引言：
Spark是一种基于内存的快速、通用、可扩展的大数据分析引擎。
参考：
[toc]
概述
历史渊源
1.X的mapreduce缺陷
- 主要用于一次性计算，不适合机器学习数据挖掘的迭代计算。
- 基于文件存储介质，非常慢。
- MR和Hadoop紧密耦合，无法动态替换
2.x
- 提出了Yarn资源调度
- 将资源管理RM和任务调度Driver解耦，可插拔。
Spark
- 计算：迭代计算之间的数据是基于内存的，基于Scale语言，非常适合迭代式计算。
- 存储：还是基于Hadoop。
- 利用2.X的Yarn解耦，变成HDFS+Yarn+Spark
模块
![image-20200211173725703]( "image-20200211173725703")
Spark运行模式
重要角色
Driver: 管理
Executor: 计算
Local模式
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| tar -zxvf spark-2.1.1-bin-hadoop2.7.tgz -C /opt/module/
mv spark-2.1.1-bin-hadoop2.7 spark
bin/spark-submit \
--class org.apache.spark.examples.SparkPi \
--executor-memory 1G \
--total-executor-cores 2 \
./examples/jars/spark-examples_2.11-2.1.1.jar \
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bin/spark-shell
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shell-WordCount
读取> 扁平化> 分组> 聚合
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sc.textFile("input")
res0: org.apache.spark.rdd.RDD[String] = input MapPartitionsRDD[1] at textFile at <console>:25
sc.textFile("input").flatMap(_.split(" "))
res2: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[4] at flatMap at <console>:25
sc.textFile("input").flatMap(_.split(" ")).map((_,1))
org.apache.spark.rdd.RDD[(String, Int)] = MapPartitionsRDD[8] at map at <console>:25
sc.textFile("input").flatMap(_.split(" ")).map((_,1)).reduceByKey(_+_)
res4: org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[13] at reduceByKey at <console>:25
sc.textFile("input").flatMap(_.split(" ")).map((_,1)).reduceByKey(_+_).collect
res5: Array[(String, Int)] = Array((scala,1), (world,1), (hello,3), (spark,1))
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程序解析
![image-20200212101811444]( "image-20200212101811444")
idea-WordCount
pom
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| <?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<groupId>com.mxx</groupId>
<artifactId>demo-spark</artifactId>
<version>1.0-SNAPSHOT</version>
<dependencies>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.11</artifactId>
<version>2.1.1</version>
</dependency>
</dependencies>
<build>
<finalName>WordCount</finalName>
<plugins>
<plugin>
<groupId>net.alchim31.maven</groupId>
<artifactId>scala-maven-plugin</artifactId>
<version>3.2.2</version>
<executions>
<execution>
<goals>
<goal>compile</goal>
<goal>testCompile</goal>
</goals>
</execution>
</executions>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-assembly-plugin</artifactId>
<version>3.0.0</version>
<configuration>
<archive>
<manifest>
<mainClass>WordCount</mainClass>
</manifest>
</archive>
<descriptorRefs>
<descriptorRef>jar-with-dependencies</descriptorRef>
</descriptorRefs>
</configuration>
<executions>
<execution>
<id>make-assembly</id>
<phase>package</phase>
<goals>
<goal>single</goal>
</goals>
</execution>
</executions>
</plugin>
</plugins>
</build>
</project>
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WordCount
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| package com.mxx.spark
import org.apache.spark.{SparkConf, SparkContext}
object WordCount {
def main(args: Array[String]): Unit = {
val sparkConf = new SparkConf().setMaster("local[*]").setAppName("WordCount")
val sc = new SparkContext(sparkConf)
val lines = sc.textFile("file:///opt/module/spark/input")
val words = lines.flatMap(_.split(" "))
val wordToOne = words.map( (_,1) )
val wordToSum = wordToOne.reduceByKey(_+_)
val result = wordToSum.collect()
result.foreach(println)
}
}
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Yarn模式
概述
有 yarn-client 和 yarn-cluster
两种模式， 主要区别在于： Driver 程序的运行节点。
安装使用
安装hadoop2.7
配置伪分布式hadoop
hdfs+yarn+JobHistory+日志聚集
配置spark
yarn-site.xml
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<property>
<name>yarn.nodemanager.pmem-check-enabled</name>
<value>false</value>
</property>
<property>
<name>yarn.nodemanager.vmem-check-enabled</name>
<value>false</value>
</property>
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spark-env.sh
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| YARN_CONF_DIR=/opt/module/hadoop/etc/hadoop
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配置日志
参考：spark history server配置使用
spark-defaults.conf
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| spark.eventLog.enabled true
spark.eventLog.dir hdfs://mxxcentos6:9000/spark_event_data
spark.eventLog.compress true
spark.yarn.historyServer.address http://mxxcentos6:18080
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spark-env.sh
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| export SPARK_HISTORY_OPTS="-Dspark.history.ui.port=18080 -Dspark.history.retainedApplications=3 -Dspark.history.fs.logDirectory=hdfs://mxxcentos6:9000/spark_event_data"
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访问：
yarn部署spark流程
yarn调度流程
![image-20200212144204404]( "image-20200212144204404")
spark程序是在container里跑的
spark yarn部署流程
![image-20200212153859818]( "image-20200212153859818")
wordcount程序打包到环境中运行
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| bin/spark-submit \
--class com.mxx.spark.WordCount \
/opt/module/WordCount-jar-with-dependencies.jar
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Standalone模式
不需要hadoop, 构建一个由 Master+Slave 构成的 Spark 集群， Spark 运行在集群中。
![image-20200212161031490]( "image-20200212161031490")
安装使用
…
Mesos 模式
…
模式对比
| 模式 |
Spark 安装机器数 |
需启动的进程 |
所属者 |
| Local |
1 |
无 |
Spark |
| Standalone |
3 |
Master 及 Worker |
Spark |
| Yarn |
1 |
Yarn 及 HDFS |
Hadoop |
