Project: Flink (App Definition and Development / Streaming & Messaging)
File: flink-1.8.md

title: "Release Notes - Flink 1.8" <!-- 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 1.7 and Flink 1.8. Please read these notes carefully if you are planning to upgrade your Flink version to 1.8. We introduced TTL (time-to-live) for Keyed state in Flink 1.6 (). This feature allowed to clean up and make inaccessible keyed state entries when accessing them. In addition state would now also being cleaned up when writing a savepoint/checkpoint. Flink 1.8 introduces continuous cleanup of old entries for both the RocksDB state backend () and the heap state backend (). This means that old entries (according to the ttl setting) are continuously being cleanup up. With Flink 1.7.0 we added support for changing the schema of state when using the `AvroSerializer` (). With Flink 1.8.0 we made great progress migrating all built-in `TypeSerializers` to a new serializer snapshot abstraction that theoretically allows schema migration. Of the serializers that come with Flink, we now support schema migration for the `PojoSerializer` (), and Java `EnumSerializer` (), As well as for Kryo in limited cases (). Savepoints from Flink 1.2 that contain a Scala `TraversableSerializer` are not compatible with Flink 1.8 anymore because of an update in this serializer (). You can get around this restriction by first upgrading to a version between Flink 1.3 and Flink 1.7 and then updating to Flink 1.8. We needed to switch to a custom build of RocksDB called FRocksDB because we need certain changes in RocksDB for supporting continuous state cleanup with TTL. The used build of FRocksDB is based on the upgraded version 5.17.2 of RocksDB. For Mac OS X, RocksDB version 5.17.2 is supported only for OS X version >= 10.13. See also: https://github.com/facebook/rocksdb/issues/4862. Convenience binaries that include hadoop are no longer released. If a deployment relies on `flink-shaded-hadoop2` being included in `flink-dist`, then you must manually download a pre-packaged Hadoop jar from the optional components section of the [download page]({{< downloads >}}) and copy it into the `/lib` directory. Alternatively, a Flink distribution that includes hadoop can be built by packaging `flink-dist` and activating the `include-hadoop` maven profile. As hadoop is no longer included in `flink-dist` by default, specifying `-DwithoutHadoop` when packaging `flink-dist` no longer impacts the build. `TaskManagers` now bind to the host IP address instead of the hostname by default . This behaviour can be controlled by the configuration option `taskmanager.network.bind-policy`. If your Flink cluster should experience inexplicable connection problems after upgrading, try to set `taskmanager.network.bind-policy: name` in your `flink-conf.yaml` to return to the pre-1.8

Flink 1.8 deprecates direct usage of the constructor of the `Table` class in the Table API. This constructor would previously be used to perform a join with a lateral table. You should now use `table.joinLateral()` or `table.leftOuterJoinLateral()` instead. This change is necessary for converting the Table class into an interface, which will make the API more maintainable and cleaner in the future. This release introduces a new format descriptor for CSV files that is compliant with RFC 4180. The new descriptor is available as `org.apache.flink.table.descriptors.Csv`. For now, this can only be used together with the Kafka connector. The old descriptor is available as `org.apache.flink.table.descriptors.OldCsv` for use with file system connectors. In order to separate API from actual implementation, the static methods `TableEnvironment.getTableEnvironment()` are deprecated. You should now use `Batch/StreamTableEnvironment.create()` instead. Users that had a `flink-table` dependency before, need to update their dependencies to `flink-table-planner` and the correct dependency of `flink-table-api-*`, depending on whether Java or Scala is used: one of `flink-table-api-java-bridge` or `flink-table-api-scala-bridge`. `ExternalCatalogTable.builder()` is deprecated in favour of `ExternalCatalogTableBuilder()`. The naming scheme for kafka/elasticsearch6 sql-jars has been changed. In maven terms, they no longer have the `sql-jar` qualifier and the artifactId is now prefixed with `flink-sql` instead of `flink`, e.g., `flink-sql-connector-kafka...`. Null literals in the Table API need to be defined with `nullOf(type)` instead of `Null(type)` from now on. The old approach is deprecated. For the Flink `KafkaConsumers`, we introduced a new `KafkaDeserializationSchema` that gives direct access to the Kafka `ConsumerRecord`. This subsumes the `KeyedSerializationSchema` functionality, which is deprecated but still available for now. Starting from Flink 1.8.0, the `FlinkKafkaConsumer` now always filters out restored partitions that are no longer associated with a specified topic to subscribe to in the restored execution. This behaviour did not exist in previous versions of the `FlinkKafkaConsumer`. If you wish to retain the previous behaviour, please use the `disableFilterRestoredPartitionsWithSubscribedTopics()` configuration method on the `FlinkKafkaConsumer`. Consider this example: if you had a Kafka Consumer that was consuming from topic `A`, you did a savepoint, then changed your Kafka consumer to instead consume from topic `B`, and then restarted your job from the savepoint. Before this change, your consumer would now consume from both topic `A` and `B` because it was stored in state that the consumer was consuming from topic `A`. With the change, your consumer would only consume from topic `B` after restore because we filter the topics that are stored in state using the configured topics. The `canEqual()` methods are usually used to make proper equality checks across hierarchies of types. The `TypeSerializer` actually doesn't require this property, so the method is now removed. The `CompositeSerializerSnapshot` utility class has been removed. You should now use `CompositeTypeSerializerSnapshot` instead, for snapshots of composite serializers that delegate serialization to multiple nested serializers. Please see for instructions on using `CompositeTypeSerializerSnapshot`. In Flink 1.8.0 and prior version, the managed memory fraction of taskmanager is controlled by `taskmanager.memory.fraction`, and with 0.7 as the default value. 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. {{< top >}}