viirya commented on a change in pull request #30789:
URL: https://github.com/apache/spark/pull/30789#discussion_r544501647
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File path: docs/structured-streaming-programming-guide.md
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@@ -1689,6 +1689,23 @@ hence the number is not same as the number of original
input rows. You'd like to
There's a known workaround: split your streaming query into multiple queries
per stateful operator, and ensure
end-to-end exactly once per query. Ensuring end-to-end exactly once for the
last query is optional.
+### State Store and task locality
+
+The stateful operations store states for events in state stores of executors.
State stores occupy resources such as memory and disk space to store the states.
+So it is more efficient to keep a state store provider running in the same
executor across different streaming batches.
+Changing the location of a state store provider requires to load from
checkpointed states from HDFS in the new executor.
+
+The stateful operations in Structured Streaming queries rely on the preferred
location feature of Spark's RDD to run the state store provider on the same
executor.
+However, generally the preferred location is not a hard requirement and it is
still possible that Spark schedules tasks to the executors other than the
preferred ones.
+
+In this case, Spark will load state store providers from checkpointed states
on HDFS to new executors. The state store providers ran in the previous batch
will not be unloaded immediately.
+If in the next batch the corresponding state store provider is scheduled on
this executor again, it could reuse the previous states and save the time of
loading checkpointed states.
+Spark runs a maintenance task which checks and unloads the state store
providers which are inactive on the executors.
+
+For some use cases such as processing very large state data, loading new state
store providers from checkpointed states can be very time-consuming and
inefficient.
+By changing the Spark configurations related to task scheduling, for example
`spark.locality.wait`, users can configure Spark how long to wait to launch a
data-local task.
+For stateful operations in Structured Streaming, it can be used to let state
store providers running on the same executors across batches.
Review comment:
Good point. I added few specific info.
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