StarRocks and Apache Iceberg

StarRocks and Apache Iceberg

StarRocks (formerly known as DorisDB, not to be confused with Apache Doris) is a high-performance, MPP (Massively Parallel Processing) OLAP database designed for real-time and interactive analytics. StarRocks supports Apache Iceberg as an external table format via its Multi-Catalog architecture, enabling StarRocks SQL to query Iceberg tables directly from object storage without data ingestion or ETL.

StarRocks is widely used in the Asia-Pacific tech ecosystem and has a growing global community, particularly for use cases requiring sub-second multi-dimensional analytics over large Iceberg datasets.

StarRocks Multi-Catalog for Iceberg

StarRocks’ Multi-Catalog feature allows creating catalog connections to external table formats including Iceberg, Hive, Delta Lake, and Hudi: alongside StarRocks’ native internal tables.

Creating an Iceberg Catalog

-- StarRocks: create an Iceberg catalog using Hive Metastore
CREATE EXTERNAL CATALOG iceberg_hms
PROPERTIES (
    "type" = "iceberg",
    "iceberg.catalog.type" = "hive",
    "hive.metastore.uris" = "thrift://hms-host:9083",
    "aws.s3.use_instance_profile" = "true",
    "aws.s3.region" = "us-east-1"
);

-- Using AWS Glue
CREATE EXTERNAL CATALOG iceberg_glue
PROPERTIES (
    "type" = "iceberg",
    "iceberg.catalog.type" = "glue",
    "aws.glue.region" = "us-east-1",
    "aws.s3.use_instance_profile" = "true"
);

-- Using Iceberg REST Catalog (Apache Polaris)
CREATE EXTERNAL CATALOG iceberg_polaris
PROPERTIES (
    "type" = "iceberg",
    "iceberg.catalog.type" = "rest",
    "iceberg.catalog.uri" = "https://my-polaris.example.com",
    "iceberg.catalog.credential" = "client-id:client-secret",
    "iceberg.catalog.warehouse" = "my-warehouse"
);

Querying Iceberg Tables

-- Set the Iceberg catalog as current
SET CATALOG iceberg_polaris;

-- List namespaces and tables
SHOW DATABASES;
SHOW TABLES FROM analytics;

-- Query Iceberg tables with full predicate pushdown
SELECT
    date_trunc('month', order_date) AS month,
    region,
    COUNT(*) AS orders,
    SUM(total) AS revenue
FROM analytics.orders
WHERE order_date >= '2026-01-01'
  AND region IN ('AMER', 'EMEA')
GROUP BY 1, 2
ORDER BY 1, 4 DESC;

Cross-Catalog Joins

StarRocks can join between internal StarRocks tables and external Iceberg tables:

-- Join StarRocks internal dimension table with Iceberg fact table
SELECT
    d.product_name,
    d.category,
    SUM(f.revenue) AS total_revenue
FROM iceberg_polaris.analytics.fact_orders f
JOIN default_catalog.dim.products d
    ON f.product_id = d.product_id
WHERE f.order_date >= '2026-01-01'
GROUP BY 1, 2;

StarRocks Performance Characteristics for Iceberg

StarRocks’ query engine applies the full Iceberg optimization stack:

• Manifest pruning: Partition elimination at the manifest level.
• File-level data skipping: Column statistics from manifests.
• Parquet row group pruning: Sub-file filtering.
• Column projection: Only reads needed columns.
• Late materialization: Defers full row reconstruction.

StarRocks also uses a vectorized execution engine (SIMD instructions, columnar processing) that makes it particularly fast for aggregation-heavy analytical queries.

StarRocks vs. Other Iceberg Query Engines

StarRocks and the Modern Iceberg Ecosystem

StarRocks fits well in architectures where:

• Real-time ingestion feeds StarRocks native tables for sub-second dashboards.
• Iceberg tables serve as the historical data store.
• StarRocks federates between live and historical data in queries.

For AI analytics, semantic layer, and natural language query capabilities on Iceberg data, Dremio complements StarRocks in the same lakehouse architecture.