Spark学习之路（一）【概述、环境搭建、基本操作】

Spark

一、概述

http://spark.apache.org/

Apache Spark™ is a unified(统一) analytics engine for large-scale data processing.

特点

• 高效：Run workloads 100x faster.
• 易用：Write applications quickly in Java, Scala, Python, R, and SQL
• 通用：Combine SQL, streaming, and complex analytics
• 兼容：Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access diverse data sources.

二、环境搭建

注： Spark支持4中集群类型，分别为Standalone、Apache Mesos 、Hadoop YARN 、Kubernetes

以下环境搭建为：Standalone 集群

环境要求

• Linux/Mac OS操作系统
• JDK
• Scala
• Hadoop HDFS环境(版本需要匹配Spark)

下载

https://spark.apache.org/downloads.html

安装

gaozhy@gaozhydeMacBook-Pro  ~ tar -zxvf Downloads/spark-2.4.3-bin-hadoop2.7.tgz -C software/

配置

gaozhy@gaozhydeMacBook-Pro  ~  cd software/spark-2.4.3-bin-hadoop2.7
gaozhy@gaozhydeMacBook-Pro  ~  cp conf/spark-env.sh.template conf/spark-env.sh
gaozhy@gaozhydeMacBook-Pro  ~  vim conf/spark-env.sh

# 在配置文件中添加如下配置
export SPARK_MASTER_HOST=spark
export SPARK_MASTER_PORT=7077

gaozhy@gaozhydeMacBook-Pro  ~/software/spark-2.4.3-bin-hadoop2.7  cp conf/slaves.template conf/slaves
gaozhy@gaozhydeMacBook-Pro  ~/software/spark-2.4.3-bin-hadoop2.7  vim conf/slaves

# 将配置文件中localhost修改为spark
spark

gaozhy@gaozhydeMacBook-Pro  ~/software/spark-2.4.3-bin-hadoop2.7  sudo vim /etc/hosts

# 在配置文件末尾添加
127.0.0.1	spark

gaozhy@gaozhydeMacBook-Pro  ~/software/spark-2.4.3-bin-hadoop2.7  sudo vim /etc/profile
# 添加spark环境变量信息
export SCALA_HOME=/Users/gaozhy/software/scala-2.11.8
export SPARK_HOME=/Users/gaozhy/software/spark-2.4.3-bin-hadoop2.7
export PATH=$PATH:$SCALA_HOME/bin:$SPARK_HOME/bin:$SPARK_HOME/sbin

gaozhy@gaozhydeMacBook-Pro  ~/software/spark-2.4.3-bin-hadoop2.7  source /etc/profile

运行Spark服务

gaozhy@gaozhydeMacBook-Pro  ~/software/spark-2.4.3-bin-hadoop2.7  start-all.sh

注：关闭服务可使用指令stop-all.sh

Spark Web UI

访问地址：http://spark:8080

三、快速开始

通过Spark Shell进行交互式分析

运行spark shell

gaozhy@gaozhydeMacBook-Pro  ~ spark-shell

基本操作

准备数据文件/Users/gaozhy/test.json

{"id":1,"name":"zs","sex":"男"}
{"id":2,"name":"ls","sex":"女"}
{"id":3,"name":"ww","sex":"男"}

spark-shell操作

scala> val dataset = spark.read.json("/Users/gaozhy/test.json")
dataset: org.apache.spark.sql.DataFrame = [id: bigint, name: string ... 1 more field]

注：DataSet是Spark抽象出来的一个弹性分布式数据集

DataSet处理

# 数据记录数
scala> dataset.count()
res0: Long = 3

# 获取数据集第一条数据
scala> dataset.first
res1: org.apache.spark.sql.Row = [1,zs,男]

# 性别为男性的记录个数
scala> dataset.filter(row => row(2).equals("男")).count()
res4: Long = 2

# 计算男性和女性用户人数
scala> dataset.rdd.map(row => (row(2),1)).reduceByKey(_+_).saveAsTextFile("/Users/gaozhy/result2")
-----------------------------------------
(男,2)
(女,1)

通过Spark API分析

创建Maven项目并导入开发依赖

<dependencies>
  <dependency>
    <groupId>org.apache.sparkgroupId>
    <artifactId>spark-core_2.11artifactId>
    <version>2.4.3version>
  dependency>
  <dependency>
    <groupId>org.apache.sparkgroupId>
    <artifactId>spark-sql_2.11artifactId>
    <version>2.4.3version>
  dependency>
dependencies>

测试代码

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


object SimpleApp2 {

  def main(args: Array[String]): Unit = {
		// 任务提交到spark集群
    val sparkConf = new SparkConf().setMaster("spark://spark:7077").setAppName("simple app").setJars(Array("/Users/gaozhy/workspace/20180429/spark01/target/spark01-1.0-SNAPSHOT.jar"))
    // 本地模拟
    // val sparkConf = new SparkConf().setMaster("local[*]").setAppName("simple app")
    val sparkContext = new SparkContext(sparkConf)
    val spark = SparkSession.builder.getOrCreate
    val dataset = spark.read.json("/Users/gaozhy/test.json")
    dataset.rdd.map(row => (row(2),1)).reduceByKey(_+_).saveAsTextFile("/Users/gaozhy/result2")
    spark.stop()
  }
}

运行结果

通过提交Jar包分析

将spark应用打成jar包

使用spark-submit提交任务

gaozhy@gaozhydeMacBook-Pro  ~ spark-submit --class "SimpleApp2" --master spark://spar:7077 /Users/gaozhy/workspace/20180429/spark01/target/spark01-1.0-SNAPSHOT.jar

官方提供的例子(计算圆周率)

提交任务

 gaozhy@gaozhydeMacBook-Pro  ~/software/spark-2.4.3-bin-hadoop2.7  bin/spark-submit \
--class org.apache.spark.examples.SparkPi \
--master spark://spark:7077 \
--executor-memory 1G \
--total-executor-cores 2 \
./examples/jars/spark-examples_2.11-2.4.3.jar \
100

计算结果