spark-通过StructType直接指定Schema

package cn.itcast.spark.sql

import org.apache.spark.sql.{Row, SQLContext}
import org.apache.spark.sql.types._
import org.apache.spark.{SparkContext, SparkConf}

/**
  * Created by ZX on 2015/12/11.
  */
object SpecifyingSchema {
  def main(args: Array[String]) {
    //创建SparkConf()并设置App名称
    val conf = new SparkConf().setAppName("SQL-2")
    //SQLContext要依赖SparkContext
    val sc = new SparkContext(conf)
    //创建SQLContext
    val sqlContext = new SQLContext(sc)
    //从指定的地址创建RDD
    val personRDD = sc.textFile(args(0)).map(_.split(" "))
    //通过StructType直接指定每个字段的schema
    val schema = StructType(
      List(
        StructField("id", IntegerType, true),
        StructField("name", StringType, true),
        StructField("age", IntegerType, true)
      )
    )
    //将RDD映射到rowRDD
    val rowRDD = personRDD.map(p => Row(p(0).toInt, p(1).trim, p(2).toInt))
    //将schema信息应用到rowRDD上
    val personDataFrame = sqlContext.createDataFrame(rowRDD, schema)
    //注册表
    personDataFrame.registerTempTable("t_person")
    //执行SQL
    val df = sqlContext.sql("select * from t_person order by age desc limit 4")
    //将结果以JSON的方式存储到指定位置
    df.write.json(args(1))
    //停止Spark Context
    sc.stop()
  }
}

将程序打成jar包，上传到spark集群，提交Spark任务

/usr/local/spark-1.5.2-bin-hadoop2.6/bin/spark-submit \

--class cn.itcast.spark.sql.InferringSchema \

--master spark://node1.itcast.cn:7077 \

/root/spark-mvn-1.0-SNAPSHOT.jar \

hdfs://node1.itcast.cn:9000/person.txt \

hdfs://node1.itcast.cn:9000/out1

 

查看结果

hdfs dfs -cat  hdfs://node1.itcast.cn:9000/out1/part-r-*