RDD的详解、创建及其操作

  • Z时代
  • 2024-01-10
  • 分类：综合

database

RDD的详解


RDD:弹性分布式数据集,是Spark中最基本的数据抽象,用来表示分布式集合,支持分布式操作!

RDD的创建

RDD中的数据可以来源于2个地方：本地集合或外部数据源

RDD操作

分类

转换算子

Map

import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}
object Demo03Map {
  def main(args: Array[String]): Unit = {
    val conf: SparkConf = new SparkConf()
    conf.setAppName("Demo03Map").setMaster("local")
    val sc: SparkContext = new SparkContext(conf)
    //读取文件数据
    val linesRDD: RDD[String] = sc.textFile("spark/data/words.txt")
    //对数据进行扁平化处理
    val flatRDD: RDD[String] = linesRDD.flatMap(_.split(","))
    //按照单词分组
    val groupRDD: RDD[(String, Iterable[String])] = flatRDD.groupBy(w => w)
    //聚合
    val wordsRDD: RDD[String] = groupRDD.map(kv => {
      val key: String = kv._1
      val words: Iterable[String] = kv._2
      key + "," + words.size
    })
    //分组+聚合
    val mapRDD1: RDD[(String, Int)] = flatRDD.map((_, 1))
    val words1: RDD[(String, Int)] = mapRDD1.reduceByKey(_ + _)
    ////分组+聚合
    val mapRDD2: RDD[(String, Int)] = flatRDD.map((_, 1))
    val words2: RDD[(String, Iterable[Int])] = mapRDD2.groupByKey()
    val wordSum: RDD[(String, Int)] = words2.mapValues(_.size)
    wordSum.foreach(println)
    //输出
    wordsRDD.foreach(println)
    words1.foreach(println)
  }
}

flatMap(数据扁平化处理)

import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}
object Demo04FlatMap {
  def main(args: Array[String]): Unit = {
    val conf: SparkConf = new SparkConf().setAppName("Demo04FlatMap").setMaster("local")
    val sc: SparkContext = new SparkContext(conf)
    val linesRDD: RDD[String] = sc.parallelize(List("java,scala,python", "map,java,scala"))
    //扁平化处理
    val flatRDD: RDD[String] = linesRDD.flatMap(_.split(","))
    flatRDD.foreach(println)
  }
}

Mappartitions

map和mapPartitions区别

1）map：每次处理一条数据

2）mapPartitions：每次处理一个分区数据

import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}
object Demo05MapPartition {
  def main(args: Array[String]): Unit = {
    val conf: SparkConf = new SparkConf().setAppName("Demo05MapPartition").setMaster("local")
    val sc: SparkContext = new SparkContext(conf)
    val stuRDD: RDD[String] = sc.textFile("spark/data/words.txt",3)
    stuRDD.mapPartitions(rdd => {
      println("map partition")
      // 按分区去处理数据
      rdd.map(line => line.split(",")(1))
    }).foreach(println)
  }
}

fliter 过滤

import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}
object Demo06Filter {
  def main(args: Array[String]): Unit = {
    val conf: SparkConf = new SparkConf().setAppName("Demo05MapPartition").setMaster("local")
    val sc: SparkContext = new SparkContext(conf)
    val linesRDD: RDD[Int] = sc.parallelize(List(1, 2, 3, 4, 5))
    //过滤，转换算子
    linesRDD.filter(kv => {
      kv % 2 == 1
    }).foreach(println)
  }
}

sample 取样

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.rdd.RDD
object Demo07Sample {
  def main(args: Array[String]): Unit = {
    val conf: SparkConf = new SparkConf().setAppName("Demo05MapPartition").setMaster("local")
    val sc: SparkContext = new SparkContext(conf)
    /**
     * sample：对数据取样
     * withReplacement 有无放回
     * fraction 抽样比例
     * withReplacement：表示抽出样本后是否在放回去，true表示会放回去
     * 这也就意味着抽出的样本可能有重复
     * fraction ：抽出多少，这是一个double类型的参数,0-1之间，eg:0.3表示抽出30%
     */
    val stuRDD: RDD[String] = sc.textFile("spark/data/students.txt",3)
    stuRDD.sample(withReplacement = true,0.1).foreach(println)
  }
}

union 将相同结结构的数据连接到一起

import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}
object Demo08Union {
  def main(args: Array[String]): Unit = {
    val conf: SparkConf = new SparkConf().setAppName("Demo05MapPartition").setMaster("local")
    val sc: SparkContext = new SparkContext(conf)
    /**union
     * 将两个相同结构的数据连接在一起
     */
    val lineRDD1: RDD[String] = sc.parallelize(List("java,scala", "data,python"))
    val lineRDD2: RDD[String] = sc.parallelize(List("spark,scala", "java,python"))
    println(lineRDD1.getNumPartitions)
    val unionRDD: RDD[String] = lineRDD1.union(lineRDD2)
    println(unionRDD.getNumPartitions)
    unionRDD.foreach(println)
  }
}

mappatitionWIthindex

    //mapPartitionsWithIndex也是一个转换算子
    // 会在处理每一个分区的时候获得一个index
    //可以选择的执行的分区
    stuRDD.mapPartitionsWithIndex((index, rdd) => {
      println("当前遍历的分区：" + index)
      // 按分区去处理数据
      rdd.map(line => line.split(",")(1))
    }).foreach(println)

join 将数据按照相同key进行关联（数据必须是（K，V））

import java.io
import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}
object Demo09Join {
  def main(args: Array[String]): Unit = {
    val conf: SparkConf = new SparkConf().setAppName("Demo05MapPartition").setMaster("local")
    val sc: SparkContext = new SparkContext(conf)
    // 构建K-V格式的RDD
    val tuple2RDD1: RDD[(String, String)] = sc.parallelize(List(("001", "张三"), "002" -> "小红", "003" -> "小明"))
    val tuple2RDD2: RDD[(String, Int)] = sc.parallelize(List(("001", 20), "002" -> 22, "003" -> 21))
    val tuple2RDD3: RDD[(String, String)] = sc.parallelize(List(("001", "男"), "002" -> "女"))
    //将文件进行join
    val joinRDD: RDD[(String, (String, Int))] = tuple2RDD1.join(tuple2RDD2)
    joinRDD.map(kv => {
      val i: String = kv._1
      val j: String = kv._2._1
      val k: Int = kv._2._2
      i + "," + j + "," + k
    }).foreach(println)
    //第二种方式
    joinRDD.map {
      case (id: String, (name: String, age: Int)) => id + "*" + name + "*" + age
    }.foreach(println)
    val leftJoinRDD: RDD[(String, (String, Option[String]))] = tuple2RDD1.leftOuterJoin(tuple2RDD3)
    leftJoinRDD.map {
          //存在关联
      case (id: String, (name: String, Some(gender))) => 
        id + "*" + name + "*" + gender
        //不存在关联
      case (id: String, (name: String, None)) =>
        id + "*" + name + "*" + "_"
    }
  }
}

groupByKey 将kv格式的数据进行key的聚合

import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}
object Demo10GroupByKey {
  def main(args: Array[String]): Unit = {
    val conf: SparkConf = new SparkConf().setAppName("Demo10GroupByKey").setMaster("local")
    val sc: SparkContext = new SparkContext(conf)
    /**
     * groupBy 指定分组的字段进行分组
     */
    // 统计班级人数
    val linesRDD: RDD[String] = sc.textFile("spark/data/students.txt")
    linesRDD.groupBy(word => word.split(",")(4))
      .map(kv => {
        val key = kv._1
        val wordsCnt = kv._2.size
        key + "," + wordsCnt
      }).foreach(println)
    val linesMap: RDD[(String, String)] = linesRDD.map(lines => (lines.split(",")(4), lines))
    //按照key进行分组
    linesMap.groupByKey()
      .map(lines=>{
        val key = lines._1
        val wordsCnt: Int = lines._2.size
        key+","+wordsCnt
      }).foreach(println)
  }
}

ReduceByKey

reduceByKey 需要接收一个聚合函数

首先会对数据按key分组 然后在组内进行聚合（一般是加和，也可以是Max、Min之类的操作）

相当于 MR 中的combiner

可以在Map端进行预聚合，减少shuffle过程需要传输的数据量，以此提高效率

相对于groupByKey来说，效率更高，但功能更弱

幂等操作

y = f(x) = f(y) = f(f(x))

reducebyKey与groupbykey的区别

reduceByKey：具有预聚合操作

groupByKey：没有预聚合

在不影响业务逻辑的前提下，优先采用reduceByKey。

import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}
object Demo11ReduceByKey {
  def main(args: Array[String]): Unit = {
    val conf: SparkConf = new SparkConf().setAppName("Demo11ReduceByKey").setMaster("local")
    val sc: SparkContext = new SparkContext(conf)
    val linesRDD: RDD[String] = sc.textFile("spark/data/students.txt")
    //统计班级人数
    linesRDD.map(lines => (lines.split(",")(4), lines))
      .groupByKey()
      .map(kv => {
        val key = kv._1
        val cnt = kv._2.size
        key + "" + cnt
      }).foreach(println)
    //ReduceByKey
    /**
     * reduceByKey 需要接收一个聚合函数
     * 首先会对数据按key分组 然后在组内进行聚合（一般是加和，也可以是Max、Min之类的操作）
     * 相当于 MR 中的combiner
     * 可以在Map端进行预聚合，减少shuffle过程需要传输的数据量，以此提高效率
     * 相对于groupByKey来说，效率更高，但功能更弱
     * 幂等操作
     * y = f(x) = f(y) = f(f(x))
     */
    linesRDD.map(lines=>(lines.split(",")(4),1))
      .reduceByKey(_+_)
      .foreach(println)
  }
}

sort 排序，默认升序

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.rdd.RDD
object Demo12Sort {
  def main(args: Array[String]): Unit = {
    val conf: SparkConf = new SparkConf().setAppName("Demo12Sort").setMaster("local")
    val sc: SparkContext = new SparkContext(conf)
    val linesRDD: RDD[String] = sc.textFile("spark/data/students.txt")
    /**
     * sortBy 转换算子
     * 指定按什么排序 默认升序
     *
     * sortByKey 转换算子
     * 需要作用在KV格式的RDD上，直接按key排序 默认升序
     */
    linesRDD.sortBy(lines => lines.split(",")(2), ascending = false) //按照年纪降序
      .take(10) //转换算子打印十行
      .foreach(println)
    val mapRDD: RDD[(String, String)] = linesRDD.map(l => (l.split(",")(2), l))
    mapRDD.sortByKey(ascending = false)
      .take(10)
      .foreach(println)
  }
}

Mapvalue

import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}
object Demo13MapValue {
  def main(args: Array[String]): Unit = {
    /**
     * mapValues 转换算子
     * 需要作用在K—V格式的RDD上
     * 传入一个函数f
     * 将RDD的每一条数据的value传给函数f，key保持不变
     * 数据规模也不会改变
     */
    val conf: SparkConf = new SparkConf().setAppName("Demo13MapValue").setMaster("local")
    val sc: SparkContext = new SparkContext(conf)
    val linesRDD: RDD[(String, Int)] = sc.parallelize(List(("zs", 10), ("zzw", 34), ("lm", 18)))
    linesRDD.mapValues(lines=>lines*2)
      .foreach(println)
  }

行为算子

import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}
object Demo14Action {
  def main(args: Array[String]): Unit = {
    val conf: SparkConf = new SparkConf().setAppName("****").setMaster("local")
    val sc: SparkContext = new SparkContext(conf)
    val linesRDD: RDD[String] = sc.textFile("spark/data/students.txt")
    linesRDD.take(10)// take 取出前n条数据 相当于limit
      .foreach(println)  //这里的foreach不是行为算子，是take里面的方法
    // count
    // 返回RDD的数据量的多少
    println(linesRDD.count())
    // collect
    // 将RDD转换为Scala中的Array
    // 注意数据量的大小 容易OOM
    val collectRDD: Array[String] = linesRDD.collect()
    collectRDD.take(10)
      .foreach(println)
    // reduce 全局聚合
    // select sum(age) from student group by 1
    val i = linesRDD.map(lines => lines.split(",")(2).toInt)
      .reduce(_ + _)
    println(i)
    //save
    linesRDD.sample(withReplacement = false,0.2)
      .saveAsTextFile("spark/data/save")
  }
}

以上是 RDD的详解、创建及其操作 的全部内容， 来源链接： utcz.com/z/536069.html

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