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
Distributed columnar OLAP database designed for petabyte-scale real-time analytics
Real-time OLAP engine optimized for low-latency dimensional analytics on streaming data.
Query Latency (P99 on 1B rows). Data Compression Ratio. Streaming Ingestion Native Support. SQL Dialect Compatibility. Storage Per Node (Typical). Community Adoption (GitHub Stars 2026). Enterprise Support Availability
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.
P99 Query Latency (1B rows, aggregation)
ClickHouse: 50-200ms | Apache Pinot: 100-500ms
Ingestion Latency (Kafka to query-ready)
ClickHouse: 5-30 seconds | Apache Pinot: 0.5-2 seconds
ClickHouse: Proprietary ANSI-SQL variant | Apache Pinot: Standard ANSI SQL
ClickHouse: 2-10TB per node | Apache Pinot: 0.5-2TB per node
ClickHouse: Kafka (basic), S3, File | Apache Pinot: Kafka, Pulsar, Kinesis, S3
ClickHouse: ClickHouse Inc (limited SLAs) | Apache Pinot: StarTree (24/7 SLA options)
ClickHouse: 1.2 seconds | Apache Pinot: N/A
ClickHouse: 1,000,000 events/sec | Apache Pinot: N/A
ClickHouse: Not native | Apache Pinot: N/A
ClickHouse: 8:1-12:1 | Apache Pinot: N/A
ClickHouse: Open-source (free) + optional support | Apache Pinot: N/A
ClickHouse: 7/10 (moderate-hard) | Apache Pinot: N/A
ClickHouse: 500-2000ms | Apache Pinot: N/A
ClickHouse: 10:1 to 40:1 | Apache Pinot: N/A
ClickHouse: 500-2000MB | Apache Pinot: N/A
ClickHouse: 1-5 million rows/sec | Apache Pinot: N/A
ClickHouse: 30-120 minutes | Apache Pinot: N/A
ClickHouse: 70% standard SQL | Apache Pinot: 85% SQL coverage
ClickHouse: 200-500ms | Apache Pinot: 50-200ms
ClickHouse: 1000-10000ms | Apache Pinot: 100-500ms
ClickHouse: 50-200MB | Apache Pinot: 100-400MB
ClickHouse: Standard SQL with extensions | Apache Pinot: PQL (Custom) + Presto Bridge
ClickHouse: Limited/requires custom logic | Apache Pinot: Native tenant isolation
ClickHouse: 10-60 seconds | Apache Pinot: N/A
ClickHouse: 50-500ms | Apache Pinot: N/A
ClickHouse: 1000+ nodes tested | Apache Pinot: N/A
ClickHouse: Limited (Kafka via TableEngine) | Apache Pinot: N/A
ClickHouse: No ACID (eventual consistency) | Apache Pinot: N/A
ClickHouse: 35,000+ | Apache Pinot: 15,000+
ClickHouse: ~1,000,000+ GB (1+ PB) | Apache Pinot: N/A
ClickHouse: 500-2000 MB | Apache Pinot: N/A
ClickHouse: 12+ formats (TSV, Native, Avro, Protobuf, etc.) | Apache Pinot: N/A
Query Latency (100M rows, simple aggregation)
ClickHouse: 500-1500ms | Apache Pinot: N/A
ClickHouse: 100TB+ efficiently | Apache Pinot: N/A
ClickHouse: 95% (PostgreSQL-compatible) | Apache Pinot: N/A
ClickHouse: Standard SQL; requires manual time bucketing | Apache Pinot: N/A
ClickHouse: 100,000-500,000 events/sec | Apache Pinot: 1,000,000-2,000,000 events/sec
ClickHouse: $40-60 | Apache Pinot: $120-180
ClickHouse: 8-32GB | Apache Pinot: 16-64GB
ClickHouse: 3-5 nodes | Apache Pinot: 5-7 nodes (3 controllers + 2-4 brokers)
ClickHouse: 1000TB+ (unlimited with tiering) | Apache Pinot: 100-500TB (hot data)
Query Latency (1B row scan, 10 column aggregate)
ClickHouse: 50-100ms | Apache Pinot: N/A
ClickHouse: 1 (can run standalone) | Apache Pinot: N/A
ClickHouse: Unlimited (resource-based) | Apache Pinot: N/A
ClickHouse: Microseconds to milliseconds | Apache Pinot: 0.1 (streaming)
ClickHouse: 5-10 (via third-party) | Apache Pinot: N/A
ClickHouse: 25000+ | Apache Pinot: 8,200+
Query Latency (1 billion rows, simple SELECT)
ClickHouse: 1,000,000 | Apache Pinot: N/A
ClickHouse: 40-80 hours | Apache Pinot: N/A
ClickHouse: $500 (ClickHouse Cloud starter) | Apache Pinot: N/A
ClickHouse: 4+ (AWS, Azure, GCP, on-premise) | Apache Pinot: N/A
ClickHouse: 60-300 (manual cluster resize required) | Apache Pinot: N/A
ClickHouse: MySQL-compatible with ClickHouse extensions | Apache Pinot: PQL + Limited SQL (60% compatibility)
Average Query Latency (1 Billion Row Scan)
ClickHouse: 1440 (self-managed) / 60 (managed) | Apache Pinot: N/A
ClickHouse: 100,000+ | Apache Pinot: N/A
ClickHouse: 99.0% (self-managed) / 99.95% (managed) | Apache Pinot: N/A
ClickHouse: 3 (S3, Kinesis via 3rd party, basic) | Apache Pinot: N/A
ClickHouse: No (third-party integration required) | Apache Pinot: N/A
ClickHouse: Native optimization, ideal for billions of events | Apache Pinot: N/A
ClickHouse: 99.5% (self-hosted dependent) | Apache Pinot: N/A
ClickHouse: 1-2 components | Apache Pinot: 5-7 components
ClickHouse: 5-10 minutes | Apache Pinot: 45-60 minutes
Average Query Latency (Standard Aggregation)
ClickHouse: 2-5 seconds | Apache Pinot: <1 second
ClickHouse: Replica, Shard (simplified) | Apache Pinot: N/A
ClickHouse: 3-5 seconds (batched) | Apache Pinot: N/A
ClickHouse: 2016 (Yandex) | Apache Pinot: N/A
ClickHouse: 30-60 minutes | Apache Pinot: N/A
ClickHouse: 1,000+ TB (distributed) | Apache Pinot: N/A
Query Latency (1B rows, COUNT aggregation)
ClickHouse: 20-100ms | Apache Pinot: N/A
ClickHouse: 40-100x compression | Apache Pinot: 3:1 to 10:1 (avg 6:1)
ClickHouse: 8 (high) | Apache Pinot: N/A
ClickHouse: 35,000+ stars | Apache Pinot: N/A
ClickHouse: AGPL - Source disclosure required | Apache Pinot: N/A
ClickHouse: N/A | Apache Pinot: 50-100ms
ClickHouse: N/A | Apache Pinot: 150-300MB
ClickHouse: N/A | Apache Pinot: $1500-3000
ClickHouse: N/A | Apache Pinot: Full support (INNER, LEFT, RIGHT, FULL)
ClickHouse: N/A | Apache Pinot: 1000+ (LinkedIn, Uber, etc)
ClickHouse: N/A | Apache Pinot: 500-2000ms
ClickHouse: N/A | Apache Pinot: 5-8 nodes
ClickHouse: N/A | Apache Pinot: 15:1 to 25:1
ClickHouse: N/A | Apache Pinot: Ad-hoc queries (generalized)
ClickHouse: N/A | Apache Pinot: Full support
ClickHouse: N/A | Apache Pinot: 100-500ms
ClickHouse: N/A | Apache Pinot: $0 (infrastructure only)
ClickHouse: N/A | Apache Pinot: 12 weeks
ClickHouse: N/A | Apache Pinot: 500,000/sec (streaming)
ClickHouse: N/A | Apache Pinot: $150,000
ClickHouse: N/A | Apache Pinot: 500+ (LinkedIn runs 1,000+)
ClickHouse: N/A | Apache Pinot: Limited (phrase queries, basic tokenization)
ClickHouse: N/A | Apache Pinot: 14-30 days (requires schema design and tuning)
ClickHouse: N/A | Apache Pinot: Native SQL with PQL extensions (ANSI-compliant subset)
ClickHouse: N/A | Apache Pinot: 16-32GB for analytics workload
ClickHouse: N/A | Apache Pinot: 100,000+
ClickHouse: N/A | Apache Pinot: Native (Kafka, S3, MQ)
ClickHouse: N/A | Apache Pinot: Java, Python (limited)
Annual Infrastructure Cost (1TB dataset)
ClickHouse: N/A | Apache Pinot: 50,000-150,000
ClickHouse: N/A | Apache Pinot: 100-500 typical
ClickHouse: N/A | Apache Pinot: Sub-second to 1 minute
ClickHouse: N/A | Apache Pinot: Apache 2.0 Open Source
ClickHouse: N/A | Apache Pinot: Kubernetes, on-premises, all cloud providers
ClickHouse: N/A | Apache Pinot: 1M+ eps native
ClickHouse: N/A | Apache Pinot: Self-hosted infrastructure costs (highly variable)
ClickHouse: N/A | Apache Pinot: Free (self-hosted) / $0.10-0.50 (managed)
ClickHouse: N/A | Apache Pinot: Self-hosted (high DevOps)
ClickHouse: N/A | Apache Pinot: 10+ billion events/day (proven at LinkedIn, Airbnb)
ClickHouse: N/A | Apache Pinot: 40-80 hours (cluster provisioning, tuning)
ClickHouse: N/A | Apache Pinot: 100-500 (depends on cluster config)
ClickHouse: N/A | Apache Pinot: 1,000,000+
ClickHouse: N/A | Apache Pinot: Yes (native distributed)
Typical Query Latency (1B rows, GROUP BY)
ClickHouse: N/A | Apache Pinot: 50-500ms
ClickHouse: N/A | Apache Pinot: 0.1-0.3x
ClickHouse: N/A | Apache Pinot: Not optimized (secondary feature)
ClickHouse: N/A | Apache Pinot: Native (Kafka, S3, HDFS)
ClickHouse: N/A | Apache Pinot: Medium-High (columnar tuning)
ClickHouse: N/A | Apache Pinot: 10+ PB (proven at scale)
ClickHouse: N/A | Apache Pinot: Native PinotSQL (full ANSI compliance)
Query Latency (Typical Analytical Query)
ClickHouse: N/A | Apache Pinot: <1 second
ClickHouse: N/A | Apache Pinot: 1,000,000+ rows/sec
ClickHouse: N/A | Apache Pinot: $0 (open-source)
ClickHouse: N/A | Apache Pinot: 0 (requires external tools)
ClickHouse: N/A | Apache Pinot: Petabyte-scale (distributed)
ClickHouse: N/A | Apache Pinot: On-prem, Kubernetes, multi-cloud
ClickHouse: N/A | Apache Pinot: 100K-500K
ClickHouse: N/A | Apache Pinot: 8+ (Broker, Server, Controller, Minion)
ClickHouse: N/A | Apache Pinot: 100M+ with dictionary encoding
ClickHouse: N/A | Apache Pinot: 1-5GB segments
ClickHouse: N/A | Apache Pinot: 1-2 months
How to choose between ClickHouse and Apache Pinot
ClickHouse vs Apache Pinot — Structured Comparison Data
Machine-readable comparison dataset for ClickHouse vs Apache Pinot. Contains 164 attribute comparisons, 5 FAQ pairs.
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.
How to Choose Between ClickHouse and Apache Pinot
Consider: Query Latency (P99 on 1B rows)
Query Latency (P99 on 1B rows): ClickHouse has 50-200ms, while Apache Pinot has 100-500ms.
Data Compression Ratio: ClickHouse has 10:1 to 50:1, while Apache Pinot has 3:1 to 10:1.
Consider: Streaming Ingestion Native Support
Streaming Ingestion Native Support: ClickHouse has Kafka, S3, limited, while Apache Pinot has Kafka, Pulsar, optimized.
SQL Dialect Compatibility: ClickHouse has Custom ANSI-SQL variant, while Apache Pinot has Standard SQL.
Storage Per Node (Typical): ClickHouse has 2-10TB, while Apache Pinot has 500GB-2TB.
Consider: Community Adoption (GitHub Stars 2026)
Community Adoption (GitHub Stars 2026): ClickHouse has 35,000+, while Apache Pinot has 15,000+.
Consider: Enterprise Support Availability
Enterprise Support Availability: ClickHouse has ClickHouse Inc, limited, while Apache Pinot has StarTree (vendor-backed).