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https://issues.apache.org/jira/browse/FLINK-3226?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15144484#comment-15144484
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ASF GitHub Bot commented on FLINK-3226:
---------------------------------------
Github user fhueske commented on a diff in the pull request:
https://github.com/apache/flink/pull/1624#discussion_r52731336
--- Diff:
flink-libraries/flink-table/src/main/scala/org/apache/flink/api/java/table/TableEnvironment.scala
---
@@ -75,5 +75,15 @@ class TableEnvironment {
TypeExtractor.createTypeInfo(clazz).asInstanceOf[TypeInformation[T]])
}
+ /**
+ * Converts the given [[org.apache.flink.api.table.Table]] to
+ * a DataSet. The given type must have exactly the same fields as the
+ * [[org.apache.flink.api.table.Table]]. That is, the names of the
--- End diff --
I would make name equivalence only required for POJOs and generic composite
types types.
Rows and tuples can be matched by position. Otherwise, fields would need to
be renamed to `f0`, `f1`, etc. for tuples.
> 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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