Multi-Engine Support

Apache Iceberg is an open standard for huge analytic tables that can be used by any processing engine. The community continuously improves Iceberg core library components to enable integrations with different compute engines that power analytics, business intelligence, machine learning, etc. Connectors for Spark, Flink and Hive are maintained in the main Iceberg repository.

Multi-Version Support

Processing engine connectors maintained in the iceberg repository are built for multiple versions.

For Spark and Flink, each new version that introduces backwards incompatible upgrade has its dedicated integration codebase and release artifacts. For example, the code for Iceberg Spark 3.1 integration is under /spark/v3.1 and the code for Iceberg Spark 3.2 integration is under /spark/v3.2. Different artifacts (iceberg-spark-3.1_2.12 and iceberg-spark-3.2_2.12) are released for users to consume. By doing this, changes across versions are isolated. New features in Iceberg could be developed against the latest features of an engine without breaking support of old APIs in past engine versions.

For Hive, Hive 2 uses the iceberg-mr package for Iceberg integration, and Hive 3 requires an additional dependency of the iceberg-hive3 package.

Runtime Jar

Iceberg provides a runtime connector jar for each supported version of Spark, Flink and Hive. When using Iceberg with these engines, the runtime jar is the only addition to the classpath needed in addition to vendor dependencies. For example, to use Iceberg with Spark 3.2 and AWS integrations, iceberg-spark-runtime-3.2_2.12 and AWS SDK dependencies are needed for the Spark installation.

Spark and Flink provide different runtime jars for each supported engine version. Hive 2 and Hive 3 currently share the same runtime jar. The runtime jar names and latest version download links are listed in the tables below.

Engine Version Lifecycle

Each engine version undergoes the following lifecycle stages:

  1. Beta: a new engine version is supported, but still in the experimental stage. Maybe the engine version itself is still in preview (e.g. Spark 3.0.0-preview), or the engine does not yet have full feature compatibility compared to old versions yet. This stage allows Iceberg to release an engine version support without the need to wait for feature parity, shortening the release time.
  2. Maintained: an engine version is actively maintained by the community. Users can expect parity for most features across all the maintained versions. If a feature has to leverage some new engine functionalities that older versions don’t have, then feature parity across maintained versions is not guaranteed.
  3. Deprecated: an engine version is no longer actively maintained. People who are still interested in the version can backport any necessary feature or bug fix from newer versions, but the community will not spend effort in achieving feature parity. Iceberg recommends users to move towards a newer version. Contributions to a deprecated version is expected to diminish over time, so that eventually no change is added to a deprecated version.
  4. End-of-life: a vote can be initiated in the community to fully remove a deprecated version out of the Iceberg repository to mark as its end of life.

Current Engine Version Lifecycle Status

Apache Spark

VersionLifecycle StageInitial Iceberg SupportLatest Iceberg SupportLatest Runtime Jar
2.4End of Life0.7.0-incubating1.2.1iceberg-spark-runtime-2.4
3.0End of Life0.
3.1Deprecated0. [1]
  • [1] Spark 3.1 shares the same runtime jar iceberg-spark3-runtime with Spark 3.0 before Iceberg 0.13.0

Based on the guideline of the Flink community, only the latest 2 minor versions are actively maintained. Users should continuously upgrade their Flink version to stay up-to-date.

VersionLifecycle StageInitial Iceberg SupportLatest Iceberg SupportLatest Runtime Jar
1.11End of Life0.
1.12End of Life0. [3]
1.13End of Life0.
1.14End of Life0.
  • [3] Flink 1.12 shares the same runtime jar iceberg-flink-runtime with Flink 1.11 before Iceberg 0.13.0

Apache Hive

VersionRecommended minor versionLifecycle StageInitial Iceberg SupportLatest Iceberg SupportLatest Runtime Jar

Developer Guide

Maintaining existing engine versions

Iceberg recommends the following for developers who are maintaining existing engine versions:

  1. New features should always be prioritized first in the latest version, which is either a maintained or beta version.
  2. For features that could be backported, contributors are encouraged to either perform backports to all maintained versions, or at least create some issues to track the backport.
  3. If the change is small enough, updating all versions in a single PR is acceptable. Otherwise, using separated PRs for each version is recommended.

Supporting new engines

Iceberg recommends new engines to build support by importing the Iceberg libraries to the engine’s project. This allows the Iceberg support to evolve with the engine. Projects such as Trino and Presto are good examples of such support strategy.

In this approach, an Iceberg version upgrade is needed for an engine to consume new Iceberg features. To facilitate engine development against unreleased Iceberg features, a daily snapshot is published in the Apache snapshot repository.

If bringing an engine directly to the Iceberg main repository is needed, please raise a discussion thread in the Iceberg community.