spark SQL实例（load和save操作）

load操作：主要用于加载数据，创建出DataFrame

save操作：主要用于将DataFrame中的数据保存到文件中

代码示例(默认为parquet数据源类型)

package wujiadong_sparkSQL

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

/**
  * Created by Administrator on 2017/2/3.
  */
object GenericLoadSave {
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setAppName("GenericLoadSave")
    val sc = new SparkContext(conf)
    val sqlContext = new SQLContext(sc)
//load默认是加载parquet格式文件
    val usersDF = sqlContext.read.load("hdfs://master:9000/student/2016113012/spark/users.parquet")
    usersDF.write.save("hdfs://master:9000/student/2016113012/parquet_out1")

  }

}

提交集群运行

hadoop@master:~/wujiadong$ spark-submit –class wujiadong_sparkSQL.GenericLoadSave –executor-memory 500m –total-executor-cores 2 /home/hadoop/wujiadong/wujiadong.spark.jar
运行后查看是否保存成功

hadoop@slave01:~$ hadoop fs -ls /student/2016113012/parquet_out1
17/02/03 12:06:26 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform… using builtin-java classes where applicable
Found 4 items
-rw-r–r– 3 hadoop supergroup 0 2017-02-03 12:05 /student/2016113012/parquet_out1/_SUCCESS
-rw-r–r– 3 hadoop supergroup 476 2017-02-03 12:05 /student/2016113012/parquet_out1/_common_metadata
-rw-r–r– 3 hadoop supergroup 841 2017-02-03 12:05 /student/2016113012/parquet_out1/_metadata
-rw-r–r– 3 hadoop supergroup 864 2017-02-03 12:05 /student/2016113012/parquet_out1/part-r-00000-8025e2a8-ab06-4558-9d76-bb2cad0042cf.gz.parquet
手动指定数据源类型（进行格式转换很方便）
默认情况下不指定数据源类型的话就是parquet类型

代码示例（手动指定数据源类型）

package wujiadong_sparkSQL

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

/**
  * Created by Administrator on 2017/2/3.
  */
object ManuallySpecifyOptions {
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setAppName("ManuallySpecifyOptions")
    val sc = new SparkContext(conf)
    val sqlContext = new SQLContext(sc)
//load读其他格式文件如json时，需要先用format指定格式
    val peopleDF = sqlContext.read.format("json").load("hdfs://master:9000/student/2016113012/people.json")
    peopleDF.select("name").write.format("parquet").save("hdfs://master:9000/sudent/2016113012/people_out1")


  }

}

提交集群运行

hadoop@master:~/wujiadong$ spark-submit –class wujiadong_sparkSQL.ManuallySpecifyOptions –executor-memory 500m –total-executor-cores 2 /home/hadoop/wujiadong/wujiadong.spark.jar
查看是否运行成功

hadoop@master:~/wujiadong$ hadoop fs -ls hdfs://master:9000/sudent/2016113012/people_out1
17/02/03 12:24:27 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform… using builtin-java classes where applicable
Found 4 items
-rw-r–r– 3 hadoop supergroup 0 2017-02-03 12:22 hdfs://master:9000/sudent/2016113012/people_out1/_SUCCESS
-rw-r–r– 3 hadoop supergroup 207 2017-02-03 12:22 hdfs://master:9000/sudent/2016113012/people_out1/_common_metadata
-rw-r–r– 3 hadoop supergroup 327 2017-02-03 12:22 hdfs://master:9000/sudent/2016113012/people_out1/_metadata
-rw-r–r– 3 hadoop supergroup 352 2017-02-03 12:22 hdfs://master:9000/sudent/2016113012/people_out1/part-r-00000-4d1a62a4-f550-4bde-899f-35e9aabfdc0c.gz.parquet

Save Mode
SaveMode.ErrorIfExists (默认)：如果目标位置已经存在数据，那么抛出一个异常
SaveMode.Append：如果目标位置已经存在数据，那么将数据追加进去
SaveMode.Overwrite：如果目标位置已经存在数据，那么就将已经存在的数据删除，用新数据进行覆盖
SaveMode.Ignore：如果目标位置已经存在数据，那么就忽略，不做任何操作
代码示例1

package wujiadong_sparkSQL

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

/**
  * Created by Administrator on 2017/2/3.
  */
object SaveModelTest {
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setAppName("SaveModelTest")
    val sc = new SparkContext(conf)
    val sqlContext = new SQLContext(sc)
    val peopleDF = sqlContext.read.format("json").load("hdfs://master:9000/student/2016113012/people.json")
    peopleDF.save("hdfs://master:9000/student/2016113012/people.json",SaveMode.ErrorIfExists)
  }

}

因为这种save mode文件已存在就报错
package wujiadong_sparkSQL

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

/**
  * Created by Administrator on 2017/2/3.
  */
object SaveModelTest {
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setAppName("SaveModelTest")
    val sc = new SparkContext(conf)
    val sqlContext = new SQLContext(sc)
    val peopleDF = sqlContext.read.format("json").load("hdfs://master:9000/student/2016113012/people.json")
    peopleDF.save("hdfs://master:9000/student/2016113012/people.json",SaveMode.Overwrite)
  }

}

这种会直接覆盖