Spark+Hadoop分布式实验

配置环境

1、配置docker

输入以下命令安装docker

sudo apt-get update
sudo apt-get install docker.io
systemctl start docker
systemctl enable docker

将当前用户添加到拥有启动docker的用户组

# 增加一个docker用户组，用于分配对应的全县
sudo su # 切换到root
groupadd docker # 添加用户组
gpasswd -a user docker # 将user添加到docker用户组

然后查看docker用户组的用户

getent group docker

若出现之前添加的user则代表操作成功

2、安装hadoop

切换回之前的用户

su user

开始安装hadoop，首先搜索镜像

docker search hadoop

然后我们选择第一个，STARS最高的一条

Setup-1.png

拉取镜像

docker pull sequenceiq/hadoop-docker

执行同样的操作，拉取spark的镜像

docker pull bitnami/spark

3、配置hadoop

接下来的命令中第一行的注释代表了在哪里执行代码，server代表主机，container代表容器（就是在hadoop容器里）

下述命令需要在三个容器中分别运行

首先进入hadoop1（在生成完公钥后要再次进入hadoop2、hadoop3分别生成）

# run in server
docker exec -it hadoop1 bash

生成公钥

# run in container
cd /root/.ssh
rm authorized_keys
rm id_rsa*
/etc/init.d/sshd start
ssh-keygen -t rsa

将公钥保存位authorized_keys

# run in container
cat /root/.ssh/id_rsa.pub >> /root/.ssh/authorized_keys 
exit # exit the continer

在主机端合并三个节点的公钥

获取三个节点的公钥，保存为key1，key2，key3

# run in server
for i in $(seq 1 3)
do
docker cp hadoop${i}:/root/.ssh/authorized_keys key${i}
done

将三个公钥组合成authorized_keys并复制到三个节点中

# run in server
cat key1 key2 key3 >> authorized_keys
for i in $(seq 1 3)
do
docker cp authorized_keys hadoop${i}:/root/.ssh/authorized_keys
done

在节点中修改文件权限

不修改可能会出现文件权限不对，导致节点无法读取authorized_keys

# run in server
docker exec -it hadoop1 bash
# run in container
cd ~/.ssh
chown `whoami` authorized_keys
chgrp `whoami` authorized_keys

编辑相关配置文件

在每一个hadoop中的 /usr/local/hadoop-2.7.0/etc/hadoop文件夹内修改以下文件

core-site.xml中添加

    hadoop.tmp.dir
    file:/usr/local/hadoop/tmp
    Abase for other temporary directories.

hdfs-site.xml中添加

    dfs.namenode.secondary.http-address
    Master:50090
    

    dfs.namenode.name.dir
     file:/usr/local/hadoop/tmp/dfs/name


    dfs.datanode.data.dir
    file:/usr/local/hadoop/tmp/dfs/data

mapred-site.xml中添加

    mapreduce.jobhistory.address
    Master:10020


    mapreduce.jobhistory.webapp.address
    Master:19888

yarn-site.xml中添加

    yarn.resourcemanager.hostname
    master

slaves修改为

slave1
slave2

如果是从外部复制的，则需要修改对应权限

chown -R root /usr/local/hadoop-2.7.0/etc/hadoop/
chgrp -R root /usr/local/hadoop-2.7.0/etc/hadoop/

启动集群

首次启动需要在master节点中格式化NameNode

# run in hadoop1
/usr/local/hadoop/bin/hdfs namenode -format

然后在master节点中启动集群

/usr/local/hadoop/sbin/stop-all.sh
/usr/local/hadoop/sbin/start-all.sh

配置环境

使用docker compose构建spark集群

配置文件如下，在一个空文件夹中存放

docker-compose.yaml

version: '2'

services:
  master:
    image: bitnami/spark:latest
    hostname: master
    environment:
      - SPARK_MODE=master
      - SPARK_RPC_AUTHENTICATION_ENABLED=no
      - SPARK_RPC_ENCRYPTION_ENABLED=no
      - SPARK_LOCAL_STORAGE_ENCRYPTION_ENABLED=no
      - SPARK_SSL_ENABLED=no
    volumes:
      - ~/Code/Java/Distribution-Spark/share:/opt/share
    ports:
      - '8080:8080'
      - '4040:4040'
    container_name: spark1
  worker-1:
    image: bitnami/spark:latest
    hostname: worker1
    environment:
      - SPARK_MODE=worker
      - SPARK_MASTER_URL=spark://master:7077
      - SPARK_WORKER_MEMORY=1G
      - SPARK_WORKER_CORES=1
      - SPARK_RPC_AUTHENTICATION_ENABLED=no
      - SPARK_RPC_ENCRYPTION_ENABLED=no
      - SPARK_LOCAL_STORAGE_ENCRYPTION_ENABLED=no
      - SPARK_SSL_ENABLED=no
    volumes:
      - ~/Code/Java/Distribution-Spark/share:/opt/share
    ports:
      - '8081:8081'
    depends_on:
      - master
    container_name: spark2
  worker-2:
    image: bitnami/spark:latest
    hostname: worker2
    environment:
      - SPARK_MODE=worker
      - SPARK_MASTER_URL=spark://master:7077
      - SPARK_WORKER_MEMORY=1G
      - SPARK_WORKER_CORES=1
      - SPARK_RPC_AUTHENTICATION_ENABLED=no
      - SPARK_RPC_ENCRYPTION_ENABLED=no
      - SPARK_LOCAL_STORAGE_ENCRYPTION_ENABLED=no
      - SPARK_SSL_ENABLED=no
    volumes:
      - ~/Code/Java/Distribution-Spark/share:/opt/share
    ports:
      - '8082:8081'
    depends_on:
      - master
    container_name: spark3

运行如下命令构建集群

docker compose up -d

构建集群的同时会构建一个network，名字是“文件夹名-default”，用于节点间通信

运行

用pyspark完成实验

import pyspark
from pyspark import SparkContext

def Map1(x):
    x = x.split(",")
    return (x[1], x[3], x[4])

def FilterAverage(x):
    return x[1] == "必修"

def Map2(x):
    return (x[0], (float(x[2]), 1))

def AddAverage(x, y):
    return (x[0]+y[0], x[1]+y[1])

def Map3(x):
    return (x[0], x[1][0]/x[1][1])

def Group(x):
    if x[1] < 60:
        return 0
    return min(int(x[1] / 10) - 5, 5)

sc = SparkContext("local", "score2")
tf = sc.textFile("./grades.txt")
# filter the compulsory class grades
tf2 = tf.map(lambda x:Map1(x)).filter(lambda x:FilterAverage(x))
# generate (name, grade) rdd
tf3 = tf2.map(lambda x: Map2(x))
# calculate the average of the grades
tf4 = tf3.reduceByKey(lambda x, y:AddAverage(x, y)).map(lambda x: Map3(x))
# save results
tf4.saveAsTextFile("./result1")

# group by average grade
tf5 = tf4.groupBy(lambda x: Group(x)).sortByKey()
resultInterval = ["[0, 60)", "[60, 70)", "[70, 80)", "[80, 90)", "[90, 100]"]
tf6 = tf5.map(lambda x:(resultInterval[x[0]], len(x[1])))
tf6.saveAsTextFile("./result2")

程序运行的结果会存放在spark目录下的result1和result2文件夹中

我们将数据存在grades.txt中，将代码保存为AverageScore.py

将数据和代码都迁移到spark中（/opt/bitnami/spark为container的默认登录地址）

docker cp ./AverageScore.py spark1:/opt/bitnami/spark/
docker cp ./grades.txt spark1:/opt/bitnami/spark/

进入spark的主节点，并运行代码

# run in server
docker exec -it spark1 bash
# run in container
spark-submit --master spark://master:7077 AverageScore.py