[jira] [Created] (SPARK-16360) Speed up SQL query performance by removing redundant analysis in `Dataset`

["Dongjoon Hyun (JIRA)" <ji...@apache.org>]

Dongjoon Hyun created SPARK-16360:
-------------------------------------

             Summary: Speed up SQL query performance by removing redundant analysis in `Dataset`
                 Key: SPARK-16360
                 URL: https://issues.apache.org/jira/browse/SPARK-16360
             Project: Spark
          Issue Type: Improvement
          Components: SQL
            Reporter: Dongjoon Hyun


Currently, there are a few reports about Spark 2.0 query performance regression for large queries.

This issue speeds up SQL query processing performance by removing redundant consecutive analysis in `Dataset.ofRows` function and `Dataset` instantiation. Specifically, this issue aims to reduce the overhead of SQL query analysis, not query execution.

**Before**
{code}
scala> :pa
// Entering paste mode (ctrl-D to finish)

val n = 4000
val values = (1 to n).map(_.toString).mkString(", ")
val columns = (1 to n).map("column" + _).mkString(", ")
val query =
  s"""
     |SELECT $columns
     |FROM VALUES ($values) T($columns)
     |WHERE 1=2 AND 1 IN ($columns)
     |GROUP BY $columns
     |ORDER BY $columns
     |""".stripMargin

def time[R](block: => R): R = {
  val t0 = System.nanoTime()
  val result = block
  println("Elapsed time: " + ((System.nanoTime - t0) / 1e9) + "s")
  result
}

time(sql(query))
time(sql(query))

// Exiting paste mode, now interpreting.

Elapsed time: 30.138142577s
Elapsed time: 25.787751452s
{code}

**After**
{code}
Elapsed time: 17.500279659s
Elapsed time: 12.364812255s
{code}





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