Release Notes - Flink 1.15

Release notes - Flink 1.15 #

These release notes discuss important aspects, such as configuration, behavior, or dependencies, that changed between Flink 1.14 and Flink 1.15. Please read these notes carefully if you are planning to upgrade your Flink version to 1.15.

Summary of changed dependency names #

There are Several changes in Flink 1.15 that require updating dependency names when upgrading from earlier versions, mainly including the effort to opting-out Scala dependencies from non-scala modules and reorganize table modules. A quick checklist of the dependency changes is as follows:

  • Any dependency to one of the following modules needs to be updated to no longer include a suffix:

  • For Table / SQL users, the new module flink-table-planner-loader replaces flink-table-planner_2.12 and avoids the need for a Scala suffix. For backwards compatibility, users can still swap it with flink-table-planner_2.12 located in opt/. flink-table-uber has been split into flink-table-api-java-uber, flink-table-planner(-loader), and flink-table-runtime. Scala users need to explicitly add a dependency to flink-table-api-scala or flink-table-api-scala-bridge.

The detail of the involved issues are listed as follows.

Add support for opting-out of Scala #

The Java DataSet/-Stream APIs are now independent of Scala and no longer transitively depend on it.

The implications are the following:

  • If you only intend to use the Java APIs, with Java types, then you can opt-in to a Scala-free Flink by removing the flink-scala jar from the lib/ directory of the distribution. You are then free to use any Scala version and Scala libraries. You can either bundle Scala itself in your user-jar; or put into the lib/ directory of the distribution.

  • If you relied on the Scala APIs, without an explicit dependency on them, then you may experience issues when building your projects. You can solve this by adding explicit dependencies to the APIs that you are using. This should primarily affect users of the Scala DataStream/CEP APIs.

  • A lot of modules have lost their Scala suffix. Further caution is advised when mixing dependencies from different Flink versions (e.g., an older connector), as you may now end up pulling in multiple versions of a single module (that would previously be prevented by the name being equal).

The new module flink-table-planner-loader replaces flink-table-planner_2.12 and avoids the need for a Scala suffix. It is included in the Flink distribution under lib/. For backwards compatibility, users can still swap it with flink-table-planner_2.12 located in opt/. As a consequence, flink-table-uber has been split into flink-table-api-java-uber, flink-table-planner(-loader), and flink-table-runtime. flink-sql-client has no Scala suffix anymore.

It is recommended to let new projects depend on flink-table-planner-loader (without Scala suffix) in provided scope.

Note that the distribution does not include the Scala API by default. Scala users need to explicitly add a dependency to flink-table-api-scala or flink-table-api-scala-bridge.

The flink-table-runtime has no Scala suffix anymore. Make sure to include flink-scala if the legacy type system (based on TypeInformation) with case classes is still used within Table API.

The table file system connector is not part of the flink-table-uber JAR anymore but is a dedicated (but removable) flink-connector-files JAR in the /lib directory of a Flink distribution.

JDK Upgrade #

The support of Java 8 is now deprecated and will be removed in a future release (FLINK-25247). We recommend all users to migrate to Java 11.

The default Java version in the Flink docker images is now Java 11 (FLINK-25251). There are images built with Java 8, tagged with “java8”.

Drop support for Scala 2.11 #

Support for Scala 2.11 has been removed in FLINK-20845. All Flink dependencies that (transitively) depend on Scala are suffixed with the Scala version that they are built for, for example flink-streaming-scala_2.12. Users should update all Flink dependecies, changing “2.11” to “2.12”.

Scala versions (2.11, 2.12, etc.) are not binary compatible with one another. That also means that there’s no guarantee that you can restore from a savepoint, made with a Flink Scala 2.11 application, if you’re upgrading to a Flink Scala 2.12 application. This depends on the data types that you have been using in your application.

The Scala Shell/REPL has been removed in FLINK-24360.

Table API & SQL #

Disable the legacy casting behavior by default #

The legacy casting behavior has been disabled by default. This might have implications on corner cases (string parsing, numeric overflows, to string representation, varchar/binary precisions). Set table.exec.legacy-cast-behaviour=ENABLED to restore the old behavior.

Enforce CHAR/VARCHAR precision when outputting to a Sink #

CHAR/VARCHAR lengths are enforced (trimmed/padded) by default now before entering the table sink.

Support the new type inference in Scala Table API table functions #

Table functions that are called using Scala implicit conversions have been updated to use the new type system and new type inference. Users are requested to update their UDFs or use the deprecated TableEnvironment.registerFunction to restore the old behavior temporarily by calling the function via name.

Propagate executor config to TableConfig #

flink-conf.yaml and other configurations from outer layers (e.g. CLI) are now propagated into TableConfig. Even though configuration set directly in TableConfig has still precedence, this change can have side effects if table configuration was accidentally set in other layers.

Remove pre FLIP-84 methods #

The previously deprecated methods TableEnvironment.execute, Table.insertInto, TableEnvironment.fromTableSource, TableEnvironment.sqlUpdate, and TableEnvironment.explain have been removed. Please use TableEnvironment.executeSql, TableEnvironment.explainSql, TableEnvironment.createStatementSet, as well as Table.executeInsert, Table.explain and Table.execute and the newly introduces classes TableResult, ResultKind, StatementSet and ExplainDetail.

Fix parser generator warnings #

STATEMENT is a reserved keyword now. Use backticks to escape tables, fields and other references.

Expose uid generator for DataStream/Transformation providers #

DataStreamScanProvider and DataStreamSinkProvider for table connectors received an additional method that might break implementations that used lambdas before. We recommend static classes as a replacement and future robustness.

Add new STATEMENT SET syntax #

It is recommended to update statement sets to the new SQL syntax:


Check & possible fix decimal precision and scale for all Aggregate functions #

This changes the result of a decimal SUM() with retraction and AVG(). Part of the behavior is restored back to be the same with 1.13 so that the behavior as a whole could be consistent with Hive / Spark.

Clarify semantics of DecodingFormat and its data type #

The DecodingFormat interface was used for both projectable and non-projectable formats which led to inconsistent implementations. The FileSystemTableSource has been updated to distinguish between those two interfaces now. Users that implement custom formats for FileSystemTableSource might need to verify the implementation and make sure to implement ProjectableDecodingFormat if necessary.

Push down partitions before filters #

This might have an impact on existing table source implementations as push down filters might not contain partition predicates anymore. However, the connector implementation for table sources that implement both partition and filter push down became easier with this change.

This changes the result of a decimal SUM() between 1.14.0 and 1.14.1. It restores the behavior of 1.13 to be consistent with Hive/Spark.

Use the new casting rules in TableResult#print #

The string representation of BOOLEAN columns from DDL results (true/false -> TRUE/FALSE), and row columns in DQL results (+I[...] -> (...)) has changed for printing.

Casting from a string to a DATE and TIME allows incomplete strings #

The defaults for casting incomplete strings like "12" to TIME have changed from 12:01:01 to 12:00:00.

Casting from STRING to TIMESTAMP_LTZ looses fractional seconds #

STRING to TIMESTAMP(_LTZ) casting now considers fractional seconds. Previously fractional seconds of any precision were ignored.

Sinks built with the unified sink framework do not receive timestamps when used in Table API #

This adds an additional operator to the topology if the new sink interfaces are used (e.g. for Kafka). It could cause issues in 1.14.1 when restoring from a 1.14 savepoint. A workaround is to cast the time attribute to a regular timestamp in the SQL statement closely before the sink.

SQL functions should return STRING instead of VARCHAR(2000) #

Functions that returned VARCHAR(2000) in 1.14, return VARCHAR with maximum length now. In particular this includes:


Support IS JSON for Table API #

This issue added IS JSON for Table API. Notes that IS JSON does not return NULL anymore but always FALSE (even if the argument is NULL).

Disabled UPSERT INTO statement. UPSERT INTO syntax was exposed by mistake in previous releases without detailed discussed. From this release every UPSERT INTO is going to throw an exception. Users of UPSERT INTO should use the documented INSERT INTO statement instead.

RuntimeException: while resolving method ‘booleanValue’ in class class java.math.BigDecimal #

Casting to BOOLEAN is not allowed from decimal numeric types anymore.

Upsert materializer is not inserted for all sink providers #

This issue aims to fix various primary key issues that effectively made it impossible to use this feature. The change might affect savepoint backwards compatibility for those incorrect pipelines. Also the resulting changelog stream might be different after these changes. Pipelines that were correct before should be restorable from a savepoint.

Propagate unique keys for fromChangelogStream #

StreamTableEnvironment.fromChangelogStream might produce a different stream because primary keys were not properly considered before.

TableResult#print() should use internal data types #

The results of Table#print have changed to be closer to actual SQL data types. E.g. decimal is printing correctly with leading/trailing zeros.

Connectors #

Remove MapR filesystem #

Support for the MapR FileSystem has been dropped.

The flink-connector-testing module has been removed and users should use flink-connector-test-utils module instead.

Support partition keys through metadata (for FileSystem connector) #

Now the formats implementing BulkWriterFormatFactory don’t need to implement partition keys reading anymore, as it’s managed internally by FileSystemTableSource.

Port ElasticSearch Sink to new Unified Sink API (FLIP-143) #

ElasticsearchXSinkBuilder supersedes ElasticsearchSink.Builder and provides at-least-once writing with the new unified sink interface supporting both batch and streaming mode of DataStream API.

For Elasticsearch 7 users that use the old ElasticsearchSink interface (org.apache.flink.streaming.connectors.elasticsearch7.ElasticsearchSink) and depend on their own elasticsearch-rest-high-level-client version, updating the client dependency to a version >= 7.14.0 is required due to internal changes.

Reduce legacy in Table API connectors #

The old JDBC connector (indicated by connector.type=jdbc in DDL) has been removed. If not done already, users need to upgrade to the newer stack (indicated by connector=jdbc in DDL).

Extensible unified Sink uses new metric to capture outgoing records #

New metrics numRecordsSend and numRecordsSendErrors have been introduced for users to monitor the number of records sent to the external system. The numRecordsOut should be used to monitor the number of records transferred between sink tasks.

Connector developers should pay attention to the usage of these metrics numRecordsOut, numRecordsSend and numRecordsSendErrors while building sink connectors. Please refer to the new Kafka Sink for details. Additionally, since numRecordsOut now only counts the records sent between sink tasks and numRecordsOutErrors was designed for counting the records sent to the external system, we deprecated numRecordsOutErrors and recommend using numRecordsSendErrors instead.

Runtime & Coordination #

Integrate retry strategy for cleanup stage #

Adds retry logic to the cleanup steps of a finished job. This feature changes the way Flink jobs are cleaned up. Instead of trying once to clean up the job, this step will be repeated until it succeeds. Users are meant to fix the issue that prevents Flink from finalizing the job cleanup. The retry functionality can be configured and disabled. More details can be found in the documentation.

Introduce explicit shutdown signaling between TaskManager and JobManager #

TaskManagers now explicitly send a signal to the JobManager when shutting down. This reduces the down-scaling delay in reactive mode (which was previously bound to the heartbeat timeout).

Release TaskManagerJobMetricGroup with the last slot rather than task #

Job metrics on the TaskManager are now removed when the last slot is released, rather than the last task. This means they may be reported for a longer time than before and when no tasks are running on the TaskManager.

Make errors happened during JobMaster initialization accessible through the exception history #

Fixes issue where the failover is not listed in the exception history but as a root cause. That could have happened if the failure occurred during JobMaster initialization.

DispatcherResourceManagerComponent fails to deregister application if no leading ResourceManager #

A new multiple component leader election service was implemented that only runs a single leader election per Flink process. If this should cause any problems, then you can set high-availability.use-old-ha-services: true in the flink-conf.yaml to use the old high availability services.

Allow idempotent job cancellation #

Attempting to cancel a FINISHED/FAILED job now returns 409 Conflict instead of 404 Not Found.

Move async savepoint operation cache into Dispatcher #

All JobManagers can now be queried for the status of a savepoint operation, irrespective of which JobManager received the initial request.

Standby per job mode Dispatchers don’t know job’s JobSchedulingStatus #

The issue of re-submitting a job in Application Mode when the job finished but failed during cleanup is fixed through the introduction of the new component JobResultStore which enables Flink to persist the cleanup state of a job to the file system. (see FLINK-25431)

Change some default config values of blocking shuffle for better usability #

Since 1.15, sort-shuffle has become the default blocking shuffle implementation and shuffle data compression is enabled by default. These changes influence batch jobs only, for more information, please refer to the official document.

Checkpoints #

FLIP-193: Snapshots ownership #

When restoring from a savepoint or retained externalized checkpoint you can choose the mode in which you want to perform the operation. You can choose from CLAIM, NO_CLAIM, LEGACY (the old behavior).

In CLAIM mode Flink takes ownership of the snapshot and will potentially try to remove the snapshot at a certain point in time. On the other hand the NO_CLAIM mode will make sure Flink does not depend on the existence of any files belonging to the initial snapshot.

For a more thorough description see the documentation.

Support native savepoints (w/o modifying the statebackend specific snapshot strategies) #

When taking a savepoint you can specify the binary format. You can choose from native (specific to a particular state backend) or canonical (unified across all state backends).

Prevent JM from discarding state on checkpoint abortion #

Shared state tracking changed to use checkpoint ID instead of reference counts. Shared state is not cleaned up on abortion anymore (but rather on subsumption or job termination).

This might result in delays in discarding the state of aborted checkpoints.

Introduce incremental/full checkpoint size stats #

Introduce metrics of persistent bytes within each checkpoint (via REST API and UI), which could help users to know how much data size had been persisted during the incremental or change-log based checkpoint.

Enables final checkpoint by default #

In 1.15 we enabled the support of checkpoints after part of tasks finished by default, and made tasks waiting for the final checkpoint before exit to ensure all data got committed.

However, it’s worth noting that this change forces tasks to wait for one more checkpoint before exiting. In other words, this change will block the tasks until the next checkpoint get triggered and completed. If the checkpoint interval is long, the tasks' execution time would also be extended largely. In the worst case if the checkpoint interval is Long.MAX_VALUE, the tasks would be in fact blocked forever.

More information about this feature and how to disable it could be found in the documentation.

Migrate state processor API to DataStream API #

The State Processor API has been migrated from Flinks legacy DataSet API to now run over DataStreams run under BATCH execution.

The internal log of RocksDB would stay under flink’s log directory by default.

Dependency upgrades #

Upgrade the minimal supported hadoop version to 2.8.5 #

Minimal supported Hadoop client version is now 2.8.5 (version of the Flink runtime dependency). The client can still talk to older server versions as the binary protocol should be backward compatible.

Update Elasticsearch Sinks to latest minor versions #

Elasticsearch libraries used by the connector are bumped to 7.15.2 and 6.8.20 respectively.

For Elasticsearch 7 users that use the old ElasticsearchSink interface (org.apache.flink.streaming.connectors.elasticsearch7.ElasticsearchSink) and depend on their own elasticsearch-rest-high-level-client version, will need to update the client dependency to a version >= 7.14.0 due to internal changes.

Drop support for Zookeeper 3.4 #

Support for using Zookeeper 3.4 for HA has been dropped. Users relying on Zookeeper need to upgrade to 3.5/3.6. By default Flink now uses a Zookeeper 3.5 client.

Upgrade Kafka dependency #

Kafka connector uses Kafka client 2.8.1 by default now.