ClickHouse vs Druid 2026: OLAP Database Comparison
ClickHouse is a column-oriented database optimized for analytical queries with superior compression and query speed, while Druid is a real-time OLAP database designed for time-series data and streaming ingestion with lower latency on aggregations. ClickHouse excels at batch analytics; Druid excels at real-time dashboarding.
ClickHouse
Distributed columnar OLAP database designed for petabyte-scale real-time analytics
Data engineers building cost-efficient analytics platforms, companies with large historical datasets, and teams prioritizing storage efficiency and batch processing
Druid
Real-time OLAP database optimized for streaming analytics and time-series data with sub-100ms query latency.
Real-time analytics platforms, monitoring systems, clickstream analytics, and teams requiring sub-second dashboard interactivity with streaming data
Quick Answer
AI SummaryClickHouse is a column-oriented database optimized for analytical queries with superior compression and query speed, while Druid is a real-time OLAP database designed for time-series data and streaming ingestion with lower latency on aggregations. ClickHouse excels at batch analytics; Druid excels at real-time dashboarding.
Our Verdict
AI-assistedChoose ClickHouse if you need cost-effective analytical data warehousing, superior compression, and batch query performance for historical data analysis. Choose Druid if you need real-time dashboard analytics, sub-100ms latency on aggregations, and streaming data ingestion from Kafka/Kinesis with minimal delay.
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Best pickData engineers building cost-efficient analytics platforms, companies with large historical datasets, and teams prioritizing storage efficiency and batch processing
Choose Druid if
Real-time analytics platforms, monitoring systems, clickstream analytics, and teams requiring sub-second dashboard interactivity with streaming data
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Key Differences at a Glance
- Primary Use Case:Batch OLAP analytics and data warehousing vs Real-time OLAP and time-series analytics
- Query Latency (Sub-second aggregations):✓ Druid wins(10-100ms for pre-aggregated data vs 100-500ms for typical queries)
- Data Compression Ratio:✓ ClickHouse wins(10:1 to 40:1 (industry-leading) vs 3:1 to 8:1)
Key Facts & Figures
80 numeric metrics compared
| Metric | ClickHouse | Druid | Ratio |
|---|---|---|---|
| P99 Query Latency (1B rows, aggregation)(milliseconds) | 50-200ms | — | — |
| Ingestion Latency (Kafka to query-ready)(seconds) | 5-30 seconds | — | — |
| Maximum Recommended Node Storage(TB) | 2-10TB 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 | 4:1-6:1 | |
| 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 | Druid SQL dialect (proprietary subset) | — |
| Query Latency (p99)(milliseconds) | 200-500ms | — | — |
| Ingestion Latency (end-to-end)(milliseconds) | 1000-10000ms | — | — |
| Memory Usage per Query(MB) | 50-200MB | — | — |
| Maximum Cluster Size(nodes) | 100+ | — | — |
| Typical Cost per TB/year(USD) | $800-1500 | — | — |
| Ingestion Latency(seconds) | 10-60 seconds | 0.1-0.5 seconds | |
| Query Latency (100M rows)(milliseconds) | 50-500ms | 100-1000ms | |
| Maximum Cluster Nodes(nodes) | 1000+ nodes tested | 500+ nodes tested | |
| GitHub Stars (2026)(stars) | 35,000+ | 16,000+ stars | |
| 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 | 100-300ms | |
| Typical Storage Cost(USD per TB per month) | $20-40 | $50-80 | |
| Max Recommended Dataset Size(terabytes) | 100TB+ efficiently | 10TB practical limit | |
| SQL Feature Completeness(percentage) | 95% (PostgreSQL-compatible) | 70% (Druid SQL dialect) | |
| Max Ingestion Throughput(events/second) | 100,000-500,000 events/sec | — | — |
| Storage Cost per TB/month(USD) | $40-60 | — | — |
| Typical Node Memory(GB) | 8-32GB | — | — |
| Minimum Recommended Cluster Size(nodes) | 3-5 nodes | 10 nodes | |
| Max Dataset Size (Practical)(TB) | 1000TB+ (unlimited with tiering) | — | — |
| 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 | 100-500ms (real-time) | |
| AWS Service Integration (native)(count) | 5-10 (via third-party) | — | — |
| GitHub Stars (as of 2026)(stars) | 25000+ | — | — |
| 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(percent) | 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 | 1,000,000+ realtime | |
| 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 | — | — |
| ANSI SQL Compliance(percentage) | 95% | — | — |
| Deployment Components(count) | 1-2 components | — | — |
| Time to First Query(minutes) | 5-10 minutes | — | — |
| Average Query Latency (Standard Aggregation)(milliseconds) | 250ms | 45ms | |
| Streaming Ingestion Latency(seconds) | 2-5 seconds | 0.1-0.5 seconds | |
| Memory Per 1TB Data(GB) | 35GB | 150GB | |
| Kafka Integration Latency(seconds) | 3-5 seconds (batched) | 0.2-0.5 seconds (real-time) | |
| First Release Year | 2016 (Yandex) | 2012 (Metamarkets) | |
| Maximum Dataset Size(TB) | 1,000+ TB (distributed) | — | — |
| Query Latency (1B rows, COUNT aggregation)(milliseconds) | 20-100ms | — | — |
| Data Compression Ratio(x) | 40-100x compression | 5:1 (average) | |
| Community Size (GitHub Stars)(stars) | 35,000+ stars | — | — |
| Query Latency (Typical)(milliseconds) | 50-200ms | 50-200ms | |
| Enterprise Customers (2025)(count) | ~400 enterprises | ~400 enterprises | |
| Base Setup Cost (Annual)(USD) | $50,000-500,000 (infrastructure) | $50,000-500,000 (infrastructure) | |
| Time to Insight (Complex Query)(seconds) | 0.2 (pre-aggregated metrics) | 0.2 (pre-aggregated metrics) | |
| Maximum Daily Data Volume(terabytes) | 500+ TB/day sustainable | 500+ TB/day sustainable | |
| Operational Complexity (1-10 scale)(complexity score) | 8/10 (high setup & tuning) | 8/10 (high setup & tuning) |
Sourced from publicly available data ·
Key Differences
7 attributes compared head-to-head
- Batch OLAP analytics and data warehousingPrimary Use CaseReal-time OLAP and time-series analytics
- 100-500ms for typical queriesQuery Latency (Sub-second aggregations)10-100ms for pre-aggregated data(winner)
- 10:1 to 40:1 (industry-leading)(winner)Data Compression Ratio3:1 to 8:1
- Kafka/S3 plugins, ~1-5 second latencyStreaming Ingestion SupportNative Kafka/Kinesis, sub-second latency(winner)
- Standard SQL with extensions(winner)SQL Dialect SupportSQL-like, proprietary Druid SQL
- Moderate (Keeper/Zookeeper required)(winner)Cluster Setup ComplexityHigher (Coordinator, Data, Query nodes)
- 20-50GB (compressed storage)(winner)Memory Footprint per 1TB Data100-200GB (in-memory indexing)
- Primary Use Case
ClickHouse
Batch OLAP analytics and data warehousing
Druid
Real-time OLAP and time-series analytics
- Query Latency (Sub-second aggregations)
ClickHouse
100-500ms for typical queries
Druid
10-100ms for pre-aggregated data(winner)
- Data Compression Ratio
ClickHouse
10:1 to 40:1 (industry-leading)(winner)
Druid
3:1 to 8:1
- Streaming Ingestion Support
ClickHouse
Kafka/S3 plugins, ~1-5 second latency
Druid
Native Kafka/Kinesis, sub-second latency(winner)
- SQL Dialect Support
ClickHouse
Standard SQL with extensions(winner)
Druid
SQL-like, proprietary Druid SQL
- Cluster Setup Complexity
ClickHouse
Moderate (Keeper/Zookeeper required)(winner)
Druid
Higher (Coordinator, Data, Query nodes)
- Memory Footprint per 1TB Data
ClickHouse
20-50GB (compressed storage)(winner)
Druid
100-200GB (in-memory indexing)
Full Comparison
| Attribute | ClickHouse | Druid |
|---|---|---|
| P99 Query Latency (1B rows, aggregation)(milliseconds) | 50-200ms | — |
| 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 17 more attributesQuery Latency (p99)(milliseconds) 200-500ms — Ingestion Latency(seconds) 10-60 seconds 0.1-0.5 seconds Query Latency (100M rows)(milliseconds) 50-500ms 100-1000ms Idle Memory Usage(MB) 500-2000 MB — Query Latency (100M rows, simple aggregation)(milliseconds) 500-1500ms 100-300ms Max Ingestion Throughput(events/second) 100,000-500,000 events/sec — Query Latency (1B row scan, 10 column aggregate)(milliseconds) 50-100ms — Data Ingestion Latency(seconds) Microseconds to milliseconds 100-500ms (real-time) 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 1,000,000+ realtime Average Query Latency (1TB dataset)(milliseconds) 85ms — Average Query Latency (Standard Aggregation)(milliseconds) 250ms 45ms Query Latency (1B rows, COUNT aggregation)(milliseconds) 20-100ms — Query Latency (Typical)(milliseconds) 50-200ms — Time to Insight (Complex Query)(seconds) 0.2 (pre-aggregated metrics) — | ||
| Ingestion Latency (Kafka to query-ready)(seconds) | 5-30 seconds | — |
| Streaming Ingestion Latency(seconds) | 2-5 seconds | 0.1-0.5 seconds(winner) |
| SQL Compliance Level(null) | Proprietary ANSI-SQL variant | — |
| Maximum Recommended Node Storage(TB) | 2-10TB per node | — |
| Maximum Cluster Size(nodes) | 100+ | — |
| Maximum Cluster Nodes(nodes) | 1000+ nodes tested(winner) | 500+ nodes tested |
| Typical Maximum Dataset Size(GB) | ~1,000,000+ GB (1+ PB) | — |
| Max Recommended Dataset Size(terabytes) | 100TB+ efficiently(winner) | 10TB practical limit |
Show 5 more attributesMax Dataset Size (Practical)(TB) 1000TB+ (unlimited with tiering) — Maximum Concurrent Queries(queries/sec) 100,000+ — Max Concurrent Queries (single cluster)(queries) 1,000+ — Maximum Dataset Size(TB) 1,000+ TB (distributed) — Maximum Daily Data Volume(terabytes) 500+ TB/day sustainable — | ||
| Native Streaming Sources(null) | Kafka (basic), S3, File | — |
| Native AWS Service Integration(count) | 3 (S3, Kinesis via 3rd party, basic) | — |
| Enterprise Support Availability | ClickHouse Inc (limited SLAs) | — |
| Monthly Cost (100 GB compressed)(USD) | $150 | — |
| Storage Cost per TB/month(USD) | $40-60 | — |
| 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 1 more attributeBase Setup Cost (Annual)(USD) $50,000-500,000 (infrastructure) — | ||
| Data Retention for Time-Travel(days) | Not native | — |
| Streaming Integration | Limited (Kafka via TableEngine) | Native (Kafka, Kinesis, Pulsar) |
| Transaction Support(consistency level) | No ACID (eventual consistency) | No ACID (eventual consistency) |
| SQL Feature Completeness(percentage) | 95% (PostgreSQL-compatible)(winner) | 70% (Druid SQL dialect) |
| Time-Series Aggregation Support(native features) | Standard SQL; requires manual time bucketing | Native time_floor, granular rollups, datasource-level aggregations |
Show 3 more attributesCloud Provider Support(providers) 4+ (AWS, Azure, GCP, on-premise) — SQL Compatibility(percentage) MySQL-compatible with ClickHouse extensions Druid SQL (subset of ANSI) Built-in ML Capabilities No (third-party integration required) — | ||
| Compression Ratio(ratio) | 8:1-12:1(winner) | 4:1-6:1 |
| Data Compression Ratio(x) | 40-100x compression(winner) | 5:1 (average) |
| Licensing Model | Open-source (free) + optional support | — |
| Typical Cost per TB/year(USD) | $800-1500 | — |
| 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 | — |
| Memory Required (minimal)(MB) | 500-2000MB | — |
| SQL Standard Compliance(% compatibility) | 70% standard SQL | Druid SQL dialect (proprietary subset) |
| Supported Data Formats(formats) | 12+ formats (TSV, Native, Avro, Protobuf, etc.) | — |
| Ingestion Latency (end-to-end)(milliseconds) | 1000-10000ms | — |
| Native SQL Support | Standard SQL with extensions | — |
| Multi-tenancy Isolation | Limited/requires custom logic | — |
| GitHub Stars (2026)(stars) | 35,000+(winner) | 16,000+ stars |
| GitHub Stars (as of 2026)(stars) | 25000+ | — |
| Community Size (GitHub Stars)(stars) | 35,000+ stars | — |
| Typical Storage Cost(USD per TB per month) | $20-40(winner) | $50-80 |
| Storage Cost (per TB/month)(USD) | $15-25 | — |
| Monthly Cost per TB Stored(USD) | $0.09 | — |
| Typical Node Memory(GB) | 8-32GB | — |
| Minimum Cluster Size (nodes)(nodes) | 1 (can run standalone) | — |
| Minimum Recommended Cluster Size(nodes) | 3-5 nodes(winner) | 10 nodes |
| Typical Data Compression Ratio(x) | 10-40x | — |
| Max Concurrent Queries (default config)(queries) | Unlimited (resource-based) | — |
| AWS Service Integration (native)(count) | 5-10 (via third-party) | — |
| Maximum Ingestion Rate(events/second) | 1,000,000 | — |
| Peak Ingestion Speed(events per second) | 100,000-500,000 | — |
| Infrastructure Management Overhead(hours per month) | 40-80 hours | — |
| Setup Time(minutes) | 30-60 minutes | — |
| Operational Complexity(1-10 scale) | 8 (high) | — |
| 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) | — |
| Initial Setup Time(minutes) | 14 days | — |
| Support for Time-Series Data | Native optimization, ideal for billions of events | — |
| ANSI SQL Compliance(percentage) | 95% | — |
| Deployment Components(count) | 1-2 components | — |
| Cluster Node Types Required | Replica, Shard (simplified) | Coordinator, Data, Query, Broker (4+ types) |
| Time to First Query(minutes) | 5-10 minutes | — |
| Memory Per 1TB Data(GB) | 35GB(winner) | 150GB |
| Kafka Integration Latency(seconds) | 3-5 seconds (batched) | 0.2-0.5 seconds (real-time)(winner) |
| First Release Year | 2016 (Yandex) | 2012 (Metamarkets) |
| License Restrictions(commercial use) | AGPL - Source disclosure required | — |
| Enterprise Customers (2025)(count) | ~400 enterprises | — |
| Operational Complexity (1-10 scale)(complexity score) | 8/10 (high setup & tuning) | — |
ClickHouseDruidShow 17 more attributes
Show 5 more attributes
Show 1 more attribute
Show 3 more attributes
Pros & Cons
10 pros·4 cons across both
ClickHouse
Pros
- Exceptional data compression (10-40x ratio) reduces storage costs by 70-80%
- Extremely fast analytical queries (100-500ms) using vectorized execution engine
- Native support for nested data structures and JSON columns
- Efficient resource utilization with lower memory overhead than competitors
- Standard SQL dialect with wide ecosystem integration (Grafana, Tableau, Superset)
Cons
- Not optimized for real-time point queries; sub-second latency requires pre-aggregation
- Steeper learning curve for distributed setup and ReplicatedMergeTree tables
Druid
Pros
- Sub-100ms latency on pre-aggregated queries enabling real-time dashboards
- Native streaming ingestion from Kafka/Kinesis with <1 second end-to-end latency
- Automatic dimension/metric indexing for flexible ad-hoc queries on streams
- Built-in time-series rollup and granularity management
- Purpose-built for monitoring, event analytics, and interactive dashboards
Cons
- Higher operational complexity requiring Coordinator, Data, and Query nodes in distributed setup
- Memory-intensive with 100-200GB footprint per 1TB of data; significantly higher TCO
Frequently Asked Questions
5 questions
Druid is purpose-built for real-time dashboards with sub-100ms latency on aggregations and native Kafka streaming ingestion. ClickHouse can support dashboards but requires pre-aggregation (materialized views) and has 3-5x higher query latency. For interactive dashboards updating every 1-5 seconds, Druid is the better choice.
Resources & Learn More
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