GitHub user HyukjinKwon opened a pull request:
https://github.com/apache/spark/pull/17589
[SPARK-16544][SQL] Support for conversion from numeric compatible schema
for Parquet readers
## What changes were proposed in this pull request?
This PR proposes schema compatibility for Parquet for normal Parquet
reader. It does not fully solve the issue for vectorized reader one.
Currently if user-given schema is different with the Parquet schema, it
throws an exception even when the user-given schema is compatible with Parquet
schema.
For example, executing the codes below:
``` scala
import org.apache.spark.sql.types._
spark.conf.set("spark.sql.parquet.enableVectorizedReader", false.toString)
val path = "/tmp/abcd"
val data = (1 to 4).map(Tuple1(_))
spark.createDataFrame(data).toDF("a").write.parquet(path)
val schema = StructType(StructField("a", LongType, true) :: Nil)
spark.read.schema(schema).parquet(path).show()
```
throws an exception as below:
```
org.apache.parquet.io.ParquetDecodingException: Can not read value at 1 in
block 0
...
```
This PR lets Parquet supports this schema compatibility for numeric types.
## How was this patch tested?
Unit tests in `ParquetIOSuite`.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/HyukjinKwon/spark SPARK-16544
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/17589.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 #17589
----
commit 1d4d40c0e6a7318cf6a2f2f783c3c110b63cd724
Author: hyukjinkwon <gurwls...@gmail.com>
Date: 2016-07-15T03:37:45Z
Support for conversion from compatible schema for Parquet data source when
data types are not matched
----
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