ClickHouse vs Postgres

Columnar analytics vs the world's most trusted relational database

PostgreSQL is the safe, general-purpose default — transactions, joins, indexes, a mature ecosystem, and it handles a surprising amount of analytics with the right indexes. ClickHouse gives up some of that relational comfort for columnar scan speed at a scale Postgres wasn't built for. Here's where each one actually wins.

Comparison matrix

ClickHouse vs PostgreSQL

Where each database actually wins — Postgres included.

	ClickHouse	PostgreSQL
Storage model	Column-oriented (MergeTree): each column stored and compressed contiguously	Row-oriented (heap + MVCC): each row stored contiguously, tuned for point lookups and transactions
OLTP: single-row reads/writes	No true row-level UPDATE/DELETE — an ALTER … DELETE rewrites entire affected parts and can visibly degrade the cluster	Purpose-built for this: MVCC, row locks and indexes make single-row transactions fast and safe
Aggregation over large tables	Vectorized execution plus columnar compression aggregates billions of rows per second without hand-tuned indexes	Fast with the right B-tree/BRIN indexes and enough RAM, but full-table scans over huge fact tables are inherently slower on row storage; Citus/columnar extensions close some of the gap
Joins	JOIN support and the query planner have improved a lot, but denormalizing wide tables is still the idiomatic ClickHouse pattern for best performance	A mature cost-based planner handles complex multi-table joins well out of the box
Indexing	Performance depends on the primary key sort order plus skip indices — no general-purpose secondary index	Rich secondary indexing: B-tree, GIN, GiST, BRIN, partial and expression indexes
Concurrency model	Tuned for fewer, heavier analytical queries rather than thousands of small concurrent transactions	MVCC handles many concurrent small transactions cleanly — the OLTP sweet spot
Compression & storage cost at scale	Columnar compression routinely reaches 10x+ on typical analytical data, cutting both storage and I/O	Standard TOAST/page compression is modest; matching ClickHouse's ratios needs a columnar extension (Citus columnar, TimescaleDB Hypercore)
Ecosystem & extensions	A narrower, fast-growing catalog: Kafka/S3/MySQL/PostgreSQL table engines, dbt-clickhouse, Grafana/Superset support	The deepest extension ecosystem in open-source databases: PostGIS, pgvector, TimescaleDB, Citus, decades of drivers and ORMs
Licensing	Apache 2.0	The PostgreSQL License — a permissive, OSI-approved license with effectively no restrictions
Best fit	Analytics dashboards, event/log analytics, and ad hoc GROUP BY over historical data at a scale where indexes stop helping	Transactional application data, referential integrity, and analytics that fit comfortably within what indexes and RAM can cover

PostgreSQL details reflect the mainline engine and its public documentation as of July 2026 — verify at postgresql.org/docs. Columnar/analytics extensions (Citus, TimescaleDB) are noted separately where relevant since they aren't part of core Postgres. ClickHouse rows reflect the open-source engine.

Decision guide

Which one fits your workload

The honest split: transactional correctness and flexibility vs raw aggregation speed at scale.

Choose ClickHouse for large-scale aggregation

Event/log analytics, product analytics, or any dashboard doing GROUP BY across billions of historical rows where indexes stop helping.

Choose ClickHouse for cheap, fast storage

10x+ columnar compression on typical analytical data cuts both storage cost and the I/O each query has to do.

Choose Postgres for transactional data

Orders, users, billing — anywhere you need ACID transactions, row-level updates, and referential integrity as first-class guarantees.

Choose Postgres for moderate-scale analytics

If your reporting queries fit comfortably within what a well-indexed table and enough RAM can cover, you don't need a second database yet.

FAQ

Questions & answers

Is ClickHouse faster than PostgreSQL for analytics?

For aggregations over large historical tables, generally yes — ClickHouse only reads the columns a query touches and executes in a vectorized, compressed form, so it scans and aggregates far more rows per second than a row-store. For single-row lookups and small, well-indexed queries, Postgres is at least as fast and often faster.

Can I just add indexes to PostgreSQL instead of adopting ClickHouse?

For moderate data volumes, yes — the right B-tree/BRIN indexes and enough RAM take Postgres a long way. The point where that stops working is roughly when full-table scans over your largest fact tables become routine; that's the signal to consider a columnar engine.

Should I replace Postgres with ClickHouse, or run both?

Most teams run both: PostgreSQL for transactional application data (orders, users, billing) and ClickHouse for analytics (events, logs, metrics), often replicating from Postgres into ClickHouse via CDC or the built-in PostgreSQL table engine.

Does ClickHouse support JOINs like PostgreSQL?

Yes, but the two differ. ClickHouse's join support and query planner have improved substantially, but denormalizing wide tables is still the idiomatic pattern for best performance at scale — Postgres's cost-based planner handles complex multi-table joins more naturally out of the box.

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