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PrunedFilteredScan — Relations with Column Pruning and Filter Pushdown

PrunedFilteredScan is the <> of <> with support for <> (i.e. eliminating unneeded columns) and <> (i.e. filtering using selected predicates only).

[[contract]] [source, scala]


package org.apache.spark.sql.sources

trait PrunedFilteredScan { def buildScan(requiredColumns: Array[String], filters: Array[Filter]): RDD[Row] }


.PrunedFilteredScan Contract [cols="1,2",options="header",width="100%"] |=== | Property | Description

| buildScan | [[buildScan]] Building distributed data scan with column pruning and filter pushdown

In other words, buildScan creates a RDD[Row] to represent a distributed data scan (i.e. scanning over data in a relation)

Used exclusively when DataSourceStrategy execution planning strategy is requested to plan a LogicalRelation with a PrunedFilteredScan. |===

Note

PrunedFilteredScan is a "lighter" and stable version of the CatalystScan abstraction.

[[implementations]] NOTE: JDBCRelation is the one and only known implementation of the <> in Spark SQL.

[[example]] [source, scala]


// Use :paste to define MyBaseRelation case class // BEGIN import org.apache.spark.sql.sources.PrunedFilteredScan import org.apache.spark.sql.sources.BaseRelation import org.apache.spark.sql.types.{StructField, StructType, StringType} import org.apache.spark.sql.SQLContext import org.apache.spark.sql.sources.Filter import org.apache.spark.rdd.RDD import org.apache.spark.sql.Row case class MyBaseRelation(sqlContext: SQLContext) extends BaseRelation with PrunedFilteredScan { override def schema: StructType = StructType(StructField("a", StringType) :: Nil) def buildScan(requiredColumns: Array[String], filters: Array[Filter]): RDD[Row] = { println(s">>> [buildScan] requiredColumns = ${requiredColumns.mkString(",")}") println(s">>> [buildScan] filters = ${filters.mkString(",")}") import sqlContext.implicits._ (0 to 4).toDF.rdd } } // END val scan = MyBaseRelation(spark.sqlContext)

import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan import org.apache.spark.sql.execution.datasources.LogicalRelation val plan: LogicalPlan = LogicalRelation(scan)

scala> println(plan.numberedTreeString) 00 Relation[a#1] MyBaseRelation(org.apache.spark.sql.SQLContext@4a57ad67)

import org.apache.spark.sql.execution.datasources.DataSourceStrategy val strategy = DataSourceStrategy(spark.sessionState.conf)

val sparkPlan = strategy(plan).head // >>> [buildScan] requiredColumns = a // >>> [buildScan] filters = scala> println(sparkPlan.numberedTreeString) 00 Scan MyBaseRelation(org.apache.spark.sql.SQLContext@4a57ad67) [a#8] PushedFilters: [], ReadSchema: struct



Last update: 2020-11-13