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https://issues.apache.org/jira/browse/FLINK-3226?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15160353#comment-15160353
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ASF GitHub Bot commented on FLINK-3226:
---------------------------------------
Github user twalthr commented on a diff in the pull request:
https://github.com/apache/flink/pull/1679#discussion_r53905238
--- Diff:
flink-libraries/flink-table/src/test/java/org/apache/flink/api/java/table/test/StringExpressionsITCase.java
---
@@ -40,46 +40,6 @@ public StringExpressionsITCase(TestExecutionMode mode) {
super(mode);
}
- @Test(expected = CodeGenException.class)
--- End diff --
No, the `ScalarFunctionsTest` does not test end-to-end. I don't think this
is necessary for scalar functions, because they don't affect optimization/plan
translation. When we will have more functions (e.g. 40 of them), we would have
40 end-to-end tests just implementing a MapFunction and evaluating an
expression. That's why I implemented the `ExpressionEvaluator`.
> 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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