docker下的spark集群，调整参数榨干硬件

调整docker-compose编排的参数，充分发挥硬件信息能运行spark集群

程序员欣宸

3510人浏览 · 2019-02-16 17:04:18

程序员欣宸 · 2019-02-16 17:04:18 发布

本文是《docker下，极速搭建spark集群(含hdfs集群)》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：

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；
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；
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：

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：

CPU：i5-6300HQ（四核四线程）
内存：16G
硬盘：256G的NVMe再加500G机械硬盘
系统：Deepin15
docker：18.09.1
docker-compose：1.17.1
spark：2.3.0
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：

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

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：

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：

"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；
4. 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：

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：

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：

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：

  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：

 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：

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：

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：

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)》；
从github下载已经构建好的spark应用jar文件：

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)》下载更多文件用来实战：

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：

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：

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：
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/
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
查看worker1的基本情况，如下图，地址是：http://192.168.1.102:8081
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：
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：

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

欢迎关注我的公众号：程序员欣宸

在这里插入图片描述

向您推荐>>Eolink开发者社区

权威｜前沿｜技术｜干货｜国内首个API全生命周期开发者社区

更多推荐

ELK实现containerd的容器日志采集展示【基于logging的全栈监测】

企业级ELK Stack构建介绍

云原生

深入理解 Mocha 测试框架：从零实现一个 Mocha

前言什么是自动化测试自动化测试在很多团队中都是Devops环节中很难执行起来的一个环节，主要原因在于测试代码的编写工作很难抽象，99%的场景都需要和业务强绑定，而且写测试代码的编写工作量往往比编写实际业务代码的工作量更多。在一些很多业务场景中投入产出比很低，适合写自动化测试的应该是那些中长期业务以及一些诸如组件一样的基础库。自动化测试是个比较大的概念，其中分类也比较多，比如单元测试，端对端测试，集

云原生

(20200916 Solved)docker-compose up创建容器自动退出

问题描述如题，创建容器后自动退出了。并且docker start container无效解决方案原因是缺失了控制终端的配置，需要在docker-compose.yml中增加tty:true ，有时候这样也不行，需要再增加一个command:/bin/bash，命令不一定是这个，需要是一个不会退出的命令，然后用-d后台启动容器。Referencesdocker-compose启动容器后自动退出...