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实战：使用Docker部署Hadoop集群

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文章目录



Hadoop简介

Hadoop是一个由Apache基金会所开发的分布式系统基础架构。用户可以在不了解分布式底层细节的情况下，开发分布式程序，充分利用集群进行高速运算和存储。Hadoop主要包含HDFS分布式文件系统和MapReduce映射规约，HDFS主要负责数据的分布式存储和管理；MapReduce的Map阶段负责任务分发各个集群节点计算获得中间结果，Reduce阶段负责将各个阶段的中间数据进行汇总获得结果。

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Hadoop优势

1.高可靠性

2.高扩展性

3.高效性

4.高容错性

5.低成本



Hadoop应用场景

1、分布式存储

2、海量并行计算

3、大规模日志处理

4、ETL数据抽取、转换、装载

5、机器学习

6、搜索引擎

7、数据挖掘



docker与docker-compose安装

#安装docker社区版
yum install docker-ce
#版本查看
docker version
#docker-compose插件安装
curl -L https://github.com/docker/compose/releases/download/1.21.2/docker-compose-`uname -s`-`uname -m` -o /usr/local/bin/docker-compose
#可执行权限
chmod +x /usr/local/bin/docker-compose
#版本查看
docker-compose version



Hadoop集群搭建



环境变量

新建hadoop数据文件夹并赋予可读写权限

mkdir ./hadoop/data

chmod -R 777 ./hodoop

新增hadoop环境变量

vim ./hadoop.env 

CORE_CONF_fs_defaultFS=hdfs://namenode:9000
CORE_CONF_hadoop_http_staticuser_user=root
CORE_CONF_hadoop_proxyuser_hue_hosts=*
CORE_CONF_hadoop_proxyuser_hue_groups=*
CORE_CONF_io_compression_codecs=org.apache.hadoop.io.compress.SnappyCodec

HDFS_CONF_dfs_webhdfs_enabled=true
HDFS_CONF_dfs_permissions_enabled=false
HDFS_CONF_dfs_namenode_datanode_registration_ip___hostname___check=false

YARN_CONF_yarn_log___aggregation___enable=true
YARN_CONF_yarn_log_server_url=http://historyserver:8188/applicationhistory/logs/
YARN_CONF_yarn_resourcemanager_recovery_enabled=true
YARN_CONF_yarn_resourcemanager_store_class=org.apache.hadoop.yarn.server.resourcemanager.recovery.FileSystemRMStateStore
YARN_CONF_yarn_resourcemanager_scheduler_class=org.apache.hadoop.yarn.server.resourcemanager.scheduler.capacity.CapacityScheduler
YARN_CONF_yarn_scheduler_capacity_root_default_maximum___allocation___mb=8192
YARN_CONF_yarn_scheduler_capacity_root_default_maximum___allocation___vcores=4
YARN_CONF_yarn_resourcemanager_fs_state___store_uri=/rmstate
YARN_CONF_yarn_resourcemanager_system___metrics___publisher_enabled=true
YARN_CONF_yarn_resourcemanager_hostname=resourcemanager
YARN_CONF_yarn_resourcemanager_address=resourcemanager:8032
YARN_CONF_yarn_resourcemanager_scheduler_address=resourcemanager:8030
YARN_CONF_yarn_resourcemanager_resource__tracker_address=resourcemanager:8031
YARN_CONF_yarn_timeline___service_enabled=true
YARN_CONF_yarn_timeline___service_generic___application___history_enabled=true
YARN_CONF_yarn_timeline___service_hostname=historyserver
YARN_CONF_mapreduce_map_output_compress=true
YARN_CONF_mapred_map_output_compress_codec=org.apache.hadoop.io.compress.SnappyCodec
YARN_CONF_yarn_nodemanager_resource_memory___mb=16384
YARN_CONF_yarn_nodemanager_resource_cpu___vcores=8
YARN_CONF_yarn_nodemanager_disk___health___checker_max___disk___utilization___per___disk___percentage=98.5
YARN_CONF_yarn_nodemanager_remote___app___log___dir=/app-logs
YARN_CONF_yarn_nodemanager_aux___services=mapreduce_shuffle

MAPRED_CONF_mapreduce_framework_name=yarn
MAPRED_CONF_mapred_child_java_opts=-Xmx4096m
MAPRED_CONF_mapreduce_map_memory_mb=4096
MAPRED_CONF_mapreduce_reduce_memory_mb=8192
MAPRED_CONF_mapreduce_map_java_opts=-Xmx3072m
MAPRED_CONF_mapreduce_reduce_java_opts=-Xmx6144m
MAPRED_CONF_yarn_app_mapreduce_am_env=HADOOP_MAPRED_HOME=/opt/hadoop-3.2.1/
MAPRED_CONF_mapreduce_map_env=HADOOP_MAPRED_HOME=/opt/hadoop-3.2.1/
MAPRED_CONF_mapreduce_reduce_env=HADOOP_MAPRED_HOME=/opt/hadoop-3.2.1/

TEZ_CONF_tez_lib_uris=hdfs://namenode:9000/tez/tez,hdfs://namenode:9000/tez/tez/lib
TEZ_CONF_tez_lib_uris_classpath=hdfs://namenode:9000/tez/tez,hdfs://namenode:9000/tez/tez/lib
TEZ_CONF_tez_use_cluster_hadoop___libs=true
TEZ_CONF_tez_history_logging_service_class=org.apache.tez.dag.history.logging.ats.ATSHistoryLoggingService



docker-compose

新增docker-compose编排文件

vim docker-compose-hadoop.yaml 

version: '3'
services:
  namenode:
    image: registry.cn-hangzhou.aliyuncs.com/senfel/hadoop-namenode:2.0.0-hadoop3.2.1-java8
    container_name: namenode
    hostname: namenode
    restart: always
    ports:
      - 9000:9000
      - 9870:9870  
    volumes:
      - ./hadoop/data/hadoop_namenode:/hadoop/dfs/name
    environment:
      - CLUSTER_NAME=test
    env_file:
      - ./hadoop.env
    networks:
      - hadoop_net

  resourcemanager:
    image: registry.cn-hangzhou.aliyuncs.com/senfel/hadoop-resourcemanager:2.0.0-hadoop3.2.1-java8
    container_name: resourcemanager
    hostname: resourcemanager
    restart: always
    ports:
      - "8088:8088"
    depends_on:
      - namenode
      - datanode1
      - datanode2
      - datanode3
    env_file:
      - ./hadoop.env
    networks:
      - hadoop_net

  historyserver:
    image: registry.cn-hangzhou.aliyuncs.com/senfel/hadoop-historyserver:2.0.0-hadoop3.2.1-java8
    container_name: historyserver
    restart: always
    hostname: historyserver
    ports:
      - "8188:8188"
    depends_on:
      - namenode
      - datanode1
      - datanode2
      - datanode3
    volumes:
      - ./hadoop/data/hadoop_historyserver:/hadoop/yarn/timeline
    env_file:
      - ./hadoop.env
    networks:
      - hadoop_net

  nodemanager:
    image: registry.cn-hangzhou.aliyuncs.com/senfel/hadoop-nodemanager:2.0.0-hadoop3.2.1-java8
    restart: always
    container_name: nodemanager
    hostname: nodemanager
    depends_on:
      - namenode
      - datanode1
      - datanode2
      - datanode3
    env_file:
      - ./hadoop.env
    ports:
      - "8042:8042"
    networks:
      - hadoop_net

  datanode1:
    image: registry.cn-hangzhou.aliyuncs.com/senfel/hadoop-datanode:2.0.0-hadoop3.2.1-java8
    restart: always
    container_name: datanode1
    hostname: datanode1
    depends_on:
      - namenode
    ports:
      - "5642:5642"
    volumes:
      - ./hadoop/data/hadoop_datanode1:/hadoop/dfs/data
    env_file:
      - ./hadoop.env
    environment:
      - HDFS_CONF_dfs_datanode_address=0.0.0.0:5640
      - HDFS_CONF_dfs_datanode_ipc_address=0.0.0.0:5641
      - HDFS_CONF_dfs_datanode_http_address=0.0.0.0:5642
    networks:
      - hadoop_net

  datanode2:
    image: registry.cn-hangzhou.aliyuncs.com/senfel/hadoop-datanode:2.0.0-hadoop3.2.1-java8
    restart: always
    container_name: datanode2
    hostname: datanode2
    depends_on:
      - namenode
    ports:
      - "5645:5645"
    volumes:
      - ./hadoop/data/hadoop_datanode2:/hadoop/dfs/data
    env_file:
      - ./hadoop.env
    environment:
      - HDFS_CONF_dfs_datanode_address=0.0.0.0:5643
      - HDFS_CONF_dfs_datanode_ipc_address=0.0.0.0:5644
      - HDFS_CONF_dfs_datanode_http_address=0.0.0.0:5645
    networks:
      - hadoop_net

  datanode3:
    image: registry.cn-hangzhou.aliyuncs.com/senfel/hadoop-datanode:2.0.0-hadoop3.2.1-java8
    restart: always
    container_name: datanode3
    hostname: datanode3
    depends_on:
      - namenode
    ports:
      - "5648:5648"
    volumes:
      - ./hadoop/data/hadoop_datanode3:/hadoop/dfs/data
    env_file:
      - ./hadoop.env
    environment:
      - HDFS_CONF_dfs_datanode_address=0.0.0.0:5646
      - HDFS_CONF_dfs_datanode_ipc_address=0.0.0.0:5647
      - HDFS_CONF_dfs_datanode_http_address=0.0.0.0:5648
    networks:
      - hadoop_net

networks:
  hadoop_net:
    driver: bridge



环境文件树结构

├── docker-compose-hadoop.yaml

├── hadoop

│ └── data

├── hadoop.env



编排并运行容器

1、编排 

docker-compose -f docker-compose-hadoop.yaml up -d

Creating namenode … done

Creating datanode3 … done

Creating datanode2 … done

Creating datanode1 … done

Creating nodemanager … done

Creating resourcemanager … done

Creating historyserver … done

2、查看运行容器 

docker ps

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3、Hadoop web页面

hdfs健康信息 http://10.10.22.91:9870

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hadoop集群信息 http://10.10.22.91:8088/

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运行wordcount例子

# 进入namenode容器
docker exec -it namenode bash

#进入测试demo WordCount程序
cd /opt/hadoop-3.2.1/share/hadoop/mapreduce

# 在namenode容器里创建目录和2个文件
mkdir input
echo "this is a hadoop test example" > input/f1.txt
echo "my name is a senfel" > input/f2.txt
echo "good study day day up" > input/f3.txt
 
# 在HDFS创建一个input目录（绝对路径为/user/root/input）
hdfs dfs -mkdir -p input
 
# 把容器/input目录下的所有文件拷贝到HDFS的input目录，如果HDFS的input目录不存在会报错
hdfs dfs -put ./input/* input
 
# 在容器里运行WordCount程序，该程序需要2个参数：HDFS输入目录和HDFS输出目录
# 需要保证dfs中output文件夹不存在
hadoop jar hadoop-mapreduce-examples-3.2.1.jar wordcount input output

# 打印输出刚才运行的结果，结果保存到HDFS的output目录下
hdfs dfs -cat output/part-r-00000

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写在最后

用docker方式部署hadoop集群较普通物理更为简单，且方便管理。本次实战过程用的bridge网络，如果需要远程访问hdfs则是需要用host网络构建容器。


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