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Posted to issues@flink.apache.org by GitBox <gi...@apache.org> on 2022/07/22 02:29:11 UTC

[GitHub] [flink] nyingping opened a new pull request, #20337: [FLINK-28504][streaming] Update watermarks across multiple oneinputstreams.

nyingping opened a new pull request, #20337:
URL: https://github.com/apache/flink/pull/20337

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## What is the purpose of the change

In local-global streaming tasks, the` LOCAL` phase is classified as `POINTWISE` by default in some versions. And in the case of data skew, the following phenomena will occur.

![image](https://user-images.githubusercontent.com/10743150/180346883-5bf24a42-feb8-43e4-8f8f-788ed5815c02.png)

When `table.exec.source-idle-timeout` is set, the watermark is not aligned in the local phase and then pushes downstream.The UI is confusing,While the `idleness` effect is achieved, But the underlying logic doesn't seem to make sense.

In the `ALL-TO-ALL` case, as shown in down picture, each Input stream task contains all channels, and all Netty Input channels share a watermark value in main memory.Watermark Value or Watermark Status is updated in different input stream tasks. Other input stream tasks are clearly visible. (volatile)

![OLWY%(4WEI 08W_}WK(YS4](https://user-images.githubusercontent.com/10743150/180347084-67a0de1a-a0b2-4d86-ab43-5ff217b1f3c8.png)

In the case of `POINTWISE`, an input stream task contains only part of the Netty input channels, and each of its channels has its own independent watermark. So its watermark value, and the Posting and updating of the watermark status are only in its own part. That's what caused this. One or a portion of independent channels will advance the watermark to downstream without updating other channels.

In order to avoid this situation, I made the following changes.
However, because the streams task&channels are independent from each other, if the watermark in a certain channel is not aligned, it will not affect the watermark to advance the down stream.

It doesn't matter how many input streams and channels there are, or how they are connected, as long as update the watermark value across the input stream with in the same Vertex.

updated effect
![image](https://user-images.githubusercontent.com/10743150/180348902-9534980b-f643-4fa0-bf0f-a12a8925c15d.png)

## Brief change log

- *When creating a stream input task, collect all dataoutputs of the same vertex. When it is necessary to update the watermark, update all outputs together.*

## Verifying this change

This change added tests and can be verified as follows:

- *Added test case verifies that across multiple OneInputStreams update watermark.*

## Does this pull request potentially affect one of the following parts:

- Dependencies (does it add or upgrade a dependency): (no)
- The public API, i.e., is any changed class annotated with `@Public(Evolving)`: (no)
- The serializers: (no)
- The runtime per-record code paths (performance sensitive): (no)
- Anything that affects deployment or recovery: JobManager (and its components), Checkpointing, Kubernetes/Yarn, ZooKeeper: (no)
- The S3 file system connector: (no)

## Documentation

- Does this pull request introduce a new feature? (no)
- If yes, how is the feature documented? (not applicable)

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