【快速入门大数据】前沿技术拓展Spark,Flink,Beam

小鱼儿 2023-01-09 15:53 186阅读 0赞

### 文章目录 ###

*  概览
 *  Spark
 *   *  mr问题
     *  Spark特征
     *  Spark生态系统对比Hadoop生态系统
     *  开发语言及运行环境
     *  Scala&Maven安装
     *  配置Spark
     *  总结
 *  Flink分布式计算框架（流处理）
 *   *  概述
     *  配置环境
     *  Flink运行
     *  检验
 *  Beam
 *   *  quickstart-java

# 概览 #

Spark、Flink、Beam  
Beam编写完适用于Spark、Flink使用

# Spark #

## mr问题 ##

mr->spark?

开发不爽 mr两个过程  
速度不快 m存硬盘r存hdfs  
框架多样性 批处理 流式处理

## Spark特征 ##

[http://spark.apache.org/][http_spark.apache.org]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70]

速度快 内存和磁盘 都比mr快  
易用 支持多语言 命令行直接运行  
通用性 同一个应用程序同时引用库

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70 1]运行 可运行在hdfs之上计算

## Spark生态系统对比Hadoop生态系统 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70 2]![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70 3]Tachyon 正式更名为 Alluxio，新的版本新增支持任意存储系统如阿里云对象存储 OSS、Amazon S3……

MPI是一个跨语言的通讯协议，，用于编写并行计算机。MPI的目标是高性能，大规模性，和可移植性。

生态对比hadoop、spark  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70 4]  
对比hadoop、spark  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70 5]对比mr和spark  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70 6]

## 开发语言及运行环境 ##

开发Spark  
![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70 7][]  
运行模式  
代码是一样的提交参数不同 导致运行模式不同  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70 8]

## Scala&Maven安装 ##

解压文件

tar -zxf apache-maven-3.6.1-bin.tar.gz -C ./

环境变量配置

export SCALA_HOME=/root/software/scala-2.10.6
    export PATH=$SCALA_HOME/bin:$PATH
    export MAVEN_HOME=/root/software/apache-maven-3.6.1
    export PATH=$MAVEN_HOME/bin:$PATH
    
    //刷新配置
    source /etc/profile

验证

scala
    
    mvn -version

## 配置Spark ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70 9]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70 10]  
手动编译适合cdh的压缩包（注意1.7的jdk可能会过时了）  
spark-2.4.3-bin-2.6.0-cdh5.15.1.tgz

进入bin目录启动模式（本地测试local好）  
/root/software/spark-2.4.3-bin-2.6.0-cdh5.15.1/bin  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70 11]  
master URL  
\[\]两个线程

//启动spark两个线程
    ./spark-shell --master local[2]

[快速指南][Link 1]  
简单helloworld

注意本地读取  
![在这里插入图片描述][2021012216374571.png]

[root@hadoop01 data]# cat hello.txt
    hello world
    hello hadoop
    hello hdfs
    
    scala
    
    scala> val textFile = spark.read.textFile("/root/data/hello.txt")
    
    scala> textFile.collect
    res1: Array[String] = Array(hello world, hello hadoop, hello hdfs, "")
    
    scala> textFile.count
    res2: Long = 4

检验  
http://hadoop01:4040/jobs/  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70 12]

## 总结 ##

spark启动：spark-shell --master local[2]
    
    spark实现wc：
    val file = sc.textFile("file:///home/hadoop/data/hello.txt")
    val a = file.flatMap(line => line.split(" "))
    val b = a.map(word => (word,1))
    Array((hadoop,1), (welcome,1), (hadoop,1), (hdfs,1), (mapreduce,1), (hadoop,1), (hdfs,1))
    
    val c = b.reduceByKey(_ + _)
    	Array((mapreduce,1), (welcome,1), (hadoop,3), (hdfs,2))
    
    
    sc.textFile("file:///home/hadoop/data/hello.txt").flatMap(line => line.split(" ")).map(word => (word,1)).reduceByKey(_ + _).collect

# Flink分布式计算框架（流处理） #

## 概述 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70 13]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70 14]  
配合使用的框架，流入流出  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70 15]  
注意hadoop版本和scala版本，新版flink并未细分下载选项

## 配置环境 ##

flink解压

tar -zxf flink-1.12.1-bin-scala_2.11.tgz -C ./

wordcount

## Flink运行 ##

./bin/flink run ./examples/batch/WordCount.jar \
    --input file:///root/data/hello.txt --output file:///root/data/tmp/flink_wc_output

## 检验 ##

[root@hadoop01 tmp]# cat flink_wc_output
    hadoop 1
    hdfs 1
    hello 3
    world 1

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70 16]

# Beam #

java\\python编写应用于批处理、流处理  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70 17][https://beam.apache.org/][https_beam.apache.org]

## quickstart-java ##

jdk1.7之后 和 maven 前置环节

tree
    
    Beam运行：
    mvn archetype:generate \
          -DarchetypeGroupId=org.apache.beam \
          -DarchetypeArtifactId=beam-sdks-java-maven-archetypes-examples \
          -DarchetypeVersion=2.27.0 \
          -DgroupId=org.example \
          -DartifactId=word-count-beam \
          -Dversion="0.1" \
          -Dpackage=org.apache.beam.examples \
          -DinteractiveMode=false
    
    #direct方式运行
    mvn compile exec:java -Dexec.mainClass=org.apache.beam.examples.WordCount \
    -Dexec.args="--inputFile=/home/hadoop/data/hello.txt --output=counts" \
    -Pdirect-runner
    
    #spark方式运行
    mvn compile exec:java -Dexec.mainClass=org.apache.beam.examples.WordCount \
    -Dexec.args="--runner=SparkRunner --inputFile=/home/hadoop/data/hello.txt --output=counts" -Pspark-runner
    
    
    #flink方式运行

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[http_spark.apache.org]: http://spark.apache.org/
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[Link 1]: http://spark.apache.org/docs/latest/quick-start.html
[2021012216374571.png]: https://img-blog.csdnimg.cn/2021012216374571.png
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[https_beam.apache.org]: https://beam.apache.org/
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