GitHub user clockfly opened a pull request:
https://github.com/apache/spark/pull/14445
[SPARK-16320][SQL] Fix performance regression for parquet table with nested
fields
## What changes were proposed in this pull request?
For non-partitioned parquet table with nested column, Spark 2.0 adds an
extra unnecessary memory copy to append partition values for each row.
By fixing this bug, we get about 30% performance gain in test case like
this:
```
// Generates parquet table with nested columns
spark.range(100000000).select(struct($"id").as("nc")).write.parquet("/tmp/data4")
val t0 = System.nanoTime()
val x = ((0 until 20).toList.map(x =>
time(spark.read.parquet("/tmp/data4").filter($"nc.id" < 100).collect()))).sum/20
println("Elapsed time: " + (System.nanoTime() - t0)/1000000 + "ms")
```
## How was this patch tested?
Existing unit tests
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/clockfly/spark fix_parquet_regression_2
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/14445.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 #14445
----
commit 272fb8100f1861d78f78d7bc34e1ff68284b773a
Author: Sean Zhong <seanzh...@databricks.com>
Date: 2016-08-01T04:29:44Z
fix parquet_regression
----
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