wuchong opened a new pull request #14905: URL: https://github.com/apache/flink/pull/14905
<!-- *Thank you very much for contributing to Apache Flink - we are happy that you want to help us improve Flink. To help the community review your contribution in the best possible way, please go through the checklist below, which will get the contribution into a shape in which it can be best reviewed.* *Please understand that we do not do this to make contributions to Flink a hassle. In order to uphold a high standard of quality for code contributions, while at the same time managing a large number of contributions, we need contributors to prepare the contributions well, and give reviewers enough contextual information for the review. Please also understand that contributions that do not follow this guide will take longer to review and thus typically be picked up with lower priority by the community.* ## Contribution Checklist - Make sure that the pull request corresponds to a [JIRA issue](https://issues.apache.org/jira/projects/FLINK/issues). Exceptions are made for typos in JavaDoc or documentation files, which need no JIRA issue. - Name the pull request in the form "[FLINK-XXXX] [component] Title of the pull request", where *FLINK-XXXX* should be replaced by the actual issue number. Skip *component* if you are unsure about which is the best component. Typo fixes that have no associated JIRA issue should be named following this pattern: `[hotfix] [docs] Fix typo in event time introduction` or `[hotfix] [javadocs] Expand JavaDoc for PuncuatedWatermarkGenerator`. - Fill out the template below to describe the changes contributed by the pull request. That will give reviewers the context they need to do the review. - Make sure that the change passes the automated tests, i.e., `mvn clean verify` passes. You can set up Azure Pipelines CI to do that following [this guide](https://cwiki.apache.org/confluence/display/FLINK/Azure+Pipelines#AzurePipelines-Tutorial:SettingupAzurePipelinesforaforkoftheFlinkrepository). - Each pull request should address only one issue, not mix up code from multiple issues. - Each commit in the pull request has a meaningful commit message (including the JIRA id) - Once all items of the checklist are addressed, remove the above text and this checklist, leaving only the filled out template below. **(The sections below can be removed for hotfixes of typos)** --> ## What is the purpose of the change Support window TVF based window aggreagte in planner. We should be able to end-to-end run the new window aggregate syntax after this PR. ## Brief change log 1. Introduce SqlOperator definitions for TUMBLE, HOP, CUMULATE table-valued function We don't use the definitions in Calcite, because we have some special needs: 1) we have time attribute type check on the DESCRIPTOR time column 2) we need to derive type of window columns from the time attribute type (e.g. TIMESTAMP_LZ) 3) we will output an additional column `window_time` which extends time attribute type 2. Support translating into TVF based window aggreagte physical node This commit makes plan test possbile. 1) [FLINK-19611] Introduce WindowProperties MetadataHandler to propagate window properties. 2) Recognize window aggregate and translate `FlinkLogicalAggregate` into `StreamPhysicalWindowAggregate`, by introduce `StreamPhysicalWindowAggregateRule` 3) Push window TVF into `StreamPhysicalWindowAggregate`, by introduce `PushWindowTableFunctionIntoWindowAggregateRule` 4) [FLINK-21304] Support split distinct aggregate for TVF based window aggregate, by introduce `ExpandWindowTableFunctionTransposeRule` 5) Support cascading window aggregate, mainly fix `RelTimeIndicatorConverter` to propagate time attribute of window_time. 3. Introduce TVF based window aggregate exec node This commit makes integration test possible. 1) Introduce StreamExecWindowAggregate to translate into slicing window aggregate operator 2) Update AggsHandlerCodeGenerator to support generate window properties based on window end timestamp 3) Fix implementation of WindowedSliceAssigner::getWindowStart, it must delegate calls to real slice assigner ## Verifying this change 1. Rename WindowAggregateTest to GroupWindowTest Because we are going to introduce the new window TVF based aggregates, in order to avoid confusing, we call the legacy window aggregate "GroupWindow", and the new window aggregate "WindowAggregate". 2. Introduce plan tests in `WindowAggregateTest` and `WindowTableFunctionTest` 3. Introduce IT cases in `WindowAggregateITCase` 4. Introduce distinct aggregate IT casess in `WindowDistinctAggregateITCase` 5. Introduce harness tests for processing-time window aggregates in `WindowAggregateHarnessTest` 6. Add tests to reproduce problems with `WindowedSliceAssigner#getWindowStart` in `WindowedSliceAssignerTest`. ## Does this pull request potentially affect one of the following parts: - Dependencies (does it add or upgrade a dependency): (yes / **no**) - The public API, i.e., is any changed class annotated with `@Public(Evolving)`: (yes / **no**) - The serializers: (yes / **no** / don't know) - The runtime per-record code paths (performance sensitive): (yes / **no** / don't know) - Anything that affects deployment or recovery: JobManager (and its components), Checkpointing, Yarn/Mesos, ZooKeeper: (yes / **no** / don't know) - The S3 file system connector: (yes / **no** / don't know) ## Documentation - Does this pull request introduce a new feature? (yes / **no**) - If yes, how is the feature documented? (**not applicable** / docs / JavaDocs / not documented) ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org