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https://issues.apache.org/jira/browse/FLINK-3226?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15145937#comment-15145937
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ASF GitHub Bot commented on FLINK-3226:
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GitHub user twalthr opened a pull request:
https://github.com/apache/flink/pull/1634
[FLINK-3226] Casting support for arithmetic operators
This PR implements auto-casting for numeric arithmetic operators.
@fhueske @vasia mergable?
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/twalthr/flink ArithmeticCasting
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/flink/pull/1634.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #1634
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commit fd783341b93d7a7ffba11c827a317adb35248ea0
Author: twalthr <twal...@apache.org>
Date: 2016-02-13T11:38:12Z
[FLINK-3226] Casting support for arithmetic operators
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> 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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