ClickHouse vs TimescaleDB

Two analytics databases. One is built for speed. One is built on Postgres. Both crush traditional databases for time-series data.

Analytics at Scale

Traditional databases choke on analytics queries. Scanning millions of rows to compute aggregates is slow in row-oriented storage. Both ClickHouse and TimescaleDB solve this, differently.

ClickHouse is a columnar database built from scratch for analytics. TimescaleDB is a Postgres extension that adds time-series superpowers.

Why ClickHouse is Faster

Columnar storage means queries only read the columns they need. Scanning a billion rows but only need 3 columns? ClickHouse reads 3 columns, not 50.

Compression is aggressive. 10:1 ratios are common. Less data to read = faster queries.

Materialized views pre-compute aggregations. Your dashboard query that would take 30 seconds runs in 10ms because the answer was pre-calculated on insert.

Why TimescaleDB for Postgres Teams

It's Postgres. Your existing Postgres knowledge, tools, and ORMs work. pg_dump, psql, Prisma, Drizzle — all compatible.

Join time-series data with your application data in the same database. No ETL pipeline, no data duplication.

Hypertables (TimescaleDB's partitioned tables) handle time-series efficiently while keeping full SQL compatibility.

Benchmarks: ClickHouse Crushes TimescaleDB on Billions of Rows

On a 10-billion-row dataset, ClickHouse queries finish in under 200ms; TimescaleDB takes 1.5–3 seconds. That’s 7–15x faster. ClickHouse’s columnar engine scans only the columns you query, while TimescaleDB’s row-based PostgreSQL engine reads entire rows—even with compression. In real-world tests on 100-billion-row IoT datasets, ClickHouse maintained sub-second aggregation; TimescaleDB buckled after 50 billion rows, needing sharding. If you’re querying billions of rows per second, ClickHouse is the only choice. TimescaleDB is a PostgreSQL extension, not a true columnar database.

Compression: ClickHouse Saves 15–30x, TimescaleDB Lags at 10–15x

ClickHouse compresses time-series data 15–30x using LZ4 and ZSTD with column-specific codecs. TimescaleDB’s native compression (based on PostgreSQL’s TOAST) hits only 10–15x for similar datasets. For example, 1 TB of raw IoT sensor data shrinks to 33–66 GB in ClickHouse vs 66–100 GB in TimescaleDB. That means lower storage costs and faster scans. ClickHouse also supports delta-of-delta and XOR codecs for timestamps and floats, which TimescaleDB lacks. If storage efficiency matters, ClickHouse is the clear winner.

Write Throughput: ClickHouse Ingests Millions of Rows/Second, TimescaleDB Stalls

ClickHouse sustains 1–5 million rows/second per node using batch inserts (1–10 MB blocks). TimescaleDB, built on PostgreSQL’s row-based engine, tops out at 100–300k rows/second for continuous inserts—and that’s with chunking. Under heavy write load, TimescaleDB’s B-tree indexes bloat and slow down ingestion; ClickHouse’s LSM-tree merges are designed for high throughput. For observability pipelines pushing 10+ TB/day, ClickHouse handles it without breaking a sweat. TimescaleDB requires constant tuning and sharding to keep up. If you need real-time ingestion at scale, ClickHouse is the only option.

Quick Comparison

Factor	ClickHouse	TimescaleDB
Query Speed (analytics)	Extremely fast	Fast
Storage Engine	Columnar	Row (Postgres)
SQL Compatibility	ClickHouse SQL	Full PostgreSQL
Postgres Ecosystem	Separate system	Extension (native)
Compression	Excellent (10:1+)	Good
Joins with App Data	Separate DB needed	Same database
Managed Options	ClickHouse Cloud	Timescale Cloud

The Verdict

Use ClickHouse if: You're building pure analytics: event tracking, log analysis, dashboards over billions of rows. Speed is everything.

Use TimescaleDB if: You're already on Postgres, need to join time-series with application data, or want one database for everything.

Consider: DuckDB for embedded analytics. Apache Druid for real-time analytics at massive scale.

ClickHouse vs TimescaleDB: FAQ

Is ClickHouse or TimescaleDB better?

ClickHouse is the Nice Pick. ClickHouse is faster for pure analytics workloads. If you're ingesting billions of events and running aggregate queries, nothing touches ClickHouse. TimescaleDB wins if you want to stay in the Postgres ecosystem.

When should you use ClickHouse?

You're building pure analytics: event tracking, log analysis, dashboards over billions of rows. Speed is everything.

When should you use TimescaleDB?

You're already on Postgres, need to join time-series with application data, or want one database for everything.

What's the main difference between ClickHouse and TimescaleDB?

Two analytics databases. One is built for speed. One is built on Postgres. Both crush traditional databases for time-series data.

How do ClickHouse and TimescaleDB compare on query speed (analytics)?

ClickHouse: Extremely fast. TimescaleDB: Fast. ClickHouse wins here.

Are there alternatives to consider beyond ClickHouse and TimescaleDB?

DuckDB for embedded analytics. Apache Druid for real-time analytics at massive scale.

The Bottom Line

ClickHouse wins

ClickHouse is faster for pure analytics workloads. If you're ingesting billions of events and running aggregate queries, nothing touches ClickHouse. TimescaleDB wins if you want to stay in the Postgres ecosystem.

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