Konstantin Shaposhnikov created SPARK-6489:
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Summary: Optimize lateral view with explode to not read
unnecessary columns
Key: SPARK-6489
URL: https://issues.apache.org/jira/browse/SPARK-6489
Project: Spark
Issue Type: Improvement
Components: SQL
Affects Versions: 1.3.0
Reporter: Konstantin Shaposhnikov
Currently a query with "lateral view explode(...)" results in an execution plan
that reads all columns of the underlying RDD.
E.g. given *ppl* table is DF created from Person case class:
{code}
case class Person(val name: String, val age: Int, val data: Array[Int])
{code}
the following SQL:
{code}
select name, sum(d) from ppl lateral view explode(data) d as d group by name
{code}
executes as follows:
{noformat}
== Physical Plan ==
Aggregate false, [name#0], [name#0,SUM(PartialSum#8L) AS _c1#3L]
Exchange (HashPartitioning [name#0], 200)
Aggregate true, [name#0], [name#0,SUM(CAST(d#6, LongType)) AS PartialSum#8L]
Project [name#0,d#6]
Generate explode(data#2), true, false
PhysicalRDD [name#0,age#1,data#2], MapPartitionsRDD[1] at mapPartitions at
ExistingRDD.scala:35
{noformat}
Note that *age* column is not needed to produce the output but it is still read
from the underlying RDD.
A sample program to demonstrate the issue:
{code}
case class Person(val name: String, val age: Int, val data: Array[Int])
object ExplodeDemo extends App {
val ppl = Array(
Person("A", 20, Array(10, 12, 19)),
Person("B", 25, Array(7, 8, 4)),
Person("C", 19, Array(12, 4, 232)))
val conf = new SparkConf().setMaster("local[2]").setAppName("sql")
val sc = new SparkContext(conf)
val sqlCtx = new HiveContext(sc)
import sqlCtx.implicits._
val df = sc.makeRDD(ppl).toDF
df.registerTempTable("ppl")
val s = sqlCtx.sql("select name, sum(d) from ppl lateral view explode(data) d
as d group by name")
s.explain(true)
}
{code}
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