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package sql
import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.sql.{Row, SQLContext, SparkSession}
import org.apache.spark.sql.types._
import org.apache.spark.sql.sources.{BaseRelation, RelationProvider, TableScan}
// Demonstrate the Spark SQL external data source API, but for
// simplicity don't connect to an external system -- just create a
// synthetic table whose size and partitioning are determined by
// configuration parameters.
// Extending TableScan allows us to describe the schema and
// provide the rows when requested
case class MyTableScan(count: Int, partitions: Int)
(@transient val sqlContext: SQLContext)
extends BaseRelation with TableScan
val schema: StructType = StructType(Seq(
StructField("val", IntegerType, nullable = false),
StructField("squared", IntegerType, nullable = false),
StructField("cubed", IntegerType, nullable = false)
private def makeRow(i: Int): Row = Row(i, i*i, i*i*i)
def buildScan: RDD[Row] = {
val values = (1 to count).map(i => makeRow(i))
sqlContext.sparkContext.parallelize(values, partitions)
// Extending RelationProvider allows us to route configuration parameters into
// our implementation -- this is the class we specify below when registering
// temporary tables.
class CustomRP extends RelationProvider {
def createRelation(sqlContext: SQLContext, parameters: Map[String, String]) = {
object CustomRelationProvider {
def main(args: Array[String]) {
val spark =
// register it as a temporary table to be queried
// (could register several of these with different parameter values)
// Note: as of Spark 1.4.0 option names can contain periods or underscores
|USING sql.CustomRP
|OPTIONS (partitions '9', rows '50')
// query the table we registered, using its column names
val data =
spark.sql("SELECT * FROM dataTable ORDER BY val")
data.foreach(r => println(r))
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