ClickHouse vs PostgreSQL

A column-oriented analytics engine versus the world's most popular relational database. Different tools for different jobs — but the overlap is growing.

Column vs Row Storage

PostgreSQL stores data in rows. Great for: insert a user, read a user, update a user. Traditional CRUD operations.

ClickHouse stores data in columns. Great for: count all users who signed up last month, average revenue per country, percentile latencies across 10 billion events.

This isn't a preference — it's physics. Column storage compresses better and scans faster for analytical queries. Row storage is faster for point lookups and updates.

When ClickHouse Destroys Postgres

At scale, the difference is absurd. A query that takes 30 seconds on Postgres finishes in 200 milliseconds on ClickHouse. Not because Postgres is bad — because columnar storage is fundamentally better for aggregations.

• Event analytics: billions of pageviews, clicks, API calls • Log analysis: searching and aggregating across terabytes of logs • Time-series metrics: server monitoring, IoT sensor data • Real-time dashboards: sub-second queries on massive datasets

PostHog, Plausible, and Cloudflare all use ClickHouse for exactly these reasons.

Why Postgres is Still the Default

ClickHouse is not a general-purpose database. It can't do:

• Transactions: No ACID. No BEGIN/COMMIT/ROLLBACK. • Updates/Deletes: Mutations are async and expensive. No UPDATE WHERE. • Foreign keys: Doesn't exist. Not even conceptually. • Joins: They work but aren't ClickHouse's strength. Denormalize instead.

You can't run a web app on ClickHouse. You need a real database for your application state. That's Postgres.

The Hybrid Approach

The winning architecture in 2026: Postgres for your application, ClickHouse for your analytics.

Stream events from Postgres to ClickHouse using CDC (Change Data Capture). Keep your CRUD in Postgres. Run your dashboards on ClickHouse.

Or use Postgres extensions: TimescaleDB for time-series, pg_analytics for columnar queries within Postgres. You lose some performance but gain operational simplicity.

Quick Comparison

Factor	ClickHouse	PostgreSQL
Aggregation Speed	Blazing fast	Good (slower at scale)
Point Lookups	Slow	Fast (indexed)
Transactions	No ACID	Full ACID
Updates/Deletes	Expensive mutations	Native support
Compression	10-40x	2-4x
Data Ingestion	Millions of rows/sec	Thousands of rows/sec
SQL Compatibility	ClickHouse SQL dialect	Full SQL standard
Managed Options	ClickHouse Cloud	Dozens of providers

The Verdict

Use ClickHouse if: You have billions of rows of event/log/metrics data and need sub-second analytical queries. You already have a separate OLTP database.

Use PostgreSQL if: You're building an application and need a general-purpose database. Postgres handles analytics fine until you hit tens of billions of rows.

Consider: DuckDB is worth looking at for embedded analytics — it's columnar like ClickHouse but runs in-process like SQLite.

The Bottom Line

PostgreSQL wins

For most teams, Postgres is the right choice because it handles both OLTP and basic analytics. ClickHouse only makes sense when you have billions of rows and your analytical queries are too slow on Postgres. That's a great problem to have — but most people don't have it yet.

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