GitHub user chenghao-intel opened a pull request:
https://github.com/apache/spark/pull/2113
[SPARK-3197] [SQL] Reduce the Expressions creation for aggregation function
(min/max)
Aggregation function min/max in catalyst will create expression tree for
each single row, however, the expression tree creation is quite expensive in a
multithreading env currently. Hence we got a very bad performance for the
min/max.
Here is the benchmark that I've done in my local.
Master | Previous Result (ms) | Current Result (ms)
------------ | ------------- | -------------
local | 3645 | 3416
local[6] | 3602 | 1002
The Benchmark source code.
```
case class Record(key: Int, value: Int)
object TestHive2 extends HiveContext(new SparkContext("local[6]",
"TestSQLContext", new SparkConf()))
object DataPrepare extends App {
import TestHive2._
val rdd = sparkContext.parallelize((1 to 10000000).map(i => Record(i %
3000, i)), 12)
runSqlHive("SHOW TABLES")
runSqlHive("DROP TABLE if exists a")
runSqlHive("DROP TABLE if exists result")
rdd.registerAsTable("records")
runSqlHive("""CREATE TABLE a (key INT, value INT)
| ROW FORMAT SERDE
|
'org.apache.hadoop.hive.serde2.columnar.LazyBinaryColumnarSerDe'
| STORED AS RCFILE
""".stripMargin)
runSqlHive("""CREATE TABLE result (key INT, value INT)
| ROW FORMAT SERDE
|
'org.apache.hadoop.hive.serde2.columnar.LazyBinaryColumnarSerDe'
| STORED AS RCFILE
""".stripMargin)
hql(s"""from records
| insert into table a
| select key, value
""".stripMargin)
}
object PerformanceTest extends App {
import TestHive2._
hql("SHOW TABLES")
hql("set spark.sql.shuffle.partitions=12")
val cmd = "select min(value), max(value) from a group by key"
val results = ("Result1", benchmark(cmd)) ::
("Result2", benchmark(cmd)) ::
("Result3", benchmark(cmd)) :: Nil
results.foreach { case (prompt, result) => {
println(s"$prompt: took ${result._1} ms (${result._2} records)")
}
}
def benchmark(cmd: String) = {
val begin = System.currentTimeMillis()
val count = hql(cmd).count
val end = System.currentTimeMillis()
((end - begin), count)
}
}
```
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/chenghao-intel/spark
aggregation_expression_optimization
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/2113.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 #2113
----
commit 03c6d4fa67eb8c07dacc2a241578da0ea364b4cd
Author: Cheng Hao <[email protected]>
Date: 2014-08-25T06:40:28Z
Reduce the Expressions creation
----
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