This documentation is for an out-of-date version of Apache Flink. We recommend you use the latest stable version.
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. 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 on how to configure memory for Flink and track the proper solution in 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)
requires the removal of the
- Due to FLINK-14105, if you have a dependency on
flink-queryable-state-runtime, the Scala suffix (
_2.12) needs to be removed from the
Table API & SQL #
Use pipeline name consistently across DataStream API and Table API #
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
Propagate unique keys for fromChangelogStream #
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 #
Table.flatMap() supports the new type system now. Users are requested to upgrade their functions.
Add Scala implicit conversions for new API methods #
The Scala implicits that convert between DataStream API and Table API have been updated to the new methods of FLIP-136.
The changes might require an update of pipelines that used
toTable or implicit conversions from
Remove YAML environment file support in SQL Client #
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.
Remove the legacy planner code base #
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 #
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-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 #
Due to the removal of
BatchTableSink have been removed
as well. Use
DynamicTableSink instead. They support the old
OutputFormat interfaces as runtime providers if necessary.
Remove TableEnvironment#connect #
TableEnvironment.connect() method has been removed. Use the
TableEnvironment.createTemporaryTable(String, TableDescriptor) to create tables
programmatically. Please note that this method only supports sources and sinks that comply with
This is also indicated by the new property design
'connector'='kafka' instead of
Deprecate toAppendStream and toRetractStream #
The outdated variants of
have been deprecated. Use the
(from|to)(Data|Changelog)Stream alternatives introduced in 1.13.
Remove old connectors and formats stack around descriptors #
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
'format.type=' options need to be updated to the new connector and formats available via the
Drop BatchTableSource/Sink HBaseTableSource/Sink and related classes #
HBaseTableSource/Sink and related classes including various
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 #
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
Drop BatchTableSource OrcTableSource and related classes #
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 #
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 #
LOAD/UNLOAD MODULE architecture for table modules has been updated to the new factory stack of
Users of this feature should update their
Migrate Table API to new KafkaSink #
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 #
processWatermarkStatusX method to classes such as
It allows to take the
WatermarkStatus into account when combining watermarks in two/multi input
Note that with this release, we renamed the previously internal
in order to better reflect its purpose.
Allow @TypeInfo annotation on POJO field declarations #
@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 #
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 #
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 #
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 #
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
Expose standardized operator metrics (FLIP-179) #
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) #
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,
needs a user-configured and unique transaction prefix, such that transactions of different
applications do not interfere with each other.
Deprecate FlinkKafkaConsumer #
FlinkKafkaConsumer has been deprecated in favor of
KafkaSource. To upgrade to the new version,
please store the offsets in Kafka with
setCommitOffsetsOnCheckpoints in the
FlinkKafkaConsumer and then stop with a savepoint. When resuming from the savepoint, please
setStartingOffsets(OffsetsInitializer.committedOffsets()) in the new
transfer the offsets to the new source.
InputStatus should not contain END_OF_RECOVERY #
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. #
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 #
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 #
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
lets you select
<nodeIp> from a desired range.
Timeout heartbeat if the heartbeat target is no longer reachable #
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
heartbeat.rpc-failure-threshold. This can speed up the detection of dead TaskManagers
RPCs fail faster when target is unreachable #
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 (
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 #
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:
(which is set to 0 by default).
Refine ShuffleMaster lifecycle management for pluggable shuffle service framework #
We improved the
ShuffleMaster interface by adding some lifecycle methods, including
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/
The ZooKeeper job-specific HA services are now grouped under a zNode with the respective
Moreover, the config options
high-availability.zookeeper.path.checkpoint-counter have been removed and, thus, no
longer have an effect.
Fallback value for taskmanager.slot.timeout #
The config option
taskmanager.slot.timeout now falls back to
akka.ask.timeout if no value has
been configured. Previously, the default value for
DuplicateJobSubmissionException after JobManager failover #
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 #
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 #
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.
The semantic of alignmentTimeout configuration has changed meaning #
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 #
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 #
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 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. #
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 #
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 #
With RocksDB upgraded to 6.20.3 (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’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
Drop Mesos support #
The support for Apache Mesos has been removed.