Release Notes - Flink 1.5

These release notes discuss important aspects, such as configuration, behavior, or dependencies, that changed between Flink 1.4 and Flink 1.5. Please read these notes carefully if you are planning to upgrade your Flink version to 1.5.

Update Configuration for Reworked Job Deployment

Flink’s reworked cluster and job deployment component improves the integration with resource managers and enables dynamic resource allocation. One result of these changes is, that you no longer have to specify the number of containers when submitting applications to YARN and Mesos. Flink will automatically determine the number of containers from the parallelism of the application.

Although the deployment logic was completely reworked, we aimed to not unnecessarily change the previous behavior to enable a smooth transition. Nonetheless, there are a few options that you should update in your conf/flink-conf.yaml or know about.

The allocation of TaskManagers with multiple slots is not fully supported yet. Therefore, we recommend to configure TaskManagers with a single slot, i.e., set taskmanager.numberOfTaskSlots: 1
If you observed any problems with the new deployment mode, you can always switch back to the pre-1.5 behavior by configuring mode: legacy.

Please report any problems or possible improvements that you notice to the Flink community, either by posting to a mailing list or by opening a JIRA issue.

Note: We plan to remove the legacy mode in the next release.

Update Configuration for Reworked Network Stack

The changes on the networking stack for credit-based flow control and improved latency affect the configuration of network buffers. In a nutshell, the networking stack can require more memory to run applications. Hence, you might need to adjust the network configuration of your Flink setup.

There are two ways to address problems of job submissions that fail due to lack of network buffers.

Reduce the number of buffers per channel, i.e., taskmanager.network.memory.buffers-per-channel or
Increase the amount of TaskManager memory that is used by the network stack, i.e., increase taskmanager.network.memory.fraction and/or taskmanager.network.memory.max.

Please consult the section about network buffer configuration in the Flink documentation for details. In case you experience issues with the new credit-based flow control mode, you can disable flow control by setting taskmanager.network.credit-model: false.

Note: We plan to remove the old model and this configuration in the next release.

Hadoop Classpath Discovery

We removed the automatic Hadoop classpath discovery via the Hadoop binary. If you want Flink to pick up the Hadoop classpath you have to export HADOOP_CLASSPATH. On cloud environments and most Hadoop distributions you would do

export HADOOP_CLASSPATH=`hadoop classpath`.

Breaking Changes of the REST API

In an effort to harmonize, extend, and improve the REST API, a few handlers and return values were changed.

The jobs overview handler is now registered under /jobs/overview (before /joboverview) and returns a list of job details instead of the pre-grouped view of running, finished, cancelled and failed jobs.
The REST API to cancel a job was changed.
The REST API to cancel a job with savepoint was changed.

Please check the REST API documentation for details.

Kafka Producer Flushes on Checkpoint by Default

The Flink Kafka Producer now flushes on checkpoints by default. Prior to version 1.5, the behaviour was disabled by default and users had to explicitly call setFlushOnCheckpoints(true) on the producer to enable it.

Updated Kinesis Dependency

The Kinesis dependencies of Flink’s Kinesis connector have been updated to the following versions.

<aws.sdk.version>1.11.319</aws.sdk.version>
<aws.kinesis-kcl.version>1.9.0</aws.kinesis-kcl.version>
<aws.kinesis-kpl.version>0.12.9</aws.kinesis-kcl.version>

Limitations of failover strategies

Flink’s non-default failover strategies are still a very experimental feature which come with a set of limitations. You should only use this feature if you are executing a stateless streaming job. In any other cases, it is highly recommended to remove the config option jobmanager.execution.failover-strategy from your flink-conf.yaml or set it to "full".

In order to avoid future problems, this feature has been removed from the documentation until it will be fixed. See FLINK-10880 for more details.