Github user nongli commented on a diff in the pull request:
https://github.com/apache/spark/pull/11749#discussion_r56277494
--- Diff:
sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetReadBenchmark.scala
---
@@ -299,10 +299,112 @@ object ParquetReadBenchmark {
}
}
+ def stringWithNullsScanBenchmark(values: Int, fractionOfNulls: Double):
Unit = {
+ withTempPath { dir =>
+ withTempTable("t1", "tempTable") {
+ sqlContext.range(values).registerTempTable("t1")
+ sqlContext.sql(s"select IF(rand(1) < $fractionOfNulls, NULL,
cast(id as STRING)) as c1, " +
+ s"IF(rand(2) < $fractionOfNulls, NULL, cast(id as STRING)) as c2
from t1")
+ .write.parquet(dir.getCanonicalPath)
+
sqlContext.read.parquet(dir.getCanonicalPath).registerTempTable("tempTable")
+
+ val benchmark = new Benchmark("String with Nulls Scan", values)
+
+ benchmark.addCase("SQL Parquet Vectorized") { iter =>
+ sqlContext.sql("select sum(length(c2)) from tempTable where c1
is " +
+ "not NULL and c2 is not NULL").collect()
+ }
+
+ val files =
SpecificParquetRecordReaderBase.listDirectory(dir).toArray
+ benchmark.addCase("PR Vectorized") { num =>
+ var sum = 0
+ files.map(_.asInstanceOf[String]).foreach { p =>
+ val reader = new UnsafeRowParquetRecordReader
+ try {
+ reader.initialize(p, ("c1" :: "c2" :: Nil).asJava)
+ val batch = reader.resultBatch()
+ while (reader.nextBatch()) {
+ val rowIterator = batch.rowIterator()
+ while (rowIterator.hasNext) {
+ val row = rowIterator.next()
+ val value = row.getUTF8String(0)
+ if (!row.isNullAt(0) && !row.isNullAt(1)) sum +=
value.numBytes()
+ }
+ }
+ } finally {
+ reader.close()
+ }
+ }
+ }
+
+ benchmark.addCase("PR Vectorized (Null Filtering)") { num =>
+ var sum = 0L
+ files.map(_.asInstanceOf[String]).foreach { p =>
+ val reader = new UnsafeRowParquetRecordReader
+ try {
+ reader.initialize(p, ("c1" :: "c2" :: Nil).asJava)
+ val batch = reader.resultBatch()
+ batch.filterNullsInColumn(0)
+ batch.filterNullsInColumn(1)
+ while (reader.nextBatch()) {
+ val rowIterator = batch.rowIterator()
+ while (rowIterator.hasNext) {
+ sum += rowIterator.next().getUTF8String(0).numBytes()
+ }
+ }
+ } finally {
+ reader.close()
+ }
+ }
+ }
+
+ /*
+ =======================
+ Fraction of NULLs: 0
+ =======================
+
+ Intel(R) Core(TM) i7-4960HQ CPU @ 2.60GHz
+ String with Nulls Scan: Best/Avg Time(ms) Rate(M/s)
Per Row(ns) Relative
+
-------------------------------------------------------------------------------------------
+ SQL Parquet Vectorized 1164 / 1333 9.0
111.0 1.0X
+ PR Vectorized 809 / 882 13.0
77.1 1.4X
+ PR Vectorized (Null Filtering) 723 / 800 14.5
69.0 1.6X
+
+ =======================
+ Fraction of NULLs: 0.5
+ =======================
+
+ Intel(R) Core(TM) i7-4960HQ CPU @ 2.60GHz
+ String with Nulls Scan: Best/Avg Time(ms) Rate(M/s)
Per Row(ns) Relative
+
-------------------------------------------------------------------------------------------
+ SQL Parquet Vectorized 983 / 1001 10.7
93.8 1.0X
+ PR Vectorized 699 / 728 15.0
66.7 1.4X
+ PR Vectorized (Null Filtering) 722 / 746 14.5
68.9 1.4X
+
+ =======================
--- End diff --
Instead of commenting it this way, can you put the fraction in the
benchmark name?
e.g. String with Nulls Scan (95%)
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastruct...@apache.org or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
