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troubleshooting

Diagnose ClickHouse cluster issues via system tables and configuration adjustments.

255|42|Updated Nov 16, 2023
Search Tags:#troubleshooting#cluster monitoring#clickhouse#oom#slow merges#query failures#stuck mutations
One-click install
npx skills add https://github.com/chmonitor/chmonitor --skill troubleshooting-chmonitor
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: troubleshooting
Source: https://github.com/chmonitor/chmonitor/tree/main/.agents/skills/troubleshooting
Command: npx skills add https://github.com/chmonitor/chmonitor --skill troubleshooting-chmonitor

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill addresses common operational disruptions in self-hosted ClickHouse deployments, including out-of-memory errors, slow background merges, stuck mutations, and failed queries that impact cluster performance and data availability.

Core Features & Use Cases

  • OOM Resolution: Diagnose memory overuse via system tables and apply query/user-level memory limits or spill-to-disk configurations to prevent crashes.
  • Merge & Mutation Management: Identify slow merges and stuck mutations, with safe cancellation steps and guidance to avoid recurring issues.
  • Query Failure Analysis: Cluster error codes from system.query_log to quickly pinpoint root causes of failed queries without manual log parsing.
  • Use Case: A ClickHouse admin experiencing recurring query timeouts can use this Skill to determine if the root cause is memory limits, excessive part counts, or inter-node network issues in minutes.

Quick Start

Use the troubleshooting skill to diagnose the root cause of recent failed queries on your self-hosted ClickHouse cluster.

Frequently Asked Questions about troubleshooting

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I troubleshoot out-of-memory errors in a self-hosted ClickHouse cluster?▼

To troubleshoot ClickHouse out-of-memory errors, you can query system tables to diagnose memory overuse and apply query or user-level memory limits, alongside spill-to-disk configurations, to prevent crashes.

What is the best way to fix slow background merges and stuck mutations in ClickHouse?▼

Fixing slow merges and stuck mutations in ClickHouse involves identifying the delays through system tables, applying safe cancellation steps, and adjusting configurations to prevent recurring background process issues.

How do I find the root cause of failed queries in ClickHouse without manual log parsing?▼

To find the root cause of failed ClickHouse queries without manual log parsing, you can cluster error codes directly from system.query_log to quickly pinpoint underlying performance disruptions.

Does this troubleshooting guidance work for ClickHouse deployments on Kubernetes and Docker?▼

Yes, this troubleshooting guidance applies to ClickHouse deployments on Docker, Kubernetes, bare metal, and ClickHouse Cloud, helping administrators resolve performance and availability disruptions across these environments.

Why should I avoid unplanned DDL operations when diagnosing ClickHouse performance issues?▼

You should avoid unplanned DDL operations during ClickHouse troubleshooting because systematic root cause identification via system tables and safe configuration adjustments prevent destructive actions and ensure cluster stability.

When do I need to query system tables to troubleshoot ClickHouse operational disruptions?▼

You need to query system tables to troubleshoot ClickHouse operational disruptions when experiencing recurring query timeouts, memory limits, excessive part counts, or inter-node network issues to systematically identify root causes.

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