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From "Hyukjin Kwon (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-25367) The column attributes obtained by Spark sql are inconsistent with hive
Date Mon, 10 Sep 2018 08:58:00 GMT

    [ https://issues.apache.org/jira/browse/SPARK-25367?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16608899#comment-16608899
] 

Hyukjin Kwon commented on SPARK-25367:
--------------------------------------

(please avoid to set target version and Critical+ which are usually reserved for committers)

> The column attributes obtained by Spark sql are inconsistent with hive
> ----------------------------------------------------------------------
>
>                 Key: SPARK-25367
>                 URL: https://issues.apache.org/jira/browse/SPARK-25367
>             Project: Spark
>          Issue Type: Bug
>          Components: Spark Shell, SQL
>    Affects Versions: 2.2.0, 2.2.1, 2.2.2, 2.3.0, 2.3.1
>         Environment: spark2.2.1-hadoop-2.6.0-chd-5.4.2
> hive-1.2.1
>            Reporter: yy
>            Priority: Major
>              Labels: sparksql
>
> We save the dataframe object as a hive table in orc/parquet format in the spark shell.
>  After we modified the column type (int to double) of this table in hive jdbc, we  found
the column type queried in spark-shell didn't change, but changed in hive jdbc. After we restarted
the spark-shell, this table's column type is still incompatible as showed in hive jdbc.
> The coding process are as follows:
> spark-shell:
> {code:java}
> val df = spark.read.json("examples/src/main/resources/people.json");
> df.write.format("orc").saveAsTable("people_test");
> spark.sql("desc people_test").show()
> +--------+---------+-------+
> |col_name|data_type|comment|
> +--------+---------+-------+
> | age| bigint| null|
> | name| string| null|
> +--------+---------+-------+
> {code}
> hive:
> {code:java}
> hive> desc people_test;
> OK
> age bigint 
> name string 
> Time taken: 0.454 seconds, Fetched: 2 row(s)
> hive> alter table people_test change column age age double;
> OK
> Time taken: 0.68 seconds
> hive> desc people_test;
> OK
> age double 
> name string 
> Time taken: 0.358 seconds, Fetched: 2 row(s){code}
> spark-shell:
> {code:java}
> spark.catalog.refreshTable("people_test")
> spark.sql("desc people_test").show()
> +--------+---------+-------+
> |col_name|data_type|comment|
> +--------+---------+-------+
> | age| bigint| null|
> | name| string| null|
> +--------+---------+-------+
> {code}
>  
> We also tested in spark-shell by creating a table using spark.sql("create table XXX()"), 
the modified columns are consistent.



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