ClickHouse is generally faster for large-scale analytics queries due to its columnar storage and OLAP design, while PostgreSQL offers greater flexibility, ACID compliance, and advanced extensions for mixed workloads.
If you need blazing-fast reporting across billions of rows, ClickHouse often wins. But for transactional analytics, complex joins, or hybrid workloads, PostgreSQL remains the better fit.
Introduction
Choosing the right open-source database for analytics can be tricky. Two of the most popular options — ClickHouse and PostgreSQL — both shine in different scenarios.
In this comparison, we’ll explore ClickHouse vs PostgreSQL for analytics workloads, looking at architecture, performance, scalability, ecosystem, and real-world use cases so you can make the right choice for your business.
For a broader view of database options, check our Ultimate Guide to Open-Source Databases (2025).
ClickHouse vs PostgreSQL: Key Differences
| Feature | ClickHouse | PostgreSQL |
|---|---|---|
| Type | Columnar OLAP database | Row-based OLTP + hybrid analytics |
| Best For | Real-time analytics, dashboards, log/event processing | Transactional workloads, mixed analytics, extensibility |
| Performance | Extremely fast for aggregation queries over billions of rows | Great for complex queries, joins, and transactions |
| Scalability | Horizontal scaling with distributed clusters | Vertical scaling; extensions help with analytics |
| Storage | Column-oriented, compressed | Row-oriented (with columnar extensions like Citus, TimescaleDB) |
| Ecosystem | Growing but newer community | Mature, extensive extensions and tooling |
When to Use PostgreSQL for Analytics
PostgreSQL is a battle-tested relational database that doubles as an analytics engine when extended. It’s particularly strong when:
- You need transactional + analytical (HTAP) workloads in one system
- Queries involve complex joins, foreign keys, or ACID transactions
- You want to extend functionality with tools like TimescaleDB for time-series analytics or Hydra for OLAP workloads
- Use cases: financial analytics, BI dashboards with transactional consistency, mixed web+analytics applications
When to Use ClickHouse for Analytics
ClickHouse was designed for one thing: speed at scale for OLAP queries. It excels when:
- Datasets are huge (billions of rows) and need sub-second query times
- Workloads are read-heavy with aggregations, filtering, and reporting
- You need real-time analytics on event logs, IoT data, or monitoring metrics
- Use cases: observability (logs/metrics), ad-tech analytics, IoT telemetry, large BI dashboards
For an extended option, ClickHouseS3 adds massive scalability with cloud storage.
Performance Benchmarks: ClickHouse vs PostgreSQL
- ClickHouse consistently outperforms PostgreSQL in OLAP-style queries, often returning results 10–100x faster on aggregation workloads.
- PostgreSQL handles smaller to mid-scale analytics very well, especially when queries combine transactions + analytics.
- With extensions (e.g., TimescaleDB), PostgreSQL can rival specialized systems for specific workloads like time-series.
Scalability & Ecosystem
- ClickHouse offers distributed clusters, replication, and sharding natively, making it highly scalable.
- PostgreSQL relies on scaling solutions like Citus, TimescaleDB, or managed services like OctaByte PostgreSQL.
- PostgreSQL’s ecosystem is unmatched for extensions, while ClickHouse focuses on raw speed + growing analytics tooling.
Real-World Examples
- ClickHouse: Used by Yandex, Cloudflare, and Uber for real-time analytics at scale.
- PostgreSQL: Trusted by financial institutions, SaaS startups, and governments for reliable, hybrid workloads.
Final Thoughts: Which Should You Choose?
The choice between ClickHouse vs PostgreSQL for analytics depends on your workload:
- Choose ClickHouse if you need real-time, large-scale analytics across billions of rows.
- Choose PostgreSQL if you need flexibility, transactions, and mixed workloads with strong community support.
Both databases are open-source and powerful. If you’d rather not manage infrastructure, OctaByte offers fully managed ClickHouse, PostgreSQL, and TimescaleDB hosting so you can focus on insights instead of operations.
FAQ
Is ClickHouse faster than PostgreSQL for analytics?
Yes. For OLAP-style queries across billions of rows, ClickHouse is often 10–100x faster than PostgreSQL.
Can PostgreSQL handle analytics workloads?
Yes. PostgreSQL supports analytics through extensions like TimescaleDB or Citus, making it suitable for mixed workloads.
When should I use ClickHouse over PostgreSQL?
Use ClickHouse when you need real-time, large-scale analytics with sub-second queries, especially for event logs, IoT, and BI dashboards.
Can I use PostgreSQL and ClickHouse together?
Yes. Many companies use PostgreSQL for transactional systems and replicate data into ClickHouse for analytics dashboards.
👉 Want more open-source hosting insights? Don’t miss The Ultimate Guide to Open-Source Databases (2025).