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Release 0.15.0

Release 0.15.0 (docs)

Apache Hudi 0.15.0 release brings enhanced engine integration, new features, and improvements in several areas. These include Spark 3.5 and Scala 2.13 support, Flink 1.18 support, better Trino Hudi native connector support with newly introduced Hadoop-agnostic storage and I/O abstractions. We encourage users to review the Release Highlights and Migration Guide down below on relevant module and API changes and behavior changes before using the 0.15.0 release.

Migration Guide

This release keeps the same table version (6) as 0.14.0 release, and there is no need for a table version upgrade if you are upgrading from 0.14.0. There are a few module and API changes and behavior changes as described below, and users are expected to take action accordingly before using 0.15.0 release.


If migrating from an older release (pre-0.14.0), please also check the upgrade instructions from each older release in sequence.

Bundle Updates

New Spark Bundles

We have expanded Hudi support to Spark 3.5 with two new bundles:

New Utilities Bundles for Scala 2.13

Besides adding a bundle for Spark 3.5 and Scala 2.13, we have added new utilities bundles to use with Scala 2.13, hudi-utilities-bundle_2.13 and hudi-utilities-slim-bundle_2.13.

We have expanded Hudi support to Flink 1.18 with a new bundle, hudi-flink1.18-bundle. This release removes Hudi support on Flink 1.13.

Module and API Changes

Hudi Storage and I/O Abstractions

This release introduces new storage and I/O abstractions that are Hadoop-agnostic to improve integration with query engines, including Trino, which uses its own native File System APIs. Core Hudi classes including HoodieTableMetaClient, HoodieBaseFile, HoodieLogFile, HoodieEngineContext, etc., now depend on new storage and I/O classes. If you’re using these classes directly in your applications, you need to change your integration code and usage. For more details, check out this section.

Module Changes

As part of introducing new storage and I/O abstractions and making core reader logic Hadoop-agnostic, this release restructures the Hudi modules to clearly reflect the layering. Specifically,

If you use hudi-common module as the dependency before and Hadoop file system APIs and implementations, you should include all three modules, hudi-io, hudi-common, and hudi-hadoop-common, as the dependency now. Note that, Presto and Trino will be released based on Hudi 0.15.0 release with such changes.

Lock Provider API Change

The LockProvider instantiation now expects the StorageConfiguration instance as the second argument of the constructor. If you extend LockProvider to implement a custom lock provider before, you need to change the constructor to match the aforementioned constructor signature. Here's an example:

public class XYZLockProvider implements LockProvider<String>, Serializable {

public XYZLockProvider(final LockConfiguration lockConfiguration, final StorageConfiguration<?> conf) {


Behavior Changes

Improving Cleaning Table Service

We have improved the default cleaner behavior to only schedule a new cleaner plan if there is no inflight plan, by flipping the default of hoodie.clean.allow.multiple from true to false. This simplifies cleaning table service when the metadata table is enabled. The config is deprecated now and will be removed after the next release.

Allowing Duplicates on Inserts

We now allow duplicate keys on INSERT operation by default, even if inserts are routed to merge with an existing file (for ensuring file sizing), by flipping the default of hoodie.merge.allow.duplicate.on.inserts from false to true. This is only relevant for INSERT operation, since UPSERT, and DELETE operations always ensure unique key constraint.

Sync MOR Snapshot to The Metastore

To better support snapshot queries on MOR tables on OLAP engines, the MOR snapshot or RT is synced to the metastore with the table name by default, by flipping the default of hoodie.meta.sync.sync_snapshot_with_table_name from false to true.

The default value of read.streaming.skip_clustering is false before this release, which could cause the situation that Flink streaming reading reads the replaced file slices of clustering and duplicated data (same concern for read.streaming.skip_compaction). The 0.15.0 release makes Flink streaming read to skip clustering and compaction instants for all cases to avoid reading the relevant file slices, by flipping the default of read.streaming.skip_clustering and read.streaming.skip_compaction from false to true.

Release Highlights

Hudi Storage and I/O Abstractions

To provide better integration experience with query engines including Trino which uses its own native File System APIs, this release introduces new storage and I/O abstractions that are Hadoop-agnostic.

To be specific, the release introduces Hudi storage abstraction HoodieStorage which provides all I/O APIs to read and write files and directories on storage, such as open, read, etc. This class can be extended to implement storage layer optimizations like caching, federated storage layout, hot/cold storage separation, etc. This class needs to be implemented based on particular systems, such as Hadoop’s FileSystem and Trino’s TrinoFileSystem. Core classes are introduced for accessing file systems:

  • StoragePath: represents a path of a file or directory on storage, which replaces Hadoop's Path.
  • StoragePathInfo: keeps the path, length, isDirectory, modification time, and other information which are used by Hudi, which replaces Hadoop's FileStatus.
  • StorageConfiguration: provides the storage configuration by wrapping the particular configuration class object used by the corresponding file system.

The HoodieIOFactory abstraction is introduced to provide APIs to create readers and writers for I/O without depending on Hadoop classes.

By using the new storage and I/O abstractions, we make the hudi-common module and core reader logic in Hudi Hadoop-independent in this release. We have introduced a new hudi-hadoop-common module which contains the implementation of HoodieStorage and HoodieIOFactory based on Hadoop’s file system APIs and implementation, and existing reader and writer logic that depends on Hadoop-dependent APIs. The hudi-hadoop-common module is used by Spark, Flink, Hive, and Presto integration where the logic remains unchanged.

For the engine that is independent of Hadoop, the integration should use the hudi-common module and plug in its own implementation of HoodieStorage and HoodieIOFactory by setting the new configs and through the storage configuration.

Engine Support

Spark 3.5 and Scala 2.13 Support

This release has added the Spark 3.5 support and Scala 2.13 support; users who are on Spark 3.5 can use the new Spark bundle based on the Scala version: hudi-spark3.5-bundle_2.12 and hudi-spark3.5-bundle_2.13. Spark 3.4, 3.3, 3.2, 3.1, 3.0 and 2.4 continue to be supported in this release. To quickly get started with Hudi and Spark 3.5, you can explore our quick start guide.

This release has added the Flink 1.18 support with a new compile maven profile flink1.18 and new Flink bundle hudi-flink1.18-bundle.

Hudi-Native HFile Reader

Hudi uses HFile format as the base file format for storing various metadata, e.g., file listing, column stats, and bloom filters, in the metadata table (MDT), as HFile format is optimized for range scans and point lookups. HFile format is originally designed and implemented by HBase. To avoid HBase dependency conflict and make engine integration easy with Hadoop-independent implementation, we have implemented a new HFile reader in Java which is independent of HBase or Hadoop dependencies. This HFile reader is backwards compatible with existing Hudi releases and storage format.

We have also written a HFile Format Specification, that defines the HFile Format required by Hudi. This makes HFile reader and writer implementation possible in any language, for example, C++ or Rust, by following this Spec.

New Features in Hudi Utilities

StreamContext and SourceProfile Interfaces

For the Hudi streamer, we have introduced the new StreamContext and SourceProfile Interfaces. These are designed to contain details about how the data should be consumed from the source and written (e.g., parallelism) in the next sync round in StreamSync. This allows users to control the behavior and performance of source reading and data writing to the target Hudi table.

Enhanced Proto Kafka Source Support

We have added the support for deserializing with the Confluent proto deserializer, through a new config hoodie.streamer.source.kafka.proto.value.deserializer.class to specify the Kafka Proto payload deserializer class.

Ignoring Checkpoint in Hudi Streamer

Hudi streamer has a new option, --ignore-checkpoint, to ignore the last committed checkpoint for the source. This options should be set with a unique value, a timestamp value or UUID as recommended. Setting this config indicates that the subsequent sync should ignore the last committed checkpoint for the source. The config value is stored in the commit history, so setting the config with same values would not have any affect. This config can be used in scenarios like kafka topic change where we would want to start ingesting from the latest or earliest offset after switching the topic (in this case we would want to ignore the previously committed checkpoint, and rely on other configs to pick the starting offset).

Meta Sync Improvements

Paralleled Listing in Glue Catalog Sync

AWS Glue Catalog sync now supports paralleled listing of partitions to improve the listing performance and reduce meta sync latency. Three new configs are added to control the listing parallelism:

BigQuery Sync Optimization with Metadata Table

The BigQuery Sync now loads all partitions from the metadata table once if the metadata table is enabled, to improve the file listing performance.

Metrics Reporting to M3

A new MetricsReporter implementation M3MetricsReporter is added to support emitting metrics to M3. Users can now enable reporting metrics to M3 by setting hoodie.metrics.reporter.type as M3 and their corresponding host address and port in and hoodie.metrics.m3.port.

Other Features and Improvements

Schema Exception Classification

This release introduces the classification of schema-related exceptions (HUDI-7486) to make it easy for users to understand the root cause, including the errors during converting the records from Avro to Spark Row due to illegal schema, or the records are incompatible with the provided schema.

Record Size Estimation Improvement

The record size estimation in Hudi is improved by considering replace commits and delta commits additionally (HUDI-7429).

Using s3 Scheme for Athena

Recent Athena version silently drops Hudi data when the partition location has a s3a scheme. Recreating the table with partition s3 scheme fixes the issue. We have added a fix to use s3 scheme for the Hudi table partitions in AWS Glue Catalog sync (HUDI-7362).

Raw Release Notes

The raw release notes are available here.