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https://issues.apache.org/jira/browse/FLINK-10972?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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sunjincheng updated FLINK-10972:
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Affects Version/s: 1.9.0
> Enhancements to Flink Table API
> -------------------------------
>
> Key: FLINK-10972
> URL: https://issues.apache.org/jira/browse/FLINK-10972
> Project: Flink
> Issue Type: New Feature
> Components: Table SQL / API
> Affects Versions: 1.9.0
> Reporter: sunjincheng
> Assignee: sunjincheng
> Priority: Major
>
> [link title|http://example.com/]With the continuous efforts from the
> community, the Flink system has been continuously improved, which has
> attracted more and more users. Flink SQL is a canonical, widely used
> relational query language. However, there are still some scenarios where
> Flink SQL failed to meet user needs in terms of functionality and ease of
> use, such as:
> * In terms of functionality
> Iteration, user-defined window, user-defined join, user-defined GroupReduce,
> etc. Users cannot express them with SQL;
> * In terms of ease of use
> * Map - e.g. “dataStream.map(mapFun)”. Although “table.select(udf1(),
> udf2(), udf3()....)” can be used to accomplish the same function., with a
> map() function returning 100 columns, one has to define or call 100 UDFs when
> using SQL, which is quite involved.
> * FlatMap - e.g. “dataStrem.flatmap(flatMapFun)”. Similarly, it can be
> implemented with “table.join(udtf).select()”. However, it is obvious that
> datastream is easier to use than SQL.
> Due to the above two reasons, In this JIRAs group, we will enhance the
> TableAPI in stages.
> -----------------------
> The first stage we seek to support (will describe the details in the sub
> issue) :
> * Table.map()
> * Table.flatMap()
> * GroupedTable.aggregate()
> * GroupedTable.flatAggregate()
> The FLIP can be find here:
> [FLIP-29|https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=97552739]
>
> The second part is about column operator/operations:
> 1) Table(schema) operators
> * Add columns
> * Replace columns
> * Drop columns
> * Rename columns
> 2）Fine-grained column/row operations
> * Column selection
> * Row package and flatten
> See [google
> doc|https://docs.google.com/document/d/1tryl6swt1K1pw7yvv5pdvFXSxfrBZ3_OkOObymis2ck/edit]
>
>
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