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https://issues.apache.org/jira/browse/SPARK-24602?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16784155#comment-16784155
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Sushanta Sen commented on SPARK-24602:
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This issue is logged prior to other JIRAs as mentioned in the Issue Links.
> In Spark SQL, ALTER TABLE--CHANGE column1 column2 datatype is not supported
> in 2.3.1
> ------------------------------------------------------------------------------------
>
> Key: SPARK-24602
> URL: https://issues.apache.org/jira/browse/SPARK-24602
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 2.3.0
> Environment: OS: SUSE11
> Spark Version: 2.3
> Reporter: Sushanta Sen
> Priority: Major
>
> Precondition:
> Spark cluster 2.3 is up and running
> Test Steps:
> # Launch Spark-sql
> # spark-sql> CREATE TABLE t1(a int,string)
> 0: jdbc:hive2://ha-cluster/default> *alter
> table t1 change a a1 int;*
> Error: org.apache.spark.sql.AnalysisException: {color:#FF0000}ALTER TABLE
> CHANGE COLUMN is not supported for changing column 'a' with type
> 'IntegerType' to 'b' with type 'IntegerType'; (state=,code=0){color}
> # Launch hive beeliine
> # repeat step1 & 2
> # 0: jdbc:hive2://10.18.108.126:10000/> desc del1;
> +-----------+------------+----------+--+
> | col_name | data_type | comment |
> +-----------+------------+----------+--+
> | *a1* | *int* | |
> | dob | int | |
> +-----------+------------+----------+--+
> 2 rows selected (1.572 seconds)
> 0: jdbc:hive2://10.18.108.126:10000/>{color:#205081} alter table del1 change
> a1 a bigint;{color}
> No rows affected (0.425 seconds)
> 0: jdbc:hive2://10.18.108.126:10000/> desc del1;
> +-----------+------------+----------+--+
> | col_name | data_type | comment |
> +-----------+------------+----------+--+
> | *a* | *bigint* | |
> | dob | int | |
> +-----------+------------+----------+--+
> 2 rows selected (0.364 seconds)
>
> Actual Result: In spark sql, alter table change is not supported, whereas in
> hive beeline it is working fine.
> Expected Result: ALTER Table CHANGE should be supported in Spark-SQL as well
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