infoverload commented on a change in pull request #17260:
URL: https://github.com/apache/flink/pull/17260#discussion_r708180426
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File path: docs/content/docs/dev/table/concepts/overview.md
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@@ -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.
+
+{{< hint info >}}
+Conceptually, source tables are never kept entirely in state. An implementer
deals with logical tables
+(i.e. [dynamic tables]({{< ref "docs/dev/table/concepts/dynamic_tables" >}})).
Their state requirements
+depend on the used operations.
Review comment:
I feel like more context could be provided in this block to make one
idea lead to the next. Wdyt?
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