Project: Flink (App Definition and Development / Streaming & Messaging)
File: flink-1.19.md

title: "Release Notes - Flink 1.19" <!-- Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to you under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> 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. 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. `RestoreMode#LEGACY` is deprecated. Please use `RestoreMode#CLAIM` or `RestoreMode#NO_CLAIM` mode instead to get a clear state file ownership when restoring. 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. `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. 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. In Flink 1.19, the old method of resolving schema compatibility has been deprecated and the new one is introduced. See for more details. Please migrate to the new method following . 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 . 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`

We strongly recommend that users and developers use the ConfigOption-based get and set methods directly. `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 (). 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 . 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. 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 `execution.batch.adaptive.auto-parallelism.default-source-parallelism` 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 `execution.batch.adaptive.auto-parallelism.max-parallelism` 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. 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. `AkkaOptions` are deprecated and replaced with `RpcOptions`. 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

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. 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. 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 ). 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 `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 . 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. According to 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`,

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. The non-ConfigOption objects in the `StreamExecutionEnvironment`, `CheckpointConfig`, and `ExecutionConfig` and their corresponding getter/setter interfaces are now be deprecated in . 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 . 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 on how to adapt your custom connectors to it. Flink introduces `env.java.opts.sql-gateway` 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. 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 . 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. Support mini-batch regular join to reduce intermediate result and resolve record amplification in cascading join scenarios. More details can be found at . 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. The Window aggregation operator (produced by Window TVF) can consume a changelog stream generated by nodes such as a CDC connector. Users can use SESSION Window TVF in streaming mode. More details can be found at . 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 and . This fix resolves the issue where the `gs.storage.root.url` 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.