infoverload commented on a change in pull request #17260:
URL: https://github.com/apache/flink/pull/17260#discussion_r708178257
##########
File path: docs/content/docs/dev/table/concepts/overview.md
##########
@@ -32,6 +32,82 @@ This means that Table API and SQL queries have the same
semantics regardless whe
The following pages explain concepts, practical limitations, and
stream-specific configuration parameters of Flink's relational APIs on
streaming data.
+State Management
+----------------
+
+Table programs that run in streaming mode leverage all capabilities of Flink
as a stateful stream
+processor.
+
+In particular, a table program can be configured with a [state backend]({{<
ref "docs/ops/state/state_backends" >}})
+and various [checkpointing options]({{< ref
"docs/dev/datastream/fault-tolerance/checkpointing" >}})
+for handling different requirements regarding state size and fault tolerance.
It is possible to take
+a savepoint of a running Table API & SQL pipeline and to restore the
application's state at a later
+point in time.
+
+### State Usage
+
+Due to the declarative nature of Table API & SQL program, it is not always
obvious where and how much
+state is used within a pipeline. The planner decides whether state is
necessary to compute a correct
+result. A pipeline is optimized to claim as little state as possible given the
current set of optimizer
+rules.
Review comment:
```suggestion
Due to the declarative nature of Table API & SQL programs, it is not always
obvious where and how much
state is used within a pipeline. The planner decides whether state is
necessary to compute a correct
result. A pipeline is optimized to claim as little state as possible given
the current set of optimizer
rules.
```
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: [email protected]
For queries about this service, please contact Infrastructure at:
[email protected]
