GitHub user wgtmac opened a pull request:

    [SPARK-17477][SQL] SparkSQL cannot handle schema evolution from Int -…

    ## What changes were proposed in this pull request?
    Using SparkSession in Spark 2.0 to read a Hive table which is stored as 
parquet files and if there has been a schema evolution from int to long of a 
column, we will get java.lang.ClassCastException: 
org.apache.spark.sql.catalyst.expressions.MutableLong cannot be cast to 
org.apache.spark.sql.catalyst.expressions.MutableInt. To be specific, if there 
are some old parquet files using int for the column while some new parquet 
files use long and the Hive metastore uses Long as its type, the aforementioned 
exception will be thrown. Because Hive and Presto deem this kind of schema 
evolution is valid, this PR allows writing a int value when its table schema is 
long in hive metastore.
    This is for non-vectorized parquet, will create a separate JIRA for 
vectorized parquet reader and come up with a fix later.
    ## How was this patch tested?
    Manual test to create parquet files with int type in the schema and create 
hive table using long as its type. Then perform spark.sql("select * from 
table") to query all data from this table.

You can merge this pull request into a Git repository by running:

    $ git pull master

Alternatively you can review and apply these changes as the patch at:

To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:

    This closes #15155
commit 2f30184ed07c50506fd5c7ede05c989f2614e8c2
Author: Gang Wu <>
Date:   2016-09-19T22:27:40Z

    [SPARK-17477][SQL] SparkSQL cannot handle schema evolution from Int -> Long 
when parquet files have Int as its type while hive metastore has Long as its 


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