[jira] [Created] (SPARK-18591) Replace hash-based aggregates with sort-based ones if inputs already sorted

["Takeshi Yamamuro (JIRA)" <ji...@apache.org>]

Takeshi Yamamuro created SPARK-18591:
----------------------------------------

             Summary: Replace hash-based aggregates with sort-based ones if inputs already sorted
                 Key: SPARK-18591
                 URL: https://issues.apache.org/jira/browse/SPARK-18591
             Project: Spark
          Issue Type: Improvement
          Components: SQL
    Affects Versions: 2.0.2
            Reporter: Takeshi Yamamuro


Spark currently uses sort-based aggregates only in limited condition; the cases where spark cannot use partial aggregates and hash-based ones.
However, if input ordering has already satisfied the requirements of sort-based aggregates, it seems sort-based ones are faster than the other.
{code}
./bin/spark-shell --conf spark.sql.shuffle.partitions=1

val df = spark.range(10000000).selectExpr("id AS key", "id % 10 AS value").sort($"key").cache

def timer[R](block: => R): R = {
  val t0 = System.nanoTime()
  val result = block
  val t1 = System.nanoTime()
  println("Elapsed time: " + ((t1 - t0 + 0.0) / 1000000000.0)+ "s")
  result
}

timer {
  df.groupBy("key").count().count
}

// codegen'd hash aggregate
Elapsed time: 7.116962977s

// non-codegen'd sort aggregarte
Elapsed time: 3.088816662s
{code}

If codegen'd sort-based aggregates are supported in SPARK-16844, this seems to make the performance gap bigger;
{code}
- codegen'd sort aggregate
Elapsed time: 1.645234684s
{code} 

Therefore, it'd be better to use sort-based ones in this case.



--
This message was sent by Atlassian JIRA
(v6.3.4#6332)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org