StarRocks is the world's fastest open query engine for sub-second, ad-hoc analytics both on and off the data lakehouse. With average query performance 3x faster than other popular alternatives, StarRocks is a query engine that eliminates the need for denormalization and adapts to your use cases, without having to move your data or rewrite SQL. A Linux Foundation project.
Features
- 🚀 Native vectorized SQL engine: StarRocks adopts vectorization technology to make full use of the parallel computing power of CPU, achieving sub-second query returns in multi-dimensional analyses, which is 5 to 10 times faster than previous systems.
- 📊 Standard SQL: StarRocks supports ANSI SQL syntax (fully supported TPC-H and TPC-DS). It is also compatible with the MySQL protocol. Various clients and BI software can be used to access StarRocks.
- 💡 Smart query optimization: StarRocks can optimize complex queries through CBO (Cost Based Optimizer). With a better execution plan, the data analysis efficiency will be greatly improved.
- ⚡ Real-time update: The updated model of StarRocks can perform upsert/delete operations according to the primary key, and achieve efficient query while concurrent updates.
- 🪟 Intelligent materialized view: The materialized view of StarRocks can be automatically updated during the data import and automatically selected when the query is executed.
- ✨ Querying data in data lakes directly: StarRocks allows direct access to data from Apache Hive™, Apache Iceberg™, Delta Lake™ and Apache Hudi™ without importing.
- 🎛️ Resource management: This feature allows StarRocks to limit resource consumption for queries and implement isolation and efficient use of resources among tenants in the same cluster.
- 💠 Easy to maintain: Simple architecture makes StarRocks easy to deploy, maintain and scale out. StarRocks tunes its query plan agilely, balances the resources when the cluster is scaled in or out, and recovers the data replica under node failure automatically.
GitHub - StarRocks/starrocks
StarRocks | A High-Performance Analytical Database
Comparisions
ChatGPT - Database Comparison Summary
You're comparing four modern, high-performance analytics databases: StarRocks, Trino, ClickHouse, and DuckDB. Each is optimized for different use cases in the data analytics ecosystem. Here's a detailed comparison across various dimensions:
⚙️ Architecture & Design
| Feature | StarRocks | Trino (Presto) | ClickHouse | DuckDB |
|---|
| Engine Type | MPP OLAP DB (vectorized) | Distributed SQL Query Engine | MPP OLAP DB (vectorized) | Embedded analytical DB (in-process) |
| Storage | Columnar | No native storage, query engine only | Columnar | Columnar |
| Execution Model | Pipeline & vectorized | Pipeline, distributed | Pipeline & vectorized | In-process, single-node |
| Scalability | High (Distributed MPP) | Very high (connect to many sources) | High (horizontal sharding/replication) | Low (mostly single-node, in-memory) |
| Language | SQL-92 + extensions | ANSI SQL | SQL-92 + extensions | SQL-92 + extensions |
| Use Case | StarRocks | Trino | ClickHouse | DuckDB |
|---|
| Interactive Analytics | Excellent | Good (depends on source and config) | Excellent | Excellent (small to mid datasets) |
| Real-time Analytics | Great (low-latency) | Moderate (pulls from source) | Great (especially with materialized views) | Not built for real-time ingestion |
| ETL / ELT | Good (batch load, broker load) | Moderate (depends on sources) | Great (good ingestion support) | Great for fast, in-process transformations |
| Data Lake Querying | Moderate (Parquet, Hudi, Iceberg) | Excellent (Iceberg, Hive, Delta, Hudi, etc.) | Moderate (limited lake support) | Basic support (Parquet, CSV, etc.) |
🔌 Integration & Ecosystem
| Feature | StarRocks | Trino | ClickHouse | DuckDB |
|---|
| Data Sources | Kafka, Hive, Iceberg, JDBC, etc. | Anything (Iceberg, Hive, Kafka, JDBC, etc.) | Kafka, JDBC, S3, etc. | CSV, Parquet, JSON, Pandas, Arrow |
| BI Tools Support | Tableau, Superset, Power BI | Everything (via JDBC/ODBC) | Tableau, Superset, Grafana, Power BI | Python, Jupyter, R, Streamlit, etc. |
| Language Bindings | JDBC/ODBC, MySQL protocol | JDBC, ODBC | JDBC, ODBC, HTTP | Python, R, Node.js |
| Cloud/Managed Options | StarRocks Cloud | Starburst (Enterprise Trino) | ClickHouse Cloud | None (local/embedded use) |
📦 Storage & Deployment
| Feature | StarRocks | Trino | ClickHouse | DuckDB |
|---|
| Deployment | On-prem, Kubernetes, Cloud | Kubernetes, Cloud, On-prem | On-prem, Kubernetes, Cloud | Local (desktop/server) |
| Storage Format | Proprietary columnar | Delegated (depends on source) | Proprietary columnar | In-memory / local columnar |
| Storage Cost | Efficient | Delegated to source | Efficient | Local file-based, minimal cost |
✅ Best Use Cases
| Use Case | Best Fit |
|---|
| High concurrency + sub-second queries | StarRocks |
| Federated querying (Data mesh/lake) | Trino |
| Ultra-fast OLAP + large volumes | ClickHouse |
| Embedded analytics, local compute | DuckDB |
📝 Summary
| Scenario | Best Choice |
|---|
| Federated querying across multiple sources | Trino |
| High-speed, large-scale OLAP workloads | ClickHouse |
| Real-time dashboards and concurrency | StarRocks |
| Local development, data science | DuckDB |
💡 TL;DR Recommendations
- Use StarRocks if you're building real-time analytics dashboards and need blazing-fast response time on large datasets.
- Use Trino when you want to query multiple data sources (e.g., S3, Hive, PostgreSQL) with a single query engine.
- Use ClickHouse for ultra-fast analytical queries and time-series workloads.
- Use DuckDB for local analytics, notebooks, and Python data science workflows.
Compare DuckDB vs StarRocks
ClickHouse vs Starrocks : r/dataengineering
⚡️ Real-Time, All the Time: How We Streamed Data from Everywhere into Dashboards and APIs in Seconds | by Swapnesh Khare | CARS24 Data Science Blog | Apr, 2025 | Medium