Github user tdas commented on a diff in the pull request:
https://github.com/apache/spark/pull/17179#discussion_r105315079
--- 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 --
Note that there is a fundamental difference between Spark Streaming and
Structured Streaming. Unlike the former, Structured Streaming queries will not
execute any batch if there is no new data. So as long as there is some data and
batch kicks off, timeouts will be processed.
I think "a trigger with any data" is confusing. A trigger by definition has
data. I will rewrite. Thank for pointing it out though.
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