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byted-bytehouse-load-analyzer AI Agent Skill
View Source: bytedance/agentkit-samples
CriticalInstallation
npx skills add bytedance/agentkit-samples --skill byted-bytehouse-load-analyzer 12
Installs
ByteHouse 负载分析 Skill
🔵 ByteHouse 品牌标识
「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，提供完整的集群负载分析和性能监控能力
描述
ByteHouse集群负载分析和性能监控工具。
当以下情况时使用此 Skill:
(1) 需要分析集群负载情况
(2) 需要监控资源使用情况
(3) 需要分析查询吞吐量
(4) 需要识别性能瓶颈
(5) 用户提到"负载分析"、"性能监控"、"资源使用"、"吞吐量"
前置条件
- Python 3.8+
- uv (已安装在
/root/.local/bin/uv) - ByteHouse MCP Server Skill - 本skill依赖
bytehouse-mcpskill提供的ByteHouse访问能力
依赖关系
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。
📁 文件说明
- SKILL.md - 本文件，技能主文档
- load_analyzer.py - 负载分析主程序
- README.md - 快速入门指南
配置信息
ByteHouse连接配置
本skill复用 bytehouse-mcp skill的配置。请确保已在 bytehouse-mcp skill中配置好：
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
1. 资源使用分析
- CPU使用率监控
- 内存使用率分析
- 磁盘空间监控
- 网络流量统计
2. 查询负载分析
- QPS (每秒查询数) 统计
- 查询并发度分析
- 查询类型分布
- 高峰时段识别
3. 表负载分析
- 表访问热度排名
- 表读写比例分析
- 表大小增长趋势
- 分区负载分布
4. 性能瓶颈识别
- 资源瓶颈识别
- 查询队列分析
- 锁等待统计
- 优化建议生成
🚀 快速开始
方法1: 运行负载分析
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分析内容包括：
- 集群资源使用情况
- 查询负载统计
- 表访问热度
- 性能瓶颈识别
- 优化建议生成
输出文件（保存在 output/ 目录）：
resource_usage_{timestamp}.json- 资源使用报告query_load_{timestamp}.json- 查询负载报告table_load_{timestamp}.json- 表负载报告bottleneck_analysis_{timestamp}.json- 瓶颈分析报告
💻 负载分析维度
资源维度
- CPU: 使用率、等待时间、上下文切换
- 内存: 使用量、缓存、Swap使用
- 磁盘: 使用率、IOPS、吞吐量
- 网络: 入流量、出流量、连接数
时间维度
- 实时: 当前负载情况
- 最近1小时: 1小时内趋势
- 最近24小时: 24小时内趋势
- 最近7天: 7天内趋势
- 历史对比: 同比环比分析
表维度
- 访问热度: 查询次数排名
- 读写比例: 读写操作比例
- 大小增长: 表大小变化趋势
- 分区分布: 分区数据分布
📊 负载报告示例
资源使用报告
{
"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
}
}
}查询负载报告
{
"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"
]
}
}📚 更多信息
详细使用说明请参考 bytehouse-mcp skill
最后更新: 2026-03-12
Installs
Security Audit
View Source
bytedance/agentkit-samples
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How to use this skill
Install byted-bytehouse-load-analyzer by running npx skills add bytedance/agentkit-samples --skill byted-bytehouse-load-analyzer in your project directory. Run the install command above in your project directory. The skill file will be downloaded from GitHub and placed in your project.
No configuration needed. Your AI agent (Claude Code, Cursor, Windsurf, etc.) automatically detects installed skills and uses them as context when generating code.
The skill enhances your agent's understanding of byted-bytehouse-load-analyzer, helping it follow established patterns, avoid common mistakes, and produce production-ready output.
What you get
Skills are plain-text instruction files — not executable code. They encode expert knowledge about frameworks, languages, or tools that your AI agent reads to improve its output. This means zero runtime overhead, no dependency conflicts, and full transparency: you can read and review every instruction before installing.
Compatibility
This skill works with any AI coding agent that supports the skills.sh format, including Claude Code (Anthropic), Cursor, Windsurf, Cline, Aider, and other tools that read project-level context files. Skills are framework-agnostic at the transport level — the content inside determines which language or framework it applies to.
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