Guides, deep dives, and troubleshooting for ClickHouse teams.
A practical comparison of ClickHouse observability tools — what each one does well, where each falls short, and how to choose the right one for your team.
Everything you need to know about diagnosing and fixing slow ClickHouse queries — from system.query_log to EXPLAIN PLAN to schema optimization.
Broken parts in ClickHouse mean data corruption. Learn what causes them, how to detect them with system.parts, and how to fix them.
A deep merge queue in ClickHouse leads to too-many-parts errors and insert failures. Learn how to monitor it and keep it healthy.
ClickHouse merges are background operations that consolidate data parts. Understanding how merges work is essential for query performance, storage efficiency, and avoiding 'too many parts' errors.
Learn how to monitor ClickHouse in production using system tables, health metrics, and automated alerts. The definitive guide for SREs and data engineers.
ClickHouse OOM errors crash queries and can destabilize a cluster. Learn what causes them, how to detect memory pressure early, and how to configure memory limits.
Use ClickHouse EXPLAIN, query_log, and flame graphs to profile expensive queries and understand exactly where time is being spent.
ClickHouse replication lag means replicas are behind the source. Learn how to detect it, what causes it, and how to fix it with system.replicas.
Learn how to identify slow queries in ClickHouse using system.query_log, understand what makes queries expensive, and optimize them.
ClickHouse system tables expose hundreds of operational metrics. Learn which tables matter most for monitoring, performance tuning, and debugging.
ClickHouse uses ZooKeeper (or ClickHouse Keeper) to coordinate replicated tables. Learn how this dependency works, what fails when ZooKeeper is unhealthy, and how to monitor it.