Github user amitsela commented on a diff in the pull request:
https://github.com/apache/spark/pull/17179#discussion_r105228392
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/streaming/KeyedState.scala ---
@@ -61,25 +65,49 @@ import
org.apache.spark.sql.catalyst.plans.logical.LogicalKeyedState
* - After that, if `update(newState)` is called, then `exists()` will
again return `true`,
* `get()` and `getOption()`will return the updated value.
*
+ * Important points to note about using `KeyedStateTimeout`.
+ * - The timeout type is a global param across all the keys (set as
`timeout` param in
+ * `[map|flatMap]GroupsWithState`, but the exact timeout duration is
configurable per key
+ * (by calling `setTimeout...()` in `KeyedState`).
+ * - When the timeout occurs for a key, the function is called with no
values, and
+ * `KeyedState.isTimingOut()` set to true.
+ * - The timeout is reset for key every time the function is called on
the key, that is,
+ * when the key has new data, or the key has timed out. So the user has
to set the timeout
+ * duration every time the function is called, otherwise there will not
be any timeout set.
+ * - Guarantees provided on processing-time-based timeout of key, when
timeout duration is D ms:
+ * - Timeout will never be called before real clock time has advanced
by D ms
+ * - Timeout will be called eventually when there is a trigger with any
data in it
--- End diff --
I understand it's expensive to "scan-through" the state, but why not enable
it periodically ? it could be useful for sparse streams.
Something a-la-updateStateByKey, but only if no data already triggered the
state store for a configured time period.
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