org.apache.spark.examples.sql.RDDRelation.scala Maven / Gradle / Ivy
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package org.apache.spark.examples.sql
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.sql.SQLContext
// One method for defining the schema of an RDD is to make a case class with the desired column
// names and types.
case class Record(key: Int, value: String)
object RDDRelation {
def main(args: Array[String]) {
val sparkConf = new SparkConf().setAppName("RDDRelation")
val sc = new SparkContext(sparkConf)
val sqlContext = new SQLContext(sc)
// Importing the SQL context gives access to all the SQL functions and implicit conversions.
import sqlContext._
val rdd = sc.parallelize((1 to 100).map(i => Record(i, s"val_$i")))
// Any RDD containing case classes can be registered as a table. The schema of the table is
// automatically inferred using scala reflection.
rdd.registerTempTable("records")
// Once tables have been registered, you can run SQL queries over them.
println("Result of SELECT *:")
sql("SELECT * FROM records").collect().foreach(println)
// Aggregation queries are also supported.
val count = sql("SELECT COUNT(*) FROM records").collect().head.getLong(0)
println(s"COUNT(*): $count")
// The results of SQL queries are themselves RDDs and support all normal RDD functions. The
// items in the RDD are of type Row, which allows you to access each column by ordinal.
val rddFromSql = sql("SELECT key, value FROM records WHERE key < 10")
println("Result of RDD.map:")
rddFromSql.map(row => s"Key: ${row(0)}, Value: ${row(1)}").collect().foreach(println)
// Queries can also be written using a LINQ-like Scala DSL.
rdd.where('key === 1).orderBy('value.asc).select('key).collect().foreach(println)
// Write out an RDD as a parquet file.
rdd.saveAsParquetFile("pair.parquet")
// Read in parquet file. Parquet files are self-describing so the schmema is preserved.
val parquetFile = sqlContext.parquetFile("pair.parquet")
// Queries can be run using the DSL on parequet files just like the original RDD.
parquetFile.where('key === 1).select('value as 'a).collect().foreach(println)
// These files can also be registered as tables.
parquetFile.registerTempTable("parquetFile")
sql("SELECT * FROM parquetFile").collect().foreach(println)
sc.stop()
}
}