[大数据]Spark-核心编程（三）使用RDD算子完成WordCount

其他WordCount解决方法

// 转换算子
// groupBy
def wordcount1(sc: SparkContext): Unit = {
    sc.makeRDD(List("Hello Scala", "Hello Spark"))
    .flatMap(_.split(" "))
    .groupBy(word => word)
    .mapValues(iter => iter.size)
    .collect()
    .foreach(println)
}

// groupByKey
def wordcount2(sc: SparkContext): Unit = {
    sc.makeRDD(List("Hello Scala", "Hello Spark"))
    .flatMap(_.split(" "))
    .map(_ -> 1)
    .groupByKey()
    .mapValues(iter => iter.size)
    .collect()
    .foreach(println)
}

// reduceByKey
def wordcount3(sc: SparkContext): Unit = {
    sc.makeRDD(List("Hello Scala", "Hello Spark"))
    .flatMap(_.split(" "))
    .map(_ -> 1)
    .reduceByKey(_ + _)
    .collect()
    .foreach(println)
}

// aggregateByKey
def wordcount4(sc: SparkContext): Unit = {
    sc.makeRDD(List("Hello Scala", "Hello Spark"))
    .flatMap(_.split(" "))
    .map(_ -> 1)
    .aggregateByKey(0)(_ + _, _ + _)
    .collect()
    .foreach(println)
}

// foldByKey
def wordcount5(sc: SparkContext): Unit = {
    sc.makeRDD(List("Hello Scala", "Hello Spark"))
    .flatMap(_.split(" "))
    .map(_ -> 1)
    .foldByKey(0)(_ + _)
    .collect()
    .foreach(println)
}

// combineByKey
def wordcount6(sc: SparkContext): Unit = {
    sc.makeRDD(List("Hello Scala", "Hello Spark"))
    .flatMap(_.split(" "))
    .map(_ -> 1)
    .combineByKey(
        x => x,
        (x: Int, y) => x + y,
        (x: Int, y: Int) => x + y
    )
    .collect()
    .foreach(println)
}

// 行动算子
// countByKey
def wordcount7(sc: SparkContext): Unit = {
    val rdd: collection.Map[String, Long] = sc.makeRDD(List("Hello Scala", "Hello Spark"))
    .flatMap(_.split(" "))
    .map(_ -> 1)
    .countByKey()

    println(rdd)
}

// countByValue
def wordcount8(sc: SparkContext): Unit = {
    val rdd: collection.Map[String, Long] = sc.makeRDD(List("Hello Scala", "Hello Spark"))
    .flatMap(_.split(" "))
    .countByValue()

    println(rdd)
}

// reduce
def wordcount9(sc: SparkContext): Unit = {
    val rdd: mutable.Map[String, Long] = sc.makeRDD(List("Hello Scala", "Hello Spark"))
    .flatMap(_.split(" "))
    .map(word => {
        mutable.Map[String, Long]((word, 1))
    })
    .reduce((map1, map2) => {
        map2.foreach {
            case (word, count) => {
                val newCount = map1.getOrElse(word, 0L) + count
                map1.update(word, newCount)
            }
        }
        map1
    })

    println(rdd)
}

// aggregate
def wordcount10(sc: SparkContext): Unit = {
    val rdd: mutable.Map[String, Long] = sc.makeRDD(List("Hello Scala", "Hello Spark"))
    .flatMap(_.split(" "))
    .map(word => {
        Map((word, 1))
    })
    .aggregate(mutable.Map[String, Long]())(
        (map1, map2) => {
            map2.foreach {
                case (word, count) => {
                    val newCount = map1.getOrElse(word, 0L) + count
                    map1.update(word, newCount)
                }
            }
            map1
        },
        (map1, map2) => {
            map2.foreach {
                case (word, count) => {
                    val newCount = map1.getOrElse(word, 0L) + count
                    map1.update(word, newCount)
                }
            }
            map1
        }
    )

    println(rdd)
}


// fold
def wordcount11(sc: SparkContext): Unit = {
    val rdd: mutable.Map[String, Long] = sc.makeRDD(List("Hello Scala", "Hello Spark"))
    .flatMap(_.split(" "))
    .map(word => {
        mutable.Map((word, 1L))
    })
    .fold(mutable.Map[String, Long]())(
        (map1, map2) => {
            map2.foreach {
                case (word, count) => {
                    val newCount: Long = map1.getOrElse(word, 0L) + count
                    map1.update(word, newCount)
                }
            }
            map1
        }
    )

    println(rdd)
}