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:
BatchTableEnvironment, and therefore Table programs ran with the planner can not be transformed to
DataSetprograms. 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
StreamTableSinkshould implement the
consumeDataStreammethod instead of
emitDataStreamif it is used with the Blink planner. Both methods work with the original planner. This is by design to make the returned
DataStreamSinkaccessible for the planner.
TableEnvironmentinstances should not be reused across multiple SQL statements when using the Blink planner.
Table.flatAggregateis not supported
CatalogAPI, and does not support
ExternalCatalogwhich is now deprecated.
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.
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.
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(...)
As a result of completing fine-grained recovery (FLIP-1),
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.
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_FORCED via the
Overall, users should see an improvement in performance.
With the support of graceful job termination with savepoints for semantic correctness (FLIP-34), 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.
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 1.9 metrics documentation 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.
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 here
for a possible workaround.
KafkaSerializationSchemato fully replace
flink-connector-kafka) supports a new
KafkaSerializationSchema that will
KeyedSerializationSchema in the long run. This new schema allows directly generating Kafka
ProducerRecords 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 FLINK-12308: Support python language in Flink Table API.
Existing users may continue to use these older APIs with future versions of Flink by copying both the
flink-python jars into the
/lib directory of the distribution and the corresponding start scripts
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 (FLIP-39). Users can still use the 1.8 version of the legacy library if their projects still rely on it.
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 regession 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.
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.
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.