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Posted to issues@flink.apache.org by "MJ Deng (JIRA)" <ji...@apache.org> on 2019/07/27 19:03:00 UTC
[jira] [Comment Edited] (FLINK-5802) Flink SQL calling Hive
User-Defined Functions
[ https://issues.apache.org/jira/browse/FLINK-5802?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16894533#comment-16894533 ]
MJ Deng edited comment on FLINK-5802 at 7/27/19 7:02 PM:
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Hey [~winipanda], are you still working on this task? If not, I'm happy to take over. cc [~fhueske]
was (Author: miaoever):
Hey [~winipanda], are you still working on this task? If not, I'm happy to work on it. cc [~fhueske]
> Flink SQL calling Hive User-Defined Functions
> ---------------------------------------------
>
> Key: FLINK-5802
> URL: https://issues.apache.org/jira/browse/FLINK-5802
> Project: Flink
> Issue Type: Sub-task
> Components: Connectors / Hive, Table SQL / Ecosystem
> Reporter: Zhuoluo Yang
> Assignee: TANG Wen-hui
> Priority: Major
> Labels: features
>
> It's important to call hive udf in Flink SQL. A great many udfs were written in hive since last ten years.
> It's really important to reuse the hive udfs. This feature will reduce the cost of migration and bring more users to flink.
> Spark SQL has already supported this function.
> https://docs.hortonworks.com/HDPDocuments/HDP2/HDP-2.4.0/bk_spark-guide/content/calling-udfs.html
> The Hive UDFs here include both built-in UDFs and customized UDFs. As many business logic had been written in UDFs, the customized UDFs are more important than the built-in UDFs.
> Generally, there are three kinds of UDFs in Hive: UDF, UDTF and UDAF.
> Here is the document of the Spark SQL: http://spark.apache.org/docs/latest/sql-programming-guide.html#compatibility-with-apache-hive
> Spark code:
> https://github.com/apache/spark/blob/master/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
> https://github.com/apache/spark/blob/master/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
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