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https://issues.apache.org/jira/browse/FLINK-3226?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15116992#comment-15116992
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Timo Walther commented on FLINK-3226:
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Once the interfaces and physical operator names are defined, I can start
porting the code generation into the FlinkRelNodes. I think it should not take
much time, because all FlinkRelNodes share the same util for generating the
Function body. (I think it was called ResultAssembler in Table API) But we can
further split up the issue if you like. Why did we expanded the scope?
> Translate optimized logical Table API plans into physical plans representing
> DataSet programs
> ---------------------------------------------------------------------------------------------
>
> Key: FLINK-3226
> URL: https://issues.apache.org/jira/browse/FLINK-3226
> Project: Flink
> Issue Type: Sub-task
> Components: Table API
> Reporter: Fabian Hueske
> Assignee: Chengxiang Li
>
> This issue is about translating an (optimized) logical Table API (see
> FLINK-3225) query plan into a physical plan. The physical plan is a 1-to-1
> representation of the DataSet program that will be executed. This means:
> - Each Flink RelNode refers to exactly one Flink DataSet or DataStream
> operator.
> - All (join and grouping) keys of Flink operators are correctly specified.
> - The expressions which are to be executed in user-code are identified.
> - All fields are referenced with their physical execution-time index.
> - Flink type information is available.
> - Optional: Add physical execution hints for joins
> The translation should be the final part of Calcite's optimization process.
> For this task we need to:
> - implement a set of Flink DataSet RelNodes. Each RelNode corresponds to one
> Flink DataSet operator (Map, Reduce, Join, ...). The RelNodes must hold all
> relevant operator information (keys, user-code expression, strategy hints,
> parallelism).
> - implement rules to translate optimized Calcite RelNodes into Flink
> RelNodes. We start with a straight-forward mapping and later add rules that
> merge several relational operators into a single Flink operator, e.g., merge
> a join followed by a filter. Timo implemented some rules for the first SQL
> implementation which can be used as a starting point.
> - Integrate the translation rules into the Calcite optimization process
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