ClickHouse vs Apache Spark: Key Differences & When to Use Each

Comprehensive side-by-side comparison of features, pricing, and metrics

Key Differences

Compare ClickHouse and Apache Spark across features, pricing, integrations, and community metrics. ClickHouse / Apache Spark.

Feature

ClickHouse

Database

Apache Spark

Data Processing

Side-by-side comparison of developer tools
Column-oriented database for OLAP
Unified analytics engine for large-scale data processing
GitHub Stars
⭐ 47,194
⭐ 43,233
Contributors
👥 2,844
👥 3,432
Pricing
✓ Free
Enterprise: Contact sales
✓ Free
Enterprise: Contact sales
Languages
C++
Scala
Features
  • • Ai
  • • Analytics
  • • Big Data
  • • Clickhouse
  • • Cloud Native
  • • Big Data
  • • Java
  • • Jdbc
  • • Python
  • • R
Integrations
No integrations listed
No integrations listed
Momentum Score
95/100 (slowing)
79/100 (stable)
Community Health
77/100 (good)
91/100 (excellent)
Maturity Index
85/100 (mature)
90/100 (mature)
Innovation Score
83/100 (innovative)
91/100 (pioneering)
Risk Score (higher is safer)
94/100 (minimal)
94/100 (minimal)
Developer Experience
95/100 (excellent)
80/100 (good)
Links
🌐 Visit Website View on GitHub
🌐 Visit Website View on GitHub

ClickHouse Strengths

  • ✓ More popular (47,194 stars)

Apache Spark Strengths

  • ✓ Larger community (3,432 contributors)

When to Use ClickHouse vs Apache Spark

Use ClickHouse when its strengths align better with your stack and team needs, and choose Apache Spark when its ecosystem, integrations, or cost profile is a better fit.

Related comparisons

ClickHouse vs Elasticsearch → Apache Spark vs Apache Flink → ClickHouse vs Redis → Apache Spark vs Apache Beam → ClickHouse vs TiDB → Apache Spark vs Elasticsearch → ClickHouse vs CockroachDB → Apache Spark vs Redis → ClickHouse vs InfluxDB → Apache Spark vs Grafana → ClickHouse vs MongoDB → Apache Spark vs Prometheus →

More Comparisons

View ClickHouse → View Apache Spark → Search More Tools →

Data source: GitHub API

Last updated: 5/4/2026