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https://issues.apache.org/jira/browse/FLINK-3779?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15306950#comment-15306950
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ASF GitHub Bot commented on FLINK-3779:
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Github user uce commented on the pull request:
https://github.com/apache/flink/pull/2051#issuecomment-222554801
Hey Gyula!
The `QueryableStateStream` is only for convenience. It's just a creating a
`AbstractQueryableStateOperator`, which takes care of setting up an operator,
which consumes data and calls the `state.update()` or `state.add()` method
respectively.
You can use the `QueryableStateClient` anywhere you like, including your
map operator. Just be aware that further Threads will be started for the
network communication (configurable). You can share it between operators though.
You can make any `KvState` queryable by calling the `setQueryable(String)`
method of its state descriptor.
> Add support for queryable state
> -------------------------------
>
> Key: FLINK-3779
> URL: https://issues.apache.org/jira/browse/FLINK-3779
> Project: Flink
> Issue Type: Improvement
> Components: Distributed Runtime
> Reporter: Ufuk Celebi
> Assignee: Ufuk Celebi
>
> Flink offers state abstractions for user functions in order to guarantee
> fault-tolerant processing of streams. Users can work with both
> non-partitioned (Checkpointed interface) and partitioned state
> (getRuntimeContext().getState(ValueStateDescriptor) and other variants).
> The partitioned state interface provides access to different types of state
> that are all scoped to the key of the current input element. This type of
> state can only be used on a KeyedStream, which is created via stream.keyBy().
> Currently, all of this state is internal to Flink and used in order to
> provide processing guarantees in failure cases (e.g. exactly-once processing).
> The goal of Queryable State is to expose this state outside of Flink by
> supporting queries against the partitioned key value state.
> This will help to eliminate the need for distributed operations/transactions
> with external systems such as key-value stores which are often the bottleneck
> in practice. Exposing the local state to the outside moves a good part of the
> database work into the stream processor, allowing both high throughput
> queries and immediate access to the computed state.
> This is the initial design doc for the feature:
> https://docs.google.com/document/d/1NkQuhIKYmcprIU5Vjp04db1HgmYSsZtCMxgDi_iTN-g.
> Feel free to comment.
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