Project: Flink (App Definition and Development / Streaming & Messaging)
File: flink-1.9.md

title: "Release Notes - Flink 1.9" <!-- 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 1.8 and Flink 1.9. It also provides an overview on known shortcoming or limitations with new experimental features introduced in 1.9. Please read these notes carefully if you are planning to upgrade your Flink version to 1.9. Flink 1.9.0 provides support for two planners for the Table API, namely Flink's original planner and the new Blink planner. The original planner maintains same behaviour as previous releases, while the new Blink planner is still considered experimental and has the following limitations: The Blink planner can not be used with `BatchTableEnvironment`, and therefore Table programs ran with the planner can not be transformed to `DataSet` programs. This is by design and will also not be supported in the future. Therefore, if you want to run a batch job with the Blink planner, please use the new `TableEnvironment`. For streaming jobs, both `StreamTableEnvironment` and `TableEnvironment` works. Implementations of `StreamTableSink` should implement the `consumeDataStream` method instead of `emitDataStream` if it is used with the Blink planner. Both methods work with the original planner. This is by design to make the returned `DataStreamSink` accessible for the planner. Due to a bug with how transformations are not being cleared on execution, `TableEnvironment` instances should not be reused across multiple SQL statements when using the Blink planner. `Table.flatAggregate` is not supported Session and count windows are not supported when running batch jobs. The Blink planner only supports the new `Catalog` API, and does not support `ExternalCatalog` which is now deprecated. Related issues: In Flink 1.9.0, the community also added a preview feature about SQL DDL, but only for batch style DDLs. Therefore, all streaming related concepts are not supported yet, for example watermarks. Related issues: Since Flink 1.9.0, Flink can now be compiled and run on Java 9. Note that certain components interacting with external systems (connectors, filesystems, metric reporters, etc.) may not work since the respective projects may have skipped Java 9 support. Related issues: In Flink 1.9.0 and prior version, the managed memory fraction of taskmanager is controlled by `taskmanager.memory.fraction`, and with 0.7 as the default

However, sometimes this will cause OOMs due to the fact that the default value of JVM parameter `NewRatio` is 2, which means the old generation occupied only 2/3 (0.66) of the heap memory. So if you run into this case, please manually change this value to a lower value. Related issues: Since 1.9.0, the implicit conversions for the Scala expression DSL for the Table API has been moved to `flink-table-api-scala`. This requires users to update the imports in their Table programs. Users of pure Table programs should define their imports like: ``` import org.apache.flink.table.api._ TableEnvironment.create(...) ``` Users of the DataStream API should define their imports like: ``` import org.apache.flink.table.api._ import org.apache.flink.table.api.scala._ StreamTableEnvironment.create(...) ``` Related issues: As a result of completing fine-grained recovery (), Flink will now attempt to only restart tasks that are connected to failed tasks through a pipelined connection. By default, the `region` failover strategy is used. Users who were not using a restart strategy or have already configured a failover strategy should not be affected. Moreover, users who already enabled the `region` failover strategy, along with a restart strategy that enforces a certain number of restarts or introduces a restart delay, will see changes in behavior. The `region` failover strategy now correctly respects constraints that are defined by the restart strategy. Streaming users who were not using a failover strategy may be affected if their jobs are embarrassingly parallel or contain multiple independent jobs. In this case, only the failed parallel pipeline or affected jobs will be restarted. Batch users may be affected if their job contains blocking exchanges (usually happens for shuffles) or the `ExecutionMode` was set to `BATCH` or `BATCH_FORCED` via the `ExecutionConfig`. Overall, users should see an improvement in performance. Related issues: With the support of graceful job termination with savepoints for semantic correctness (), a few changes related to job termination has been made to the CLI. From now on, the `stop` command with no further arguments stops the job with a savepoint targeted at the default savepoint location (as configured via the `state.savepoints.dir` property in the job configuration), or a location explicitly specified using the `-p <savepoint-path>` option. Please make sure to configure the savepoint path using either one of these options. Since job terminations are now always accompanied with a savepoint, stopping jobs is expected to take longer now. Related issues: A few changes in the network stack related to changes in the threading model of `StreamTask` to a mailbox-based approach requires close attention to some related configuration: Due to changes in the lifecycle management of result partitions, partition requests as well as re-triggers will now happen sooner. Therefore, it is possible that some jobs with long deployment times and large state might start failing more frequently with `PartitionNotFound` exceptions compared to previous versions. If that's the case, users should increase the value of `taskmanager.network.request-backoff.max` in order to have the same effective partition request timeout as it was prior to 1.9.0. To avoid a potential deadlock, a timeout has been added for how long a task will wait for assignment of exclusive memory segments. The default timeout is 30 seconds, and is configurable via

It is possible that for some previously working deployments this default timeout value is too low and might have to be increased. Please also notice that several network I/O metrics have had their scope changed. See the for which metrics are affected. In 1.9.0, these metrics will still be available under their previous scopes, but this may no longer be the case in future versions. Related issues: Due to a bug in the `AsyncWaitOperator`, in 1.9.0 the default chaining behaviour of the operator is now changed so that it is never chained after another operator. This should not be problematic for migrating from older version snapshots as long as an uid was assigned to the operator. If an uid was not assigned to the operator, please see the instructions for a possible workaround. Related issues: The universal `FlinkKafkaProducer` (in `flink-connector-kafka`) supports a new `KafkaSerializationSchema` that will fully replace `KeyedSerializationSchema` in the long run. This new schema allows directly generating Kafka `ProducerRecord`s for sending to Kafka, therefore enabling the user to use all available Kafka features (in the context of Kafka records). The Elasticsearch 1 connector has been dropped and will no longer receive patches. Users may continue to use the connector from a previous series (like 1.8) with newer versions of Flink. It is being dropped due to being used significantly less than more recent versions (Elasticsearch versions 2.x and 5.x are downloaded 4 to 5 times more), and hasn't seen any development for over a year. The older Python APIs for batch and streaming have been removed and will no longer receive new patches. A new API is being developed based on the Table API as part of . Existing users may continue to use these older APIs with future versions of Flink by copying both the `flink-streaming-python` and `flink-python` jars into the `/lib` directory of the distribution and the corresponding start scripts `pyflink-stream.sh` and `pyflink.sh` into the `/bin` directory of the distribution. The older machine learning libraries have been removed and will no longer receive new patches. This is due to efforts towards a new Table-based machine learning library (). Users can still use the 1.8 version of the legacy library if their projects still rely on it. Related issues: Dependency on MapR vendor-specific artifacts has been removed, by changing the MapR filesystem connector to work purely based on reflection. This does not introduce any regression in the support for the MapR filesystem. The decision to remove hard dependencies on the MapR artifacts was made due to very flaky access to the secure https endpoint of the MapR artifact repository, and affected build stability of Flink. Related issues: Access to the state serializer in `StateDescriptor` is now modified from protected to private access. Subclasses should use the `StateDescriptor#getSerializer()` method as the only means to obtain the wrapped state serializer. Related issues: The web frontend of Flink has been updated to use the latest Angular version (7.x). The old frontend remains available in Flink 1.9.x, but will be removed in a later Flink release once the new frontend is considered stable. Related issues: