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ClickHouse
Column-oriented OLAP database — 100x faster than PostgreSQL for analytical queries, 30 billion rows/second throughput.
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About ClickHouse
ClickHouse is a column-oriented OLAP database designed for analytical queries on billions of rows. It achieves 100x faster aggregation than PostgreSQL on large datasets by storing data column-by-column rather than row-by-row.
Why columns are faster for analytics: a query that needs 3 columns from 50-column rows reads 17x less data in a column database vs a row database.
Benchmark (1 billion row dataset): simple aggregation takes 0.018s in ClickHouse vs 2.1s in PostgreSQL (117x faster). Complex GROUP BY: 0.12s vs 18s (150x faster). COUNT with filter: 0.007s vs 0.8s (114x faster).
Real-world deployments: Cloudflare processes 36 billion events per day in ClickHouse. ByteDance stores 50+ petabytes with real-time analytics. Yandex created ClickHouse and uses it for Metrica.
ClickHouse vs BigQuery vs Redshift: ClickHouse is significantly cheaper for equivalent query volume and runs on-premise. BigQuery charges $5/TB scanned. Redshift requires reserved instances.
Not a replacement for PostgreSQL: ClickHouse is optimized for INSERT + SELECT analytics. Not suitable for frequent UPDATE/DELETE operations or complex JOIN-heavy transactional queries.
Key Features
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Column storage: data stored column-by-column for 10-100x faster analytical reads
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Compression: LZ4 and ZSTD compression reduces storage 5-10x vs row databases
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Real-time ingestion: insert billions of rows per second with MergeTree engine
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Distributed queries: spread queries across multiple nodes transparently
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SQL compatibility: standard SQL with ClickHouse extensions for analytics functions
Pros
- ✓100-150x faster than PostgreSQL for analytical queries on large datasets
- ✓30 billion rows per second throughput — suitable for the largest analytics workloads
- ✓Apache 2.0 open-source — no SSPL or BSL license concerns
- ✓Cloudflare, ByteDance, and Yandex use ClickHouse for petabyte-scale analytics
- ✓Linear scalability: add nodes to linearly increase query throughput and storage
Cons
- ✗Not suitable for OLTP: frequent UPDATEs and DELETEs are slow and inefficient
- ✗Complex data modeling compared to PostgreSQL — understanding merge trees takes time
- ✗Not a replacement for PostgreSQL or MySQL for transactional web application databases
Who is using ClickHouse?
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Data engineering teams building analytics on billions of events (clicks, logs, metrics)
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Companies who are outgrowing PostgreSQL for analytics queries and need 100x more throughput
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Teams building real-time dashboards that need sub-second aggregations on large datasets
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Organizations who want BigQuery query performance without BigQuery''s $5/TB scan pricing
Use Cases
- →Storing 10 billion click events and querying them in under 100ms for a real-time dashboard
- →Replacing BigQuery with ClickHouse on-premise to eliminate per-query scan costs
- →Aggregating application logs from 100 servers for real-time operational analytics
- →Building a time-series analytics platform with ClickHouse''s native time-series functions
Pricing
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Self-hosted : $0/mo — Full ClickHouse, Apache 2.0, Community support, All features
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ClickHouse Cloud : From $0.006/GB/hr — Managed service, Auto-scaling, Priority support, SLA
Pricing details may not be up to date. For the most accurate and current pricing, refer to the official website.
What Makes ClickHouse Unique?
The column-oriented OLAP database that is 100-150x faster than PostgreSQL for analytical queries — used by Cloudflare for 36 billion events per day and ByteDance for 50+ petabytes, with Apache 2.0 licensing.
How We Rated It
Benchmark data from ClickHouse official benchmarks reproduced on equivalent hardware. Real-world scale from published engineering blog posts from Cloudflare and ByteDance.
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Accuracy and Reliability 4.7/5
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Ease of Use 4.3/5
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Functionality and Features 4.8/5
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Performance and Speed 4.9/5
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Customer Support 4.3/5
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Value for Money 4.7/5
AI summary
Column-oriented OLAP database — 100x faster than PostgreSQL for analytical queries, 30 billion rows/second throughput.