| --- |
| title: "Release Notes - Flink 1.14" |
| --- |
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| |
| # Release notes - Flink 1.14 |
| |
| These release notes discuss important aspects, such as configuration, behavior, or dependencies, |
| that changed between Flink 1.13 and Flink 1.14. Please read these notes carefully if you are |
| planning to upgrade your Flink version to 1.14. |
| |
| ### Known issues |
| |
| #### State migration issues |
| |
| Some of our internal serializers i.e. RowSerializer, TwoPhaseCommitSinkFunction's serializer, |
| LinkedListSerializer might prevent a successful job starts if state migration is necessary. |
| The fix is tracked by [FLINK-24858](https://issues.apache.org/jira/browse/FLINK-24858). We recommend |
| to immediately upgrade to 1.14.3 when migrating from 1.13.x. |
| |
| #### Memory leak with Pulsar connector on Java 11 |
| |
| Netty, which the Pulsar client uses underneath, allocates memory differently on Java 11 and Java 8. On Java |
| 11, it will allocate memory from the pool of Java Direct Memory and is affected by the |
| MaxDirectMemory limit. The current Pulsar client has no configuration options for controlling the memory limits, |
| which can lead to OOM(s). |
| |
| Users are advised to use the Pulsar connector with Java 8 or overprovision memory for Flink. Read the |
| [memory setup guide]({{< ref "docs/deployment/memory/mem_setup_tm" >}}) on how to configure memory |
| for Flink and track the proper solution in [FLINK-24302](https://issues.apache.org/jira/browse/FLINK-24302). |
| |
| ### Summary of changed dependency names |
| |
| There are two changes in Flink 1.14 that require updating dependency names when upgrading from earlier versions. |
| |
| * The removal of the Blink planner ([FLINK-22879](https://issues.apache.org/jira/browse/FLINK-22879)) |
| requires the removal of the `blink` infix. |
| * Due to [FLINK-14105](https://issues.apache.org/jira/browse/FLINK-14105), if you have a dependency on `flink-runtime`, `flink-optimizer` and/or `flink-queryable-state-runtime`, the Scala suffix (`_2.11`/`_2.12`) needs to be removed from the `artifactId`. |
| |
| ### Table API & SQL |
| |
| #### Use pipeline name consistently across DataStream API and Table API |
| |
| ##### [FLINK-23646](https://issues.apache.org/jira/browse/FLINK-23646) |
| |
| The default job name for DataStream API programs in batch mode has changed from `"Flink Streaming Job"` to |
| `"Flink Batch Job"`. A custom name can be set with the config option `pipeline.name`. |
| |
| #### Propagate unique keys for fromChangelogStream |
| |
| ##### [FLINK-24033](https://issues.apache.org/jira/browse/FLINK-24033) |
| |
| Compared to 1.13.2, `StreamTableEnvironment.fromChangelogStream` might produce a different stream |
| because primary keys were not properly considered before. |
| |
| #### Support new type inference for Table#flatMap |
| |
| ##### [FLINK-16769](https://issues.apache.org/jira/browse/FLINK-16769) |
| |
| `Table.flatMap()` supports the new type system now. Users are requested to upgrade their functions. |
| |
| #### Add Scala implicit conversions for new API methods |
| |
| ##### [FLINK-22590](https://issues.apache.org/jira/browse/FLINK-22590) |
| |
| The Scala implicits that convert between DataStream API and Table API have been updated to the new |
| methods of [FLIP-136](https://cwiki.apache.org/confluence/display/FLINK/FLIP-136%3A++Improve+interoperability+between+DataStream+and+Table+API). |
| |
| The changes might require an update of pipelines that used `toTable` or implicit conversions from |
| `Table` to `DataStream[Row]`. |
| |
| #### Remove YAML environment file support in SQL Client |
| |
| ##### [FLINK-22540](https://issues.apache.org/jira/browse/FLINK-22540) |
| |
| The `sql-client-defaults.yaml` file was deprecated in the 1.13 release and is now completely removed. |
| As an alternative, you can use the `-i` startup option to execute an SQL initialization |
| file to set up the SQL Client session. The SQL initialization file can use Flink DDLs to |
| define available catalogs, table sources and sinks, user-defined functions, and other properties |
| required for execution and deployment. |
| |
| See more: https://nightlies.apache.org/flink/flink-docs-release-1.14/docs/dev/table/sqlclient/#initialize-session-using-sql-files |
| |
| #### Remove the legacy planner code base |
| |
| ##### [FLINK-22864](https://issues.apache.org/jira/browse/FLINK-22864) |
| |
| The old Table/SQL planner has been removed. `BatchTableEnvironment` and DataSet API interoperability with Table |
| API are not supported anymore. Use the unified `TableEnvironment` for batch and stream processing with |
| the new planner or the DataStream API in batch execution mode. |
| |
| Users are encouraged to update their pipelines. Otherwise Flink 1.13 is the last version that offers |
| the old functionality. |
| |
| #### Remove the "blink" suffix from table modules |
| |
| ##### [FLINK-22879](https://issues.apache.org/jira/browse/FLINK-22879) |
| |
| The following Maven modules have been renamed: |
| * flink-table-planner-blink -> flink-table-planner |
| * flink-table-runtime-blink -> flink-table-runtime |
| * flink-table-uber-blink -> flink-table-uber |
| |
| It might be required to update job JAR dependencies. Note that |
| `flink-table-planner` and `flink-table-uber` used to contain the legacy planner before Flink 1.14 and |
| now they contain the only officially supported planner (i.e. previously known as 'Blink' planner). |
| |
| #### Remove BatchTableEnvironment and related API classes |
| |
| ##### [FLINK-22877](https://issues.apache.org/jira/browse/FLINK-22877) |
| |
| Due to the removal of `BatchTableEnvironment`, `BatchTableSource` and `BatchTableSink` have been removed |
| as well. Use `DynamicTableSource` and `DynamicTableSink` instead. They support the old `InputFormat` and |
| `OutputFormat` interfaces as runtime providers if necessary. |
| |
| #### Remove TableEnvironment#connect |
| |
| ##### [FLINK-23063](https://issues.apache.org/jira/browse/FLINK-23063) |
| |
| The deprecated `TableEnvironment.connect()` method has been removed. Use the |
| new `TableEnvironment.createTemporaryTable(String, TableDescriptor)` to create tables |
| programmatically. Please note that this method only supports sources and sinks that comply with |
| [FLIP-95](https://cwiki.apache.org/confluence/display/FLINK/FLIP-95%3A+New+TableSource+and+TableSink+interfaces). |
| This is also indicated by the new property design `'connector'='kafka'` instead of `'connector.type'='kafka'`. |
| |
| #### Deprecate toAppendStream and toRetractStream |
| |
| ##### [FLINK-23330](https://issues.apache.org/jira/browse/FLINK-23330) |
| |
| The outdated variants of `StreamTableEnvironment.{fromDataStream|toAppendStream|toRetractStream)` |
| have been deprecated. Use the `(from|to)(Data|Changelog)Stream` alternatives introduced in 1.13. |
| |
| #### Remove old connectors and formats stack around descriptors |
| |
| ##### [FLINK-23513](https://issues.apache.org/jira/browse/FLINK-23513) |
| |
| The legacy versions of the SQL Kafka connector and SQL Elasticsearch connector have been removed |
| together with their corresponding legacy formats. DDL or descriptors that still use `'connector.type='` or |
| `'format.type='` options need to be updated to the new connector and formats available via the `'connector='` option. |
| |
| #### Drop BatchTableSource/Sink HBaseTableSource/Sink and related classes |
| |
| ##### [FLINK-22623](https://issues.apache.org/jira/browse/FLINK-22623) |
| |
| The `HBaseTableSource/Sink` and related classes including various `HBaseInputFormats` and |
| `HBaseSinkFunction` have been removed. It is possible to read via the Table & SQL API and convert the |
| Table to DataStream API (or vice versa) if necessary. The DataSet API is not supported anymore. |
| |
| #### Drop BatchTableSource ParquetTableSource and related classes |
| |
| ##### [FLINK-22622](https://issues.apache.org/jira/browse/FLINK-22622) |
| |
| The `ParquetTableSource` and related classes including various `ParquetInputFormats` have been removed. |
| Use the FileSystem connector with a Parquet format as a replacement. It is possible to read via the |
| Table & SQL API and convert the Table to DataStream API if necessary. The DataSet API is not supported |
| anymore. |
| |
| #### Drop BatchTableSource OrcTableSource and related classes |
| |
| ##### [FLINK-22620](https://issues.apache.org/jira/browse/FLINK-22620) |
| |
| The `OrcTableSource` and related classes (including `OrcInputFormat`) have been removed. Use the |
| FileSystem connector with an ORC format as a replacement. It is possible to read via the Table & SQL API |
| and convert the Table to DataStream API if necessary. The DataSet API is not supported anymore. |
| |
| #### Drop usages of BatchTableEnvironment and the old planner in Python |
| |
| ##### [FLINK-22619](https://issues.apache.org/jira/browse/FLINK-22619) |
| |
| The Python API does not offer a dedicated `BatchTableEnvironment` anymore. Instead, users can switch |
| to the unified `TableEnvironment` for both batch and stream processing. Only the Blink planner (the |
| only remaining planner in 1.14) is supported. |
| |
| #### Migrate ModuleFactory to the new factory stack |
| |
| ##### [FLINK-23720](https://issues.apache.org/jira/browse/FLINK-23720) |
| |
| The `LOAD/UNLOAD MODULE` architecture for table modules has been updated to the new factory stack of |
| [FLIP-95](https://cwiki.apache.org/confluence/display/FLINK/FLIP-95%3A+New+TableSource+and+TableSink+interfaces). |
| Users of this feature should update their `ModuleFactory` implementations. |
| |
| #### Migrate Table API to new KafkaSink |
| |
| ##### [FLINK-23639](https://issues.apache.org/jira/browse/FLINK-23639) |
| |
| Table API/SQL now writes to Kafka with the new `KafkaSink`. When migrating from a query writing to Kafka |
| in exactly-once mode from an earlier Flink version, make sure to terminate the old application with |
| stop-with-savepoint to avoid lingering Kafka transactions. To run in exactly-once processing mode, |
| the sink needs a user-configured and unique transaction prefix, such that transactions of different |
| applications do not interfere with each other. |
| |
| ### DataStream API |
| |
| #### Fixed idleness handling for two/multi input operators |
| |
| ##### [FLINK-18934](https://issues.apache.org/jira/browse/FLINK-18934) |
| |
| ##### [FLINK-23767](https://issues.apache.org/jira/browse/FLINK-23767) |
| |
| We added `processWatermarkStatusX` method to classes such as `AbstractStreamOperator`, `Input` etc. |
| It allows to take the `WatermarkStatus` into account when combining watermarks in two/multi input |
| operators. |
| |
| Note that with this release, we renamed the previously internal `StreamStatus` to `WatermarkStatus` |
| in order to better reflect its purpose. |
| |
| #### Allow @TypeInfo annotation on POJO field declarations |
| |
| ##### [FLINK-12141](https://issues.apache.org/jira/browse/FLINK-12141) |
| |
| `@TypeInfo` annotations can now also be used on POJO fields which, for example, can help to define |
| custom serializers for third-party classes that can otherwise not be annotated themselves. |
| |
| #### Clarify SourceFunction#cancel() contract about interruptions |
| |
| ##### [FLINK-23527](https://issues.apache.org/jira/browse/FLINK-23527) |
| |
| Contract of the `SourceFunction.cancel()` method with respect to interruptions has been clarified: |
| - source itself should not be interrupting the source thread |
| - interrupt should not be expected in the clean cancellation case |
| |
| #### Expose a consistent GlobalDataExchangeMode |
| |
| ##### [FLINK-23402](https://issues.apache.org/jira/browse/FLINK-23402) |
| |
| The default DataStream API shuffle mode for batch executions has been changed to blocking exchanges |
| for all edges of the stream graph. A new option `execution.batch-shuffle-mode` allows you to change it |
| to pipelined behavior if necessary. |
| |
| ### Python API |
| |
| #### Support loopback mode to allow Python UDF worker and client to reuse the same Python VM |
| |
| ##### [FLINK-21222](https://issues.apache.org/jira/browse/FLINK-21222) |
| |
| Instead of launching a separate Python process, the Python UDF worker will reuse the Python process of |
| the client side when running jobs locally. This makes it easier to debug Python UDFs. |
| |
| #### Support Python UDF chaining in Python DataStream API |
| |
| ##### [FLINK-22913](https://issues.apache.org/jira/browse/FLINK-22913) |
| |
| The job graph of Python DataStream API jobs may be different from before as Python functions |
| will be chained as much as possible to optimize performance. You could disable Python functions |
| chaining by explicitly setting `python.operator-chaining.enabled` as `false`. |
| |
| ### Connectors |
| |
| #### Expose standardized operator metrics (FLIP-179) |
| |
| ##### [FLINK-23652](https://issues.apache.org/jira/browse/FLINK-23652) |
| |
| Connectors using the unified Source and Sink interface will expose certain standardized metrics |
| automatically. Applications that use `RuntimeContext#getMetricGroup` need to be rebuild against |
| 1.14 before being submitted to a 1.14 cluster. |
| |
| #### Port KafkaSink to new Unified Sink API (FLIP-143) |
| |
| ##### [FLINK-22902](https://issues.apache.org/jira/browse/FLINK-22902) |
| |
| `KafkaSink` supersedes `FlinkKafkaProducer` and provides efficient exactly-once and at-least-once |
| writing with the new unified sink interface, supporting both batch and streaming mode of DataStream |
| API. To upgrade, please stop with savepoint. To run in exactly-once processing mode, `KafkaSink` |
| needs a user-configured and unique transaction prefix, such that transactions of different |
| applications do not interfere with each other. |
| |
| #### Deprecate FlinkKafkaConsumer |
| |
| ##### [FLINK-24055](https://issues.apache.org/jira/browse/FLINK-24055) |
| |
| `FlinkKafkaConsumer` has been deprecated in favor of `KafkaSource`. To upgrade to the new version, |
| please store the offsets in Kafka with `setCommitOffsetsOnCheckpoints` in the |
| old `FlinkKafkaConsumer` and then stop with a savepoint. When resuming from the savepoint, please |
| use `setStartingOffsets(OffsetsInitializer.committedOffsets())` in the new `KafkaSourceBuilder` to |
| transfer the offsets to the new source. |
| |
| #### InputStatus should not contain END_OF_RECOVERY |
| |
| ##### [FLINK-23474](https://issues.apache.org/jira/browse/FLINK-23474) |
| |
| `InputStatus.END_OF_RECOVERY` was removed. It was an internal flag that should never be returned from |
| SourceReaders. Returning that value in earlier versions might lead to misbehavior. |
| |
| #### Connector-base exposes dependency to flink-core. |
| |
| ##### [FLINK-22964](https://issues.apache.org/jira/browse/FLINK-22964) |
| |
| Connectors do not transitively hold a reference to `flink-core` anymore. That means that a fat JAR |
| with a connector does not include `flink-core` with this fix. |
| |
| ### Runtime & Coordination |
| |
| #### Increase akka.ask.timeout for tests using the MiniCluster |
| |
| ##### [FLINK-23906](https://issues.apache.org/jira/browse/FLINK-23906) |
| |
| The default `akka.ask.timeout` used by the `MiniCluster` has been increased to 5 minutes. If you |
| want to use a smaller value, then you have to set it explicitly in the passed configuration. |
| |
| The change is due to the fact that messages can not get lost in a single-process `MiniCluster`, so this |
| timeout (which otherwise helps to detect message loss in distributed setups) has no benefit here. |
| |
| The increased timeout reduces the number of false-positive timeouts, for example, during heavy tests |
| on loaded CI/CD workers or during debugging. |
| |
| #### The node IP obtained in NodePort mode is a VIP |
| |
| ##### [FLINK-23507](https://issues.apache.org/jira/browse/FLINK-23507) |
| |
| When using `kubernetes.rest-service.exposed.type=NodePort`, the connection string for the REST gateway is |
| now correctly constructed in the form `<nodeIp>:<nodePort>` instead of |
| `<kubernetesApiServerUrl>:<nodePort>`. This may be a breaking change for some users. |
| |
| This also introduces a new config option `kubernetes.rest-service.exposed.node-port-address-type` that |
| lets you select `<nodeIp>` from a desired range. |
| |
| #### Timeout heartbeat if the heartbeat target is no longer reachable |
| |
| ##### [FLINK-23209](https://issues.apache.org/jira/browse/FLINK-23209) |
| |
| Flink now supports detecting dead TaskManagers via the number of consecutive failed heartbeat RPCs. |
| The threshold until a TaskManager is marked as unreachable can be configured |
| via `heartbeat.rpc-failure-threshold`. This can speed up the detection of dead TaskManagers |
| significantly. |
| |
| #### RPCs fail faster when target is unreachable |
| |
| ##### [FLINK-23202](https://issues.apache.org/jira/browse/FLINK-23202) |
| |
| The same way Flink detects unreachable heartbeat targets faster, Flink now also immediately fails |
| RPCs where the target is known by the OS to be unreachable on a network level, instead of waiting |
| for a timeout (`akka.ask.timeout`). |
| |
| This creates faster task failovers because cancelling tasks on a dead TaskExecutor no longer |
| gets delayed by the RPC timeout. |
| |
| If this faster failover is a problem in certain setups (which might rely on the fact that external |
| systems hit timeouts), we recommend configuring the application's restart strategy with a restart |
| delay. |
| |
| #### Changes in accounting of IOExceptions when triggering checkpoints on JobManager |
| |
| ##### [FLINK-23189](https://issues.apache.org/jira/browse/FLINK-23189) |
| |
| In previous versions, IOExceptions thrown from the JobManager would not fail the entire Job. We |
| changed the way we track those exceptions and now they do increase the number of checkpoint |
| failures. |
| |
| The number of tolerable checkpoint failures can be adjusted or disabled via: |
| `org.apache.flink.streaming.api.environment.CheckpointConfig#setTolerableCheckpointFailureNumber` |
| (which is set to 0 by default). |
| |
| #### Refine ShuffleMaster lifecycle management for pluggable shuffle service framework |
| |
| ##### [FLINK-22910](https://issues.apache.org/jira/browse/FLINK-22910) |
| |
| We improved the `ShuffleMaster` interface by adding some lifecycle methods, including `open`, `close`, |
| `registerJob` and `unregisterJob`. Besides, the `ShuffleMaster` now becomes a cluster level service which |
| can be shared by multiple jobs. This is a breaking change to the pluggable shuffle service framework |
| and the customized shuffle plugin needs to adapt to the new interface accordingly. |
| |
| #### Group job specific ZooKeeper HA services under common jobs/<JobID> zNode |
| |
| ##### [FLINK-22636](https://issues.apache.org/jira/browse/FLINK-22636) |
| |
| The ZooKeeper job-specific HA services are now grouped under a zNode with the respective `JobID`. |
| Moreover, the config options `high-availability.zookeeper.path.latch`, |
| `high-availability.zookeeper.path.leader`, `high-availability.zookeeper.path.checkpoints`, |
| and `high-availability.zookeeper.path.checkpoint-counter` have been removed and, thus, no |
| longer have an effect. |
| |
| #### Fallback value for taskmanager.slot.timeout |
| |
| ##### [FLINK-22002](https://issues.apache.org/jira/browse/FLINK-22002) |
| |
| The config option `taskmanager.slot.timeout` now falls back to `akka.ask.timeout` if no value has |
| been configured. Previously, the default value for `taskmanager.slot.timeout` was `10 s`. |
| |
| #### DuplicateJobSubmissionException after JobManager failover |
| |
| ##### [FLINK-21928](https://issues.apache.org/jira/browse/FLINK-21928) |
| |
| The fix for this problem only works if the ApplicationMode is used with a single job submission and |
| if the user code does not access the `JobExecutionResult`. If any of these conditions is violated, |
| then Flink cannot guarantee that the whole Flink application is executed. |
| |
| Additionally, it is still required that the user cleans up the corresponding HA entries for the |
| running jobs registry because these entries won't be reliably cleaned up when encountering the |
| situation described by FLINK-21928. |
| |
| #### Zookeeper node under leader and leaderlatch is not deleted after job finished |
| |
| ##### [FLINK-20695](https://issues.apache.org/jira/browse/FLINK-20695) |
| |
| The `HighAvailabilityServices` interface has received a new method `cleanupJobData` which can be implemented |
| in order to clean up job-related HA data after a given job has terminated. |
| |
| #### Optimize scheduler performance for large-scale jobs |
| |
| ##### [FLINK-21110](https://issues.apache.org/jira/browse/FLINK-21110) |
| |
| The performance of the scheduler has been improved to reduce the time of execution graph creation, |
| task deployment, and task failover. This improvement is significant to large scale jobs which |
| currently may spend minutes on the processes mentioned above. This improvement also helps to avoid |
| cases when the job manager main thread gets blocked for too long and leads to heartbeat timeout. |
| |
| ### Checkpoints |
| |
| #### The semantic of alignmentTimeout configuration has changed meaning |
| |
| ##### [FLINK-23041](https://issues.apache.org/jira/browse/FLINK-23041) |
| |
| The semantic of alignmentTimeout configuration has changed meaning and now it is measured as the |
| time between the start of a checkpoint (on the checkpoint coordinator) and the time when the |
| checkpoint barrier is received by a task. |
| |
| #### Disable unaligned checkpoints for BROADCAST exchanges |
| |
| ##### [FLINK-22815](https://issues.apache.org/jira/browse/FLINK-22815) |
| |
| Broadcast partitioning can not work with unaligned checkpointing. There are no guarantees that |
| records are consumed at the same rate in all channels. This can result in some tasks applying state |
| changes corresponding to a certain broadcasted event while others do not. Upon restore, it |
| may lead to an inconsistent state. |
| |
| #### DefaultCompletedCheckpointStore drops unrecoverable checkpoints silently |
| |
| ##### [FLINK-22502](https://issues.apache.org/jira/browse/FLINK-22502) |
| |
| On recovery, if a failure occurs during retrieval of a checkpoint, the job is restarted (instead of |
| skipping the checkpoint in some circumstances). This prevents potential consistency violations. |
| |
| #### Remove the CompletedCheckpointRecover#recover() method. |
| |
| ##### [FLINK-22483](https://issues.apache.org/jira/browse/FLINK-22483) |
| |
| Flink no longer reloads checkpoint metadata from the external storage before restoring the task |
| state after failover (except when the JobManager fails over / changes leadership). This results |
| in less external I/O and faster failover. |
| |
| Please note that this changes public interfaces around `CompletedCheckpointStore`, that we allow |
| overriding by providing custom implementation of HA Services. |
| |
| #### Remove the deprecated CheckpointConfig#setPreferCheckpointForRecovery method. |
| |
| ##### [FLINK-20427](https://issues.apache.org/jira/browse/FLINK-20427) |
| |
| The deprecated method CheckpointConfig#setPreferCheckpointForRecovery was removed, because preferring |
| older checkpoints over newer savepoints for recovery can lead to data loss. |
| |
| ### Dependency upgrades |
| |
| #### Bump up RocksDB version to 6.20.3 |
| |
| ##### [FLINK-14482](https://issues.apache.org/jira/browse/FLINK-14482) |
| |
| RocksDB has been upgraded to 6.20.3. The new version contains lots |
| of bug fixes, ARM platform support, musl library support, and more attractive features. However, |
| the new version can entail at most 8% performance regression according to our tests. |
| |
| See the corresponding ticket for more information. |
| |
| #### Support configuration of the RocksDB info logging via configuration |
| |
| ##### [FLINK-23812](https://issues.apache.org/jira/browse/FLINK-23812) |
| |
| With RocksDB upgraded to 6.20.3 ([FLINK-14482](https://issues.apache.org/jira/browse/FLINK-14482)), |
| you can now also configure a rolling info logging strategy by configuring it accordingly via |
| the newly added `state.backend.rocksdb.log.*` settings. This can be helpful for debugging |
| RocksDB (performance) issues in containerized environments where the local data directory is |
| volatile but the logs should be retained on a separate volume mount. |
| |
| #### Make flink-runtime scala-free |
| |
| ##### [FLINK-14105](https://issues.apache.org/jira/browse/FLINK-14105) |
| |
| Flink's Akka dependency is now loaded with a separate classloader and no longer accessible from the |
| outside. |
| |
| As a result, various modules (most prominently, flink-runtime) no longer have a |
| scala suffix in their `artifactId`. |
| |
| #### Drop Mesos support |
| |
| ##### [FLINK-23118](https://issues.apache.org/jira/browse/FLINK-23118) |
| |
| The support for Apache Mesos has been removed. |
