Iceberg is a high-performance format for huge analytic tables. Iceberg brings the reliability and simplicity of SQL tables to big data, while making it possible for engines like Spark, Trino, Flink, Presto, Hive and Impala to safely work with the same tables, at the same time.

Background and documentation is available at https://iceberg.apache.org

Status

Iceberg is under active development at the Apache Software Foundation.

The core Java library that tracks table snapshots and metadata is complete, but still evolving. Current work is focused on adding row-level deletes and upserts, and integration work with new engines like Flink and Hive.

The Iceberg format specification is being actively updated and is open for comment. Until the specification is complete and released, it carries no compatibility guarantees. The spec is currently evolving as the Java reference implementation changes.

Java API javadocs are available for the main.

Collaboration

Iceberg tracks issues in GitHub and prefers to receive contributions as pull requests.

Community discussions happen primarily on the dev mailing list or on specific issues.

Building

Iceberg is built using Gradle with Java 8, 11, or 17.

• To invoke a build and run tests: ./gradlew build
• To skip tests: ./gradlew build -x test -x integrationTest
• To fix code style for default versions: ./gradlew spotlessApply
• To fix code style for all versions of Spark/Hive/Flink:./gradlew spotlessApply -DallVersions

Iceberg table support is organized in library modules:

• iceberg-common contains utility classes used in other modules
• iceberg-api contains the public Iceberg API
• iceberg-core contains implementations of the Iceberg API and support for Avro data files, this is what processing engines should depend on
• iceberg-parquet is an optional module for working with tables backed by Parquet files
• iceberg-arrow is an optional module for reading Parquet into Arrow memory
• iceberg-orc is an optional module for working with tables backed by ORC files
• iceberg-hive-metastore is an implementation of Iceberg tables backed by the Hive metastore Thrift client
• iceberg-data is an optional module for working with tables directly from JVM applications

Iceberg also has modules for adding Iceberg support to processing engines:

• iceberg-spark is an implementation of Spark's Datasource V2 API for Iceberg with submodules for each spark versions (use runtime jars for a shaded version)
• iceberg-flink contains classes for integrating with Apache Flink (use iceberg-flink-runtime for a shaded version)
• iceberg-mr contains an InputFormat and other classes for integrating with Apache Hive
• iceberg-pig is an implementation of Pig's LoadFunc API for Iceberg

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NOTE

The tests require Docker to execute. On MacOS (with Docker Desktop), you might need to create a symbolic name to the docker socket in order to be detected by the tests:

sudo ln -s $HOME/.docker/run/docker.sock /var/run/docker.sock

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Engine Compatibility

See the Multi-Engine Support page to know about Iceberg compatibility with different Spark, Flink and Hive versions. For other engines such as Presto or Trino, please visit their websites for Iceberg integration details.

Implementations

This repository contains the Java implementation of Iceberg. Other implementations can be found at:

• Go: iceberg-go
• PyIceberg (Python): iceberg-python
• Rust: iceberg-rust