# bigdata-docker **Repository Path**: usench/bigdata-docker ## Basic Information - **Project Name**: bigdata-docker - **Description**: No description available - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 1 - **Created**: 2025-04-17 - **Last Updated**: 2025-04-17 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # Bigdata-Docker构建大数据学习开发环境 ### 介绍 ##### 1、镜像环境 * 系统：centos 7 * Java ：java7 * Zookeeper: 3.4.6 * Hadoop: 2.7.1 * mysql: 5.6.29 * Hive: 1.2.1 * Spark: 1.6.2 * Hbase: 1.1.2 ##### 2、镜像介绍 * tonywell/centos-java：openssh、java7，基础镜像 * tonywell/docker-zk: 基于tonywell/centos-java构建，zookeeper，用于启动zk集群 * tonywell/docker-hadoop：基于tonywell/centos-java构建, hadoop，用于启动hadoop集群 * tonywell/docker-mysql：openssh、mysql，用于启动mysql容器提供给hive集群 * tonywell/docker-hive：基于tonywell/docker-hadoop镜像构建，包含hadoop、hive，用于启动hadoop、hive集群 * tonywell/docker-spark：基于tonywell/docker-hive镜像构建，包含hadoop、hive、spark，用于启动hadoo、hive、spark集群 * tonywell/docker-hbase：基于tonywell/docker-spark镜像构建，包含hadoop、hive、spark、hbase，用于启动hadoop、hive、spark、hbase集群 ### Quick Start #### 1、构建镜像 ``` $ sh build.sh ``` 可以根据需求注释掉不需要的镜像 #### 2、创建大数据集群网络 ``` $ docker network create zoo ``` #### 3、启动zk集群 ``` $ docker-compose -f docker-compose-zk.yml up -d ``` 根据需要可在compose膜拜中增减集群数量，注意同时要增减myid配置 #### 4、启动mysql容器 如何仅仅想使用hadoop集群的，可省略此步。 ``` $ docker-compose -f docker-compose-mysql.yml up -d ``` 然后就要修改密码和配置远程访问mysql了 ``` $ docker exec -it hadoop-mysql bash $ cd /usr/local/mysql-5.6.29/bin $ ./mysql -u root -p #默认密码为空，回车即可 $ mysql> use mysql; $ mysql> UPDATE user SET Password=PASSWORD('新密码') where USER='root'; $ mysql> FLUSH PRIVILEGES; #授权远程访问 $ mysql> grant ALL PRIVILEGES ON *.* to root@"%" identified by "root" WITH GRANT OPTION; $ mysql> FLUSH PRIVILEGES; #配置字符集，解决后面hive建表报错 #FAILED: Execution Error, return code 1 from org.apache.hadoop.hive.ql.exec.DDLTask. MetaException(message:For direct MetaStore DB connections, we don't support retries at the client level.) $ mysql> alter database hive character set latin1; ``` ok mysql容器配置完成 #### 4、大数据集群 ##### a）启动Hadoop集群 ``` $ docker-compose -f docker-compose-hadoop.yml up -d ``` 启动集群，格式化namenode ``` $ docker exec -it hadoop-master bash $ cd /usr/local/hadoop/bin $ hdfs namenode -format ``` 然后启动hdfs和yarn ``` $ cd /usr/local/hadoop/sbin $ ./start-all.sh ``` 访问http://localhost:50070，看集群是否启动成功 ##### b）启动Hive集群 需要依赖mysql容器 ``` $ docker-compose -f docker-compose-hive.yml up -d ``` 启动hadoo集群的操作和上面启动hadoop集群一样 ##### c）启动Spark集群 需要依赖mysql容器 ``` $ docker-compose -f docker-compose-spark.yml up -d ``` 启动hadoop集群同a。 启动spark集群 ``` $ sh /usr/local/spark/sbin/start-all.sh ``` 使用 spark 自带样例中的计算 Pi 的应用来验证一下 ``` /usr/local/spark/bin/spark-submit --master spark://hadoop-master:7077 --class org.apache.spark.examples.SparkPi /usr/local/spark/lib/spark-examples-1.6.2-hadoop2.2.0.jar 1000 ``` 计算结果输出如下 ``` starting org.apache.spark.deploy.master.Master, logging to /usr/local/spark/logs/spark--org.apache.spark.deploy.master.Master-1-1bdfd98bccc7.out hadoop-slave2: starting org.apache.spark.deploy.worker.Worker, logging to /usr/local/spark/logs/spark-root-org.apache.spark.deploy.worker.Worker-1-9dd7e2ebbf13.out hadoop-slave3: starting org.apache.spark.deploy.worker.Worker, logging to /usr/local/spark/logs/spark-root-org.apache.spark.deploy.worker.Worker-1-97a87730dd03.out hadoop-slave1: starting org.apache.spark.deploy.worker.Worker, logging to /usr/local/spark/logs/spark-root-org.apache.spark.deploy.worker.Worker-1-adb07707f15b.out