Spark SQL 算子实例_sparksql算子案例

package sqlText

import org.apache.spark.rdd.RDD
import org.apache.spark.sql.SQLContext
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.sql.types.{StructType, IntegerType, StringType, StructField}

/**
  * Created by xiaoxu
  */
object SparkSQLAgg {
  def main(args: Array[String]) {
    System.setProperty("hadoop.home.dir", "E:\\winutils-hadoop-2.6.4\\hadoop-2.6.4")
    val conf = new SparkConf().setMaster("local[2]").setAppName(this.getClass.getName)
    val sc = new SparkContext(conf)
    val sqlContext = new SQLContext(sc)
    import sqlContext.implicits._
    val userData = Array(
      "2016-04-15,1001,http://spark.apache.org,1000",
      "2016-04-15,1001,http://hadoop.apache.org,1001",
      "2016-04-15,1002,http://fink.apache.org,1002",
      "2016-04-16,1003,http://kafka.apache.org,1020",
      "2016-04-16,1004,http://spark.apache.org,1010",
      "2016-04-16,1002,http://hive.apache.org,1200",
      "2016-04-16,1001,http://parquet.apache.org,1500",
      "2016-04-16,1001,http://spark.apache.org,1800"
    )
    import org.apache.spark.sql._
    val parallelize: RDD[String] = sc.parallelize(userData)
    val userDateRDDRow = parallelize.map(row => {
      val splitted = row.split(",")
      Row(splitted(0).replaceAll("-", ""), splitted(1).toInt, splitted(2), splitted(3).toInt)
    })
    // 构造字段，与数据匹配，便于今后查询
    val structTypes = StructType(Array(
      StructField("date", StringType, true),
      StructField("id", IntegerType, true),
      StructField("url", StringType, true),
      StructField("amount", IntegerType, true)
    ))
    val createDataFrame = sqlContext.createDataFrame(userDateRDDRow, structTypes)
    //统计每个月的数量，直接显示
    createDataFrame.groupBy("date").agg("amount" -> "sum").write.json("")
    // 统计每个月的数量，直接显示,数据量比较大时不能用collect,用write.json("")方法直接保存数据即可
    createDataFrame.groupBy("date").agg("amount" -> "sum").map(row => Row(row(0), row(1))).collect.foreach(println)
    // 停止改程序
    sc.stop()
  }}