[jira] [Comment Edited] (SPARK-34212) For parquet table, after changing the precision and scale of decimal type in hive, spark reads incorrect value

[Dongjoon Hyun (Jira)]

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https://issues.apache.org/jira/browse/SPARK-34212?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17271071#comment-17271071
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Dongjoon Hyun edited comment on SPARK-34212 at 1/25/21, 5:22 AM:
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As a workaround, you may want to turn off 
`spark.sql.hive.convertMetastoreParquet`.
{code:java}
spark-sql> set spark.sql.hive.convertMetastoreParquet=false;
spark.sql.hive.convertMetastoreParquet  false
Time taken: 0.04 seconds, Fetched 1 row(s)

spark-sql> select * from test_decimal;
100.000
Time taken: 1.695 seconds, Fetched 1 row(s)
{code}


was (Author: dongjoon):
As a workaround, you may want to turn off 
`spark.sql.hive.convertMetastoreParquet`.
{code:java}
spark-sql> set spark.sql.hive.convertMetastoreParquet=false;
spark.sql.hive.convertMetastoreParquet  false
Time taken: 0.04 seconds, Fetched 1 row(s)

spark-sql> set spark.sql.hive.convertMetastoreParquet;
spark.sql.hive.convertMetastoreParquet  false
Time taken: 0.038 seconds, Fetched 1 row(s)

spark-sql> select * from test_decimal;
21/01/24 21:20:34 WARN SessionState: METASTORE_FILTER_HOOK will be ignored, 
since hive.security.authorization.manager is set to instance of 
HiveAuthorizerFactory.
100.000
Time taken: 1.695 seconds, Fetched 1 row(s)
{code}

> For parquet table, after changing the precision and scale of decimal type in 
> hive, spark reads incorrect value
> --------------------------------------------------------------------------------------------------------------
>
>                 Key: SPARK-34212
>                 URL: https://issues.apache.org/jira/browse/SPARK-34212
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 2.4.5, 3.1.1
>            Reporter: Yahui Liu
>            Priority: Major
>
> In Hive, 
> {code}
> create table test_decimal(amt decimal(18,2)) stored as parquet; 
> insert into test_decimal select 100;
> alter table test_decimal change amt amt decimal(19,3);
> {code}
> In Spark,
> {code}
> select * from test_decimal;
> {code}
> {code}
> +--------+
> |    amt |
> +--------+
> | 10.000 |
> +--------+
> {code}



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