ClickHouse and StarRocks are both high-performance analytical databases designed for real-time queries on large datasets. While ClickHouse has the longer track record, StarRocks (formerly DorisDB) has gained significant momentum with its MPP architecture and MySQL compatibility. This comparison helps you understand the trade-offs between these two leading OLAP databases.
Platform Overview
ClickHouse
ClickHouse is a columnar database developed at Yandex, now maintained by ClickHouse Inc., known for exceptional query performance and compression.
Key characteristics:
- Fastest single-query performance
- Excellent compression (10-20x)
- Mature and battle-tested
- Large community and ecosystem
- Open source with cloud option
StarRocks
StarRocks is an MPP analytical database designed for sub-second queries on fresh data, with strong MySQL compatibility.
Key characteristics:
- MySQL-compatible interface
- Real-time data updates
- Materialised view acceleration
- Vectorised execution
- Strong JOIN performance
Architecture Comparison
ClickHouse Architecture
┌─────────────────────────────────────────┐
│ ClickHouse Cluster │
├─────────────────────────────────────────┤
│ ┌─────────┐ ┌─────────┐ ┌─────────┐ │
│ │ Shard 1 │ │ Shard 2 │ │ Shard N │ │
│ │┌───────┐│ │┌───────┐│ │┌───────┐│ │
│ ││Replica││ ││Replica││ ││Replica││ │
│ │└───────┘│ │└───────┘│ │└───────┘│ │
│ └─────────┘ └─────────┘ └─────────┘ │
│ │
│ Shared-Nothing, MergeTree Engine │
└─────────────────────────────────────────┘StarRocks Architecture
┌─────────────────────────────────────────┐
│ StarRocks Cluster │
├─────────────────────────────────────────┤
│ ┌─────────────────────────────────┐ │
│ │ Frontend (FE) │ │
│ │ Query Planning & Metadata │ │
│ └───────────────┬─────────────────┘ │
│ │ │
│ ┌───────────────▼─────────────────┐ │
│ │ Backend (BE) │ │
│ │ ┌─────┐ ┌─────┐ ┌─────┐ │ │
│ │ │ BE1 │ │ BE2 │ │ BEN │ │ │
│ │ └─────┘ └─────┘ └─────┘ │ │
│ │ MPP Distributed Execution │ │
│ └─────────────────────────────────┘ │
└─────────────────────────────────────────┘Performance Comparison
Query Performance
| Query Type | ClickHouse | StarRocks |
|---|---|---|
| Simple aggregation | 0.3s | 0.5s |
| Complex GROUP BY | 0.8s | 1.0s |
| Multi-table JOIN | 3-5s | 1-2s |
| Point lookup | 10ms | 5ms |
| High concurrency (100 QPS) | Good | Excellent |
| Single large query | Fastest | Very fast |
ClickHouse advantages:
- Superior single-query performance
- Better compression ratios
- More efficient for scan-heavy workloads
- Mature optimisation for aggregations
StarRocks advantages:
- Better multi-table JOIN performance
- More efficient point lookups
- Better query concurrency handling
- Faster for complex queries with JOINs
Data Freshness
| Capability | ClickHouse | StarRocks |
|---|---|---|
| Batch ingestion | Excellent | Excellent |
| Stream ingestion | Good | Excellent |
| Real-time updates | Limited (ReplacingMergeTree) | Native (Primary Key) |
| Upsert support | Via table engines | Native |
| Delete support | Async mutations | Native |
StarRocks provides better support for real-time data updates:
-- StarRocks: Native upsert with Primary Key table
CREATE TABLE orders (
order_id BIGINT,
customer_id BIGINT,
status VARCHAR(50),
amount DECIMAL(10,2),
updated_at DATETIME
) PRIMARY KEY (order_id)
DISTRIBUTED BY HASH(order_id);
-- Direct update
UPDATE orders SET status = 'shipped' WHERE order_id = 12345;Feature Comparison
| Feature | ClickHouse | StarRocks |
|---|---|---|
| Query language | ClickHouse SQL | MySQL-compatible SQL |
| JOINs | Supported (with caveats) | Excellent |
| Real-time updates | Limited | Native |
| Materialised views | Basic | Advanced (automatic refresh) |
| External tables | S3, HDFS, etc. | Extensive (lakes, databases) |
| CDC integration | Via Kafka | Native CDC connectors |
| Compression | Excellent (20x+) | Good (10-15x) |
| UDFs | C++, SQL | Java, SQL |
| Ecosystem | Large | Growing |
Use Case Recommendations
Choose ClickHouse When:
High-volume log analytics
-- ClickHouse excels at log analysis
SELECT
toStartOfMinute(timestamp) AS minute,
level,
count() AS count,
uniqExact(trace_id) AS unique_traces
FROM logs
WHERE timestamp >= now() - INTERVAL 1 HOUR
GROUP BY minute, level
ORDER BY minute DESCTime-series and metrics
- Monitoring dashboards
- IoT sensor data
- Financial tick data
Maximum compression requirements
- Long-term data retention
- Cost-sensitive storage
Established ecosystem needs
- Grafana integration
- dbt support
- Wide tooling compatibility
Choose StarRocks When:
Real-time data updates
-- StarRocks handles updates natively
INSERT INTO customer_metrics
SELECT
customer_id,
count(*) AS total_orders,
sum(amount) AS total_spend,
NOW() AS updated_at
FROM orders
WHERE order_date >= CURRENT_DATE - INTERVAL 30 DAY
GROUP BY customer_id
ON DUPLICATE KEY UPDATE
total_orders = VALUES(total_orders),
total_spend = VALUES(total_spend),
updated_at = VALUES(updated_at);Complex JOIN workloads
- Multi-dimensional analysis
- Star/snowflake schemas
- Ad-hoc exploration with JOINs
MySQL compatibility requirements
- Existing MySQL applications
- Familiar tooling and drivers
- Easy migration from MySQL
High-concurrency dashboards
- Many concurrent users
- Mixed query patterns
- Interactive analytics
Materialised Views Comparison
ClickHouse Materialised Views
-- Basic materialised view
CREATE MATERIALIZED VIEW hourly_stats
ENGINE = SummingMergeTree()
ORDER BY (hour, product_id)
AS SELECT
toStartOfHour(event_time) AS hour,
product_id,
count() AS views,
sum(revenue) AS total_revenue
FROM events
GROUP BY hour, product_id;StarRocks Materialised Views
-- Async materialised view with auto-refresh
CREATE MATERIALIZED VIEW hourly_stats
REFRESH ASYNC EVERY (INTERVAL 5 MINUTE)
AS SELECT
date_trunc('hour', event_time) AS hour,
product_id,
count(*) AS views,
sum(revenue) AS total_revenue
FROM events
GROUP BY hour, product_id;
-- Query routing is automatic
SELECT * FROM events
WHERE date_trunc('hour', event_time) = '2026-01-27 10:00:00';
-- Automatically uses materialised viewStarRocks provides more sophisticated materialised view management with automatic query rewriting and refresh policies.
Integration and Ecosystem
ClickHouse Ecosystem
Data ingestion:
- Kafka (native)
- Vector, Fluent Bit
- Airbyte, Fivetran
- dbt adapter
Visualisation:
- Grafana (excellent)
- Superset, Metabase
- Tableau, Looker
Orchestration:
- Airflow
- Dagster, Prefect
StarRocks Ecosystem
Data ingestion:
- Kafka connector
- Flink CDC
- Spark connector
- Routine Load (streaming)
Data lake integration:
- Apache Hudi
- Apache Iceberg
- Delta Lake
- External catalogs
Visualisation:
- Any MySQL-compatible tool
- Superset, Metabase
- Grafana
Operational Comparison
ClickHouse Operations
Strengths:
- Mature, well-documented
- Predictable behaviour
- Strong community support
- ClickHouse Cloud option
Challenges:
- Schema design critical
- Mutations can be slow
- JOINs require careful planning
StarRocks Operations
Strengths:
- MySQL-familiar operations
- Better schema flexibility
- Easier updates and deletes
- Good cloud options (CelerData)
Challenges:
- Younger project
- Smaller community
- Fewer third-party integrations
Cost Comparison
| Factor | ClickHouse | StarRocks |
|---|---|---|
| Storage efficiency | Better (20x compression) | Good (10-15x) |
| Compute efficiency | Excellent for scans | Better for JOINs |
| Operational cost | Lower (simpler) | Moderate |
| Cloud pricing | ClickHouse Cloud | CelerData Cloud |
For cost-optimisation strategies, see our cloud cost guide.
Migration Considerations
From ClickHouse to StarRocks
Consider when:
- Need better UPDATE/DELETE support
- Complex JOIN performance is critical
- Want MySQL compatibility
From StarRocks to ClickHouse
Consider when:
- Maximum query performance needed
- Better compression required
- Larger ecosystem preferred
SQL Compatibility
Many queries work in both with minor modifications:
-- ClickHouse
SELECT toStartOfHour(ts) AS hour, count() FROM t GROUP BY hour
-- StarRocks
SELECT date_trunc('hour', ts) AS hour, count(*) FROM t GROUP BY hourBenchmark Considerations
When benchmarking ClickHouse vs StarRocks:
- Test your actual queries - Synthetic benchmarks may not reflect your workload
- Include JOINs if relevant - StarRocks shines here
- Test update scenarios - If you need real-time updates
- Measure concurrency - Not just single-query speed
- Consider total cost - Storage, compute, and operations
Conclusion
Both ClickHouse and StarRocks are excellent choices for real-time analytics:
Choose ClickHouse for maximum single-query performance, best-in-class compression, log analytics, time-series workloads, and when you have a mature ecosystem requirement.
Choose StarRocks for complex JOIN workloads, real-time data updates, MySQL compatibility, high-concurrency scenarios, and when native CDC/lake integration is important.
Consider your primary workload: Scan-heavy analytics favour ClickHouse; JOIN-heavy and update-intensive workloads favour StarRocks.
For help selecting the right analytical database, contact our team to discuss your requirements.
Related Resources
- How Tasrie IT Services Uses ClickHouse
- ClickHouse vs PostgreSQL 2026
- Top 10 NoSQL Databases 2026
- Cloud Native Database Guide 2026
External Resources: