GitHub user vanzin opened a pull request:
https://github.com/apache/spark/pull/14272
[SPARK-16632][sql] Respect Hive schema when merging parquet schema.
When Hive (or at least certain versions of Hive) creates parquet files
containing tinyint or smallint columns, it stores them as int32, but
doesn't annotate the parquet field as containing the corresponding
int8 / int16 data. When Spark reads those files using the vectorized
reader, it follows the parquet schema for these fields, but when
actually reading the data it tries to use the type fetched from
the metastore, and then fails because data has been loaded into the
wrong fields in OnHeapColumnVector.
So instead of blindly trusting the parquet schema, check whether the
Catalyst-provided schema disagrees with it, and adjust the types so
that the necessary metadata is present when loading the data into
the ColumnVector instance.
Tested with unit tests and with tests that create byte / short columns
in Hive and try to read them from Spark.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/vanzin/spark SPARK-16632
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/14272.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #14272
----
commit d853ba0769189b0557d801b9e3e1ed1a9d65cea9
Author: Marcelo Vanzin <[email protected]>
Date: 2016-07-19T22:51:16Z
[SPARK-16632][sql] Respect Hive schema when merging parquet schema.
When Hive (or at least certain versions of Hive) creates parquet files
containing tinyint or smallint columns, it stores them as int32, but
doesn't annotate the parquet field as containing the corresponding
int8 / int16 data. When Spark reads those files using the vectorized
reader, it follows the parquet schema for these fields, but when
actually reading the data it tries to use the type fetched from
the metastore, and then fails because data has been loaded into the
wrong fields in OnHeapColumnVector.
So instead of blindly trusting the parquet schema, check whether the
Catalyst-provided schema disagrees with it, and adjust the types so
that the necessary metadata is present when loading the data into
the ColumnVector instance.
Tested with unit tests and with tests that create byte / short columns
in Hive and try to read them from Spark.
----
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