GitHub user clockfly opened a pull request:
https://github.com/apache/spark/pull/14446
[SPARK-16841][SQL] Improves the row level metrics performance when reading
Parquet table
## What changes were proposed in this pull request?
When reading from Parquet table, Spark updates row level metrics like
recordsRead, bytesRead.
The implementation is not very efficient. It may take 20% of read them to
update these metrics.
Test benchmark:
```
// Generates parquet table with nested columns
spark.range(100000000).select(struct($"id").as("nc")).write.parquet("/tmp/data4")
def time[R](block: => R): Long = {
val t0 = System.nanoTime()
val result = block // call-by-name
val t1 = System.nanoTime()
println("Elapsed time: " + (t1 - t0)/1000000 + "ms")
(t1 - t0)/1000000
}
val x = ((0 until 20).toList.map(x =>
time(spark.read.parquet("/tmp/data4").filter($"nc.id" < 100).collect()))).sum/20
```
## How was this patch tested?
Exisiting unit tests.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/clockfly/spark improve_metrics_performance
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/14446.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 #14446
----
commit 1054b74f18193378942b7fde26df36e06bff765e
Author: Sean Zhong <seanzh...@databricks.com>
Date: 2016-08-01T23:35:30Z
improve row level metrics performance
----
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