Guides, deep dives, and troubleshooting for ClickHouse teams.
What we learned from ClusterSight's first 10 production deployments. The features operators actually use, what they ignore, and how real-world feedback reshaped the PULSE Framework.
OPTIMIZE TABLE FINAL is the most misused command in ClickHouse. It rewrites every part, blocks merges, and can crash your cluster. Learn when it's safe, when it's dangerous, and what to do instead.
ClickHouse's system.parts_columns reveals exactly which columns in which tables consume the most storage. Find disk waste, oversized columns, and compression opportunities with these SQL queries.
A single SQL query tells you if your ClickHouse merge queue is keeping pace with inserts. Learn the part creation rate vs merge rate check, what the numbers mean, and when to act.
ClickHouse's system.errors table tracks 40+ error types that predict failures before they happen. Learn which errors matter, how to detect chronic patterns, and the queries that turn error noise into operational signal.
A transparent look at three months of building ClusterSight — the ClickHouse operational health platform. What we shipped, what users actually wanted, lessons learned, and the roadmap ahead.
ClickHouse's system.query_log has 60+ columns. Most are noise. These 15 fields diagnose every performance issue — slow queries, memory spikes, excessive reads, and error patterns. The operational guide to query_log.
ClickHouse replicas go read-only when ZooKeeper sessions fail — but they keep serving stale queries. Learn the SQL checks to detect read-only state, understand why load balancers miss it, and prevent silent stale data.
A ClickHouse mutation stuck for 72 hours served stale data to every query while monitoring showed healthy. Learn how stuck mutations form, why they're invisible to standard monitoring, and how to detect them before they corrupt analytics.
A behind-the-scenes look at how ClusterSight's PULSE health score works — how we weight 400+ ClickHouse metrics across five dimensions, handle edge cases, and turn operational complexity into a single 0-100 number.
A deep reference guide to ClickHouse's system.merges table. Learn what every column means, how to diagnose slow merges, detect I/O contention, and prevent merge backlogs before they cause too-many-parts errors.
Why Grafana dashboards give a false sense of security for ClickHouse clusters. The 70% of operational metrics Prometheus never scrapes, and what purpose-built monitoring actually looks like.
Three SQL queries to verify your ClickHouse compression is effective. Find tables wasting disk with poor compression ratios, wrong codecs, and uncompressed columns eating your storage budget.
ClickHouse part counts silently grow until inserts fail. Learn how merge backlogs form, why standard monitoring misses them, and the SQL queries that catch part count explosions before they become incidents.
The story behind ClusterSight — why standard monitoring tools fail ClickHouse operators, what the PULSE Framework is, and how we're building an operational health platform that catches the 70% of metrics Grafana misses.
A deep reference guide to ClickHouse's system.replicas table. Learn what every column means operationally, which ones matter for production monitoring, and the queries that catch replication problems before they cause incidents.
Before upgrading ClickHouse, run these 5 diagnostic SQL queries to verify cluster health. Catch broken parts, stuck mutations, replication drift, and merge backlogs before they turn an upgrade into a rollback.
ClickHouse replicas can fall out of sync without triggering alerts. Learn how silent replication drift corrupts analytics dashboards, why standard monitoring misses it, and how to detect it before stakeholders notice.
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.