🚀 Quick start

Method 1: Run load analysis

cd /root/.openclaw/workspace/skills/bytehouse-load-analyzer

# 先设置环境变量（复用bytehouse-mcp的配置）
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"

# 运行负载分析
uv run load_analyzer.py

Analysis contents include:

Cluster resource usage
Query load statistics
Table access popularity
Performance bottleneck identification
Optimization suggestion generation

Output files (saved in the output/ directory):

resource_usage_{timestamp}.json - Resource usage report
query_load_{timestamp}.json - Query load report
table_load_{timestamp}.json - Table load report
bottleneck_analysis_{timestamp}.json - Bottleneck analysis report

💻 Load analysis dimensions

Resource dimensions

CPU: Usage rate, wait time, context switching
Memory: Usage, cache, Swap usage
Disk: Usage rate, IOPS, throughput
Network: Inbound traffic, outbound traffic, number of connections

Time Dimension

Real-time: Current load status
Last 1 hour: Trend within 1 hour
Last 24 hours: Trend within 24 hours
Last 7 days: Trend within 7 days
Historical comparison: Year-over-year and month-over-month analysis

Table Dimension

Access popularity: Query count ranking
Read/write ratio: Read/write operation ratio
Size growth: Table size change trend
Partition distribution: Partition data distribution

📊 Load Report Example

Resource Usage Report

{
  "analysis_time": "2026-03-12T21:00:00",
  "cluster_name": "bh_log_boe",
  "resources": {
    "cpu": {
      "usage_percent": 65.5,
      "wait_time_ms": 15,
      "context_switches": 10000
    },
    "memory": {
      "used_gb": 128.5,
      "total_gb": 256.0,
      "usage_percent": 50.2
    },
    "disk": {
      "used_gb": 5120.0,
      "total_gb": 10240.0,
      "usage_percent": 50.0,
      "iops_read": 5000,
      "iops_write": 3000
    }
  }
}

Query Load Report

{
  "analysis_time": "2026-03-12T21:00:00",
  "query_load": {
    "qps": 500,
    "concurrent_queries": 50,
    "query_types": {
      "SELECT": 70,
      "INSERT": 20,
      "UPDATE": 5,
      "DELETE": 3,
      "DDL": 2
    },
    "peak_hours": [
      "10:00-11:00",
      "14:00-15:00",
      "20:00-21:00"
    ]
  }
}

📚 More Information

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

Last updated: 2026-03-12