Github user tdas commented on a diff in the pull request:
https://github.com/apache/spark/pull/17179#discussion_r105989084
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/streaming/KeyedState.scala ---
@@ -61,25 +65,50 @@ 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 in the
query
+ * (i.e. after D ms). So there is a no strict upper bound on when the
timeout would occur.
+ * For example, the trigger interval of the query will affect when
the timeout is actually hit.
+ * If there is no data in the stream (for any key) for a while, then
their will not be
+ * any trigger and timeout will not be hit until there is data.
--- End diff --
Shouldnt be tough. We will have to occasionally and periodically run a
trigger with an empty batch DF. I think that should be a separate PR as that
touch the StreamExecution, and we need figure out what the policy and the APIs
to specify the policy should be.
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