StarRocks v3.2/3.3

Vectorized OLAP engine with read-write Iceberg support, async materialized views, CBO optimization, and strong analytical performance for lakehouse analytics

Key Features

100

REST + Cloud Native

Modern Catalog Integration

Hive Metastore, AWS Glue, REST (Nessie/Tabular) with credential vending support for modern lakehouse architectures

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90

High Performance Reads

Vectorized OLAP Engine

Full reads including MoR (position & equality-delete files); INSERT/INSERT OVERWRITE, CREATE/DROP (v3.1+). Vectorized execution for analytical workloads

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40

INSERT Only

Limited DML Operations

Supports INSERT & INSERT OVERWRITE (partition-level). No UPDATE/DELETE/MERGE operations available in current versions

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70

MoR Read + CoW Write

Read-Optimized Storage

Reads MoR (position & equality-delete files) efficiently. Writes CoW only (partition overwrite) - no equality-delete file production

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80

Unique Feature

Async Materialized Views

No native streaming; supports Async Materialized Views for incremental ingest patterns and low-latency dashboard performance

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60

v1/v2 Only

Limited Format Support

Iceberg v1 & v2 (Parquet & ORC) support. No Iceberg v3 or Avro format support in current versions

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20

v3.4+ Required

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+

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90

External Catalog Security

StarRocks RBAC Integration

Catalog ACLs respected (IAM/HMS). StarRocks RBAC on external catalogs for fine-grained access control and governance

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95

CBO + Vectorization

Advanced Performance Features

Vectorized Parquet/ORC reader, Cost-based optimizer uses Iceberg stats, metadata caching (3.3.3+), data-file/output-size tuning

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85

Rapid Evolution

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

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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+	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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Use Cases

High-Performance Analytics

Vectorized OLAP engine optimized for analytical workloads

• Business intelligence and real-time dashboards
• Large-scale data warehouse analytics
• Complex analytical queries on lakehouse data
• High-performance reporting with sub-second latency

Materialized View Acceleration

Async materialized views for performance optimization

• Low-latency dashboard queries on large datasets
• Incremental data processing and aggregation
• Performance acceleration for frequent analytical queries
• Near-real-time analytics with scheduled refresh patterns

Read-Heavy Lakehouse Analytics

Optimal for analytical workloads consuming data from other engines

• Query layer for data written by Spark/Flink/Dremio
• Cross-engine analytical workloads in multi-engine environments
• Performance-critical read operations on MoR tables
• Analytical consumption of frequently updated datasets

Enterprise OLAP Platform

Comprehensive analytical platform with enterprise security

• Multi-tenant analytical platforms with RBAC
• Enterprise data warehouse modernization projects
• Cloud-native analytical platforms with catalog integration
• Compliance-aware analytical environments

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