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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:
--------------------------------
Description:
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]
was:
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 design document and the discussion mail list can be find here:
Google doc:
[https://docs.google.com/document/d/1tnpxg31EQz2-MEzSotwFzqatsB4rNLz0I-l_vPa5H4Q/edit#|https://docs.google.com/document/d/1tnpxg31EQz2-MEzSotwFzqatsB4rNLz0I-l_vPa5H4Q/edit]
[DISCUSS] Enhancing the functionality and productivity of Table API
[https://lists.apache.org/thread.html/881b34fe79991870c099132b4723dde882cffcfff8e9a1f5bbe92bee@%3Cdev.flink.apache.org%3E]
[DISCUSS] Table API Enhancement Outline:
[https://lists.apache.org/thread.html/a75f5d0a938333503a0f1881f800d37ba0ec662b44624d4be9c6fdd9@%3Cdev.flink.apache.org%3E]
> 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 API & SQL
> Reporter: sunjincheng
> Assignee: sunjincheng
> Priority: Major
> Fix For: 1.8.0
>
>
> 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]
>
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