### 欢迎访问我的GitHub
> 这里分类和汇总了欣宸的全部原创(含配套源码)：[https://github.com/zq2599/blog_demos](https://github.com/zq2599/blog_demos)

### 本篇概览
- 本文是[《docker下，极速搭建spark集群(含hdfs集群)》](https://blog.51cto.com/u_13674465/5584009)的续篇，前文将spark集群搭建成功并进行了简单的验证，但是存在以下几个小问题：
1.  spark只有一个work节点，只适合处理小数据量的任务，遇到大量数据的任务要消耗更多时间；
2. hdfs的文件目录和docker安装目录在一起，如果要保存大量文件，很可能由于磁盘空间不足导致上传失败；
3. master的4040和work的8080端口都没有开放，看不到job、stage、executor的运行情况；

- 今天就来调整系统参数，解决上述问题；

### 最初的docker-compose.yml内容
- 优化前的docker-compose.yml内容如下所示：

```yml
version: "2.2"
services:
  namenode:
    image: bde2020/hadoop-namenode:1.1.0-hadoop2.7.1-java8
    container_name: namenode
    volumes:
      - hadoop_namenode:/hadoop/dfs/name
      - ./input_files:/input_files
    environment:
      - CLUSTER_NAME=test
    env_file:
      - ./hadoop.env
    ports:
      - 50070:50070
  
  resourcemanager:
    image: bde2020/hadoop-resourcemanager:1.1.0-hadoop2.7.1-java8
    container_name: resourcemanager
    depends_on:
      - namenode
      - datanode1
      - datanode2
    env_file:
      - ./hadoop.env
  
  historyserver:
    image: bde2020/hadoop-historyserver:1.1.0-hadoop2.7.1-java8
    container_name: historyserver
    depends_on:
      - namenode
      - datanode1
      - datanode2
    volumes:
      - hadoop_historyserver:/hadoop/yarn/timeline
    env_file:
      - ./hadoop.env
  
  nodemanager1:
    image: bde2020/hadoop-nodemanager:1.1.0-hadoop2.7.1-java8
    container_name: nodemanager1
    depends_on:
      - namenode
      - datanode1
      - datanode2
    env_file:
      - ./hadoop.env
  
  datanode1:
    image: bde2020/hadoop-datanode:1.1.0-hadoop2.7.1-java8
    container_name: datanode1
    depends_on:
      - namenode
    volumes:
      - hadoop_datanode1:/hadoop/dfs/data
    env_file:
      - ./hadoop.env
  
  datanode2:
    image: bde2020/hadoop-datanode:1.1.0-hadoop2.7.1-java8
    container_name: datanode2
    depends_on:
      - namenode
    volumes:
      - hadoop_datanode2:/hadoop/dfs/data
    env_file:
      - ./hadoop.env
  
  datanode3:
    image: bde2020/hadoop-datanode:1.1.0-hadoop2.7.1-java8
    container_name: datanode3
    depends_on:
      - namenode
    volumes:
      - hadoop_datanode3:/hadoop/dfs/data
    env_file:
      - ./hadoop.env

master:
    image: gettyimages/spark:2.3.0-hadoop-2.8
    container_name: master
    command: bin/spark-class org.apache.spark.deploy.master.Master -h master
    hostname: master
    environment:
      MASTER: spark://master:7077
      SPARK_CONF_DIR: /conf
      SPARK_PUBLIC_DNS: localhost
    links:
      - namenode
    expose:
      - 7001
      - 7002
      - 7003
      - 7004
      - 7005
      - 7077
      - 6066
    ports:
      - 6066:6066
      - 7077:7077
      - 8080:8080
    volumes:
      - ./conf/master:/conf
      - ./data:/tmp/data
      - ./jars:/root/jars

worker:
    image: gettyimages/spark:2.3.0-hadoop-2.8
    container_name: worker
    command: bin/spark-class org.apache.spark.deploy.worker.Worker spark://master:7077
    hostname: worker
    environment:
      SPARK_CONF_DIR: /conf
      SPARK_WORKER_CORES: 2
      SPARK_WORKER_MEMORY: 1g
      SPARK_WORKER_PORT: 8881
      SPARK_WORKER_WEBUI_PORT: 8081
      SPARK_PUBLIC_DNS: localhost
    links:
      - master
    expose:
      - 7012
      - 7013
      - 7014
      - 7015
      - 8881
    ports:
      - 8081:8081
    volumes:
      - ./conf/worker:/conf
      - ./data:/tmp/data

volumes:
  hadoop_namenode:
  hadoop_datanode1:
  hadoop_datanode2:
  hadoop_datanode3:
  hadoop_historyserver:
```

- 接下来开始优化；

### 实战环境信息
- 本次实战所用的电脑是联想笔记本：
1. CPU：i5-6300HQ（四核四线程）
2. 内存：16G
3. 硬盘：256G的NVMe再加500G机械硬盘
4. 系统：Deepin15
5. docker：18.09.1
6. docker-compose：1.17.1
7. spark：2.3.0
8. hdfs：2.7.1

### 调整work节点数量
- 由于内存有16G，于是打算将work节点数从1个调整到6个，调整后work容器的配置如下：

```yml
worker1:
    image: gettyimages/spark:2.3.0-hadoop-2.8
    container_name: worker1
    command: bin/spark-class org.apache.spark.deploy.worker.Worker spark://master:7077
    hostname: worker1
    environment:
      SPARK_CONF_DIR: /conf
      SPARK_WORKER_CORES: 2
      SPARK_WORKER_MEMORY: 2g
      SPARK_WORKER_PORT: 8881
      SPARK_WORKER_WEBUI_PORT: 8081
      SPARK_PUBLIC_DNS: localhost
    links:
      - master
    expose:
      - 7012
      - 7013
      - 7014
      - 7015
      - 8881
    volumes:
      - ./conf/worker1:/conf
      - ./data/worker1:/tmp/data
worker2:
    image: gettyimages/spark:2.3.0-hadoop-2.8
    container_name: worker2
    command: bin/spark-class org.apache.spark.deploy.worker.Worker spark://master:7077
    hostname: worker2
    environment:
      SPARK_CONF_DIR: /conf
      SPARK_WORKER_CORES: 2
      SPARK_WORKER_MEMORY: 2g
      SPARK_WORKER_PORT: 8881
      SPARK_WORKER_WEBUI_PORT: 8082
      SPARK_PUBLIC_DNS: localhost
    links:
      - master
    expose:
      - 7012
      - 7013
      - 7014
      - 7015
      - 8881
    volumes:
      - ./conf/worker2:/conf
      - ./data/worker2:/tmp/data
```
- 如上所示，注意volumes参数，都映射在了docker-compose.yml同一层级的conf和data两个目录下，这里只贴出了worker1和worker2的内容，worker3-worker6的内容都是类似的；

### hdfs的文件目录导致的磁盘空间不足问题
- 先来看下hdfs的文件目录配置：

```yml
volumes:
      - hadoop_datanode1:/hadoop/dfs/data
```

- 上面的**hadoop_datanode1**数据卷的配置在docker-compose.yml的最底部，是默认声明，如下：

```yml
volumes:
  hadoop_namenode:
  hadoop_datanode1:
  hadoop_datanode2:
  hadoop_datanode3:
  hadoop_historyserver:
```

- 在容器运行状态，执行命令**docker inspect datanode1**查看容器信息，和数据卷相关的信息如下所示：

```shell
"Mounts": [
            {
                "Type": "volume",
                "Name": "temp_hadoop_datanode1",
                "Source": "/var/lib/docker/volumes/temp_hadoop_datanode1/_data",
                "Destination": "/hadoop/dfs/data",
                "Driver": "local",
                "Mode": "rw",
                "RW": true,
                "Propagation": ""
            }
        ]
```
- 可见hdfs容器的文件目录对应的是宿主机的**/var/lib/docker/volumes**；
- 用**df -m**看看磁盘空间情况，如下所示，"/var/lib/docker/volumes"所在的"/dev/nvme0n1p3"设备可用空间只有20多G（29561），显然在保存大量文件时这个空间是不够的，而且hdfs的默认副本数为3：

```shell
root@willzhao-deepin:/data/work/spark/temp# df -m
文件系统        1M-块   已用   可用 已用% 挂载点
udev             7893      0   7893    0% /dev
tmpfs            1584      4   1581    1% /run
/dev/nvme0n1p3  43927  12107  29561   30% /
tmpfs            7918      0   7918    0% /dev/shm
tmpfs               5      1      5    1% /run/lock
tmpfs            7918      0   7918    0% /sys/fs/cgroup
/dev/nvme0n1p4  87854    181  83169    1% /home
/dev/nvme0n1p1    300      7    293    3% /boot/efi
/dev/sda1      468428 109152 335430   25% /data
tmpfs            1584      1   1584    1% /run/user/108
tmpfs            1584      0   1584    0% /run/user/0
```

- 上面的磁盘信息显示设备**/dev/sda1**还有300G，所以hdfs的文件目录映射到/dev/sda1就能缓解磁盘空间问题了，于是修改docker-compose.yml文件中hdfs的三个数据节点的配置，修改后如下：

```yml
datanode1:
    image: bde2020/hadoop-datanode:1.1.0-hadoop2.7.1-java8
    container_name: datanode1
    depends_on:
      - namenode
    volumes:
      - ./hadoop/datanode1:/hadoop/dfs/data
    env_file:
      - ./hadoop.env
  
  datanode2:
    image: bde2020/hadoop-datanode:1.1.0-hadoop2.7.1-java8
    container_name: datanode2
    depends_on:
      - namenode
    volumes:
      - ./hadoop/datanode2:/hadoop/dfs/data
    env_file:
      - ./hadoop.env
  
  datanode3:
    image: bde2020/hadoop-datanode:1.1.0-hadoop2.7.1-java8
    container_name: datanode3
    depends_on:
      - namenode
    volumes:
      - ./hadoop/datanode3:/hadoop/dfs/data
    env_file:
      - ./hadoop.env
```
- 再将下面这段配置删除：

```yml
volumes:
  hadoop_namenode:
  hadoop_datanode1:
  hadoop_datanode2:
  hadoop_datanode3:
  hadoop_historyserver:
```

### 开发master的4040和work的8080端口
- 任务运行过程中，如果有UI页面来观察详情，可以帮助我们更全面直观的了解运行情况，所以需要修改配置开放端口；
- 如下所示，**expose**参数增加4040，表示对外暴露4040端口，**ports**参数增加4040:4040，表示容器的4040映射到宿主机的4040端口：

```yml
  master:
    image: gettyimages/spark:2.3.0-hadoop-2.8
    container_name: master
    command: bin/spark-class org.apache.spark.deploy.master.Master -h master
    hostname: master
    environment:
      MASTER: spark://master:7077
      SPARK_CONF_DIR: /conf
      SPARK_PUBLIC_DNS: localhost
    links:
      - namenode
    expose:
      - 4040
      - 7001
      - 7002
      - 7003
      - 7004
      - 7005
      - 7077
      - 6066
    ports:
      - 4040:4040
      - 6066:6066
      - 7077:7077
      - 8080:8080
    volumes:
      - ./conf/master:/conf
      - ./data:/tmp/data
      - ./jars:/root/jars
```

- worker的web端口同样需要打开，访问worker的web页面可以观察worker的状态，并且查看任务日志（这个很重要），这里要注意的是由于有多个worker，所以要映射到宿主机的多个端口，如下配置，workder1的environment.SPARK_WORKER_WEBUI_PORT配置为8081，并且暴露8081，再将容器的8081映射到宿主机的8081，workder2的environment.SPARK_WORKER_WEBUI_PORT配置为8082，并且暴露8082，再将容器的8082映射到宿主机的8082：

```yml
 worker1:
    image: gettyimages/spark:2.3.0-hadoop-2.8
    container_name: worker1
    command: bin/spark-class org.apache.spark.deploy.worker.Worker spark://master:7077
    hostname: worker1
    environment:
      SPARK_CONF_DIR: /conf
      SPARK_WORKER_CORES: 2
      SPARK_WORKER_MEMORY: 2g
      SPARK_WORKER_PORT: 8881
      SPARK_WORKER_WEBUI_PORT: 8081
      SPARK_PUBLIC_DNS: localhost
    links:
      - master
    expose:
      - 7012
      - 7013
      - 7014
      - 7015
      - 8881
      - 8081
    ports:
      - 8081:8081
    volumes:
      - ./conf/worker1:/conf
      - ./data/worker1:/tmp/data

worker2:
    image: gettyimages/spark:2.3.0-hadoop-2.8
    container_name: worker2
    command: bin/spark-class org.apache.spark.deploy.worker.Worker spark://master:7077
    hostname: worker2
    environment:
      SPARK_CONF_DIR: /conf
      SPARK_WORKER_CORES: 2
      SPARK_WORKER_MEMORY: 2g
      SPARK_WORKER_PORT: 8881
      SPARK_WORKER_WEBUI_PORT: 8082
      SPARK_PUBLIC_DNS: localhost
    links:
      - master
    expose:
      - 7012
      - 7013
      - 7014
      - 7015
      - 8881
      - 8082
    ports:
      - 8082:8082
    volumes:
      - ./conf/worker2:/conf
      - ./data/worker2:/tmp/data  
```
- worker3-worker6的配置与上面类似，注意用不同的端口号；

- 至此，修改已经完成，最终版的docker-compose.yml内容如下：

```yml
version: "2.2"
services:
  namenode:
    image: bde2020/hadoop-namenode:1.1.0-hadoop2.7.1-java8
    container_name: namenode
    volumes:
      - ./hadoop/namenode:/hadoop/dfs/name
      - ./input_files:/input_files
    environment:
      - CLUSTER_NAME=test
    env_file:
      - ./hadoop.env
    ports:
      - 50070:50070
  
  resourcemanager:
    image: bde2020/hadoop-resourcemanager:1.1.0-hadoop2.7.1-java8
    container_name: resourcemanager
    depends_on:
      - namenode
      - datanode1
      - datanode2
    env_file:
      - ./hadoop.env
  
  historyserver:
    image: bde2020/hadoop-historyserver:1.1.0-hadoop2.7.1-java8
    container_name: historyserver
    depends_on:
      - namenode
      - datanode1
      - datanode2
    volumes:
      - ./hadoop/historyserver:/hadoop/yarn/timeline
    env_file:
      - ./hadoop.env
  
  nodemanager1:
    image: bde2020/hadoop-nodemanager:1.1.0-hadoop2.7.1-java8
    container_name: nodemanager1
    depends_on:
      - namenode
      - datanode1
      - datanode2
    env_file:
      - ./hadoop.env
  
  datanode1:
    image: bde2020/hadoop-datanode:1.1.0-hadoop2.7.1-java8
    container_name: datanode1
    depends_on:
      - namenode
    volumes:
      - ./hadoop/datanode1:/hadoop/dfs/data
    env_file:
      - ./hadoop.env
  
  datanode2:
    image: bde2020/hadoop-datanode:1.1.0-hadoop2.7.1-java8
    container_name: datanode2
    depends_on:
      - namenode
    volumes:
      - ./hadoop/datanode2:/hadoop/dfs/data
    env_file:
      - ./hadoop.env
  
  datanode3:
    image: bde2020/hadoop-datanode:1.1.0-hadoop2.7.1-java8
    container_name: datanode3
    depends_on:
      - namenode
    volumes:
      - ./hadoop/datanode3:/hadoop/dfs/data
    env_file:
      - ./hadoop.env

master:
    image: gettyimages/spark:2.3.0-hadoop-2.8
    container_name: master
    command: bin/spark-class org.apache.spark.deploy.master.Master -h master
    hostname: master
    environment:
      MASTER: spark://master:7077
      SPARK_CONF_DIR: /conf
      SPARK_PUBLIC_DNS: localhost
    links:
      - namenode
    expose:
      - 4040
      - 7001
      - 7002
      - 7003
      - 7004
      - 7005
      - 7077
      - 6066
    ports:
      - 4040:4040
      - 6066:6066
      - 7077:7077
      - 8080:8080
    volumes:
      - ./conf/master:/conf
      - ./data:/tmp/data
      - ./jars:/root/jars

worker1:
    image: gettyimages/spark:2.3.0-hadoop-2.8
    container_name: worker1
    command: bin/spark-class org.apache.spark.deploy.worker.Worker spark://master:7077
    hostname: worker1
    environment:
      SPARK_CONF_DIR: /conf
      SPARK_WORKER_CORES: 2
      SPARK_WORKER_MEMORY: 2g
      SPARK_WORKER_PORT: 8881
      SPARK_WORKER_WEBUI_PORT: 8081
      SPARK_PUBLIC_DNS: localhost
    links:
      - master
    expose:
      - 7012
      - 7013
      - 7014
      - 7015
      - 8881
      - 8081
    ports:
      - 8081:8081
    volumes:
      - ./conf/worker1:/conf
      - ./data/worker1:/tmp/data

worker2:
    image: gettyimages/spark:2.3.0-hadoop-2.8
    container_name: worker2
    command: bin/spark-class org.apache.spark.deploy.worker.Worker spark://master:7077
    hostname: worker2
    environment:
      SPARK_CONF_DIR: /conf
      SPARK_WORKER_CORES: 2
      SPARK_WORKER_MEMORY: 2g
      SPARK_WORKER_PORT: 8881
      SPARK_WORKER_WEBUI_PORT: 8082
      SPARK_PUBLIC_DNS: localhost
    links:
      - master
    expose:
      - 7012
      - 7013
      - 7014
      - 7015
      - 8881
      - 8082
    ports:
      - 8082:8082
    volumes:
      - ./conf/worker2:/conf
      - ./data/worker2:/tmp/data

worker3:
    image: gettyimages/spark:2.3.0-hadoop-2.8
    container_name: worker3
    command: bin/spark-class org.apache.spark.deploy.worker.Worker spark://master:7077
    hostname: worker3
    environment:
      SPARK_CONF_DIR: /conf
      SPARK_WORKER_CORES: 2
      SPARK_WORKER_MEMORY: 2g
      SPARK_WORKER_PORT: 8881
      SPARK_WORKER_WEBUI_PORT: 8083
      SPARK_PUBLIC_DNS: localhost
    links:
      - master
    expose:
      - 7012
      - 7013
      - 7014
      - 7015
      - 8881
      - 8083
    ports:
      - 8083:8083
    volumes:
      - ./conf/worker3:/conf
      - ./data/worker3:/tmp/data

worker4:
    image: gettyimages/spark:2.3.0-hadoop-2.8
    container_name: worker4
    command: bin/spark-class org.apache.spark.deploy.worker.Worker spark://master:7077
    hostname: worker4
    environment:
      SPARK_CONF_DIR: /conf
      SPARK_WORKER_CORES: 2
      SPARK_WORKER_MEMORY: 2g
      SPARK_WORKER_PORT: 8881
      SPARK_WORKER_WEBUI_PORT: 8084
      SPARK_PUBLIC_DNS: localhost
    links:
      - master
    expose:
      - 7012
      - 7013
      - 7014
      - 7015
      - 8881
      - 8084
    ports:
      - 8084:8084
    volumes:
      - ./conf/worker4:/conf
      - ./data/worker4:/tmp/data

worker5:
    image: gettyimages/spark:2.3.0-hadoop-2.8
    container_name: worker5
    command: bin/spark-class org.apache.spark.deploy.worker.Worker spark://master:7077
    hostname: worker5
    environment:
      SPARK_CONF_DIR: /conf
      SPARK_WORKER_CORES: 2
      SPARK_WORKER_MEMORY: 2g
      SPARK_WORKER_PORT: 8881
      SPARK_WORKER_WEBUI_PORT: 8085
      SPARK_PUBLIC_DNS: localhost
    links:
      - master
    expose:
      - 7012
      - 7013
      - 7014
      - 7015
      - 8881
      - 8085
    ports:
      - 8085:8085
    volumes:
      - ./conf/worker5:/conf
      - ./data/worker5:/tmp/data

worker6:
    image: gettyimages/spark:2.3.0-hadoop-2.8
    container_name: worker6
    command: bin/spark-class org.apache.spark.deploy.worker.Worker spark://master:7077
    hostname: worker6
    environment:
      SPARK_CONF_DIR: /conf
      SPARK_WORKER_CORES: 2
      SPARK_WORKER_MEMORY: 2g
      SPARK_WORKER_PORT: 8881
      SPARK_WORKER_WEBUI_PORT: 8086
      SPARK_PUBLIC_DNS: localhost
    links:
      - master
    expose:
      - 7012
      - 7013
      - 7014
      - 7015
      - 8881
      - 8086
    ports:
      - 8086:8086
    volumes:
      - ./conf/worker6:/conf
      - ./data/worker6:/tmp/data
```

- 接下来我们运行一个实例来验证；

### 验证

- 在docker-compose.yml所在目录创建hadoop.env文件，内容如下：

```shell
CORE_CONF_fs_defaultFS=hdfs://namenode:8020
CORE_CONF_hadoop_http_staticuser_user=root
CORE_CONF_hadoop_proxyuser_hue_hosts=*
CORE_CONF_hadoop_proxyuser_hue_groups=*

HDFS_CONF_dfs_webhdfs_enabled=true
HDFS_CONF_dfs_permissions_enabled=false

YARN_CONF_yarn_log___aggregation___enable=true
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_fs_state___store_uri=/rmstate
YARN_CONF_yarn_nodemanager_remote___app___log___dir=/app-logs
YARN_CONF_yarn_log_server_url=http://historyserver:8188/applicationhistory/logs/
YARN_CONF_yarn_timeline___service_enabled=true
YARN_CONF_yarn_timeline___service_generic___application___history_enabled=true
YARN_CONF_yarn_resourcemanager_system___metrics___publisher_enabled=true
YARN_CONF_yarn_resourcemanager_hostname=resourcemanager
YARN_CONF_yarn_timeline___service_hostname=historyserver
YARN_CONF_yarn_resourcemanager_address=resourcemanager:8032
YARN_CONF_yarn_resourcemanager_scheduler_address=resourcemanager:8030
YARN_CONF_yarn_resourcemanager_resource___tracker_address=resourcemanager:8031
```

- 修改好docker-composes.yml后，执行以下命令启动容器：

```shell
docker-compose up -d
```

- 此次验证所用的spark应用的功能是分析维基百科的网站统计信息，找出访问量最大的网页，本次实战用现成的jar包，不涉及编码，该应用的源码和开发详情请参照[《spark实战之：分析维基百科网站统计数据(java版)》](https://blog.51cto.com/u_13674465/5591775)；

- 从github下载已经构建好的spark应用jar文件：

```shell
wget https://raw.githubusercontent.com/zq2599/blog_demos/master/files/sparkdemo-1.0-SNAPSHOT.jar
```

- 从github下载维基百科的网站统计信息大数据集，这里只下载了一个文件，建议您参照[《寻找海量数据集用于大数据开发实战(维基百科网站统计数据)》](https://blog.51cto.com/u_13674465/5587921)下载更多文件用来实战：

```shell
wget https://raw.githubusercontent.com/zq2599/blog_demos/master/files/pagecounts-20160801-000000
```

- 将下载的sparkdemo-1.0-SNAPSHOT.jar文件放在docker-compose.xml所在目录的jars目录下；
- 在docker-compose.xml所在目录的input_files目录内创建input目录，再将下载的pagecounts-20160801-000000文件放在这个input目录下；
- 执行以下命令，将整个input目录放入hdfs：

```shell
docker exec namenode hdfs dfs -put /input_files/input /
```

- 执行以下命令，提交一个任务，使用了12个executor，每个1G内存：

```shell
docker exec -it master spark-submit \
--class com.bolingcavalry.sparkdemo.app.WikiRank \
--executor-memory 1g \
--total-executor-cores 12 \
/root/jars/sparkdemo-1.0-SNAPSHOT.jar \
namenode \
8020
```

- 宿主机的状态如下所示，CPU和内存都被榨干：
![在这里插入图片描述](https://img-blog.csdnimg.cn/20190216161742366.jpg?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2JvbGluZ19jYXZhbHJ5,size_16,color_FFFFFF,t_70)

- 宿主机的IP地址是192.168.1.102，以下是状态信息，地址：http://192.168.1.102:8080/
![在这里插入图片描述](https://img-blog.csdnimg.cn/20190216161936490.jpg?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2JvbGluZ19jYXZhbHJ5,size_16,color_FFFFFF,t_70)

- 查看job的Stage情况，如下图，这些信息对学习和掌握spark至关重要，地址：http://192.168.1.102:4040
![在这里插入图片描述](https://img-blog.csdnimg.cn/20190216162315332.jpg?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2JvbGluZ19jYXZhbHJ5,size_16,color_FFFFFF,t_70)

- 查看worker1的基本情况，如下图，地址是：http://192.168.1.102:8081
![在这里插入图片描述](https://img-blog.csdnimg.cn/20190216162801657.jpg?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2JvbGluZ19jYXZhbHJ5,size_16,color_FFFFFF,t_70)

- 如果想查看worker1上的业务日志，请点击下图红框中的链接，但此时会提示页面访问失败，对应的url是"http://localhost:8081/logPage?appId=app-20190216081637-0002&executorId=5&logType=stdout"，这个地址是页面生成的，我们只要把其中的"localhost"改成宿主机的IP地址就好了：
![在这里插入图片描述](https://img-blog.csdnimg.cn/20190216163002753.jpg?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2JvbGluZ19jYXZhbHJ5,size_16,color_FFFFFF,t_70)

- 修改后的链接可以访问，看到的业务日志如下图，红框中就是业务代码输出的日志：
![在这里插入图片描述](https://img-blog.csdnimg.cn/20190216163351925.jpg?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2JvbGluZ19jYXZhbHJ5,size_16,color_FFFFFF,t_70)

- 以上就是优化和验证的全部过程，您可以根据自己机器的实际情况来调整参数，将电脑的性能充分的利用起来；

- 后来我用24个300M的文件做数据集，大约1.5亿条记录，在上述硬件环境运行上述命令，最终耗时30分钟完成，如下图：
![在这里插入图片描述](https://img-blog.csdnimg.cn/20190216171125764.jpg?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2JvbGluZ19jYXZhbHJ5,size_16,color_FFFFFF,t_70)

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