Release Notes - Flink 1.19
This documentation is for an unreleased version of Apache Flink. We recommend you use the latest stable version.

Release notes - Flink 1.19 #

These release notes discuss important aspects, such as configuration, behavior or dependencies, that changed between Flink 1.18 and Flink 1.19. Please read these notes carefully if you are planning to upgrade your Flink version to 1.19.

Dependency upgrades #

Drop support for python 3.7 #

Add support for python 3.11 #

Build System #

Support Java 21 #

Apache Flink was made ready to compile and run with Java 21. This feature is still in beta mode. Issues should be reported in Flink’s bug tracker.

Checkpoints #

Deprecate RestoreMode#LEGACY #

RestoreMode#LEGACY is deprecated. Please use RestoreMode#CLAIM or RestoreMode#NO_CLAIM mode instead to get a clear state file ownership when restoring.

CheckpointsCleaner clean individual checkpoint states in parallel #

Now when disposing of no longer needed checkpoints, every state handle/state file will be disposed in parallel by the ioExecutor, vastly improving the disposing speed of a single checkpoint (for large checkpoints, the disposal time can be improved from 10 minutes to < 1 minute). The old behavior can be restored by setting state.checkpoint.cleaner.parallel-mode to false.

Support using larger checkpointing interval when source is processing backlog #

ProcessingBacklog is introduced to demonstrate whether a record should be processed with low latency or high throughput. ProcessingBacklog can be set by source operators and can be used to change the checkpoint interval of a job during runtime.

Allow triggering Checkpoints through command line client #

The command line interface supports triggering a checkpoint manually. Usage:

./bin/flink checkpoint $JOB_ID [-full]

By specifying the ‘-full’ option, a full checkpoint is triggered. Otherwise an incremental checkpoint is triggered if the job is configured to take incremental ones periodically.

Runtime & Coordination #

Migrate TypeSerializerSnapshot#resolveSchemaCompatibility #

In Flink 1.19, the old method of resolving schema compatibility has been deprecated and the new one is introduced. See FLIP-263 for more details. Please migrate to the new method following link.

Deprecate old serialization config methods and options #

Configuring serialization behavior through hard codes is deprecated, because you need to modify the codes when upgrading the job version. You should configure this via options pipeline.serialization-config, pipeline.force-avro, pipeline.force-kryo, and pipeline.generic-types. Registration of instance-level serializers is deprecated, using class-level serializers instead. For more information and code examples, please refer to link.

Migrate string configuration key to ConfigOption #

We have deprecated all setXxx and getXxx methods except getString(String key, String defaultValue) and setString(String key, String value), such as: setInteger, setLong, getInteger and getLong etc. We strongly recommend that users and developers use the ConfigOption-based get and set methods directly.

Support System out and err to be redirected to LOG or discarded #

System.out and System.err output the content to the taskmanager.out and taskmanager.err files. In a production environment, if flink users use them to print a lot of content, the limits of yarn or kubernetes may be exceeded, eventually causing the TaskManager to be killed. Flink supports redirecting the System.out and System.err to the log file, and the log file can be rolled to avoid unlimited disk usage.

Starting with Flink 1.19, Flink has officially introduced full support for the standard YAML 1.2 syntax (FLIP-366). The default configuration file has been changed to config.yaml and placed in the conf/ directory. Users should directly modify this file to configure Flink. If users want to use the legacy configuration file flink-conf.yaml, they need to copy this file into the conf/ directory. Once the legacy configuration file flink-conf.yaml is detected, Flink will prioritize using it as the configuration file. In the upcoming Flink 2.0, the flink-conf.yaml configuration file will no longer work. More details could be found at flink-configuration-file.

Add config options for administrator JVM options #

A set of administrator JVM options are available, which prepend the user-set extra JVM options for platform-wide JVM tuning.

Fixed a bug where the leader election wasn’t able to pick up leadership again after renewing the lease token caused a leadership loss. This required fabric8io:kubernetes-client to be upgraded from v6.6.2 to v6.9.0.

Support dynamic source parallelism inference for batch jobs #

In Flink 1.19, we have supported dynamic source parallelism inference for batch jobs, which allows source connectors to dynamically infer the parallelism based on the actual amount of data to consume. This feature is a significant improvement over previous versions, which only assigned a fixed default parallelism to source vertices. Source connectors need to implement the inference interface to enable dynamic parallelism inference. Currently, the FileSource connector has already been developed with this functionality in place. Additionally, the configuration will be used as the upper bound of source parallelism inference. And now it will be set to 1 by default. If it is not set, the upper bound of allowed parallelism set via will be used instead. If that configuration is also not set, the default parallelism set via parallelism.default or StreamExecutionEnvironment#setParallelism() will be used instead.

Improve the exponential-delay restart-strategy #

Flink 1.19 makes a series of improvements to exponential-delay restart-strategy, including: optimizing the default values of related options, support for max attempts, and solving the issue of inaccurate attempts in region failover. After these improvements, Flink 1.19 uses exponential-delay restart-strategy as the default restart-strategy.

Renaming AkkaOptions into RpcOptions #

AkkaOptions are deprecated and replaced with RpcOptions.

Add min number of slots configuration to limit total number of slots #

Flink now supports defining the minimum resource requirements that the Flink cluster allocates using the configuration options slotmanager.min-total-resource.cpu, slotmanager.min-total-resource.memory, and slotmanager.number-of-slots.min. These options are intended to ensure that a certain minimum level of resources is allocated to initialize specific workers during startup, thereby speeding up the job startup process. Please note that these configuration options do not have any effect on standalone clusters, as resource allocation in such clusters is not controlled by Flink.

Support adding custom metrics in Recovery Spans #

A breaking change has been introduced to the StateBackend interface. This is relevant only to users that are implementing their own custom state backends. Newly added methods org.apache.flink.runtime.state.StateBackend#createKeyedStateBackend(KeyedStateBackendParameters<K> parameters) and org.apache.flink.runtime.state.StateBackend#createOperatorStateBackend(OperatorStateBackendParameters parameters) have replaced previous versions of the createKeyedStateBackend and createOperatorStateBackend methods. The new parameters POJO classes contain as fields all of the arguments that were passed directly to those methods.

Unify the Representation of TaskManager Location in REST API and Web UI #

Unify the representation of TaskManager location in REST API and Web UI. The host field is deprecated in favor of the newly introduced endpoint field that includes both the host and port information to distinguish multiple TaskManagers on the same host.

In Flink 1.19, we support triggering profiling at the JobManager/TaskManager level, allowing users to create a profiling instance with arbitrary intervals and event modes (supported by async-profiler). Users can easily submit profiles and export results in the Flink Web UI.

For example,

  • First, users should identify the candidate TaskManager/JobManager with performance bottleneck for profiling and switch to the corresponding TaskManager/JobManager page (profiler tab).
  • The user simply clicks on the Create Profiling Instance button to submit a profiling instance with specified period and mode. (The description of the profiling mode will be displayed when hovering over the corresponding mode.)
  • Once the profiling instance is complete, the user can easily download the interactive HTML file by clicking on the link.

More Information


Deprecate RuntimeContext#getExecutionConfig #

RuntimeContext#getExecutionConfig is now being deprecated in Flink 1.19. And this method is planned to be removed in Flink 2.0. More details can be found at FLIP-391.

Deprecate RichFunction#open(Configuration parameters) #

The RichFunction#open(Configuration parameters) method has been deprecated and will be removed in future versions. Users are encouraged to migrate to the new RichFunction#open(OpenContext openContext) method, which provides a more comprehensive context for initialization. Here are the key changes and recommendations for migration: The open(Configuration parameters) method is now marked as deprecated. A new method open(OpenContext openContext) has been added as a default method to the RichFunction interface. Users should implement the new open(OpenContext openContext) method for function initialization tasks. The new method will be called automatically before the execution of any processing methods(map, join, etc.). If the new open(OpenContext openContext) method is not implemented, Flink will fall back to invoking the deprecated open(Configuration parameters) method.

Flink’s Time classes are deprecated now and will be subject to deletion with the release of Flink 2.0. Please start to use Java’s own Duration class, instead. Methods supporting the Duration class that replace the deprecated Time-based methods were introduced.

Add new interfaces for SinkV2 to synchronize the API with the Source API #

According to FLIP-372 the SinkV2 API has been changed. The following interfaces are deprecated: TwoPhaseCommittingSink, StatefulSink, WithPreWriteTopology, WithPreCommitTopology, WithPostCommitTopology. The following new interfaces have been introduced: CommitterInitContext, CommittingSinkWriter, WriterInitContext, StatefulSinkWriter. The following interface method’s parameter has been changed: Sink#createWriter The original interfaces will remain available during the 1.19 release line, but they will be removed in consecutive releases. For the changes required when migrating, please consult the Migration Plan detailed in the FLIP.

Deprecate configuration getters/setters that return/set complex Java objects #

The non-ConfigOption objects in the StreamExecutionEnvironment, CheckpointConfig, and ExecutionConfig and their corresponding getter/setter interfaces are now be deprecated in FLINK-33581. And these objects and methods are planned to be removed in Flink 2.0. The deprecated interfaces include the getter and setter methods of RestartStrategy, CheckpointStorage, and StateBackend. More details can be found at FLIP-381.

Table SQL / API #

Support Setting Parallelism for Table/SQL Sources #

Scan table sources can now be set a custom parallelism for performance tuning via the scan.parallelism option. Currently, only the DataGen connector has been adapted to support that, Kafka connector is on the way. Please check scan-table-source on how to adapt your custom connectors to it.

Adding a separate configuration for specifying Java Options of the SQL Gateway #

Flink introduces for specifying the Java options for the SQL Gateway, which allows you to fine-tune the memory settings, garbage collection behavior, and other relevant Java parameters.

Support Configuring Different State TTLs using SQL Hint #

This is a new feature in Apache Flink 1.19 that enhances the flexibility and user experience when managing SQL state time-to-live (TTL) settings. Users can now specify custom TTL values for regular joins and group aggregations directly within their queries by utilizing the STATE_TTL hint. This improvement means that you no longer need to alter your compiled plan to set specific TTLs for these operators. With the introduction of STATE_TTL hints, you can streamline your workflow and dynamically adjust the TTL based on your operational requirements.

MiniBatch Optimization for Regular Joins #

Support mini-batch regular join to reduce intermediate result and resolve record amplification in cascading join scenarios. More details can be found at minibatch-regular-joins.

Support named parameters for functions and procedures #

When calling a function or stored procedure now, named parameters can be used. With named parameters, we do not need to strictly specify the parameter position, just specify the parameter name and its corresponding value. At the same time, if non-essential parameters are not specified, they will default to being filled with null.

Window TVF Aggregation Supports Changelog Inputs #

The Window aggregation operator (produced by Window TVF) can consume a changelog stream generated by nodes such as a CDC connector.

Supports SESSION Window TVF in Streaming Mode #

Users can use SESSION Window TVF in streaming mode. More details can be found at session window-tvf.

Connectors #

Add committer metrics to track the status of committables #

The TwoPhaseCommittingSink#createCommitter method parameterization has been changed, a new CommitterInitContext parameter has been added. The original method will remain available during the 1.19 release line, but they will be removed in consecutive releases. When migrating please also consider changes introduced by FLINK-33973 and FLIP-372.

FileSystems #

GCS filesystem does not respect config option #

This fix resolves the issue where the setting in the Hadoop configuration was not being acknowledged by the Sink. Warning: If you have been using this property to configure the GCS Source, please ensure that your tests or pipelines are not adversely affected by the GCS Sink now also correctly adhering to this configuration.