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Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2018/09/01 05:34:00 UTC
[jira] [Commented] (SPARK-25301) When a view uses an UDF from a non
default database, Spark analyser throws AnalysisException
[ https://issues.apache.org/jira/browse/SPARK-25301?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16599540#comment-16599540 ]
Apache Spark commented on SPARK-25301:
--------------------------------------
User 'vinodkc' has created a pull request for this issue:
https://github.com/apache/spark/pull/22307
> When a view uses an UDF from a non default database, Spark analyser throws AnalysisException
> --------------------------------------------------------------------------------------------
>
> Key: SPARK-25301
> URL: https://issues.apache.org/jira/browse/SPARK-25301
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.4.0
> Reporter: Vinod KC
> Priority: Minor
>
> When a hive view uses an UDF from a non default database, Spark analyser throws AnalysisException
> Steps to simulate this issue
> -----------------------------
> In Hive
> --------
> 1) CREATE DATABASE d100;
> 2) ADD JAR /usr/udf/masking.jar // masking.jar has a custom udf class 'com.uzx.udf.Masking'
> 3) create function d100.udf100 as "com.uzx.udf.Masking"; // Note: udf100 is created in d100
> 4) create view d100.v100 as select *d100.udf100*(name) from default.emp; // Note : table default.emp has two columns 'nanme', 'address',
> 5) select * from d100.v100; // query on view d100.v100 gives correct result
> In Spark
> -------------
> 1) spark.sql("select * from d100.v100").show
> throws
> ```
> org.apache.spark.sql.AnalysisException: Undefined function: '*d100.udf100*'. This function is neither a registered temporary function nor a permanent function registered in the database '*default*'
> ```
> This is because, while parsing the SQL statement of the View 'select `d100.udf100`(`emp`.`name`) from `default`.`emp`' , spark parser fails to split database name and udf name and hence Spark function registry tries to load the UDF 'd100.udf100' from 'default' database.
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