Release Notes - Flink 1.17

Release notes - Flink 1.17 #

These release notes discuss important aspects, such as configuration, behavior or dependencies, that changed between Flink 1.16 and Flink 1.17. Please read these notes carefully if you are planning to upgrade your Flink version to 1.17.

Clusters & Deployment #

The Flink distribution no longer bundles 2 different Zookeeper client jars (one in lib, one in lib/opt respectively). Instead, only 1 client will be bundled within the flink-dist jar. This has no effect on the supported Zookeeper server versions.

Table API & SQL #

Incompatible plan change of event time temporal join on an upsert source #

A correctness issue when do event time temporal join with a versioned table backed by an upsert source was resolved. When the right input of the join is an upsert source, it no longer generates a ChangelogNormalize node for it. This is an incompatible plan change compared to 1.16.0.

Incompatible plan change of filter after temporal join #

After the patch is applied, the filter will no longer be pushed down into both inputs of the event time temporal join. Note this may cause incompatible plan changes compared to 1.16.0, e.g., when left input is an upsert source (use upsert-kafka connector), the query plan will remove the ChangelogNormalize node which appeared in 1.16.0.

Connectors & Libraries #

Remove cassandra connector from master branch #

The Cassandra connector has been externalized and is no longer released as part of the main Flink release. Downloads can be found at https://flink.apache.org/downloads.html and the source code at https://github.com/apache/flink-connector-cassandra.

Remove Pulsar connector from master branch #

The Pulsar connector has been externalized and is no longer bundled and released as part of the main Flink release. Downloads can be found at https://flink.apache.org/downloads.html and the source code at https://github.com/apache/flink-connector-pulsar.

Remove HCatalog #

The HCatalog connector has been removed from Flink. You can use the Hive connector as a replacement.

Remove Gelly #

Gelly has been removed from Flink. Current users of Gelly should not upgrade to Flink 1.17 but stay on an older version. If you’re looking for iterations support, you could investigate Flink ML Iteration as a potential successor.

Support watermark alignment of source splits #

Since Flink 1.17, source connectors have to implement watermark alignment of source split in order to use the watermark alignment feature. The required methods to implement are: SourceReader#pauseOrResumeSplits and SplitReader#pauseOrResumeSplits.

If you are migrating from Flink <= 1.16.x, and you are using watermark alignment, but at the same time you are not able to upgrade/modify your connector, you can disable per split alignment via setting pipeline.watermark-alignment.allow-unaligned-source-splits to true. Note that by doing so, watermark alignment will be working properly only when your number of splits equals to the parallelism of the source operator.

Remove deprecated MiniClusterResource #

The deprecated MiniClusterResource in flink-test-utils has been removed. The MiniClusterWithClientResource is a drop-in replacement.

Kinesis connector doesn’t shade jackson dependency #

Kinesis connector now shades and relocates transitive Jackson dependencies of flink-connector-kinesis. If your Flink job was transitively relying on the these, you may need to include additional Jackson dependencies into your project.

Runtime & Coordination #

Speculative execution take input data amount into account when detecting slow tasks #

The slow task detecting is improved for speculative execution. Previously, it only considered the execution time of tasks when deciding which tasks are slow. It now takes the input data volume of tasks into account. Tasks which have a longer execution time but consumes more data may not be considered as slow. This improvement helps to eliminate the negative impacts of data skew on slow task detecting.

Use adaptive batch scheduler as default scheduler for batch jobs #

Adaptive batch scheduler is now used for batch jobs by default. It will automatically decide the parallelism of operators. The keys and values of related configuration items are improved for ease of use. More details can be found in the document.

Simplify network memory configurations for TaskManager #

The default value of taskmanager.memory.network.max has changed from 1g to Long#MAX_VALUE, to reduce the number of config options the user needs to tune when trying to increase the network memory size. This may affect the performance when this option is not explicitly configured, due to potential changes of network memory size, heap and managed memory size when the total memory size is fixed. To go back to the previous behavior, the user can explicitly configure this option to the previous default value of 1g.

A threshold is introduced for controlling the number of required buffers among all buffers needed for reading data from upstream tasks. Reducing the number of required buffers helps reduce the chance of failures due to insufficient network buffers, at the price of potential performance impact. By default, the number of required buffers is only reduced for batch workloads, while stay unchanged for streaming workloads. This can be tuned via taskmanager.network.memory.read-buffer.required-per-gate.max. See the description of the config option for more details.

Metric Reporters #

Only support reporter factories for instantiation #

Configuring reporters by their class is no longer supported. Reporter implementations must provide a MetricReporterFactory, and all configurations must be migrated to such a factory.

UseLogicalIdentifier makes datadog consider metric as custom #

The Datadog reporter now adds a “flink.” prefix to metric identifiers if “useLogicalIdentifier” is enabled. This is required for these metrics to be recognized as Flink metrics, not custom ones.

Use separate Prometheus CollectorRegistries #

The PrometheusReporters now use a separate CollectorRegistry for each reporter instance instead of the singleton default registry. This generally shouldn’t impact setups, but it may break code that indirectly interacts with the reporter via the singleton instance (e.g., a test trying to assert what metrics are reported).

Checkpoints #

Drop TypeSerializerConfigSnapshot and savepoint support from Flink versions < 1.8.0 #

Savepoints using TypeSerializerConfigSnapshot are no longer supported. That means that all savepoints from Flink < 1.8.0 are no longer supported. Furthermore, savepoints from Flink < 1.17.0 created with custom serializer using deprecated since Flink 1.8.0 class TypeSerializerConfigSnapshot are also no longer supported.

If you are only using built-in serializers (Pojo, Kryo, Avro, Tuple, …), and your savepoint is from Flink >= 1.8.0, you don’t have to do anything.

If you are only using built-in serializers (Pojo, Kryo, Avro, Tuple, …), and your savepoint is from Flink < 1.8.0, please first upgrade your job to 1.8.x <= Flink <= 1.16.x, before upgrading in a second step to Flink >= 1.17.x.

If previously you were using a custom serializer that depends on TypeSerializerConfigSnapshot, please upgrade your serializer to TypeSerializerSnapshot while still using 1.8.x <= Flink <= 1.16.x, take a savepoint and restore from that savepoint in Flink >= 1.7.0.

Python #

Remove duplicated operators in the StreamGraph for Python DataStream API jobs #

This duplicated operator issue has been addressed since 1.15.4, 1.16.2 and 1.17.0. For jobs which are not affected by this issue, there are no backward compatibility issues. However, for jobs which are affected, it may not be possible to restore from savepoints generated from versions 1.15.0 ~ 1.15.3 and 1.16.0 ~ 1.16.1.

Adds support of Python 3.10 and removes support of Python 3.6 #

PyFlink 1.17 will support Python 3.10 and remove the support of Python 3.6.

Dependency upgrades #

Upgrade FRocksDB to 6.20.3-ververica-2.0 #

Upgrade FRocksDB to 6.20.3-ververica-2.0.

Upgrade the minimal supported hadoop version to 2.10.2 #

The minimum Hadoop version supported by Apache Flink has been updated to version 2.10.2. For Hadoop 3, the minimum Hadoop version that is now supported is version 3.2.3.

Upgrade Calcite version to 1.29.0 #

Calcite upgrade brings optimizations that change logical plans for some queries involving Sarg and queries with count of non-distinct values. Please note that the execution plan changes for certain SQL queries after the upgrade, so savepoints are not backward compatible in these cases.

For support of Python 3.10, PyFlink updates some dependencies:

  • apache-beam: 2.43.0
  • pemja: 0.3.0
  • cloudpickle: 2.2.0