ClickHouse vs TimescaleDB vs QuestDB (2026)
When PostgreSQL can't keep up with analytical queries on millions of rows, you need a purpose-built analytics database. Here's how the three leaders compare.
Quick Comparison
| Feature | ClickHouse | TimescaleDB | QuestDB |
|---|---|---|---|
| Type | Column-oriented OLAP | PostgreSQL extension | Time-series native |
| SQL Compatible | ✅ ClickHouse SQL | ✅ Full PostgreSQL | ✅ PostgreSQL wire |
| Best For | Analytics, aggregations | Time-series + PostgreSQL | High-ingest time-series |
| Ingest Speed | Very fast | Fast | Fastest |
| Query Speed | Fastest for analytics | Good | Fast for time-series |
| Joins | ✅ | ✅ Full SQL | ⚠️ Limited |
| Managed Cloud | ClickHouse Cloud | Timescale Cloud | QuestDB Cloud |
| Open Source | ✅ Apache 2.0 | ✅ Apache 2.0 | ✅ Apache 2.0 |
ClickHouse — The Analytics Powerhouse
Best for: OLAP queries, dashboards, aggregating billions of rows.
ClickHouse scans billions of rows in seconds because it's column-oriented — it reads only the columns needed for your query.
-- Aggregate 1 billion events in seconds
SELECT
toStartOfHour(timestamp) as hour,
countIf(event = 'page_view') as views,
countIf(event = 'purchase') as purchases,
uniqExact(user_id) as unique_users
FROM events
WHERE timestamp > now() - INTERVAL 7 DAY
GROUP BY hour
ORDER BY hour
Pros: Fastest analytical queries, scales to petabytes, excellent compression, materialized views. Cons: Not great for point lookups, limited UPDATE/DELETE, different SQL dialect.
TimescaleDB — PostgreSQL for Time-Series
Best for: Teams already on PostgreSQL who need time-series capabilities.
TimescaleDB is a PostgreSQL extension. Your existing PostgreSQL knowledge, tools, and ORMs work.
-- Create a hypertable (time-partitioned table)
SELECT create_hypertable('metrics', 'time');
-- Standard PostgreSQL queries, accelerated
SELECT time_bucket('1 hour', time) as hour, avg(cpu_usage)
FROM metrics
WHERE time > now() - INTERVAL '7 days'
GROUP BY hour;
Pros: Full PostgreSQL compatibility, easy migration, JOINs work, continuous aggregates. Cons: Slower than ClickHouse for pure analytics, higher storage usage.
QuestDB — The Ingest Speed Champion
Best for: High-frequency data ingestion (IoT, financial data, metrics).
-- QuestDB uses PostgreSQL wire protocol
SELECT timestamp, symbol, avg(price), max(price)
FROM trades
WHERE timestamp > dateadd('d', -7, now())
SAMPLE BY 1h
Pros: Fastest ingest (millions of rows/second), low latency, efficient storage. Cons: Limited JOIN support, smaller ecosystem, less mature.
Performance Benchmarks
Ingest (rows/second)
| Database | Single Node |
|---|---|
| QuestDB | 4M+ rows/s |
| ClickHouse | 1M+ rows/s |
| TimescaleDB | 300K+ rows/s |
Analytical Query (1B rows aggregation)
| Database | Time |
|---|---|
| ClickHouse | ~0.5s |
| QuestDB | ~1.5s |
| TimescaleDB | ~5s |
Decision Guide
| Use Case | Choose |
|---|---|
| Product analytics dashboards | ClickHouse |
| IoT sensor data | QuestDB |
| Already on PostgreSQL | TimescaleDB |
| Need JOINs and complex queries | TimescaleDB or ClickHouse |
| Highest ingest rate | QuestDB |
| Petabyte scale | ClickHouse |
| Simplest migration | TimescaleDB |
FAQ
Can I replace PostgreSQL with ClickHouse?
No. ClickHouse is for analytics, not transactional workloads. Use PostgreSQL for your app, ClickHouse for analytics on top.
Is TimescaleDB just PostgreSQL?
Yes. It's a PostgreSQL extension. All PostgreSQL features work. You add time-series optimizations on top.
Which is cheapest to run?
ClickHouse has the best compression (10-40x). Less storage = lower costs. QuestDB is also storage-efficient. TimescaleDB uses more storage.
Bottom Line
ClickHouse for analytics dashboards and aggregations at scale. TimescaleDB if you're on PostgreSQL and want to add time-series. QuestDB for maximum ingest speed (IoT, financial). Most web apps: start with PostgreSQL, add ClickHouse when you need analytics on millions of rows.