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 vs Druid — Side-by-side comparison on A Versus B
Distributed column-oriented OLAP database for petabyte-scale analytics
Real-time OLAP database for streaming analytics and time-series data
Primary Use Case. Query Latency (Sub-second aggregations). Data Compression Ratio. Streaming Ingestion Support. SQL Dialect Support. Cluster Setup Complexity. Memory Footprint per 1TB Data
Choose 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.
P99 Query Latency (1B rows, aggregation)
Ingestion Latency (Kafka to query-ready)
ClickHouse: Proprietary ANSI-SQL variant | Druid: N/A
ClickHouse: 2-10TB per node | Druid: N/A
ClickHouse: Kafka (basic), S3, File | Druid: N/A
ClickHouse: ClickHouse Inc (limited SLAs) | Druid: N/A
ClickHouse: 1,000,000 events/sec | Druid: N/A
ClickHouse: Open-source (free) + optional support | Druid: N/A
ClickHouse: 7/10 (moderate-hard) | Druid: N/A
ClickHouse: 1M+ with batching | Druid: N/A
ClickHouse: 70% standard SQL | Druid: Druid SQL dialect (proprietary subset)
ClickHouse: Standard SQL with extensions | Druid: N/A
ClickHouse: Limited/requires custom logic | Druid: N/A
ClickHouse: 10-60 seconds | Druid: 0.1-0.5 seconds
ClickHouse: 50-500ms | Druid: 100-1000ms
ClickHouse: 1000+ nodes tested | Druid: 500+ nodes tested
ClickHouse: Limited (Kafka via TableEngine) | Druid: Native (Kafka, Kinesis, Pulsar)
ClickHouse: No ACID (eventual consistency) | Druid: No ACID (eventual consistency)
ClickHouse: 35,000+ | Druid: 16,000+ stars
ClickHouse: ~1,000,000+ GB (1+ PB) | Druid: N/A
ClickHouse: 12+ formats (TSV, Native, Avro, Protobuf, etc.) | Druid: N/A
Query Latency (100M rows, simple aggregation)
ClickHouse: 500-1500ms | Druid: 100-300ms
ClickHouse: 100TB+ efficiently | Druid: 10TB practical limit
ClickHouse: 95% (PostgreSQL-compatible) | Druid: 70% (Druid SQL dialect)
ClickHouse: Standard SQL; requires manual time bucketing | Druid: Native time_floor, granular rollups, datasource-level aggregations
ClickHouse: 100,000-500,000 events/sec | Druid: N/A
ClickHouse: 1000TB+ (unlimited with tiering) | Druid: N/A
Query Latency (1B row scan, 10 column aggregate)
ClickHouse: 1 (can run standalone) | Druid: N/A
ClickHouse: Unlimited (resource-based) | Druid: N/A
ClickHouse: Microseconds to milliseconds | Druid: 100-500ms (real-time)
ClickHouse: 5-10 (via third-party) | Druid: N/A
Query Latency (1 billion rows, simple SELECT)
ClickHouse: $500 (ClickHouse Cloud starter) | Druid: N/A
ClickHouse: 4+ (AWS, Azure, GCP, on-premise) | Druid: N/A
ClickHouse: 60-300 (manual cluster resize required) | Druid: N/A
ClickHouse: MySQL-compatible with ClickHouse extensions | Druid: Druid SQL (subset of ANSI)
Average Query Latency (1 Billion Row Scan)
ClickHouse: 1440 (self-managed) / 60 (managed) | Druid: N/A
ClickHouse: 99.0% (self-managed) / 99.95% (managed) | Druid: N/A
ClickHouse: 3 (S3, Kinesis via 3rd party, basic) | Druid: N/A
ClickHouse: 1-5 | Druid: 1,000,000+ realtime
ClickHouse: No (third-party integration required) | Druid: N/A
ClickHouse: Native optimization, ideal for billions of events | Druid: N/A
ClickHouse: 99.5% (self-hosted dependent) | Druid: N/A
Average Query Latency (Standard Aggregation)
ClickHouse: 2-5 seconds | Druid: 0.1-0.5 seconds
ClickHouse: Replica, Shard (simplified) | Druid: Coordinator, Data, Query, Broker (4+ types)
ClickHouse: 3-5 seconds (batched) | Druid: 0.2-0.5 seconds (real-time)
ClickHouse: 2016 (Yandex) | Druid: 2012 (Metamarkets)
Query Latency (1B rows, COUNT aggregation)
ClickHouse: 10-40x for analytics | Druid: 8:1 to 15:1
ClickHouse: 40-100 (cluster setup) | Druid: N/A
ClickHouse: AGPL - Source disclosure required | Druid: N/A
ClickHouse: Petabytes (1000+ TB) | Druid: N/A
ClickHouse: 95% (custom dialect) | Druid: N/A
ClickHouse: Native (Zookeeper/Keeper) | Druid: N/A
ClickHouse: N/A | Druid: ~400 enterprises
ClickHouse: N/A | Druid: $50,000-500,000 (infrastructure)
ClickHouse: N/A | Druid: 0.2 (pre-aggregated metrics)
ClickHouse: N/A | Druid: 500+ TB/day sustainable
ClickHouse: N/A | Druid: 8/10 (high setup & tuning)
ClickHouse: N/A | Druid: Limited (no native implementation)
Maximum Concurrent Queries (Standard Node)
ClickHouse: N/A | Druid: 32+ (distributed)
ClickHouse: N/A | Druid: Partial (60-70% ANSI SQL)
ClickHouse: N/A | Druid: Linear scaling with added nodes
How to choose between ClickHouse and Druid
ClickHouse vs Druid — Structured Comparison Data
Machine-readable comparison dataset for ClickHouse vs Druid. Contains 117 attribute comparisons, 5 FAQ pairs.
ClickHouse vs Druid — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for ClickHouse vs Druid.
Which database should I use for real-time dashboards?
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.
How much cheaper is ClickHouse on storage?
ClickHouse's 20:1 compression ratio vs Druid's 5:1 means you need 4x less storage for equivalent data. For 1TB of raw data, ClickHouse requires ~50GB storage while Druid requires ~200GB. This translates to 70-80% lower cloud storage costs, though Druid's memory requirements for indexing add operational cost.
Can I use ClickHouse for time-series data instead of Druid?
Yes, ClickHouse excels at time-series storage and historical analysis with ReplicatedMergeTree tables and exceptional compression. However, it's not ideal for real-time dashboards because aggregations take 100-500ms vs Druid's <100ms. Use ClickHouse for time-series data warehousing; use Druid if you need real-time interactivity.
ClickHouse is simpler operationally with fewer node types and less complex configuration. Druid requires managing Coordinators, Data nodes, Query nodes, and Brokers, making it 2-3x more complex to deploy and maintain. For small teams with DevOps constraints, ClickHouse is significantly easier.
Which database integrates better with existing tools?
ClickHouse has broader third-party integration support due to standard SQL compliance (JDBC, ODBC, Python, Go drivers). Druid requires specialized adapters. Both work with Grafana and Tableau, but ClickHouse has better support in Apache Superset, Metabase, and traditional BI tools without custom configuration.
How to Choose Between ClickHouse and Druid
Primary Use Case: ClickHouse has Batch OLAP analytics and data warehousing, while Druid has Real-time OLAP and time-series analytics.
Consider: Query Latency (Sub-second aggregations)
Query Latency (Sub-second aggregations): ClickHouse has 100-500ms for typical queries, while Druid has 10-100ms for pre-aggregated data.
Data Compression Ratio: ClickHouse has 10:1 to 40:1 (industry-leading), while Druid has 3:1 to 8:1.
Streaming Ingestion Support: ClickHouse has Kafka/S3 plugins, ~1-5 second latency, while Druid has Native Kafka/Kinesis, sub-second latency.
SQL Dialect Support: ClickHouse has Standard SQL with extensions, while Druid has SQL-like, proprietary Druid SQL.
Cluster Setup Complexity: ClickHouse has Moderate (Keeper/Zookeeper required), while Druid has Higher (Coordinator, Data, Query nodes).
Memory Footprint per 1TB Data: ClickHouse has 20-50GB (compressed storage), while Druid has 100-200GB (in-memory indexing).