201 Spark SQL查询程序

前面我们学习了如何在Spark Shell中使用SQL完成查询，现在我们来实现在自定义的程序中编写Spark SQL查询程序。

首先在maven项目的pom.xml中添加Spark SQL的依赖:

<dependency>
    <groupId>org.apache.spark</groupId>
    <artifactId>spark-sql_2.10</artifactId>
    <version>1.5.2</version>
</dependency>

1.通过反射推断Schema

创建一个object为cn.itcast.spark.sql.InferringSchema

package cn.itcast.spark.sql

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

object InferringSchema {
  def main(args: Array[String]) {

    //创建SparkConf()并设置App名称
    val conf = new SparkConf().setAppName("SQL-1")
  
    //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 lineRDD = sc.textFile(args(0)).map(_.split(" "))

    //创建case class
    //将RDD和case class关联
    val personRDD = lineRDD.map(x => Person(x(0).toInt, x(1), x(2).toInt))
   
    //导入隐式转换，如果不到人无法将RDD转换成DataFrame
    //将RDD转换成DataFrame
    import sqlContext.implicits._
    val personDF = personRDD.toDF
   
    //注册表
    personDF.registerTempTable("t_person")
   
    //传入SQL
    val df = sqlContext.sql("select * from t_person order by age desc limit 2")
  
    //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()
  }
}
//case class一定要放到外面
case class Person(id: Int, name: String, age: Int)

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/out 

查看运行结果

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

2.通过StructType直接指定Schema

创建一个object为cn.itcast.spark.sql.SpecifyingSchema

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-*