[jira] [Created] (FLINK-20038) Rectify the usage of ResultPartitionType#isPipelined() in partition tracker.

["Jin Xing (Jira)" <ji...@apache.org>]

Jin Xing created FLINK-20038:
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             Summary: Rectify the usage of ResultPartitionType#isPipelined() in partition tracker.
                 Key: FLINK-20038
                 URL: https://issues.apache.org/jira/browse/FLINK-20038
             Project: Flink
          Issue Type: Improvement
          Components: Runtime / Coordination
            Reporter: Jin Xing


After "FLIP-31: Pluggable Shuffle Service", users can extend and plug in new shuffle manner, thus to benefit different scenarios. New shuffle manner tend to bring in new abilities which could be leveraged by scheduling layer to provide better performance.

From my understanding, the characteristics of shuffle manner is exposed by ResultPartitionType (e.g. isPipelined, isBlocking, hasBackPressure ...), and leveraged by scheduling layer to conduct job. But seems that Flink doesn't provide a way to describe the new characteristics from a plugged in shuffle manner. I also find that scheduling layer have some weak assumptions on ResultPartitionType. I detail by below example.

In our internal Flink, we develop a new shuffle manner for batch jobs. Characteristics can be summarized as below briefly:
1. Upstream task shuffle writes data to DISK;
2. Upstream task commits data while producing and notify "consumable" to downstream BEFORE task finished;
3. Downstream is notified when upstream data is consumable and can be scheduled according to resource;
4. When downstream task failover, only itself needs to be restarted because upstream data is written into disk and replayable;

We can tell the character of this new shuffle manner as:
a. isPipelined=true – downstream task can consume data before upstream finished;
b. hasBackPressure=false – upstream task shuffle writes data to disk and can finish by itself no matter if there's downstream task consumes the data in time.

But above new ResultPartitionType(isPipelined=true, hasBackPressure=false) seems contradicts the partition lifecycle management in current scheduling layer:
1. The above new shuffle manner needs partition tracker for lifecycle management, but current Flink assumes that ALL "isPipelined=true" result partitions are released on consumption and will not be taken care of by partition tracker ([link|https://github.com/apache/flink/blob/master/flink-runtime/src/main/java/org/apache/flink/runtime/io/network/partition/JobMasterPartitionTrackerImpl.java#L66]) – the limitation is not correct for this case.

From my understanding, the method of ResultPartitionType#isPipelined() indicates whether data can be consumed while being produced, and it's orthogonal to whether the partition is released on consumption. I propose to have a fix on this and fully respect to the original meaning of ResultPartitionType#isPipelined().



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