dawidwys 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_r235296908
 
 

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 File path: docs/dev/stream/state/schema_evolution.md
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 @@ -0,0 +1,92 @@
+---
+title: "State Schema Evolution"
+nav-parent_id: streaming_state
+nav-pos: 6
+---
+<!--
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+
+  http://www.apache.org/licenses/LICENSE-2.0
+
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+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
 
 Review comment:
   How about adding a very high level description of how does the migration 
happens? Something along the lines that old serializer is used to read state, 
new serializer is used to write state with new schema. I would also mention 
here already that the compatibility check between those serializers is 
performed. Then I would link to more detailed description for particular state 
backends.
   
   I guess that you left it out, because you intended this page to be for 
"basic/simple" users, but I think if somebody ended up looking in for state 
migration he/she already has some deeper knowledge. 

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