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High-performance analytical database with vectorized OLAP engine for fast analytics on large datasets. Supports reading and writing to Apache Iceberg tables with async materialized views, CBO optimization, and strong analytical performance for modern data lakehouse architectures
Key Features
Modern Catalog Integration
Hive Metastore, AWS Glue, REST (Nessie/Tabular) with credential vending support for modern lakehouse architectures
Vectorized OLAP Engine
Full reads including MoR (position & equality-delete files); INSERT/INSERT OVERWRITE, CREATE/DROP (v3.1+). Vectorized execution for analytical workloads
Limited DML Operations
Supports INSERT & INSERT OVERWRITE (partition-level). No UPDATE/DELETE/MERGE operations available in current versions
Read-Optimized Storage
Reads MoR (position & equality-delete files) efficiently. Writes CoW only (partition overwrite) - no equality-delete file production
Async Materialized Views
No native streaming; supports Async Materialized Views for incremental ingest patterns and low-latency dashboard performance
Limited Format Support
Iceberg v1 & v2 (Parquet & ORC) support. No Iceberg v3 or Avro format support in current versions
Limited Time Travel
No SQL 'AS OF' in v3.2/3.3 - use separate catalog pointing at older snapshot. SQL time travel supported from v3.4.0+
StarRocks RBAC Integration
Catalog ACLs respected (IAM/HMS). StarRocks RBAC on external catalogs for fine-grained access control and governance
Advanced Performance Features
Vectorized Parquet/ORC reader, Cost-based optimizer uses Iceberg stats, metadata caching (3.3.3+), data-file/output-size tuning
Version-Dependent Features
2.4: read-only; 3.1: create & insert; 3.2: insert-overwrite, equality-deletes; 3.3: Iceberg views, metadata cache; 3.4+: time travel
StarRocks Iceberg Feature Matrix
Comprehensive breakdown of Iceberg capabilities in StarRocks v3.2/3.3
Dimension | Support Level | Implementation Details | Since Version |
|---|---|---|---|
Catalog Types | FullREST + Cloud | Hive Metastore, AWS Glue, REST (Nessie/Tabular) with credential vending | 2.4+ |
SQL Analytics | PartialOLAP Optimized | Vectorized reads + MoR support; INSERT/INSERT OVERWRITE; no UPDATE/DELETE/MERGE | 3.1+ |
DML Operations | LimitedINSERT Only | INSERT & INSERT OVERWRITE (partition-level); no UPDATE/DELETE/MERGE operations | 3.1+ |
Storage Strategy | PartialMoR Read + CoW Write | Reads position/equality-deletes efficiently; writes CoW only (partition overwrite) | 3.2+ |
Streaming Support | LimitedAsync MV | No native streaming; Async Materialized Views for incremental patterns | 2.5+ |
Format Support | Partialv1/v2 + Parquet/ORC | Iceberg v1/v2, Parquet/ORC vectorized; no v3 or Avro support | 2.4+ |
Time Travel | Limitedv3.4+ Required | No SQL AS OF in v3.2/3.3; separate catalog workaround; SQL time travel v3.4+. See time travel for details | 3.4+ |
Schema Evolution | FullAuto-detected | Add/Drop columns auto-detected; metadata tables supported from v3.4.1+ | 2.4+ |
Security & Governance | FullRBAC + Catalog ACLs | StarRocks RBAC on external catalogs; catalog ACLs respected (IAM/HMS) | 3.0+ |
Performance Features | FullVectorized + CBO | Vectorized engine, CBO with Iceberg stats, metadata caching, write tuning | 2.4+ |
Unique Features | InnovativeAsync Materialized Views | Async materialized views over Iceberg; scheduled refresh for low-latency dashboards | 2.5+ |
Maturity & Roadmap | EvolvingRapid Development | Rapid version-based feature progression; clear roadmap for UPDATE/DELETE/MERGE | 2.4+ |
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For issues, click here (GitHub)Use Cases
High-Performance Analytics
Vectorized OLAP engine optimized for analytical workloads
- Real-world example: A retail company uses StarRocks to power sales dashboards that update every minute, showing current inventory levels, sales trends, and customer behavior across 500+ stores. The vectorized OLAP engine processes millions of transactions in seconds, enabling store managers to make data-driven decisions in real-time
- Large-scale data warehouse analytics with sub-second query response times
- Complex analytical queries on lakehouse data with automatic optimization
- High-performance reporting dashboards for business intelligence teams
Materialized View Acceleration
Async materialized views for performance optimization
- Real-world example: An e-commerce platform maintains complex aggregation queries for product recommendations. Instead of recalculating these aggregations on every request, StarRocks uses async materialized views to pre-compute results every 5 minutes, reducing dashboard load times from 30 seconds to under 1 second
- Low-latency dashboard queries on large datasets with automatic refresh
- Incremental data processing and aggregation for analytics workloads
- Near-real-time analytics with scheduled refresh patterns for operational reporting
Read-Heavy Lakehouse Analytics
Optimal for analytical workloads consuming data from other engines
- Real-world example: A financial services company uses Apache Spark to write transaction data to Iceberg tables throughout the day. StarRocks acts as the query layer, allowing analysts to run complex SQL queries on this data without waiting for Spark jobs. The MoR read support ensures analysts always see the latest data
- Query layer for data written by Spark/Flink/Dremio in multi-engine architectures
- Cross-engine analytical workloads with consistent read performance
- Performance-critical read operations on frequently updated MoR tables
Enterprise OLAP Platform
Comprehensive analytical platform with enterprise security
- Real-world example: A healthcare organization uses StarRocks to provide secure access to patient analytics across multiple departments. RBAC integration with external catalogs ensures that doctors only see their patients' data, while administrators can access hospital-wide metrics, all querying the same Iceberg tables
- Multi-tenant analytical platforms with fine-grained RBAC controls
- Enterprise data warehouse modernization with cloud-native architecture
- Compliance-aware analytical environments with audit trail requirements
Resources & Documentation
Official Documentation
Complete API reference and guides
Getting Started Guide
Quick start tutorials and examples
Iceberg Catalog Configuration
Documentation
StarRocks 3.3 Release
Documentation
Apache Iceberg Guide
Documentation
Data Lake Analytics Features
Documentation
Time Travel Documentation
Documentation
StarRocks Features Overview
Documentation
Release 3.2 Notes
Documentation
Apache Iceberg Blog
Documentation