Docker-Compose搭建Spark集群的实现方法

一、前言

在前文中,我们使用Docker-Compose完成了hdfs集群的构建。本文将继续使用Docker-Compose,实现Spark集群的搭建。

二、docker-compose.yml

对于Spark集群,我们采用一个mater节点和两个worker节点进行构建。其中,所有的work节点均分配1一个core和 1GB的内存。

Docker镜像选择了bitnami/spark的开源镜像,选择的spark版本为2.4.3,docker-compose配置如下:

  master:
    image: bitnami/spark:2.4.3
    container_name: master
    user: root
    environment:
      - SPARK_MODE=master
      - SPARK_RPC_AUTHENTICATION_ENABLED=no
      - SPARK_RPC_ENCRYPTION_ENABLED=no
      - SPARK_LOCAL_STORAGE_ENCRYPTION_ENABLED=no
      - SPARK_SSL_ENABLED=no
    ports:
      - '8080:8080'
      - '7077:7077'
    volumes:
      - ./python:/python

  worker1:
    image: bitnami/spark:2.4.3
    container_name: worker1
    user: root
    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
  worker2:
    image: bitnami/spark:2.4.3
    container_name: worker2
    user: root
    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

在master节点中,也映射了一个/python目录,用于存放pyspark代码,方便运行。

对于master节点,暴露出7077端口和8080端口分别用于连接spark以及浏览器查看spark UI,在spark UI中,集群状态如下图(启动后):

Docker-Compose搭建Spark集群的实现方法_第1张图片

如果有需要,可以自行添加worker节点,其中可以修改SPARK_WORKER_MEMORYSPARK_WORKER_CORES对节点分配的资源进行修改。

对于该镜像而言,默认exec进去是无用户的,会导致一些安装命令权限的不足,无法安装。例如需要运行pyspark,可能需要安装numpy、pandas等库,就无法使用pip完成安装。而通过user: root就能设置默认用户为root用户,避免上述问题。

三、启动集群

同上文一样,在docker-compose.yml的目录下执行docker-compose up -d命令,就能一键构建集群(但是如果需要用到numpy等库,还是需要自己到各节点内进行安装)。

进入master节点执行spark-shell,成功进入:

Docker-Compose搭建Spark集群的实现方法_第2张图片

四、结合hdfs使用

将上文的Hadoop的docker-compose.yml与本次的结合,得到新的docker-compose.yml:

version: "1.0"
services:
  namenode:
    image: bde2020/hadoop-namenode:2.0.0-hadoop3.2.1-java8
    container_name: namenode
    ports:
      - 9870:9870
      - 9000:9000
    volumes:
      - ./hadoop/dfs/name:/hadoop/dfs/name
      - ./input:/input
    environment:
      - CLUSTER_NAME=test
    env_file:
      - ./hadoop.env

  datanode:
    image: bde2020/hadoop-datanode:2.0.0-hadoop3.2.1-java8
    container_name: datanode
    depends_on:
      - namenode
    volumes:
      - ./hadoop/dfs/data:/hadoop/dfs/data
    environment:
      SERVICE_PRECONDITION: "namenode:9870"
    env_file:
      - ./hadoop.env
  
  resourcemanager:
    image: bde2020/hadoop-resourcemanager:2.0.0-hadoop3.2.1-java8
    container_name: resourcemanager
    environment:
      SERVICE_PRECONDITION: "namenode:9000 namenode:9870 datanode:9864"
    env_file:
      - ./hadoop.env

  nodemanager1:
    image: bde2020/hadoop-nodemanager:2.0.0-hadoop3.2.1-java8
    container_name: nodemanager
    environment:
      SERVICE_PRECONDITION: "namenode:9000 namenode:9870 datanode:9864 resourcemanager:8088"
    env_file:
      - ./hadoop.env
  
  historyserver:
    image: bde2020/hadoop-historyserver:2.0.0-hadoop3.2.1-java8
    container_name: historyserver
    environment:
      SERVICE_PRECONDITION: "namenode:9000 namenode:9870 datanode:9864 resourcemanager:8088"
    volumes:
      - ./hadoop/yarn/timeline:/hadoop/yarn/timeline
    env_file:
      - ./hadoop.env
    
  master:
    image: bitnami/spark:2.4.3-debian-9-r81
    container_name: master
    user: root
    environment:
      - SPARK_MODE=master
      - SPARK_RPC_AUTHENTICATION_ENABLED=no
      - SPARK_RPC_ENCRYPTION_ENABLED=no
      - SPARK_LOCAL_STORAGE_ENCRYPTION_ENABLED=no
      - SPARK_SSL_ENABLED=no
    ports:
      - '8080:8080'
      - '7077:7077'
    volumes:
      - ./python:/python

  worker1:
    image: bitnami/spark:2.4.3-debian-9-r81
    container_name: worker1
    user: root
    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
  worker2:
    image: bitnami/spark:2.4.3-debian-9-r81
    container_name: worker2
    user: root
    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

运行集群(还需要一个hadoop.env文件见上文)长这样:

Docker-Compose搭建Spark集群的实现方法_第3张图片

通过Docker容器的映射功能,将本地文件与spark集群的master节点的/python进行了文件映射,编写的pyspark通过映射可与容器中进行同步,并通过docker exec指令,完成代码执行:

Docker-Compose搭建Spark集群的实现方法_第4张图片

运行了一个回归程序,集群功能正常:

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