software
ClickHouse vs Pinot: OLAP Database Comparison 2026
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
Distributed columnar OLAP database designed for petabyte-scale real-time analytics
Teams building data warehouses, analytics platforms, and real-time dashboards prioritizing query speed and storage efficiency at scale.
Score63%
VS
Apache Pinot
Real-time OLAP engine optimized for low-latency dimensional analytics on streaming data.
Organizations running real-time analytics platforms, user-facing dashboards, and streaming applications requiring strict SLA guarantees and standard SQL.
Score63%
Quick Answer
AI SummaryClickHouse 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.
Our Verdict
AI-assistedChoose 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.
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ClickHouse
7.9/10
Apache Pinot
7.1/10
Choose ClickHouse if
Best pickTeams building data warehouses, analytics platforms, and real-time dashboards prioritizing query speed and storage efficiency at scale.
Choose Apache Pinot if
Organizations running real-time analytics platforms, user-facing dashboards, and streaming applications requiring strict SLA guarantees and standard SQL.
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Key Differences at a Glance
- Query Latency (P99 on 1B rows):✓ ClickHouse wins(50-200ms vs 100-500ms)
- Data Compression Ratio:✓ ClickHouse wins(10:1 to 50:1 vs 3:1 to 10:1)
- Streaming Ingestion Native Support:✓ Apache Pinot wins(Kafka, Pulsar, optimized vs Kafka, S3, limited)
Key Facts & Figures
128 numeric metrics compared
| Metric | ClickHouse | Apache Pinot | Ratio |
|---|---|---|---|
| P99 Query Latency (1B rows, aggregation)(milliseconds) | 50-200ms | 100-500ms | |
| Ingestion Latency (Kafka to query-ready)(seconds) | 5-30 seconds | 0.5-2 seconds | |
| Maximum Recommended Node Storage(TB) | 2-10TB per node | 0.5-2TB per node | |
| Query Latency (1 billion rows)(seconds) | 1.2 seconds | — | — |
| Monthly Cost (100 GB compressed)(USD) | $150 | — | — |
| Ingestion Throughput(events/sec) | 1,000,000 events/sec | — | — |
| Compression Ratio(ratio) | 8:1-12:1 | — | — |
| Learning Curve (1-10 scale)(difficulty) | 7/10 (moderate-hard) | — | — |
| Query Latency (1GB aggregation)(milliseconds) | 500-2000ms | — | — |
| Compression Ratio (typical)(ratio) | 10:1 to 40:1 | — | — |
| Memory Required (minimal)(MB) | 500-2000MB | — | — |
| Ingest Throughput(million rows/second) | 1-5 million rows/sec | — | — |
| Setup Time to First Query(minutes) | 30-120 minutes | — | — |
| SQL Standard Compliance(% compatibility) | 70% standard SQL | 85% SQL coverage | |
| Query Latency (p99)(milliseconds) | 200-500ms | 50-200ms | |
| Ingestion Latency (end-to-end)(milliseconds) | 1000-10000ms | 100-500ms | |
| Memory Usage per Query(MB) | 50-200MB | 100-400MB | |
| Maximum Cluster Size(nodes) | 100+ | 1000+ | |
| Typical Cost per TB/year(USD) | $800-1500 | $2000-3500 | |
| Ingestion Latency(seconds) | 10-60 seconds | — | — |
| Query Latency (100M rows)(milliseconds) | 50-500ms | — | — |
| Maximum Cluster Nodes(nodes) | 1000+ nodes tested | — | — |
| GitHub Stars (2026)(stars) | 35,000+ | 15,000+ | |
| Typical Maximum Dataset Size(GB) | ~1,000,000+ GB (1+ PB) | — | — |
| Idle Memory Usage(MB) | 500-2000 MB | — | — |
| Supported Data Formats(formats) | 12+ formats (TSV, Native, Avro, Protobuf, etc.) | — | — |
| Query Latency (100M rows, simple aggregation)(milliseconds) | 500-1500ms | — | — |
| Typical Storage Cost(USD per TB per month) | $20-40 | — | — |
| Max Recommended Dataset Size(terabytes) | 100TB+ efficiently | — | — |
| SQL Feature Completeness(percentage) | 95% (PostgreSQL-compatible) | — | — |
| Max Ingestion Throughput(events/second) | 100,000-500,000 events/sec | 1,000,000-2,000,000 events/sec | |
| Storage Cost per TB/month(USD) | $40-60 | $120-180 | |
| Typical Node Memory(GB) | 8-32GB | 16-64GB | |
| Minimum Recommended Cluster Size(nodes) | 3-5 nodes | 5-7 nodes (3 controllers + 2-4 brokers) | |
| Max Dataset Size (Practical)(TB) | 1000TB+ (unlimited with tiering) | 100-500TB (hot data) | |
| Query Latency (1B row scan, 10 column aggregate)(milliseconds) | 50-100ms | — | — |
| Storage Cost (per TB/month)(USD) | $15-25 | — | — |
| Typical Data Compression Ratio(x) | 10-40x | — | — |
| Minimum Cluster Size (nodes)(nodes) | 1 (can run standalone) | — | — |
| Data Ingestion Latency(seconds) | Microseconds to milliseconds | 0.1 (streaming) | |
| AWS Service Integration (native)(count) | 5-10 (via third-party) | — | — |
| GitHub Stars (as of 2026)(stars) | 25000+ | 8,200+ | |
| Query Latency (1 billion rows, simple SELECT)(milliseconds) | 150ms | — | — |
| Cost per GB Scanned(USD) | $0.015 | — | — |
| Maximum Ingestion Rate(events/second) | 1,000,000 | — | — |
| Infrastructure Management Overhead(hours per month) | 40-80 hours | — | — |
| Minimum Monthly Cost (basic setup)(USD) | $500 (ClickHouse Cloud starter) | — | — |
| Cloud Provider Support(providers) | 4+ (AWS, Azure, GCP, on-premise) | — | — |
| Automatic Scaling Time(seconds) | 60-300 (manual cluster resize required) | — | — |
| Average Query Latency (1 Billion Row Scan)(ms) | 75ms | — | — |
| Monthly Cost per TB Stored(USD) | $0.09 | — | — |
| Time to Production Deployment(hours) | 1440 (self-managed) / 60 (managed) | — | — |
| Maximum Concurrent Queries(queries/sec) | 100,000+ | — | — |
| Uptime SLA Guarantee(%) | 99.0% (self-managed) / 99.95% (managed) | — | — |
| Native AWS Service Integration(count) | 3 (S3, Kinesis via 3rd party, basic) | — | — |
| Data Ingestion Rate(GB/sec) | 1-5 | 500,000-1,000,000 | |
| Average Query Latency (1TB dataset)(milliseconds) | 85ms | — | — |
| Cost per TB Scanned(USD) | $0.01 | — | — |
| Initial Setup Time(minutes) | 14 days | — | — |
| Max Concurrent Queries (single cluster)(queries) | 1,000+ | — | — |
| Enterprise SLA Availability(percent) | 99.5% (self-hosted dependent) | — | — |
| Peak Ingestion Speed(events per second) | 100,000-500,000 | 1,000,000+ | |
| ANSI SQL Compliance(percentage) | 95% | 70% | |
| Deployment Components(count) | 1-2 components | 5-7 components | |
| Time to First Query(minutes) | 5-10 minutes | 45-60 minutes | |
| Average Query Latency (Standard Aggregation)(milliseconds) | 250ms | — | — |
| Streaming Ingestion Latency(seconds) | 2-5 seconds | <1 second | |
| Memory Per 1TB Data(GB) | 35GB | — | — |
| Kafka Integration Latency(seconds) | 3-5 seconds (batched) | — | — |
| First Release Year | 2016 (Yandex) | — | — |
| Maximum Dataset Size(TB) | 1,000+ TB (distributed) | — | — |
| Query Latency (1B rows, COUNT aggregation)(milliseconds) | 20-100ms | — | — |
| Data Compression Ratio(x) | 40-100x compression | 3:1 to 10:1 (avg 6:1) | |
| Community Size (GitHub Stars)(stars) | 35,000+ stars | — | — |
| Query Latency (1B rows, 100 dimensions)(milliseconds) | 50-100ms | 50-100ms | |
| Memory Footprint per 1GB Data(MB) | 150-300MB | 150-300MB | |
| Maximum Events/Sec per Node(events/sec) | 10K-50K | 10K-50K | |
| Typical Cluster Setup Cost(USD/month (3-node)) | $1500-3000 | $1500-3000 | |
| Enterprise Deployments(thousands) | 1000+ (LinkedIn, Uber, etc) | 1000+ (LinkedIn, Uber, etc) | |
| Query Latency (p95 on Real-Time Data)(milliseconds) | 500-2000ms | 500-2000ms | |
| Minimum Cluster Size for 1TB Dataset(nodes) | 5-8 nodes | 5-8 nodes | |
| GitHub Stars (Community Activity)(count) | 5,300 | 5,300 | |
| Storage Compression Ratio(ratio) | 15:1 to 25:1 | 15:1 to 25:1 | |
| Query Latency (P95)(milliseconds) | 100-500ms | 100-500ms | |
| Per-Query Cost (1TB scan)(USD) | $0 (infrastructure only) | $0 (infrastructure only) | |
| Events/Second Ingestion(events/sec) | 500,000/sec (streaming) | 500,000/sec (streaming) | |
| Annual TCO (100TB dataset)(USD) | $150,000 | $150,000 | |
| Maximum Recommended Cluster Size(nodes) | 500+ (LinkedIn runs 1,000+) | 500+ (LinkedIn runs 1,000+) | |
| Time to First Production Query(days) | 14-30 days (requires schema design and tuning) | 14-30 days (requires schema design and tuning) | |
| Typical Memory Per Node(GB) | 16-32GB for analytics workload | 16-32GB for analytics workload | |
| Setup Time (minutes)(minutes) | 240-480 | 240-480 | |
| Query Latency on 1GB Dataset(milliseconds) | 100-500 | 100-500 | |
| Maximum Scalable Dataset Size(GB) | 100,000+ | 100,000+ | |
| Minimum Cluster Nodes Required(nodes) | 5-7 | 5-7 | |
| Concurrent Queries Supported(queries) | 1000+ | 1000+ | |
| Supported Programming Languages(languages) | Java, Python (limited) | Java, Python (limited) | |
| Annual Infrastructure Cost (1TB dataset)(USD) | 50,000-150,000 | 50,000-150,000 | |
| Concurrent User Support(users) | 100-500 typical | 100-500 typical | |
| Query Latency (Average)(milliseconds) | <100ms | <100ms | |
| Data Freshness(seconds) | Sub-second to 1 minute | Sub-second to 1 minute | |
| Ingestion Streaming Support(events per second) | 1M+ eps native | 1M+ eps native | |
| Query Latency (Median)(milliseconds) | <100 ms | <100 ms | |
| On-Demand Query Pricing(USD per TB scanned) | Free (self-hosted) / $0.10-0.50 (managed) | Free (self-hosted) / $0.10-0.50 (managed) | |
| Maximum Daily Event Throughput(billion events/day) | 10+ billion events/day (proven at LinkedIn, Airbnb) | 10+ billion events/day (proven at LinkedIn, Airbnb) | |
| Time to Deploy(seconds (average)) | 40-80 hours (cluster provisioning, tuning) | 40-80 hours (cluster provisioning, tuning) | |
| Concurrent Users Supported(users) | 100-500 (depends on cluster config) | 100-500 (depends on cluster config) | |
| Ingestion Rate (events/second)(events/sec) | 1,000,000+ | 1,000,000+ | |
| Query Latency (1B rows)(seconds) | 2-5 | 2-5 | |
| Maximum Recommended Dataset Size(rows) | 10,000+ | 10,000+ | |
| Deployment Time(seconds) | 6 | 6 | |
| Minimum Cluster Size(nodes) | 3-5 | 3-5 | |
| Memory Per Node(GB per 1M events/sec) | 50-200 | 50-200 | |
| Typical Query Latency (1B rows, GROUP BY)(milliseconds) | 50-500ms | 50-500ms | |
| Index Size to Data Ratio(multiplier) | 0.1-0.3x | 0.1-0.3x | |
| GitHub Stars (Community Size Proxy)(stars) | 9,200+ | 9,200+ | |
| Typical Deployment Complexity(relative score) | Medium-High (columnar tuning) | Medium-High (columnar tuning) | |
| Maximum Practical Dataset Size(petabytes) | 10+ PB (proven at scale) | 10+ PB (proven at scale) | |
| Query Latency (Typical Analytical Query)(seconds) | <1 second | <1 second | |
| Data Ingestion Throughput(rows/second) | 1,000,000+ rows/sec | 1,000,000+ rows/sec | |
| Per-Query Cost (1 TB scanned)(USD) | $0 (open-source) | $0 (open-source) | |
| Built-in ML Models(count) | 0 (requires external tools) | 0 (requires external tools) | |
| P99 Query Latency(milliseconds) | 500-2000ms | 500-2000ms | |
| Streaming Ingestion Rate(events/second) | 500K | 500K | |
| Aggregation Query Throughput(queries/second) | 100K-500K | 100K-500K | |
| Operational Components(count) | 8+ (Broker, Server, Controller, Minion) | 8+ (Broker, Server, Controller, Minion) | |
| High-Cardinality Dimension Support(unique values) | 100M+ with dictionary encoding | 100M+ with dictionary encoding | |
| Segment Size (typical)(GB) | 1-5GB segments | 1-5GB segments | |
| Learning Curve (Complexity)(months to production) | 1-2 months | 1-2 months |
Sourced from publicly available data ·
Key Differences
7 attributes compared head-to-head
ClickHouse leads
ClickHouse
Attribute
Apache Pinot
- 50-200ms(winner)Query Latency (P99 on 1B rows)100-500ms
- 10:1 to 50:1(winner)Data Compression Ratio3:1 to 10:1
- Kafka, S3, limitedStreaming Ingestion Native SupportKafka, Pulsar, optimized(winner)
- Custom ANSI-SQL variantSQL Dialect CompatibilityStandard SQL(winner)
- 2-10TB(winner)Storage Per Node (Typical)500GB-2TB
- 35,000+(winner)Community Adoption (GitHub Stars 2026)15,000+
- ClickHouse Inc, limitedEnterprise Support AvailabilityStarTree (vendor-backed)(winner)
- Query Latency (P99 on 1B rows)
ClickHouse
50-200ms(winner)
Apache Pinot
100-500ms
- Data Compression Ratio
ClickHouse
10:1 to 50:1(winner)
Apache Pinot
3:1 to 10:1
- Streaming Ingestion Native Support
ClickHouse
Kafka, S3, limited
Apache Pinot
Kafka, Pulsar, optimized(winner)
- SQL Dialect Compatibility
ClickHouse
Custom ANSI-SQL variant
Apache Pinot
Standard SQL(winner)
- Storage Per Node (Typical)
ClickHouse
2-10TB(winner)
Apache Pinot
500GB-2TB
- Community Adoption (GitHub Stars 2026)
ClickHouse
35,000+(winner)
Apache Pinot
15,000+
- Enterprise Support Availability
ClickHouse
ClickHouse Inc, limited
Apache Pinot
StarTree (vendor-backed)(winner)
Full Comparison
| Attribute | ClickHouse | Apache Pinot |
|---|---|---|
| P99 Query Latency (1B rows, aggregation)(milliseconds) | 50-200ms(winner) | 100-500ms |
| Query Latency (1 billion rows)(seconds) | 1.2 seconds | — |
| Ingestion Throughput(events/sec) | 1,000,000 events/sec | — |
| Query Latency (1GB aggregation)(milliseconds) | 500-2000ms | — |
| Ingest Throughput(million rows/second) | 1-5 million rows/sec | — |
Show 31 more attributesQuery Latency (p99)(milliseconds) 200-500ms 50-200ms Ingestion Latency(seconds) 10-60 seconds — Query Latency (100M rows)(milliseconds) 50-500ms — Query Latency (100M rows, simple aggregation)(milliseconds) 500-1500ms — Max Ingestion Throughput(events/second) 100,000-500,000 events/sec 1,000,000-2,000,000 events/sec Query Latency (1B row scan, 10 column aggregate)(milliseconds) 50-100ms — Data Ingestion Latency(seconds) Microseconds to milliseconds 0.1 (streaming) Query Latency (1 billion rows, simple SELECT)(milliseconds) 150ms — Automatic Scaling Time(seconds) 60-300 (manual cluster resize required) — Average Query Latency (1 Billion Row Scan)(ms) 75ms — Data Ingestion Rate(GB/sec) 1-5 500,000-1,000,000 Average Query Latency (1TB dataset)(milliseconds) 85ms — Average Query Latency (Standard Aggregation)(milliseconds) 250ms — Query Latency (1B rows, COUNT aggregation)(milliseconds) 20-100ms — Query Latency (1B rows, 100 dimensions)(milliseconds) 50-100ms — Query Latency (p95 on Real-Time Data)(milliseconds) 500-2000ms — Query Latency (P95)(milliseconds) 100-500ms — Query Latency on 1GB Dataset(milliseconds) 100-500 — Concurrent Queries Supported(queries) 1000+ — Query Latency (Average)(milliseconds) <100ms — Query Latency (Median)(milliseconds) <100 ms — Time to Deploy(seconds (average)) 40-80 hours (cluster provisioning, tuning) — Ingestion Rate (events/second)(events/sec) 1,000,000+ — Query Latency (1B rows)(seconds) 2-5 — Maximum Recommended Dataset Size(rows) 10,000+ — Deployment Time(seconds) 6 — Typical Query Latency (1B rows, GROUP BY)(milliseconds) 50-500ms — Query Latency (Typical Analytical Query)(seconds) <1 second — Data Ingestion Throughput(rows/second) 1,000,000+ rows/sec — P99 Query Latency(milliseconds) 500-2000ms — Aggregation Query Throughput(queries/second) 100K-500K — | ||
| Ingestion Latency (Kafka to query-ready)(seconds) | 5-30 seconds | 0.5-2 seconds(winner) |
| Streaming Ingestion Latency(seconds) | 2-5 seconds | <1 second(winner) |
| SQL Compliance Level(null) | Proprietary ANSI-SQL variant | Standard ANSI SQL |
| Maximum Recommended Node Storage(TB) | 2-10TB per node(winner) | 0.5-2TB per node |
| Maximum Cluster Size(nodes) | 100+ | 1000+(winner) |
| Maximum Cluster Nodes(nodes) | 1000+ nodes tested | — |
| Typical Maximum Dataset Size(GB) | ~1,000,000+ GB (1+ PB) | — |
| Max Recommended Dataset Size(terabytes) | 100TB+ efficiently | — |
Show 10 more attributesMax Dataset Size (Practical)(TB) 1000TB+ (unlimited with tiering) 100-500TB (hot data) Maximum Concurrent Queries(queries/sec) 100,000+ — Max Concurrent Queries (single cluster)(queries) 1,000+ — Maximum Dataset Size(TB) 1,000+ TB (distributed) — Maximum Recommended Cluster Size(nodes) 500+ (LinkedIn runs 1,000+) — Maximum Scalable Dataset Size(GB) 100,000+ — Maximum Daily Event Throughput(billion events/day) 10+ billion events/day (proven at LinkedIn, Airbnb) — Concurrent Users Supported(users) 100-500 (depends on cluster config) — Maximum Practical Dataset Size(petabytes) 10+ PB (proven at scale) — Maximum Table Size(TB) Petabyte-scale (distributed) — | ||
| Native Streaming Sources(null) | Kafka (basic), S3, File | Kafka, Pulsar, Kinesis, S3 |
| Native AWS Service Integration(count) | 3 (S3, Kinesis via 3rd party, basic) | — |
| Enterprise Support Availability(null) | ClickHouse Inc (limited SLAs) | StarTree (24/7 SLA options) |
| Monthly Cost (100 GB compressed)(USD) | $150 | — |
| Storage Cost per TB/month(USD) | $40-60(winner) | $120-180 |
| Cost per GB Scanned(USD) | $0.015 | — |
| Minimum Monthly Cost (basic setup)(USD) | $500 (ClickHouse Cloud starter) | — |
| Cost per TB Scanned(USD) | $0.01 | — |
Show 2 more attributesPer-Query Cost (1TB scan)(USD) $0 (infrastructure only) — Per-Query Cost (1 TB scanned)(USD) $0 (open-source) — | ||
| Data Retention for Time-Travel(days) | Not native | — |
| Streaming Integration | Limited (Kafka via TableEngine) | — |
| Transaction Support(consistency level) | No ACID (eventual consistency) | — |
| SQL Feature Completeness(percentage) | 95% (PostgreSQL-compatible) | — |
| Time-Series Aggregation Support(native features) | Standard SQL; requires manual time bucketing | — |
Show 10 more attributesCloud Provider Support(providers) 4+ (AWS, Azure, GCP, on-premise) — SQL Compatibility(percentage) MySQL-compatible with ClickHouse extensions PQL + Limited SQL (60% compatibility) Built-in ML Capabilities No (third-party integration required) — Typical Use Case Flexibility Ad-hoc queries (generalized) — JOIN Operation Support Full support — Full-Text Search Capability Limited (phrase queries, basic tokenization) — Real-time Streaming Ingestion Native (Kafka, S3, MQ) — Real-time Upsert Support(boolean) Yes — Built-in ML Models(count) 0 (requires external tools) — High-Cardinality Dimension Support(unique values) 100M+ with dictionary encoding — | ||
| Compression Ratio(ratio) | 8:1-12:1 | — |
| Data Compression Ratio(x) | 40-100x compression(winner) | 3:1 to 10:1 (avg 6:1) |
| Storage Compression Ratio(ratio) | 15:1 to 25:1 | — |
| Segment Size (typical)(GB) | 1-5GB segments | — |
| Licensing Model | Open-source (free) + optional support | — |
| Typical Cost per TB/year(USD) | $800-1500(winner) | $2000-3500 |
| Typical Cluster Setup Cost(USD/month (3-node)) | $1500-3000 | — |
| Learning Curve (1-10 scale)(difficulty) | 7/10 (moderate-hard) | — |
| Setup Time to First Query(minutes) | 30-120 minutes | — |
| Compression Ratio (typical)(ratio) | 10:1 to 40:1 | — |
| Memory Usage per Query(MB) | 50-200MB(winner) | 100-400MB |
| Memory Required (minimal)(MB) | 500-2000MB | — |
| SQL Standard Compliance(% compatibility) | 70% standard SQL | 85% SQL coverage(winner) |
| Supported Data Formats(formats) | 12+ formats (TSV, Native, Avro, Protobuf, etc.) | — |
| Ingestion Latency (end-to-end)(milliseconds) | 1000-10000ms | 100-500ms(winner) |
| Native SQL Support | Standard SQL with extensions | PQL (Custom) + Presto Bridge |
| Multi-tenancy Isolation | Limited/requires custom logic | Native tenant isolation |
| Deployment Flexibility(options) | Kubernetes, on-premises, all cloud providers | — |
| Multi-node Support(boolean) | Yes (native distributed) | — |
| GitHub Stars (2026)(stars) | 35,000+(winner) | 15,000+ |
| Community Size (GitHub Stars)(stars) | 35,000+ stars | — |
| GitHub Stars (Community Activity)(count) | 5,300 | — |
| Idle Memory Usage(MB) | 500-2000 MB | — |
| Memory Footprint per 1GB Data(MB) | 150-300MB | — |
| Typical Memory Per Node(GB) | 16-32GB for analytics workload | — |
| Memory Per Node(GB per 1M events/sec) | 50-200 | — |
| Typical Storage Cost(USD per TB per month) | $20-40 | — |
| Storage Cost (per TB/month)(USD) | $15-25 | — |
| Monthly Cost per TB Stored(USD) | $0.09 | — |
| Annual Infrastructure Cost (1TB dataset)(USD) | 50,000-150,000 | — |
| Base Monthly Cost (Small Cluster)(USD) | Self-hosted infrastructure costs (highly variable) | — |
Show 1 more attributeOn-Demand Query Pricing(USD per TB scanned) Free (self-hosted) / $0.10-0.50 (managed) — | ||
| Typical Node Memory(GB) | 8-32GB(winner) | 16-64GB |
| Minimum Cluster Size (nodes)(nodes) | 1 (can run standalone) | — |
| Minimum Cluster Size for 1TB Dataset(nodes) | 5-8 nodes | — |
| Minimum Cluster Nodes Required(nodes) | 5-7 | — |
| Minimum Cluster Size(nodes) | 3-5 | — |
Show 1 more attributeDeployment Options On-prem, Kubernetes, multi-cloud — | ||
| Minimum Recommended Cluster Size(nodes) | 3-5 nodes(winner) | 5-7 nodes (3 controllers + 2-4 brokers) |
| Setup Time to Production(minutes) | 12 weeks | — |
| Typical Data Compression Ratio(x) | 10-40x | — |
| Index Size to Data Ratio(multiplier) | 0.1-0.3x | — |
| Max Concurrent Queries (default config)(queries) | Unlimited (resource-based) | — |
| Maximum Events/Sec per Node(events/sec) | 10K-50K | — |
| Events/Second Ingestion(events/sec) | 500,000/sec (streaming) | — |
| AWS Service Integration (native)(count) | 5-10 (via third-party) | — |
| GitHub Stars (Community Size Proxy)(stars) | 9,200+ | — |
| GitHub Stars (as of 2026)(stars) | 25000+(winner) | 8,200+ |
| Maximum Ingestion Rate(events/second) | 1,000,000 | — |
| Peak Ingestion Speed(events per second) | 100,000-500,000 | 1,000,000+(winner) |
| Real-Time Ingestion Support | Native (Kafka, S3, HDFS) | — |
| Infrastructure Management Overhead(hours per month) | 40-80 hours | — |
| Setup Time(minutes) | 30-60 minutes | — |
| Operational Complexity(1-10 scale) | 8 (high) | — |
| Infrastructure Management | Self-hosted (high DevOps) | — |
| Typical Deployment Complexity(relative score) | Medium-High (columnar tuning) | — |
Show 2 more attributesOperational Components(count) 8+ (Broker, Server, Controller, Minion) — Learning Curve (Complexity)(months to production) 1-2 months — | ||
| Time to Production Deployment(hours) | 1440 (self-managed) / 60 (managed) | — |
| Uptime SLA Guarantee(%) | 99.0% (self-managed) / 99.95% (managed) | — |
| Enterprise SLA Availability(percent) | 99.5% (self-hosted dependent) | — |
| Initial Setup Time(minutes) | 14 days | — |
| Support for Time-Series Data | Native optimization, ideal for billions of events | — |
| ANSI SQL Compliance(percentage) | 95%(winner) | 70% |
| Deployment Components(count) | 1-2 components(winner) | 5-7 components |
| Cluster Node Types Required | Replica, Shard (simplified) | — |
| Time to First Query(minutes) | 5-10 minutes(winner) | 45-60 minutes |
| Memory Per 1TB Data(GB) | 35GB | — |
| Kafka Integration Latency(seconds) | 3-5 seconds (batched) | — |
| First Release Year | 2016 (Yandex) | — |
| License Restrictions(commercial use) | AGPL - Source disclosure required | — |
| License Type | Apache 2.0 Open Source | — |
| Multi-table JOIN Support(capability level) | Full support (INNER, LEFT, RIGHT, FULL) | — |
| Enterprise Deployments(thousands) | 1000+ (LinkedIn, Uber, etc) | — |
| Annual TCO (100TB dataset)(USD) | $150,000 | — |
| Time to First Production Query(days) | 14-30 days (requires schema design and tuning) | — |
| Setup Time (minutes)(minutes) | 240-480 | — |
| SQL Support | Native SQL with PQL extensions (ANSI-compliant subset) | — |
| Supported Programming Languages(languages) | Java, Python (limited) | — |
| Concurrent User Support(users) | 100-500 typical | — |
| Data Freshness(seconds) | Sub-second to 1 minute | — |
| Ingestion Streaming Support(events per second) | 1M+ eps native | — |
| Full-Text Search Optimization | Not optimized (secondary feature) | — |
| SQL Query Support | Native PinotSQL (full ANSI compliance) | — |
| Streaming Ingestion Rate(events/second) | 500K | — |
ClickHouseApache PinotP99 Query Latency (1B rows, aggregation)(milliseconds)
50-200ms
100-500ms
Query Latency (1 billion rows)(seconds)
1.2 seconds
—
Ingestion Throughput(events/sec)
1,000,000 events/sec
—
Query Latency (1GB aggregation)(milliseconds)
500-2000ms
—
Ingest Throughput(million rows/second)
1-5 million rows/sec
—
Show 31 more attributes
Query Latency (p99)(milliseconds)
200-500ms
50-200ms
Ingestion Latency(seconds)
10-60 seconds
—
Query Latency (100M rows)(milliseconds)
50-500ms
—
Query Latency (100M rows, simple aggregation)(milliseconds)
500-1500ms
—
Max Ingestion Throughput(events/second)
100,000-500,000 events/sec
1,000,000-2,000,000 events/sec
Query Latency (1B row scan, 10 column aggregate)(milliseconds)
50-100ms
—
Data Ingestion Latency(seconds)
Microseconds to milliseconds
0.1 (streaming)
Query Latency (1 billion rows, simple SELECT)(milliseconds)
150ms
—
Automatic Scaling Time(seconds)
60-300 (manual cluster resize required)
—
Average Query Latency (1 Billion Row Scan)(ms)
75ms
—
Data Ingestion Rate(GB/sec)
1-5
500,000-1,000,000
Average Query Latency (1TB dataset)(milliseconds)
85ms
—
Average Query Latency (Standard Aggregation)(milliseconds)
250ms
—
Query Latency (1B rows, COUNT aggregation)(milliseconds)
20-100ms
—
Query Latency (1B rows, 100 dimensions)(milliseconds)
50-100ms
—
Query Latency (p95 on Real-Time Data)(milliseconds)
500-2000ms
—
Query Latency (P95)(milliseconds)
100-500ms
—
Query Latency on 1GB Dataset(milliseconds)
100-500
—
Concurrent Queries Supported(queries)
1000+
—
Query Latency (Average)(milliseconds)
<100ms
—
Query Latency (Median)(milliseconds)
<100 ms
—
Time to Deploy(seconds (average))
40-80 hours (cluster provisioning, tuning)
—
Ingestion Rate (events/second)(events/sec)
1,000,000+
—
Query Latency (1B rows)(seconds)
2-5
—
Maximum Recommended Dataset Size(rows)
10,000+
—
Deployment Time(seconds)
6
—
Typical Query Latency (1B rows, GROUP BY)(milliseconds)
50-500ms
—
Query Latency (Typical Analytical Query)(seconds)
<1 second
—
Data Ingestion Throughput(rows/second)
1,000,000+ rows/sec
—
P99 Query Latency(milliseconds)
500-2000ms
—
Aggregation Query Throughput(queries/second)
100K-500K
—
Ingestion Latency (Kafka to query-ready)(seconds)
5-30 seconds
0.5-2 seconds
Streaming Ingestion Latency(seconds)
2-5 seconds
<1 second
SQL Compliance Level(null)
Proprietary ANSI-SQL variant
Standard ANSI SQL
Maximum Recommended Node Storage(TB)
2-10TB per node
0.5-2TB per node
Maximum Cluster Size(nodes)
100+
1000+
Maximum Cluster Nodes(nodes)
1000+ nodes tested
—
Typical Maximum Dataset Size(GB)
~1,000,000+ GB (1+ PB)
—
Max Recommended Dataset Size(terabytes)
100TB+ efficiently
—
Show 10 more attributes
Max Dataset Size (Practical)(TB)
1000TB+ (unlimited with tiering)
100-500TB (hot data)
Maximum Concurrent Queries(queries/sec)
100,000+
—
Max Concurrent Queries (single cluster)(queries)
1,000+
—
Maximum Dataset Size(TB)
1,000+ TB (distributed)
—
Maximum Recommended Cluster Size(nodes)
500+ (LinkedIn runs 1,000+)
—
Maximum Scalable Dataset Size(GB)
100,000+
—
Maximum Daily Event Throughput(billion events/day)
10+ billion events/day (proven at LinkedIn, Airbnb)
—
Concurrent Users Supported(users)
100-500 (depends on cluster config)
—
Maximum Practical Dataset Size(petabytes)
10+ PB (proven at scale)
—
Maximum Table Size(TB)
Petabyte-scale (distributed)
—
Native Streaming Sources(null)
Kafka (basic), S3, File
Kafka, Pulsar, Kinesis, S3
Native AWS Service Integration(count)
3 (S3, Kinesis via 3rd party, basic)
—
Enterprise Support Availability(null)
ClickHouse Inc (limited SLAs)
StarTree (24/7 SLA options)
Monthly Cost (100 GB compressed)(USD)
$150
—
Storage Cost per TB/month(USD)
$40-60
$120-180
Cost per GB Scanned(USD)
$0.015
—
Minimum Monthly Cost (basic setup)(USD)
$500 (ClickHouse Cloud starter)
—
Cost per TB Scanned(USD)
$0.01
—
Show 2 more attributes
Per-Query Cost (1TB scan)(USD)
$0 (infrastructure only)
—
Per-Query Cost (1 TB scanned)(USD)
$0 (open-source)
—
Data Retention for Time-Travel(days)
Not native
—
Streaming Integration
Limited (Kafka via TableEngine)
—
Transaction Support(consistency level)
No ACID (eventual consistency)
—
SQL Feature Completeness(percentage)
95% (PostgreSQL-compatible)
—
Time-Series Aggregation Support(native features)
Standard SQL; requires manual time bucketing
—
Show 10 more attributes
Cloud Provider Support(providers)
4+ (AWS, Azure, GCP, on-premise)
—
SQL Compatibility(percentage)
MySQL-compatible with ClickHouse extensions
PQL + Limited SQL (60% compatibility)
Built-in ML Capabilities
No (third-party integration required)
—
Typical Use Case Flexibility
Ad-hoc queries (generalized)
—
JOIN Operation Support
Full support
—
Full-Text Search Capability
Limited (phrase queries, basic tokenization)
—
Real-time Streaming Ingestion
Native (Kafka, S3, MQ)
—
Real-time Upsert Support(boolean)
Yes
—
Built-in ML Models(count)
0 (requires external tools)
—
High-Cardinality Dimension Support(unique values)
100M+ with dictionary encoding
—
Compression Ratio(ratio)
8:1-12:1
—
Data Compression Ratio(x)
40-100x compression
3:1 to 10:1 (avg 6:1)
Storage Compression Ratio(ratio)
15:1 to 25:1
—
Segment Size (typical)(GB)
1-5GB segments
—
Licensing Model
Open-source (free) + optional support
—
Typical Cost per TB/year(USD)
$800-1500
$2000-3500
Typical Cluster Setup Cost(USD/month (3-node))
$1500-3000
—
Learning Curve (1-10 scale)(difficulty)
7/10 (moderate-hard)
—
Setup Time to First Query(minutes)
30-120 minutes
—
Compression Ratio (typical)(ratio)
10:1 to 40:1
—
Memory Usage per Query(MB)
50-200MB
100-400MB
Memory Required (minimal)(MB)
500-2000MB
—
SQL Standard Compliance(% compatibility)
70% standard SQL
85% SQL coverage
Supported Data Formats(formats)
12+ formats (TSV, Native, Avro, Protobuf, etc.)
—
Ingestion Latency (end-to-end)(milliseconds)
1000-10000ms
100-500ms
Native SQL Support
Standard SQL with extensions
PQL (Custom) + Presto Bridge
Multi-tenancy Isolation
Limited/requires custom logic
Native tenant isolation
Deployment Flexibility(options)
Kubernetes, on-premises, all cloud providers
—
Multi-node Support(boolean)
Yes (native distributed)
—
GitHub Stars (2026)(stars)
35,000+
15,000+
Community Size (GitHub Stars)(stars)
35,000+ stars
—
GitHub Stars (Community Activity)(count)
5,300
—
Idle Memory Usage(MB)
500-2000 MB
—
Memory Footprint per 1GB Data(MB)
150-300MB
—
Typical Memory Per Node(GB)
16-32GB for analytics workload
—
Memory Per Node(GB per 1M events/sec)
50-200
—
Typical Storage Cost(USD per TB per month)
$20-40
—
Storage Cost (per TB/month)(USD)
$15-25
—
Monthly Cost per TB Stored(USD)
$0.09
—
Annual Infrastructure Cost (1TB dataset)(USD)
50,000-150,000
—
Base Monthly Cost (Small Cluster)(USD)
Self-hosted infrastructure costs (highly variable)
—
Show 1 more attribute
On-Demand Query Pricing(USD per TB scanned)
Free (self-hosted) / $0.10-0.50 (managed)
—
Typical Node Memory(GB)
8-32GB
16-64GB
Minimum Cluster Size (nodes)(nodes)
1 (can run standalone)
—
Minimum Cluster Size for 1TB Dataset(nodes)
5-8 nodes
—
Minimum Cluster Nodes Required(nodes)
5-7
—
Minimum Cluster Size(nodes)
3-5
—
Show 1 more attribute
Deployment Options
On-prem, Kubernetes, multi-cloud
—
Minimum Recommended Cluster Size(nodes)
3-5 nodes
5-7 nodes (3 controllers + 2-4 brokers)
Setup Time to Production(minutes)
12 weeks
—
Typical Data Compression Ratio(x)
10-40x
—
Index Size to Data Ratio(multiplier)
0.1-0.3x
—
Max Concurrent Queries (default config)(queries)
Unlimited (resource-based)
—
Maximum Events/Sec per Node(events/sec)
10K-50K
—
Events/Second Ingestion(events/sec)
500,000/sec (streaming)
—
AWS Service Integration (native)(count)
5-10 (via third-party)
—
GitHub Stars (Community Size Proxy)(stars)
9,200+
—
GitHub Stars (as of 2026)(stars)
25000+
8,200+
Maximum Ingestion Rate(events/second)
1,000,000
—
Peak Ingestion Speed(events per second)
100,000-500,000
1,000,000+
Real-Time Ingestion Support
Native (Kafka, S3, HDFS)
—
Infrastructure Management Overhead(hours per month)
40-80 hours
—
Setup Time(minutes)
30-60 minutes
—
Operational Complexity(1-10 scale)
8 (high)
—
Infrastructure Management
Self-hosted (high DevOps)
—
Typical Deployment Complexity(relative score)
Medium-High (columnar tuning)
—
Show 2 more attributes
Operational Components(count)
8+ (Broker, Server, Controller, Minion)
—
Learning Curve (Complexity)(months to production)
1-2 months
—
Time to Production Deployment(hours)
1440 (self-managed) / 60 (managed)
—
Uptime SLA Guarantee(%)
99.0% (self-managed) / 99.95% (managed)
—
Enterprise SLA Availability(percent)
99.5% (self-hosted dependent)
—
Initial Setup Time(minutes)
14 days
—
Support for Time-Series Data
Native optimization, ideal for billions of events
—
ANSI SQL Compliance(percentage)
95%
70%
Deployment Components(count)
1-2 components
5-7 components
Cluster Node Types Required
Replica, Shard (simplified)
—
Time to First Query(minutes)
5-10 minutes
45-60 minutes
Memory Per 1TB Data(GB)
35GB
—
Kafka Integration Latency(seconds)
3-5 seconds (batched)
—
First Release Year
2016 (Yandex)
—
License Restrictions(commercial use)
AGPL - Source disclosure required
—
License Type
Apache 2.0 Open Source
—
Multi-table JOIN Support(capability level)
Full support (INNER, LEFT, RIGHT, FULL)
—
Enterprise Deployments(thousands)
1000+ (LinkedIn, Uber, etc)
—
Annual TCO (100TB dataset)(USD)
$150,000
—
Time to First Production Query(days)
14-30 days (requires schema design and tuning)
—
Setup Time (minutes)(minutes)
240-480
—
SQL Support
Native SQL with PQL extensions (ANSI-compliant subset)
—
Supported Programming Languages(languages)
Java, Python (limited)
—
Concurrent User Support(users)
100-500 typical
—
Data Freshness(seconds)
Sub-second to 1 minute
—
Ingestion Streaming Support(events per second)
1M+ eps native
—
Full-Text Search Optimization
Not optimized (secondary feature)
—
SQL Query Support
Native PinotSQL (full ANSI compliance)
—
Streaming Ingestion Rate(events/second)
500K
—
Pros & Cons
10 pros·6 cons across both
ClickHouse
+5-3
Pros
- Exceptional compression ratios (10:1 to 50:1) reducing storage costs by 80%+
- Sub-second query execution on billion-row datasets with vectorized processing
- Handles petabyte-scale analytics with single-node clusters storing 10TB+
- Excellent support for time-series data with native DateTime and decimal types
- Active development with 3-4 releases monthly, backed by company resources
Cons
- Non-standard SQL dialect with proprietary functions requires developer ramp-up
- Limited streaming ingestion compared to competitors, Kafka integration is basic
- Smaller ecosystem of third-party integrations (200+ vs competitors' 500+)
Apache Pinot
+5-3
Pros
- Native streaming ingestion from Kafka, Pulsar, and event systems with sub-second latency
- Standard ANSI SQL support enabling immediate team productivity without learning curve
- StarTree vendor backing provides enterprise SLAs, professional support, and roadmap transparency
- Optimized for real-time dashboards with predictable sub-100ms P99 latency guarantees
- Advanced aggregation support (GROUP BY, DISTINCT COUNT) on streaming data without late-binding
Cons
- Lower compression efficiency (3:1 to 10:1) results in 3-5x higher storage costs vs ClickHouse
- Single-node capacity limited to 2TB, horizontal scaling adds operational complexity
- Smaller community (15K GitHub stars) with less third-party integration support
Frequently Asked Questions
5 questions
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.
Resources & Learn More
Curated sources to dive deeper
Where to Buy
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Wikipedia
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