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[GitHub] spark issue #22482: WIP - [SPARK-10816][SS] Support session window natively
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[GitHub] spark issue #22482: WIP - [SPARK-10816][SS] Support session window natively
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[GitHub] spark issue #22482: WIP - [SPARK-10816][SS] Support session window natively
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[GitHub] spark issue #22482: WIP - [SPARK-10816][SS] Support session window natively
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[GitHub] spark issue #22482: WIP - [SPARK-10816][SS] Support session window natively
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[GitHub] spark issue #22482: WIP - [SPARK-10816][SS] Support session window natively
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[GitHub] spark issue #22482: WIP - [SPARK-10816][SS] Support session window natively
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[GitHub] spark issue #22482: WIP - [SPARK-10816][SS] Support session window natively
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[GitHub] spark issue #22482: WIP - [SPARK-10816][SS] Support session window natively
Github user HeartSaVioR commented on the issue: https://github.com/apache/spark/pull/22482 According to the discussion on SPARK-10816, I'm holding up effort to improve and plan to discuss further from JIRA issue. I guess someone interested for this patch can still review or try this out and share feedback. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #22482: WIP - [SPARK-10816][SS] Support session window natively
Github user HeartSaVioR commented on the issue: https://github.com/apache/spark/pull/22482 If we are fine with ignoring the optimal delta of state, or OK with addressing it in follow-up issue (it should be addressed in same release version to avoid having state V1, V2, etc...), I think the only TODO is writing javadoc as well as deduplicate some codes. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #22482: WIP - [SPARK-10816][SS] Support session window natively
Github user HeartSaVioR commented on the issue: https://github.com/apache/spark/pull/22482 @arunmahadevan We may want to be aware is that the requirement is pretty different from other streaming frameworks like Flink, which normally set a long period of checkpoint interval and do a full snapshot (though it supports incremental checkpoint, which deals with how it minimizes amount of storing data). Here in Spark, we are expecting smaller batch interval, and Spark deals with the requirement as storing "delta" of state change. The behavior brings concern about the strategy of how we store and how we remove the state. Let's say we have 3 rows in group in batch result and there're also 3 rows in same group in state, and we want to replace state with new batch result. For full snapshot removing 3 rows first and putting 3 rows may not matter much, but with delta approach, we should compare them side-by-side and bring less changes on state. The difference is not trivial one for session window, because arbitrary changes are required: for example, two different sessions in state can be merged later when late events come in, then we should have to overwrite one and remove others. Some new sessions can be created as well as existing session, and we want to overwrite session if the new output session is originated from old state, and append session if not. For other window, it is just a "put" because there's no group and we are just safe to put (overwrite if any, and without evict there's no need to remove). The different requirements between time window and session window are not easy to combine into one. That's what I realized the difficulty of state part for session window, and that's why I feel I need to make change on streaming part. For batch part current patch is doing OK. Btw, we can assume `AssignWindows` as `TimeWindowing` and `SessionWindowing` as we are logically assign rows to individual window. So unless we would like to support custom window like dynamic gap session window, I think we can address it later whenever needed. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #22482: WIP - [SPARK-10816][SS] Support session window natively
Github user arunmahadevan commented on the issue: https://github.com/apache/spark/pull/22482 +1 for the idea to provide native session window support. On the approach, it would be ideal if all windowing aggregations can be handled via single plan and state store (v/s the separate plan and state store the patch proposes for session window). Underlying steps are more or less the same for Fixed, Session and Sliding windows. The sort/merge operations have to be part of a window merge function rather than the plan itself. K,Values -> AssignWindows (produces [k, v, timestamp, window]) -> GroupByKey (shuffle) -> MergeWindows (optional step) -> GroupWindows -> aggregate values. Based on how we want to approach it, it could be handled now or as a follow up item (with major refactoring). --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user HeartSaVioR commented on the issue: https://github.com/apache/spark/pull/22482 Please review the general approach and direction first. I'm planning to spend time to rewrite streaming part to tightly integrate logic with state so that updating state is going to be minimized. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #22482: WIP - [SPARK-10816][SS] Support session window natively
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[GitHub] spark issue #22482: WIP - [SPARK-10816][SS] Support session window natively
Github user HeartSaVioR commented on the issue: https://github.com/apache/spark/pull/22482 retest this, please --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
