software
Pinot vs ClickHouse 2026: Real-Time vs Analytics
Pinot is optimized for real-time analytics on streaming data with lower latency (sub-second queries), while ClickHouse excels at batch processing and complex analytical queries with superior compression (10-40x) and broader SQL compatibility. Pinot uses a distributed architecture with brokers and servers, whereas ClickHouse employs a simpler peer-to-peer model.
Apache Pinot
Real-time distributed OLAP database optimized for low-latency analytics on streaming data.
Companies needing real-time analytics on high-volume streaming data (ad-tech platforms, user behavior analytics, fraud detection) with dedicated DevOps teams
Score63%
VS
ClickHouse
Open-source columnar OLAP database with exceptional compression and SQL compatibility for analytical queries.
Analytics teams, data warehouses, and enterprises needing cost-effective storage with complex SQL queries and manageable deployment complexity
Score71%
Quick Answer
AI SummaryPinot is optimized for real-time analytics on streaming data with lower latency (sub-second queries), while ClickHouse excels at batch processing and complex analytical queries with superior compression (10-40x) and broader SQL compatibility. Pinot uses a distributed architecture with brokers and servers, whereas ClickHouse employs a simpler peer-to-peer model.
Our Verdict
AI-assistedChoose Pinot if you need sub-100ms query latency on streaming data with 1M+ events per second ingestion (common in ad-tech, user analytics, real-time dashboards). Choose ClickHouse if you prioritize ease of deployment, superior compression, complex analytical queries, and can tolerate 200-500ms latency (ideal for business intelligence, log analytics, metrics aggregation).
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Apache Pinot
6.8/10
ClickHouse
8.2/10
Choose Apache Pinot if
Companies needing real-time analytics on high-volume streaming data (ad-tech platforms, user behavior analytics, fraud detection) with dedicated DevOps teams
Choose ClickHouse if
Best pickAnalytics teams, data warehouses, and enterprises needing cost-effective storage with complex SQL queries and manageable deployment complexity
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Key Differences at a Glance
- Query Latency (P99):✓ Apache Pinot wins(50-200ms vs 200-500ms)
- Data Compression Ratio:✓ ClickHouse wins(10-40x vs 3-8x)
- Real-Time Ingestion Speed:✓ Apache Pinot wins(1M+ events/sec vs 100k-500k events/sec)
Key Facts & Figures
108 numeric metrics compared
| Metric | Apache Pinot | ClickHouse | Ratio |
|---|---|---|---|
| Query Latency (1B rows, 100 dimensions)(milliseconds) | 50-100ms | — | — |
| Memory Footprint per 1GB Data(MB) | 150-300MB | — | — |
| Maximum Events/Sec per Node(events/sec) | 10K-50K | — | — |
| Typical Cluster Setup Cost(USD/month (3-node)) | $1500-3000 | — | — |
| Enterprise Deployments(thousands) | 1000+ (LinkedIn, Uber, etc) | — | — |
| Data Compression Ratio(x compression) | 3-8x | 10-40x | |
| Ingestion Latency (end-to-end)(milliseconds) | 100-500ms | 1000-10000ms | |
| Memory Usage per Query(MB) | 100-400MB | 50-200MB | |
| Typical Cost per TB/year(USD) | $2000-3500 | $800-1500 | |
| Query Latency (p95 on Real-Time Data)(milliseconds) | 500-2000ms | — | — |
| Data Ingestion Rate(GB/sec) | 500,000-1,000,000 | 1-5 | |
| Minimum Cluster Size for 1TB Dataset(nodes) | 5-8 nodes | — | — |
| GitHub Stars (Community Activity)(count) | 5,300 | — | — |
| Storage Compression Ratio(x reduction) | ~4-6x columnar | — | — |
| Max Ingestion Throughput(events/second) | 1,000,000-2,000,000 events/sec | 100,000-500,000 events/sec | |
| Query Latency (p99)(milliseconds) | 50-200ms | 200-500ms | |
| Storage Cost per TB/Month(USD) | $120-180 | $40-60 | |
| Typical Node Memory(GB) | 16-64GB | 8-32GB | |
| Minimum Recommended Cluster Size(nodes) | 5-7 nodes (3 controllers + 2-4 brokers) | 3-5 nodes | |
| Max Dataset Size (Practical)(TB) | 100-500TB (hot data) | 1000TB+ (unlimited with tiering) | |
| Query Latency (P95)(milliseconds) | 100-500ms | — | — |
| Per-Query Cost (1TB scan)(USD) | $0 (infrastructure only) | — | — |
| Data Ingestion Latency(seconds) | 0.1 (streaming) | Microseconds to milliseconds | |
| Maximum Cluster Size(nodes) | 1000+ | 100+ | |
| SQL Standard Compliance(percent) | ~60% (Pinot Query Language variant) | 70% ANSI SQL | |
| Events/Second Ingestion(events/sec) | 500,000/sec (streaming) | — | — |
| Annual TCO (100TB dataset)(USD) | $150,000 | — | — |
| P99 Query Latency(milliseconds) | 50-200ms | — | — |
| Maximum Recommended Cluster Size(nodes) | 500+ (LinkedIn runs 1,000+) | — | — |
| Time to First Production Query(days) | 14-30 days (requires schema design and tuning) | — | — |
| Typical Memory Per Node(GB) | 16-32GB for analytics workload | — | — |
| GitHub Stars (as of 2026)(stars) | 8,200+ | 25000+ | |
| Setup Time (Minutes)(minutes) | 240-480 | — | — |
| Query Latency on 1GB Dataset(milliseconds) | 100-500 | — | — |
| Maximum Scalable Dataset Size(GB) | 100,000+ | — | — |
| Minimum Cluster Nodes Required(nodes) | 5-7 | — | — |
| Concurrent Queries Supported(queries) | 1000+ | — | — |
| Supported Programming Languages(languages) | Java, Python (limited) | — | — |
| Annual Infrastructure Cost (1TB dataset)(USD) | 50,000-150,000 | — | — |
| Concurrent User Support(users) | 100-500 typical | — | — |
| Query Latency (Average)(milliseconds) | <100ms | — | — |
| Data Freshness(seconds) | Sub-second to 1 minute | — | — |
| Ingestion Streaming Support(events per second) | 1M+ eps native | — | — |
| Query Latency (Median)(milliseconds) | <100 ms | — | — |
| Streaming Ingestion Latency(seconds) | <1 second | — | — |
| On-Demand Query Pricing(USD per TB scanned) | Free (self-hosted) / $0.10-0.50 (managed) | — | — |
| Maximum Daily Event Throughput(billion events/day) | 10+ billion events/day (proven at LinkedIn, Airbnb) | — | — |
| Time to Deploy(hours) | 40-80 hours (cluster provisioning, tuning) | — | — |
| Concurrent Users Supported(users) | 100-500 (depends on cluster config) | — | — |
| 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(months) | 6 | — | — |
| Minimum Cluster Size(nodes) | 3-5 | — | — |
| Memory Per Node(GB per 1M events/sec) | 50-200 | — | — |
| Typical Query Latency (1B rows, GROUP BY)(milliseconds) | 50-500ms | — | — |
| Index Size to Data Ratio(multiplier) | 0.1-0.3x | — | — |
| GitHub Stars (Community Size Proxy)(stars) | 9,200+ | — | — |
| Typical Deployment Complexity(relative score) | Medium-High (columnar tuning) | — | — |
| Maximum Practical Dataset Size(petabytes) | 10+ PB (proven at scale) | — | — |
| Peak Ingestion Speed(events per second) | 1,000,000+ | 100,000-500,000 | |
| ANSI SQL Compliance(percentage) | 70% | 95% | |
| Deployment Components(count) | 5-7 components | 1-2 components | |
| Time to First Query(minutes) | 45-60 minutes | 5-10 minutes | |
| Query Latency (1 billion rows)(seconds) | 1.2 seconds | 1.2 seconds | |
| Monthly Cost (100 GB compressed)(USD) | $150 | $150 | |
| Ingestion Throughput(events/sec) | 1,000,000 events/sec | 1,000,000 events/sec | |
| Compression Ratio(ratio) | 8:1-12:1 | 8:1-12:1 | |
| Learning Curve (1-10 scale)(difficulty level) | 7/10 (moderate-hard) | 7/10 (moderate-hard) | |
| Query Latency (1GB aggregation)(milliseconds) | 500-2000ms | 500-2000ms | |
| Compression Ratio (typical)(ratio) | 10:1 to 40:1 | 10:1 to 40:1 | |
| Memory Required (minimal)(MB) | 500-2000MB | 500-2000MB | |
| Ingest Throughput(million rows/second) | 1-5 million rows/sec | 1-5 million rows/sec | |
| Setup Time to First Query(minutes) | 30-120 minutes | 30-120 minutes | |
| Ingestion Latency(seconds) | 10-60 seconds | 10-60 seconds | |
| Query Latency (100M rows)(milliseconds) | 50-500ms | 50-500ms | |
| Maximum Cluster Nodes(nodes) | 1000+ nodes tested | 1000+ nodes tested | |
| GitHub Stars (2026)(stars) | 34,000+ | 34,000+ | |
| Typical Maximum Dataset Size(GB) | ~1,000,000+ GB (1+ PB) | ~1,000,000+ GB (1+ PB) | |
| Idle Memory Usage(MB) | 500-2000 MB | 500-2000 MB | |
| Supported Data Formats(formats) | 12+ formats (TSV, Native, Avro, Protobuf, etc.) | 12+ formats (TSV, Native, Avro, Protobuf, etc.) | |
| Query Latency (100M rows, simple aggregation)(milliseconds) | 500-1500ms | 500-1500ms | |
| Typical Storage Cost(USD per TB per month) | $20-40 | $20-40 | |
| Max Recommended Dataset Size(terabytes) | 100TB+ efficiently | 100TB+ efficiently | |
| SQL Feature Completeness(percentage) | 95% (PostgreSQL-compatible) | 95% (PostgreSQL-compatible) | |
| Query Latency (1B row scan, 10 column aggregate)(milliseconds) | 50-100ms | 50-100ms | |
| Storage Cost (per TB/month)(USD) | $15-25 | $15-25 | |
| Typical Data Compression Ratio(x) | 10-40x | 10-40x | |
| Minimum Cluster Size (nodes)(nodes) | 1 (can run standalone) | 1 (can run standalone) | |
| AWS Service Integration (native)(count) | 5-10 (via third-party) | 5-10 (via third-party) | |
| Query Latency (1 billion rows, simple SELECT)(milliseconds) | 150ms | 150ms | |
| Cost per GB Scanned(USD) | $0.015 | $0.015 | |
| Maximum Ingestion Rate(events/second) | 1,000,000 | 1,000,000 | |
| Infrastructure Management Overhead(hours per month) | 40-80 hours | 40-80 hours | |
| Minimum Monthly Cost (basic setup)(USD) | $500 (ClickHouse Cloud starter) | $500 (ClickHouse Cloud starter) | |
| Cloud Provider Support(providers) | 4+ (AWS, Azure, GCP, on-premise) | 4+ (AWS, Azure, GCP, on-premise) | |
| Automatic Scaling Time(seconds) | 60-300 (manual cluster resize required) | 60-300 (manual cluster resize required) | |
| Average Query Latency (1 Billion Row Scan)(ms) | 75ms | 75ms | |
| Monthly Cost per TB Stored(USD) | $0.09 | $0.09 | |
| Time to Production Deployment(hours) | 1440 (self-managed) / 60 (managed) | 1440 (self-managed) / 60 (managed) | |
| Maximum Concurrent Queries(queries/sec) | 100,000+ | 100,000+ | |
| Uptime SLA Guarantee(percent) | 99.0% (self-managed) / 99.95% (managed) | 99.0% (self-managed) / 99.95% (managed) | |
| Native AWS Service Integration(count) | 3 (S3, Kinesis via 3rd party, basic) | 3 (S3, Kinesis via 3rd party, basic) | |
| Average Query Latency (1TB dataset)(milliseconds) | 85ms | 85ms | |
| Cost per TB Scanned(USD) | $0.01 | $0.01 | |
| Initial Setup Time(hours) | 14 days | 14 days | |
| Max Concurrent Queries (single cluster)(queries) | 1,000+ | 1,000+ | |
| Enterprise SLA Availability(percent) | 99.5% (self-hosted dependent) | 99.5% (self-hosted dependent) |
Sourced from publicly available data ·
Key Differences
7 attributes compared head-to-head
ClickHouse leads
Apache Pinot
Attribute
ClickHouse
- 50-200ms(winner)Query Latency (P99)200-500ms
- 3-8xData Compression Ratio10-40x(winner)
- 1M+ events/sec(winner)Real-Time Ingestion Speed100k-500k events/sec
- 70% ANSI SQLSQL Standard Compliance95% ANSI SQL(winner)
- 125-330MBTypical Storage per GB Data25-100MB(winner)
- High (5+ components)Deployment ComplexityLow (single binary)(winner)
- Limited (point lookups only)OLTP Query SupportFull OLTP capability(winner)
- Query Latency (P99)
Apache Pinot
50-200ms(winner)
ClickHouse
200-500ms
- Data Compression Ratio
Apache Pinot
3-8x
ClickHouse
10-40x(winner)
- Real-Time Ingestion Speed
Apache Pinot
1M+ events/sec(winner)
ClickHouse
100k-500k events/sec
- SQL Standard Compliance
Apache Pinot
70% ANSI SQL
ClickHouse
95% ANSI SQL(winner)
- Typical Storage per GB Data
Apache Pinot
125-330MB
ClickHouse
25-100MB(winner)
- Deployment Complexity
Apache Pinot
High (5+ components)
ClickHouse
Low (single binary)(winner)
- OLTP Query Support
Apache Pinot
Limited (point lookups only)
ClickHouse
Full OLTP capability(winner)
Full Comparison
| Attribute | Apache Pinot | ClickHouse |
|---|---|---|
| Query Latency (1B rows, 100 dimensions)(milliseconds) | 50-100ms | — |
| Query Latency (p95 on Real-Time Data)(milliseconds) | 500-2000ms | — |
| Data Ingestion Rate(GB/sec) | 500,000-1,000,000(winner) | 1-5 |
| Storage Compression Ratio(x reduction) | ~4-6x columnar | — |
| Max Ingestion Throughput(events/second) | 1,000,000-2,000,000 events/sec(winner) | 100,000-500,000 events/sec |
Show 24 more attributesQuery Latency (p99)(milliseconds) 50-200ms 200-500ms Query Latency (P95)(milliseconds) 100-500ms — Data Ingestion Latency(seconds) 0.1 (streaming) Microseconds to milliseconds P99 Query Latency(milliseconds) 50-200ms — Query Latency on 1GB Dataset(milliseconds) 100-500 — Concurrent Queries Supported(queries) 1000+ — Query Latency (Average)(milliseconds) <100ms — Query Latency (Median)(milliseconds) <100 ms — Ingestion Rate (events/second)(events/sec) 1,000,000+ — Query Latency (1B rows)(seconds) 2-5 — Maximum Recommended Dataset Size(rows) 10,000+ — Typical Query Latency (1B rows, GROUP BY)(milliseconds) 50-500ms — 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 — Ingestion Latency(seconds) 10-60 seconds — Query Latency (100M rows)(milliseconds) 50-500ms — Query Latency (100M rows, simple aggregation)(milliseconds) 500-1500ms — Query Latency (1B row scan, 10 column aggregate)(milliseconds) 50-100ms — 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 — Average Query Latency (1TB dataset)(milliseconds) 85ms — | ||
| 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 | — |
| Idle Memory Usage(MB) | 500-2000 MB | — |
| Maximum Events/Sec per Node(events/sec) | 10K-50K | — |
| Events/Second Ingestion(events/sec) | 500,000/sec (streaming) | — |
| Max Concurrent Queries (default config)(queries) | Unlimited (resource-based) | — |
| Typical Cluster Setup Cost(USD/month (3-node)) | $1500-3000 | — |
| Typical Cost per TB/year(USD) | $2000-3500 | $800-1500(winner) |
| Licensing Model | Open-source (free) + optional support | — |
| Multi-table JOIN Support(capability level) | Full support (INNER, LEFT, RIGHT, FULL) | — |
| SQL Compatibility(percentage) | PQL + Limited SQL (60% compatibility) | MySQL-compatible with ClickHouse extensions |
| Typical Use Case Flexibility | Ad-hoc queries (generalized) | — |
| JOIN Operation Support | Full support | — |
| SQL Standard Compliance(percent) | ~60% (Pinot Query Language variant) | 70% ANSI SQL(winner) |
| Full-Text Search Capability | Limited (phrase queries, basic tokenization) | — |
Show 10 more attributesReal-time Streaming Ingestion Native (Kafka, S3, MQ) — Real-time Upsert Support(boolean) Yes — SQL Query Support Native PinotSQL (full ANSI compliance) — 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 — Cloud Provider Support(providers) 4+ (AWS, Azure, GCP, on-premise) — Built-in ML Capabilities No (third-party integration required) — | ||
| Enterprise Deployments(thousands) | 1000+ (LinkedIn, Uber, etc) | — |
| Data Compression Ratio(x compression) | 3-8x | 10-40x(winner) |
| Index Size to Data Ratio(multiplier) | 0.1-0.3x | — |
| Typical Data Compression Ratio(x) | 10-40x | — |
| Ingestion Latency (end-to-end)(milliseconds) | 100-500ms(winner) | 1000-10000ms |
| Memory Usage per Query(MB) | 100-400MB | 50-200MB(winner) |
| Compression Ratio (typical)(ratio) | 10:1 to 40:1 | — |
| Native SQL Support | PQL (Custom) + Presto Bridge | Standard SQL with extensions |
| Multi-tenancy Isolation | Native tenant isolation | Limited/requires custom logic |
| Multi-node Support(boolean) | Yes (native distributed) | — |
| Minimum Cluster Size for 1TB Dataset(nodes) | 5-8 nodes | — |
| Typical Node Memory(GB) | 16-64GB | 8-32GB(winner) |
| Minimum Cluster Nodes Required(nodes) | 5-7 | — |
| Deployment Flexibility | Kubernetes, on-premises, all cloud providers | — |
| Minimum Cluster Size(nodes) | 3-5 | — |
Show 1 more attributeMinimum Cluster Size (nodes)(nodes) 1 (can run standalone) — | ||
| GitHub Stars (Community Activity)(count) | 5,300 | — |
| Storage Cost per TB/Month(USD) | $120-180 | $40-60(winner) |
| Annual Infrastructure Cost (1TB dataset)(USD) | 50,000-150,000 | — |
| Base Monthly Cost (Small Cluster)(USD) | Self-hosted infrastructure costs (highly variable) | — |
| On-Demand Query Pricing(USD per TB scanned) | Free (self-hosted) / $0.10-0.50 (managed) | — |
| Typical Storage Cost(USD per TB per month) | $20-40 | — |
Show 2 more attributesStorage Cost (per TB/month)(USD) $15-25 — Monthly Cost per TB Stored(USD) $0.09 — | ||
| Minimum Recommended Cluster Size(nodes) | 5-7 nodes (3 controllers + 2-4 brokers) | 3-5 nodes(winner) |
| Setup Time to Production(minutes) | 12 weeks | — |
| Max Dataset Size (Practical)(TB) | 100-500TB (hot data) | 1000TB+ (unlimited with tiering)(winner) |
| Maximum Cluster Size(nodes) | 1000+(winner) | 100+ |
| 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) | — |
Show 7 more attributesConcurrent Users Supported(users) 100-500 (depends on cluster config) — Maximum Practical Dataset Size(petabytes) 10+ PB (proven at scale) — 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 — Maximum Concurrent Queries(queries/sec) 100,000+ — Max Concurrent Queries (single cluster)(queries) 1,000+ — | ||
| Per-Query Cost (1TB scan)(USD) | $0 (infrastructure only) | — |
| Monthly Cost (100 GB compressed)(USD) | $150 | — |
| Cost per GB Scanned(USD) | $0.015 | — |
| Minimum Monthly Cost (basic setup)(USD) | $500 (ClickHouse Cloud starter) | — |
| Cost per TB Scanned(USD) | $0.01 | — |
| Annual TCO (100TB dataset)(USD) | $150,000 | — |
| Time to First Production Query(days) | 14-30 days (requires schema design and tuning) | — |
| Setup Time to First Query(minutes) | 30-120 minutes | — |
| Initial Setup Time(hours) | 14 days | — |
| SQL Support | Native SQL with PQL extensions (ANSI-compliant subset) | — |
| GitHub Stars (as of 2026)(stars) | 8,200+ | 25000+(winner) |
| GitHub Stars (2026)(stars) | 34,000+ | — |
| Setup Time (Minutes)(minutes) | 240-480 | — |
| Setup Time(hours) | 240 minutes | — |
| Supported Programming Languages(languages) | Java, Python (limited) | — |
| Supported Data Formats(formats) | 12+ formats (TSV, Native, Avro, Protobuf, etc.) | — |
| Concurrent User Support(users) | 100-500 typical | — |
| Data Freshness(seconds) | Sub-second to 1 minute | — |
| Ingestion Streaming Support(events per second) | 1M+ eps native | — |
| License Type | Apache 2.0 Open Source | — |
| Streaming Ingestion Latency(seconds) | <1 second | — |
| Infrastructure Management | Manual cluster management required | — |
| Typical Deployment Complexity(relative score) | Medium-High (columnar tuning) | — |
| Infrastructure Management Overhead(hours per month) | 40-80 hours | — |
| Time to Deploy(hours) | 40-80 hours (cluster provisioning, tuning) | — |
| Deployment Time(months) | 6 | — |
| Full-Text Search Optimization | Not optimized (secondary feature) | — |
| GitHub Stars (Community Size Proxy)(stars) | 9,200+ | — |
| AWS Service Integration (native)(count) | 5-10 (via third-party) | — |
| Real-Time Ingestion Support | Native (Kafka, S3, HDFS) | — |
| Peak Ingestion Speed(events per second) | 1,000,000+(winner) | 100,000-500,000 |
| Maximum Ingestion Rate(events/second) | 1,000,000 | — |
| ANSI SQL Compliance(percentage) | 70% | 95%(winner) |
| Deployment Components(count) | 5-7 components | 1-2 components(winner) |
| Time to First Query(minutes) | 45-60 minutes | 5-10 minutes(winner) |
| Compression Ratio(ratio) | 8:1-12:1 | — |
| Learning Curve (1-10 scale)(difficulty level) | 7/10 (moderate-hard) | — |
| Memory Required (minimal)(MB) | 500-2000MB | — |
| Time to Production Deployment(hours) | 1440 (self-managed) / 60 (managed) | — |
| Uptime SLA Guarantee(percent) | 99.0% (self-managed) / 99.95% (managed) | — |
| Enterprise SLA Availability(percent) | 99.5% (self-hosted dependent) | — |
| Native AWS Service Integration(count) | 3 (S3, Kinesis via 3rd party, basic) | — |
| Support for Time-Series Data | Native optimization, ideal for billions of events | — |
Apache PinotClickHouseQuery Latency (1B rows, 100 dimensions)(milliseconds)
50-100ms
—
Query Latency (p95 on Real-Time Data)(milliseconds)
500-2000ms
—
Data Ingestion Rate(GB/sec)
500,000-1,000,000
1-5
Storage Compression Ratio(x reduction)
~4-6x columnar
—
Max Ingestion Throughput(events/second)
1,000,000-2,000,000 events/sec
100,000-500,000 events/sec
Show 24 more attributes
Query Latency (p99)(milliseconds)
50-200ms
200-500ms
Query Latency (P95)(milliseconds)
100-500ms
—
Data Ingestion Latency(seconds)
0.1 (streaming)
Microseconds to milliseconds
P99 Query Latency(milliseconds)
50-200ms
—
Query Latency on 1GB Dataset(milliseconds)
100-500
—
Concurrent Queries Supported(queries)
1000+
—
Query Latency (Average)(milliseconds)
<100ms
—
Query Latency (Median)(milliseconds)
<100 ms
—
Ingestion Rate (events/second)(events/sec)
1,000,000+
—
Query Latency (1B rows)(seconds)
2-5
—
Maximum Recommended Dataset Size(rows)
10,000+
—
Typical Query Latency (1B rows, GROUP BY)(milliseconds)
50-500ms
—
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
—
Ingestion Latency(seconds)
10-60 seconds
—
Query Latency (100M rows)(milliseconds)
50-500ms
—
Query Latency (100M rows, simple aggregation)(milliseconds)
500-1500ms
—
Query Latency (1B row scan, 10 column aggregate)(milliseconds)
50-100ms
—
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
—
Average Query Latency (1TB dataset)(milliseconds)
85ms
—
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
—
Idle Memory Usage(MB)
500-2000 MB
—
Maximum Events/Sec per Node(events/sec)
10K-50K
—
Events/Second Ingestion(events/sec)
500,000/sec (streaming)
—
Max Concurrent Queries (default config)(queries)
Unlimited (resource-based)
—
Typical Cluster Setup Cost(USD/month (3-node))
$1500-3000
—
Typical Cost per TB/year(USD)
$2000-3500
$800-1500
Licensing Model
Open-source (free) + optional support
—
Multi-table JOIN Support(capability level)
Full support (INNER, LEFT, RIGHT, FULL)
—
SQL Compatibility(percentage)
PQL + Limited SQL (60% compatibility)
MySQL-compatible with ClickHouse extensions
Typical Use Case Flexibility
Ad-hoc queries (generalized)
—
JOIN Operation Support
Full support
—
SQL Standard Compliance(percent)
~60% (Pinot Query Language variant)
70% ANSI SQL
Full-Text Search Capability
Limited (phrase queries, basic tokenization)
—
Show 10 more attributes
Real-time Streaming Ingestion
Native (Kafka, S3, MQ)
—
Real-time Upsert Support(boolean)
Yes
—
SQL Query Support
Native PinotSQL (full ANSI compliance)
—
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
—
Cloud Provider Support(providers)
4+ (AWS, Azure, GCP, on-premise)
—
Built-in ML Capabilities
No (third-party integration required)
—
Enterprise Deployments(thousands)
1000+ (LinkedIn, Uber, etc)
—
Data Compression Ratio(x compression)
3-8x
10-40x
Index Size to Data Ratio(multiplier)
0.1-0.3x
—
Typical Data Compression Ratio(x)
10-40x
—
Ingestion Latency (end-to-end)(milliseconds)
100-500ms
1000-10000ms
Memory Usage per Query(MB)
100-400MB
50-200MB
Compression Ratio (typical)(ratio)
10:1 to 40:1
—
Native SQL Support
PQL (Custom) + Presto Bridge
Standard SQL with extensions
Multi-tenancy Isolation
Native tenant isolation
Limited/requires custom logic
Multi-node Support(boolean)
Yes (native distributed)
—
Minimum Cluster Size for 1TB Dataset(nodes)
5-8 nodes
—
Typical Node Memory(GB)
16-64GB
8-32GB
Minimum Cluster Nodes Required(nodes)
5-7
—
Deployment Flexibility
Kubernetes, on-premises, all cloud providers
—
Minimum Cluster Size(nodes)
3-5
—
Show 1 more attribute
Minimum Cluster Size (nodes)(nodes)
1 (can run standalone)
—
GitHub Stars (Community Activity)(count)
5,300
—
Storage Cost per TB/Month(USD)
$120-180
$40-60
Annual Infrastructure Cost (1TB dataset)(USD)
50,000-150,000
—
Base Monthly Cost (Small Cluster)(USD)
Self-hosted infrastructure costs (highly variable)
—
On-Demand Query Pricing(USD per TB scanned)
Free (self-hosted) / $0.10-0.50 (managed)
—
Typical Storage Cost(USD per TB per month)
$20-40
—
Show 2 more attributes
Storage Cost (per TB/month)(USD)
$15-25
—
Monthly Cost per TB Stored(USD)
$0.09
—
Minimum Recommended Cluster Size(nodes)
5-7 nodes (3 controllers + 2-4 brokers)
3-5 nodes
Setup Time to Production(minutes)
12 weeks
—
Max Dataset Size (Practical)(TB)
100-500TB (hot data)
1000TB+ (unlimited with tiering)
Maximum Cluster Size(nodes)
1000+
100+
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)
—
Show 7 more attributes
Concurrent Users Supported(users)
100-500 (depends on cluster config)
—
Maximum Practical Dataset Size(petabytes)
10+ PB (proven at scale)
—
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
—
Maximum Concurrent Queries(queries/sec)
100,000+
—
Max Concurrent Queries (single cluster)(queries)
1,000+
—
Per-Query Cost (1TB scan)(USD)
$0 (infrastructure only)
—
Monthly Cost (100 GB compressed)(USD)
$150
—
Cost per GB Scanned(USD)
$0.015
—
Minimum Monthly Cost (basic setup)(USD)
$500 (ClickHouse Cloud starter)
—
Cost per TB Scanned(USD)
$0.01
—
Annual TCO (100TB dataset)(USD)
$150,000
—
Time to First Production Query(days)
14-30 days (requires schema design and tuning)
—
Setup Time to First Query(minutes)
30-120 minutes
—
Initial Setup Time(hours)
14 days
—
SQL Support
Native SQL with PQL extensions (ANSI-compliant subset)
—
GitHub Stars (as of 2026)(stars)
8,200+
25000+
GitHub Stars (2026)(stars)
34,000+
—
Setup Time (Minutes)(minutes)
240-480
—
Setup Time(hours)
240 minutes
—
Supported Programming Languages(languages)
Java, Python (limited)
—
Supported Data Formats(formats)
12+ formats (TSV, Native, Avro, Protobuf, etc.)
—
Concurrent User Support(users)
100-500 typical
—
Data Freshness(seconds)
Sub-second to 1 minute
—
Ingestion Streaming Support(events per second)
1M+ eps native
—
License Type
Apache 2.0 Open Source
—
Streaming Ingestion Latency(seconds)
<1 second
—
Infrastructure Management
Manual cluster management required
—
Typical Deployment Complexity(relative score)
Medium-High (columnar tuning)
—
Infrastructure Management Overhead(hours per month)
40-80 hours
—
Time to Deploy(hours)
40-80 hours (cluster provisioning, tuning)
—
Deployment Time(months)
6
—
Full-Text Search Optimization
Not optimized (secondary feature)
—
GitHub Stars (Community Size Proxy)(stars)
9,200+
—
AWS Service Integration (native)(count)
5-10 (via third-party)
—
Real-Time Ingestion Support
Native (Kafka, S3, HDFS)
—
Peak Ingestion Speed(events per second)
1,000,000+
100,000-500,000
Maximum Ingestion Rate(events/second)
1,000,000
—
ANSI SQL Compliance(percentage)
70%
95%
Deployment Components(count)
5-7 components
1-2 components
Time to First Query(minutes)
45-60 minutes
5-10 minutes
Compression Ratio(ratio)
8:1-12:1
—
Learning Curve (1-10 scale)(difficulty level)
7/10 (moderate-hard)
—
Memory Required (minimal)(MB)
500-2000MB
—
Time to Production Deployment(hours)
1440 (self-managed) / 60 (managed)
—
Uptime SLA Guarantee(percent)
99.0% (self-managed) / 99.95% (managed)
—
Enterprise SLA Availability(percent)
99.5% (self-hosted dependent)
—
Native AWS Service Integration(count)
3 (S3, Kinesis via 3rd party, basic)
—
Support for Time-Series Data
Native optimization, ideal for billions of events
—
Pros & Cons
10 pros·5 cons across both
Apache Pinot
+5-3
Pros
- Sub-100ms P99 query latency for real-time dashboards
- Handles 1M+ events per second ingestion natively
- Automatic segment pruning and query optimization reduces query time by 70%
- Native support for push-based and pull-based ingestion from Kafka
- Distributed broker-server architecture enables horizontal scaling to 1000+ nodes
Cons
- Requires 5+ operational components (Zookeeper, brokers, servers, minions), increasing DevOps complexity
- Limited SQL compatibility (only 70% ANSI SQL standard), requires query rewrites
- Storage overhead of 3-8x compression versus competitors, making per-GB costs 2-3x higher
ClickHouse
+5-2
Pros
- Superior data compression (10-40x) reduces storage costs to 25-100MB per GB of raw data
- 95% ANSI SQL compliance enables direct porting of existing analytical queries
- Single-binary deployment with minimal configuration, reducing operational overhead by 80%
- Native support for mutations and updates, bridging OLAP and OLTP workloads
- Built-in support for JSON, arrays, and nested structures without flattening
Cons
- Query latency of 200-500ms on P99 makes it unsuitable for real-time dashboards requiring <100ms response
- Ingestion speed capped at 100k-500k events per second, requiring batching for high-volume streams
Frequently Asked Questions
5 questions
Use Pinot when you need real-time analytics with sub-100ms latency on streaming data at scale (1M+ events/sec). Pinot excels at powering live dashboards, real-time fraud detection, and ad-tech analytics where freshness is critical. It's built specifically for these use cases with optimized segment pruning and distributed query execution.
Resources & Learn More
Curated sources to dive deeper
Where to Buy
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Wikipedia
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