ClickHouse excels at real-time OLAP analytics with superior compression and faster query performance on large datasets, while Apache Pinot specializes in low-latency dimensional analytics with better support for streaming data ingestion and complex aggregations at scale.
ClickHouse vs Apache Pinot — Side-by-side comparison on A Versus B
Choose ClickHouse if you need maximum query performance, superior compression for cost-efficiency, and can work with its SQL dialect for batch-oriented analytics at petabyte scale. Choose Apache Pinot if you require sub-100ms latency for real-time streaming dashboards, standard SQL compliance, and native Kafka/Pulsar integration with active vendor support.
Distributed columnar OLAP database designed for petabyte-scale real-time analytics
Real-time OLAP engine optimized for low-latency dimensional analytics on streaming data.
ClickHouse vs Apache Pinot — Comparison Data
Structured comparison data for ClickHouse vs Apache Pinot with 164 attributes.
P99 Query Latency (1B rows, aggregation)
Ingestion Latency (Kafka to query-ready)
Query Latency (100M rows, simple aggregation)
Query Latency (1B row scan, 10 column aggregate)
Query Latency (1 billion rows, simple SELECT)
Average Query Latency (1 Billion Row Scan)
Average Query Latency (Standard Aggregation)
Query Latency (1B rows, COUNT aggregation)
Annual Infrastructure Cost (1TB dataset)
Typical Query Latency (1B rows, GROUP BY)
Query Latency (Typical Analytical Query)
ClickHouse vs Apache Pinot — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for ClickHouse vs Apache Pinot.
Which database is faster for analytical queries?
ClickHouse typically achieves 50-200ms P99 latency on billion-row aggregations due to superior columnar compression and vectorized execution, compared to Pinot's 100-500ms. However, Pinot excels at streaming ingestion latency (sub-second vs ClickHouse's 5-30 seconds), making Pinot better for real-time dashboard freshness.
How much storage will each require for 100GB of raw data?
ClickHouse with 30:1 compression would need ~3.3TB, while Pinot with 6:1 compression would need ~16.7TB. ClickHouse's superior compression saves approximately 80% storage costs, but requires more CPU for decompression during queries. For cost-sensitive workloads processing cold historical data, ClickHouse wins; for real-time systems with frequent updates, Pinot's lower compression overhead may be acceptable.
Can I use standard SQL with both databases?
Apache Pinot supports standard ANSI SQL directly, allowing teams to use existing SQL expertise immediately. ClickHouse uses a SQL variant with proprietary functions (arrayMap, groupArray, etc.), requiring 2-4 weeks of developer ramp-up. Teams with large existing SQL codebases should choose Pinot; new projects can optimize for ClickHouse's superior performance.
Which is better for real-time streaming dashboards?
Apache Pinot is purpose-built for this with native Kafka/Pulsar support achieving sub-second ingestion-to-query latency and StarTree vendor SLAs. ClickHouse's 5-30 second ingestion delay and basic Kafka integration make it unsuitable for sub-100ms dashboard refresh requirements. Choose Pinot if your product requires real-time user-facing dashboards.
ClickHouse scales vertically with single nodes handling 2-10TB, ideal for consolidated analytics infrastructure. Pinot requires horizontal distribution, with each node holding 0.5-2TB, making it better for distributed cloud deployments. ClickHouse is simpler to operate (fewer nodes); Pinot requires orchestration expertise but provides better fault tolerance across regions.