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https://issues.apache.org/jira/browse/FLINK-5991?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15954859#comment-15954859
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ASF GitHub Bot commented on FLINK-5991:
---------------------------------------
Github user aljoscha commented on a diff in the pull request:
https://github.com/apache/flink/pull/3508#discussion_r109610444
--- Diff: docs/dev/stream/state.md ---
@@ -233,45 +229,44 @@ val counts: DataStream[(String, Int)] = stream
## Using Managed Operator State
-A stateful function can implement either the more general
`CheckpointedFunction`
+To use managed operator state, a stateful function can implement either
the more general `CheckpointedFunction`
interface, or the `ListCheckpointed<T extends Serializable>` interface.
-In both cases, the non-keyed state is expected to be a `List` of
*serializable* objects, independent from each other,
-thus eligible for redistribution upon rescaling. In other words, these
objects are the finest granularity at which
-non-keyed state can be repartitioned. As an example, if with parallelism 1
the checkpointed state of the `BufferingSink`
-contains elements `(test1, 2)` and `(test2, 2)`, when increasing the
parallelism to 2, `(test1, 2)` may end up in task 0,
-while `(test2, 2)` will go to task 1.
-
-##### ListCheckpointed
+#### CheckpointedFunction
-The `ListCheckpointed` interface requires the implementation of two
methods:
-
-{% highlight java %}
-List<T> snapshotState(long checkpointId, long timestamp) throws Exception;
-
-void restoreState(List<T> state) throws Exception;
-{% endhighlight %}
-
-On `snapshotState()` the operator should return a list of objects to
checkpoint and
-`restoreState` has to handle such a list upon recovery. If the state is
not re-partitionable, you can always
-return a `Collections.singletonList(MY_STATE)` in the `snapshotState()`.
-
-##### CheckpointedFunction
-
-The `CheckpointedFunction` interface also requires the implementation of
two methods:
+The `CheckpointedFunction` interface provides access to non-keyed state
with different
+redistribution schemes. It requires the implementation of two methods:
{% highlight java %}
void snapshotState(FunctionSnapshotContext context) throws Exception;
void initializeState(FunctionInitializationContext context) throws
Exception;
{% endhighlight %}
-Whenever a checkpoint has to be performed `snapshotState()` is called. The
counterpart, `initializeState()`, is called every time the user-defined
function is initialized, be that when the function is first initialized
-or be that when actually recovering from an earlier checkpoint. Given
this, `initializeState()` is not
+Whenever a checkpoint has to be performed, `snapshotState()` is called.
The counterpart, `initializeState()`,
+is called every time the user-defined function is initialized, be that
when the function is first initialized
+or be that when the function is actually recovering from an earlier
checkpoint. Given this, `initializeState()` is not
only the place where different types of state are initialized, but also
where state recovery logic is included.
-This is an example of a function that uses `CheckpointedFunction`, a
stateful `SinkFunction` that
-uses state to buffer elements before sending them to the outside world:
+Currently, list-style managed operator state is supported. The state
+is expected to be a `List` of *serializable* objects, independent from
each other,
+thus eligible for redistribution upon rescaling. In other words, these
objects are the finest granularity at which
+non-keyed state can be redistributed. Depending on the state accessing
method,
+the following redistribution schemes are defined:
+
+ - **Even-split redistribution:** Each operator returns a List of state
elements. The whole state is logically a concatenation of
--- End diff --
Could use "Round-Robin redistribution". Maybe...
> Expose Broadcast Operator State through public APIs
> ---------------------------------------------------
>
> Key: FLINK-5991
> URL: https://issues.apache.org/jira/browse/FLINK-5991
> Project: Flink
> Issue Type: New Feature
> Components: DataStream API, State Backends, Checkpointing
> Reporter: Tzu-Li (Gordon) Tai
> Assignee: Tzu-Li (Gordon) Tai
> Fix For: 1.3.0
>
>
> The broadcast operator state functionality was added in FLINK-5265, it just
> hasn't been exposed through any public APIs yet.
> Currently, we have 2 streaming connector features for 1.3 that are pending on
> broadcast state: rescalable Kinesis / Kafka consumers with shard / partition
> discovery (FLINK-4821 & FLINK-4022). We should consider exposing broadcast
> state for the 1.3 release also.
> This JIRA also serves the purpose to discuss how we want to expose it.
> To initiate the discussion, I propose:
> 1. For the more powerful {{CheckpointedFunction}}, add the following to the
> {{OperatorStateStore}} interface:
> {code}
> <S> ListState<S> getBroadcastOperatorState(ListStateDescriptor<S>
> stateDescriptor);
> <T extends Serializable> ListState<T>
> getBroadcastSerializableListState(String stateName);
> {code}
> 2. For a simpler {{ListCheckpointed}} variant, we probably should have a
> separate {{BroadcastListCheckpointed}} interface.
> Extending {{ListCheckpointed}} to let the user define either the list state
> type of either {{PARTITIONABLE}} or {{BROADCAST}} might also be possible, if
> we can rely on a contract that the value doesn't change. Or we expose a
> defining method (e.g. {{getListStateType()}}) that is called only once in the
> operator. This would break user code, but can be considered because it is
> marked as {{PublicEvolving}}.
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