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>
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-*