igalshilman commented on a change in pull request #7124: [FLINK-9574] [doc] Rework documentation for custom state serializers and state evolution URL: https://github.com/apache/flink/pull/7124#discussion_r234964879
########## File path: docs/dev/stream/state/schema_evolution.md ########## @@ -0,0 +1,92 @@ +--- +title: "State Schema Evolution" +nav-parent_id: streaming_state +nav-pos: 6 +--- +<!-- +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. +--> + +* ToC +{:toc} + +## Overview + +Apache Flink streaming applications are typically designed to run indefinitely for long periods of time. +As with all long-running services, the applications need to be updated to adapt to changing requirements. +This goes the same for data schemas that the applications work against; they evolve along with the application. + +This page provides an overview of how you can evolve your state type's data schema. +The current restrictions varies across different type's and state structures (et.c `ValueState`, `ListState`, etc.). + +Note that the information on this page is relevant only if you are using state serializers that is +generated by Flink's own [type serialization framework]({{ site.baseurl }}/dev/types_serialization.html). +That is, when declaring your state, the provided state descriptor is not configured to use a specific `TypeSerializer` +or `TypeInformation`, and therefore allowing Flink to infer information about the state type: + +<div data-lang="java" markdown="1"> +{% highlight java %} +ListStateDescriptor<MyPojoType> descriptor = + new ListStateDescriptor<>( + "state-name", + MyPojoType.class); + +checkpointedState = getRuntimeContext().getListState(descriptor); +{% endhighlight %} +</div> + +Under the hood, whether or not the schema of state can be evolved depends on the serializer used to read / write +persisted state bytes. Simply put, a registered state's schema can only be evolved if its serializer properly +supports it. This is handled transparently by serializers generated by Flink's type serialization framework +(current scope of support is listed [below]({{ site.baseurl }}/dev/stream/state/schema_evolution#supported-data-types-for-schema-evolution)). + +If you intend to implement a custom `TypeSerializer` for your state type and would like to learn how to implement +the serializer to support state schema evolution, please refer to +[Custom State Serialization]({{ site.baseurl }}/dev/stream/state/custom_serialization). +The documentation there also covers necessary internal details about the interplay between state serializers and Flink's +state backends to support state schema evolution. + +## Evolving state schema + +To evolve the schema of a given state type, you would take the following steps: + + 1. Take a savepoint of your Flink streaming job. + 2. Update state types in your application (e.g., modifying your Avro / POJO type schema). + 3. Restore the job from the savepoint. When accessing state for the first time, Flink will assess whether or not + the schema had been changed for the state, and migrate state schema if necessary. + +The process of migrating state to adapt to changed schemas happens automatically, and independently for each state. +Further details about the migration process is out of the scope of this documentation; please refer to +[here]({{ site.baseurl }}/dev/stream/state/custom_serialization). + +## Supported data types for schema evolution + +Currently, schema evolution is supported only for Avro. Therefore, if you care about schema evolution for +state, it is currently recommended to always use Avro for state data types. + +There are plans to extend the support for more composite types, such as POJOs; for more details, +please refer to [FLINK-10897](https://issues.apache.org/jira/browse/FLINK-10897). + +### Avro types + +Flink fully supports evolving schema of Avro type state, as long as the schema change is considered compatible by +[Avro's rules for schema resolution](http://avro.apache.org/docs/current/spec.html#Schema+Resolution). + +Moreover, it is possible on restore to switch from using Avro-generated `SpecificRecord`s to `GenericRecord`s, Review comment: Would it make sense to mention the limitation here? For example, you can't change the namespace, or relocated the Avro generated classes. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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