ClickHouse cost & performance
Practical, no-fluff guides on making ClickHouse faster and cheaper — with the exact SQL.
Anatomy of a ClickHouse bill: finding $14,800/month of waste in one read-only scan
A transparent teardown of a representative 2.4 PB ClickHouse cluster — FINAL on every read, missing PREWHERE, weak codecs, zombie materialized views — and the exact SQL to reclaim ~$14,800/month, read-only.
Common ClickHouse problems — and how to diagnose and fix each one
A troubleshooting hub for the most common ClickHouse errors and performance problems — Too Many Parts, Memory Limit Exceeded, slow queries, duplicate rows, and runaway storage — with the system-table queries to diagnose each.
ClickHouse best practices: 12 rules for fast, cheap, stable analytics
A practical, opinionated guide to ClickHouse best practices — data types, ORDER BY, batching and async inserts, materialized views, projections, skip indexes, and the schema decisions that decide whether your cluster scales.
ORDER BY mistakes in ClickHouse (and what they cost you)
The table-level ORDER BY is the single biggest lever on ClickHouse scan speed and compression. Here are the five most common ORDER BY mistakes — and how to fix them.
ClickHouse MergeTree explained: how the engine really works
A deep, practical guide to the ClickHouse MergeTree engine — the sparse primary index, parts and merges, ORDER BY vs PARTITION BY, and the design mistakes that quietly kill query performance.
ClickHouse cost optimization: a complete guide to cutting your bill
Where ClickHouse spend actually goes — compute on bad queries, storage on cold data, parts and merge overhead — and the exact SQL to reclaim 20–40% of it, read-only and risk-free.
5 kinds of unused tables hiding in your ClickHouse
Most ClickHouse clusters carry tables nobody reads anymore — abandoned staging, zombie materialized views, backups, deprecated rollups. Here's how to find them and what they cost.
Anatomy of a ClickHouse audit: what 5 minutes reveals
What does a read-only ClickHouse scan actually find? A walkthrough of a typical audit of a large cluster — unused tables, expensive queries, reclaimable storage — with the numbers behind each.
ClickHouse FINAL: when to use it (and how to avoid the cost)
FROM ... FINAL guarantees deduplicated reads on ReplacingMergeTree, but it forces a merge-on-read across all parts. Here's when you actually need it and how argMax replaces it cheaply.
Too many parts in ClickHouse: causes and fixes
ClickHouse 'too many parts' errors and creeping query latency usually trace back to tiny, frequent inserts. Here's how to diagnose part fragmentation and fix it at the source.
ClickHouse cost optimization: a practical checklist
A no-fluff checklist for cutting ClickHouse compute and storage costs: query tuning, storage tiering, part hygiene, schema design and cost attribution — each with where to look.
Why is my ClickHouse query slow? 7 common causes
Slow ClickHouse queries almost always read data they don't need. Here are the 7 most common causes — broken partition pruning, missing PREWHERE, FINAL on every read, bad JOINs — and how to fix each.
How to reduce ClickHouse storage costs
A practical guide to cutting ClickHouse storage bills: find the biggest tables, drop unused ones, tier cold data with TTL, and fix poorly-compressed columns — with the exact SQL.