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

org.apache.spark
spark-sql_2.10
1.5.2

通过反射推断Schema
val sc:SparkContext //定义一个SparkContext类型的常量sc，SparkContext是Spark中提交作业的唯一通道
val sqlContext = new SqlContext(sc)//根据sc new一个SqlContext对象，该对象是处理SparkSQL的
import sqlContext._ //引入sqlContext中的所有方法，这些方法是处理SQL语句的基础
case class Person(name:String,age:String)//定义一个Person类，case class是后面数据能够生产SchemaRDD的关键
val people:RDD[Person] = sc.textFile(“people.txt”).map(_.split(“,”)).map(p => Person(p(0),p(1).toInt))//定义一个RDD数组，类型为Person，从people.txt文件中读取数据生成RDD，根据，进行split之后进行map操作，将每一行记录都生成对应的Person对象
people.registerAsTable(“people”)//将得到的RDD数组注册为表“people”
val teenagers = sql(“select name from people where age >= 10 && age <= 19”)//定义要执行的sql语句
teenagers.map(t => “Name:” + t(0)).collect().foreach(println)//循环打印出teenagers中的每个对象的名字

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SparkContextval sc = new SparkContext(conf)//创建SQLContextval sqlContext = new SQLContext(sc)//从指定的地址创建RDDval 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DataFrameimport sqlContext.implicits._val personDF = personRDD.toDF//注册表personDF.registerTempTable("t_person")//传入SQLval 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 Contextsc.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://cosa:7077 \
/root/spark-mvn-1.0-SNAPSHOT.jar \
hdfs://cosa:9000/person.txt \
hdfs://cosa:9000/out

查看运行结果
hdfs dfs -cat hdfs://cosa:9000/out/part-r-*

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://cosa:7077 \
/root/spark-mvn-1.0-SNAPSHOT.jar \
hdfs://cosa:9000/person.txt \
hdfs://cosa:9000/out1

查看结果
hdfs dfs -cat hdfs://cosa:9000/out1/part-r-*