《OD大数据实战》Spark入门实例

一、环境搭建

1. 编译spark 1.3.0

1）安装apache-maven-3.0.5

2）下载并解压 spark-1.3.0.tgz

3）修改make-distribution.sh 

VERSION=1.3.
SCALA_VERSION=2.10
SPARK_HADOOP_VERSION=2.5.-cdh5.3.6
SPARK_HIVE=
#VERSION=$("$MVN" help:evaluate -Dexpression=project.version >/dev/null | grep -v "INFO" | tail -n )
#SPARK_HADOOP_VERSION=$("$MVN" help:evaluate -Dexpression=hadoop.version $@ >/dev/null\
#    | grep -v "INFO"\
#    | tail -n )
#SPARK_HIVE=$("$MVN" help:evaluate -Dexpression=project.activeProfiles -pl sql/hive $@ >/dev/null\
#    | grep -v "INFO"\
#    | fgrep --count "<id>hive</id>";\
#    # Reset exit status to , otherwise the script stops here if the last grep finds nothing\
#    # because we use "set -o pipefail"
#    echo -n)

4）替换maven仓库jar包

5）打包编译

（1）MAVEN编译

build/mvn clean package -DskipTests -Phadoop-2.4 -Dhadoop.version=2.5.-cdh5.3.6 -Pyarn -Phive-0.13. -Phive-thriftserver 

（2）使用CDH5.3.6版本的hadoop

./make-distribution.sh --tgz -Phadoop-2.4 -Dhadoop.version=2.5.-cdh5.3.6 -Pyarn -Phive-0.13. -Phive-thriftserver 

（3）使用Apache版本的hadoop

./make-distribution.sh --tgz -Phadoop-2.4 -Dhadoop.version=2.5. -Pyarn -Phive-0.13. -Phive-thriftserver 

二、测试程序

1. 准备

bin/spark-shell

val textFile = sc.textFile("README.md")

textFile.count()

textFile.count 方法没有参数时，括号可以省略

textFile.first

textFile.take(10)

可以将函数A作为参数传递给函数B，此时这个函数B叫做高阶函数

textFile.filter((line: String) =>line.contains("Spark"))

textFile.filter(line =>line.contains("Spark"))

textFile.filter(_.contains("Spark"))

scala中_标示任意元素

匿名函数

(line: String) =>line.contains("Spark")

def func01(line : String){

　　line.contains("Spark")

}

def func01(line: String) => line.contains("Spark")

sc.parallelize(Array(1,2,3,4,5))

三、Scala集合操作

Method on Seq[T]

map(f: T => U): Seq[U]

flatMap(f: T=> Req[U]): Seq[U]

filter(f: T => Boolean): Seq[T]

exists(f: T => Boolean): Boolean

forall(f: T => Boolean): Boolean

reduce(f: (T, T) => T): T

groupBy(f: T => K): Map[K, List[T]]

sortBy(f: T => K): Seq[T]

　　(line: String) =>line.contains("Spark")

　　T:   (line: String)

　　Boolean: line.contains("Spark")　　

三、 wordcount

val linesRdd = sc.textFile("hdfs://beifeng-hadoop-02:9000/user/beifeng/mapreduce/input01/wc_input")

val wordsRdd = linesRdd.map(line => line.split(" "))

val wordsRdd = linesRdd.flatMap(line => line.split(" "))

val keyvalRdd = wordsRdd.map(word => (word, 1))

val countRdd = keyvalRdd.reduceByKey((a, b) =>(a + b))

countRdd.collect()

countRdd.cache

变成一行

sc.textFile("hdfs://beifeng-hadoop-02:9000/user/beifeng/mapreduce/input01/wc_input").flatMap(line => line.split(" ")).map( (_, 1)).reduceByKey(_ + _).collect