【按此回上一頁目錄】
因自身是機器學習初學者，此份作業參考各方資料並加入自己的理解，若有誤還請多指教（建立 Github Issue 來協助我修正），謝謝。
01、接續上一章節，接下來要使用製作好的 Container 部署成 Hadoop 叢集，首先進行 Hadoop 安裝及相關設置。
$ sudo mkdir -p /opt && sudo chown -R "$USER":"$USER" /opt && sudo chmod -R 775 /opt01-002、安裝 Hadoop 與相關設置。至 Apache Hadoop 官網，點擊進入目前最新穩定版（2.9.0）binary 連結。
$ cd /tmp && wget "http://apache.stu.edu.tw/hadoop/common/hadoop-2.9.0/hadoop-2.9.0.tar.gz"
$ tar -zxv -f "/tmp/hadoop-2.9.0.tar.gz" -C "/opt"$ ln -sf "/opt/hadoop-2.9.0" "/opt/hadoop"$ ls -al /opttotal 4
drwxrwxr-x. 1 hadoop hadoop 42 Nov 24 16:34 .
dr-xr-xr-x. 1 root root 132 Nov 24 16:04 ..
drwxrwxr-x. 1 hadoop hadoop 32 Nov 24 16:35 bin
lrwxrwxrwx. 1 hadoop hadoop 17 Nov 24 16:33 hadoop -> /opt/hadoop-2.9.0
drwxr-xr-x. 1 hadoop hadoop 126 Nov 14 07:28 hadoop-2.9.0
$ cd /tmp && wget "https://github.com/oneleo/PythonSparkMLBookClub/raw/master/Homework-PythonSparkML_08/Appendix-002-ConfigurationFiles/hdopt17.11.23.tar.gz"
$ tar -zxv -f "hdopt17.11.23.tar.gz" -C "/"
$ sudo chown -R "$USER":"$USER" /opt && sudo chmod -R 775 /opt$ ls -al /optdrwxrwxr-x. 1 hadoop hadoop 140 Nov 24 16:36 .
dr-xr-xr-x. 1 root root 132 Nov 24 16:04 ..
drwxrwxr-x. 1 hadoop hadoop 92 Nov 24 16:04 bin
drwxrwxr-x. 1 hadoop hadoop 10 Nov 24 16:04 conf
lrwxrwxrwx. 1 hadoop hadoop 17 Nov 24 16:33 hadoop -> /opt/hadoop-2.9.0
drwxrwxr-x. 1 hadoop hadoop 126 Nov 14 07:28 hadoop-2.9.0
drwxrwxr-x. 1 hadoop hadoop 216 Nov 24 16:04 hadoop.etc.hadoop.template
drwxrwxr-x. 1 hadoop hadoop 74 Nov 24 16:04 spark.conf.template
- 設定檔分別為「core-site.xml」、「hdfs-site.xml」、「mapred-site.xml」、「yarn-site.xml」、「slaves」、「hdfs.include」、「hdfs.exclude」、「yarn.include」、「yarn.exclude」，請點入連結察看設定說明。
- 「core-default.xml」所有的可用參數請參考：官方網站
- 「hdfs-default.xml」所有的可用參數請參考：官方網站
- 「mapred-default.xml」所有的可用參數請參考：官方網站
- 「yarn-default.xml」所有的可用參數請參考：官方網站
- 新舊版本 Hadoop 參數對照請參考：官網參數對照表
$ cp -pRf /opt/hadoop.etc.hadoop.template/* /opt/hadoop/etc/hadoop02-001、至 Apache Spark 官網，點擊進入目前最新穩定版（2.2.0）binary 連結。
- 「1. Choose a Spark release:」=【2.2.0 (Jul 11 2017)】。
- 「2. Choose a package type:」=【Pre-built for Apache Hadoop 2.7 and later】。
- 點選【spark-2.2.0-bin-hadoop2.7.tgz】進入下載頁面。
$ cd /tmp && wget "http://apache.stu.edu.tw/spark/spark-2.2.0/spark-2.2.0-bin-hadoop2.7.tgz"
$ tar -zxv -f "/tmp/spark-2.2.0-bin-hadoop2.7.tgz" -C "/opt"
$ sudo chown -R "$USER":"$USER" /opt && sudo chmod -R 775 /opt$ ln -sf "/opt/spark-2.2.0-bin-hadoop2.7" "/opt/spark"$ ls -al /opttotal 8
drwxrwxr-x. 1 hadoop hadoop 200 Nov 24 16:53 .
dr-xr-xr-x. 1 root root 132 Nov 24 16:04 ..
drwxrwxr-x. 1 hadoop hadoop 92 Nov 24 16:04 bin
drwxrwxr-x. 1 hadoop hadoop 10 Nov 24 16:04 conf
lrwxrwxrwx. 1 hadoop hadoop 17 Nov 24 16:33 hadoop -> /opt/hadoop-2.9.0
drwxrwxr-x. 1 hadoop hadoop 126 Nov 14 07:28 hadoop-2.9.0
drwxrwxr-x. 1 hadoop hadoop 216 Nov 24 16:04 hadoop.etc.hadoop.template
lrwxrwxrwx. 1 hadoop hadoop 30 Nov 24 16:53 spark -> /opt/spark-2.2.0-bin-hadoop2.7
drwxrwxr-x. 1 hadoop hadoop 150 Jul 1 07:09 spark-2.2.0-bin-hadoop2.7
drwxrwxr-x. 1 hadoop hadoop 74 Nov 24 16:04 spark.conf.template
- 設定檔分別為「spark-defaults.conf」、「spark-env.sh」、「slaves」，請點入連結察看設定說明。
- 相關參數請參考：官方網站。
$ cp -pRf /opt/spark.conf.template/* /opt/spark/conf03-001、至 Anaconda 官網，請在站點上按滑鼠【右鍵】→【複製連結網址(E)】，並取代 wget 指令後方位址。
$ cd /tmp && wget https://repo.continuum.io/archive/Anaconda3-5.0.1-Linux-x86_64.sh
- Anaconda*-Linux-x86_64.sh 是一個可執行的安裝檔，可直接使用 /bin/bash 來執行。
- 注意：在安裝過程中，請依序按此順序操作：按下【Enter】繼續→按下【q】離開授權條條→輸入【yes】後按下【Enter】→輸入安裝資料夾【/opt/anaconda3-5.0.1】→按下【Enter】鍵→安裝好輸入【no】之後按下【Enter】。
$ sudo /bin/bash /tmp/Anaconda3-5.0.1-Linux-x86_64.sh$ sudo chown -R "$USER":"$USER" /opt && sudo chmod -R 775 /opt$ ln -sf "/opt/anaconda3-5.0.1" "/opt/anaconda"$ ls -al /opttotal 12
drwxrwxr-x. 1 hadoop hadoop 246 Nov 24 17:12 .
dr-xr-xr-x. 1 root root 132 Nov 24 16:04 ..
lrwxrwxrwx. 1 hadoop hadoop 20 Nov 24 17:12 anaconda -> /opt/anaconda3-5.0.1
drwxrwxr-x. 1 hadoop hadoop 302 Nov 24 17:12 anaconda3-5.0.1
drwxrwxr-x. 1 hadoop hadoop 92 Nov 24 16:04 bin
drwxrwxr-x. 1 hadoop hadoop 10 Nov 24 16:04 conf
lrwxrwxrwx. 1 hadoop hadoop 17 Nov 24 16:33 hadoop -> /opt/hadoop-2.9.0
drwxrwxr-x. 1 hadoop hadoop 126 Nov 14 07:28 hadoop-2.9.0
drwxrwxr-x. 1 hadoop hadoop 216 Nov 24 16:04 hadoop.etc.hadoop.template
lrwxrwxrwx. 1 hadoop hadoop 30 Nov 24 16:53 spark -> /opt/spark-2.2.0-bin-hadoop2.7
drwxrwxr-x. 1 hadoop hadoop 150 Jul 1 07:09 spark-2.2.0-bin-hadoop2.7
drwxrwxr-x. 1 hadoop hadoop 74 Nov 24 16:04 spark.conf.template
- 敬翔自製的腳本放置於 /opt/bin 目錄內，腳本會參照 /opt/conf/hosts 檔（設計圖）來執行，故要依需求修改此設計圖，請將此設計圖的【dockerimage】參數修改成上一章節所製作的 Docker Image。
- 敬翔自製的腳本分別為「hosts」、「dkc.boot」、「dkc.bash」、「dkrstart」、「dkrstop」、「dkrremove」、「dkrnet」，請點入連結察看設定說明。
- 文件最下方（未被註解處）可再自行追加或減少 Hostname 及對映的 IP 數，腳本會自動偵測並供裝。
$ vim /opt/conf/hosts# appadmin=hadoop # Application Administrator # dockerimage=oneleo/worker:17.11.2 # Docker Image Selection # clusterconf= #/opt/conf/cluster # Application Configuration Folder # sparkpython=python2 # python3 for Spark, python2 for Jupyter (IPython) Notebook # hadoophome=/opt/hadoop # Apache Hadoop Home # hbasehome= #/opt/hbase-1.2.6 # Apache HBase Home # pighome= #/opt/pig-0.17.0 # Apache Pig Home # hivehome= #/opt/apache-hive-2.2.0-bin # Apache Hive Home # flumehome= #/opt/apache-flume-1.7.0-bin # Apache Flume Home # kafkahome= #/opt/kafka_2.12-0.11.0.0 # Apache Kafka Home # zookeeperhome= #/opt/zookeeper-3.4.10 # Apache ZooKeeper Home # sparkhome=/opt/spark # Spark Home # anacondahome=/opt/anaconda # Anaconda Home # netmask=255.255.0.0 # Docker Netmask # gateway=172.17.0.1 # Docker Gateway # nameserver= #168.95.1.1 # Docker Name Server # namenodehost=master # Name Node, Secondary Name Node, Resource Manager, Spark Master 172.17.0.10 master # Name Node, Secondary Name Node, Resource Manager, Spark Master 172.17.0.11 data1 # Data Node, Node Manager, Spark Worker 172.17.0.12 data2 # Data Node, Node Manager, Spark Worker 172.17.0.13 data3 # Data Node, Node Manager, Spark Worker
$ /opt/bin/dkrstartf8b2ab7fb309ef79e3d6654a2c812a2762bb8178b8376674873802de6a20d85d
（註解：容器 data1 建好且執行中。）
Container data1 is created.
（註解：容器 data1 的 hostname 成功覆寫至 hadoop 設定檔 slvaes、hdfs.include、yarn.include。）
Container Hostname data1 is overwritten to /opt/hadoop/etc/hadoop/slaves file.
Container Hostname data1 is overwritten to /opt/hadoop/etc/hadoop/hdfs.include file.
Container Hostname data1 is overwritten to /opt/hadoop/etc/hadoop/yarn.include file.
Container Hostname data1 is overwritten to /opt/spark/conf/slaves file.
5b67272625d0c20bc5e7faa919bace31887d06205b45996ca1d12da18cd0f048
Container data2 is created.
（註解：容器 data2 的 hostname 成功追加至 hadoop 設定檔 slvaes、hdfs.include、yarn.include 最下方。）
Container Hostname data2 is written to /opt/hadoop/etc/hadoop/slaves file.
Container Hostname data2 is written to /opt/hadoop/etc/hadoop/hdfs.include file.
Container Hostname data2 is written to /opt/hadoop/etc/hadoop/yarn.include file.
Container Hostname data2 is written to /opt/spark/conf/slaves file.
3eaab5a6c21c2c59a465e1fa60e50269f1c81d1ef950f478e2023cac5dceb946
Container data3 is created.
Container Hostname data3 is written to /opt/hadoop/etc/hadoop/slaves file.
Container Hostname data3 is written to /opt/hadoop/etc/hadoop/hdfs.include file.
Container Hostname data3 is written to /opt/hadoop/etc/hadoop/yarn.include file.
Container Hostname data3 is written to /opt/spark/conf/slaves file.
5c196b66d43a5a2eee00f8e307c3862fef84bd63043e8b093537d06d8ce03c5e
Container master is created.
（註解：成功將設計圖 /opt/conf/hosts 覆寫至宿主的 /etc/hosts 檔。）
Successfully overwrite file from /opt/conf/hosts to "/etc/hosts".
（註解：成功將容器的 SSH 金鑰複製到宿主的 /home/hadoop/.ssh 內；使其 SSH 登入容器免密碼。）
Successfully copy id_rsa, id_rsa.pub, id_rsa.pub from Container to "/home/hadoop/.ssh".
$ ssh "$USER"@master hdfs namenode -format$ ssh "$USER"@master start-dfs.sh && ssh "$USER"@master start-yarn.sh && ssh "$USER"@master /opt/spark/sbin/start-all.shStarting namenodes on [master]
master: Warning: Permanently added 'master,172.17.0.10' (ECDSA) to the list of known hosts.
master: starting namenode, logging to /opt/logs/hadoop/hadoop-hadoop-namenode-master.out
data3: Warning: Permanently added 'data3,172.17.0.13' (ECDSA) to the list of known hosts.
data3: starting datanode, logging to /opt/logs/hadoop/hadoop-hadoop-datanode-data3.out
data1: Warning: Permanently added 'data1,172.17.0.11' (ECDSA) to the list of known hosts.
data1: starting datanode, logging to /opt/logs/hadoop/hadoop-hadoop-datanode-data1.out
data2: Warning: Permanently added 'data2,172.17.0.12' (ECDSA) to the list of known hosts.
data2: starting datanode, logging to /opt/logs/hadoop/hadoop-hadoop-datanode-data2.out
Starting secondary namenodes [master]
master: starting secondarynamenode, logging to /opt/logs/hadoop/hadoop-hadoop-secondarynamenode-master.out
starting yarn daemons
starting resourcemanager, logging to /opt/logs/yarn/yarn-hadoop-resourcemanager-master.out
data3: starting nodemanager, logging to /opt/logs/yarn/yarn-hadoop-nodemanager-data3.out
data2: starting nodemanager, logging to /opt/logs/yarn/yarn-hadoop-nodemanager-data2.out
data1: starting nodemanager, logging to /opt/logs/yarn/yarn-hadoop-nodemanager-data1.out
starting org.apache.spark.deploy.master.Master, logging to /opt/logs/spark/spark-hadoop-org.apache.spark.deploy.master.Master-1-master.out
failed to launch: nice -n 0 /opt/spark/bin/spark-class org.apache.spark.deploy.master.Master --host master --port 7077 --webui-port 8080
full log in /opt/logs/spark/spark-hadoop-org.apache.spark.deploy.master.Master-1-master.out
data2: starting org.apache.spark.deploy.worker.Worker, logging to /opt/logs/spark/spark-hadoop-org.apache.spark.deploy.worker.Worker-1-data2.out
data3: starting org.apache.spark.deploy.worker.Worker, logging to /opt/logs/spark/spark-hadoop-org.apache.spark.deploy.worker.Worker-1-data3.out
data1: starting org.apache.spark.deploy.worker.Worker, logging to /opt/logs/spark/spark-hadoop-org.apache.spark.deploy.worker.Worker-1-data1.out
$ ssh "$USER"@master jps1169 Jps
870 Master
780 ResourceManager
431 NameNode
623 SecondaryNameNode
$ ssh "$USER"@data1 jps
$ ssh "$USER"@data2 jps
$ ssh "$USER"@data3 jpsWarning: Permanently added 'datax,172.17.0.1x' (ECDSA) to the list of known hosts.
418 NodeManager
470 Worker
311 DataNode
618 Jps
04-009、接下來要將 CentOS VM 宿主的第二張網路卡插到 Master Container 內，使得外部也可使用瀏覽器連至 Master Container 的服務。首先查看目前的網路卡。
- 【-A 1】：連同找到那一列的上下一列也顯示出來。
- 在這邊要記下第一張 NAT 網路的名稱為「ens33」，而第二張 NAT 網路的名稱為「ens34」。
ifconfig | grep -E "^[0-9a-z]*:" -A 1docker0: flags=4163<UP,BROADCAST,RUNNING,MULTICAST> mtu 1500
inet 172.17.0.1 netmask 255.255.0.0 broadcast 0.0.0.0
--
ens33: flags=4163<UP,BROADCAST,RUNNING,MULTICAST> mtu 1500
inet 192.168.133.137 netmask 255.255.255.0 broadcast 192.168.133.255
--
ens34: flags=4163<UP,BROADCAST,RUNNING,MULTICAST> mtu 1500
inet 192.168.133.138 netmask 255.255.255.0 broadcast 192.168.133.255
--
lo: flags=73<UP,LOOPBACK,RUNNING> mtu 65536
inet 127.0.0.1 netmask 255.0.0.0
--
veth1d9774a: flags=4163<UP,BROADCAST,RUNNING,MULTICAST> mtu 1500
inet6 fe80::54ff:b7ff:fef4:6feb prefixlen 64 scopeid 0x20<link>
--
veth85574eb: flags=4163<UP,BROADCAST,RUNNING,MULTICAST> mtu 1500
inet6 fe80::fc2f:81ff:fee4:9388 prefixlen 64 scopeid 0x20<link>
--
vetheda8a2b: flags=4163<UP,BROADCAST,RUNNING,MULTICAST> mtu 1500
inet6 fe80::84cc:51ff:fe2a:86d5 prefixlen 64 scopeid 0x20<link>
--
vethfc95c78: flags=4163<UP,BROADCAST,RUNNING,MULTICAST> mtu 1500
inet6 fe80::f8e3:16ff:fec2:4c48 prefixlen 64 scopeid 0x20<link>
--
virbr0: flags=4099<UP,BROADCAST,MULTICAST> mtu 1500
inet 192.168.122.1 netmask 255.255.255.0 broadcast 192.168.122.255
$ /opt/bin/dkrnet ens33 ens34 masterContainer master's ens34 IP is 192.168.133.10.
- 以這邊為例，Hadoop HDFS 服務網址為：http://192.168.133.10:50070。
- 以這邊為例，Hadoop Yarn 服務網址為：http://192.168.133.10:8088。
- 以這邊為例，Spark Worker 服務網址為：http://192.168.133.10:8080/。
- 因 CentOS 宿主的第二張網路卡已給 master Container 使用，所以要連至第一張網路卡的 IP，以這邊為例為：https://192.168.133.137:10000
- 注意：請觀察「/opt/spark/conf/spark-defaults.conf」檔內設置，在這邊我們指定將函式庫放在「hdfs://master:9000/hadoop/jars」目錄內。
$ ssh "$USER"@master hadoop fs -mkdir -p "/hadoop/jars"
$ ssh "$USER"@master hadoop fs -put "/opt/spark/jars/*" "/hadoop/jars"- 【-copyFromLocal A B】指令：將本地的 A 檔案複製到 HDFS 內的 B 目錄內，或成為 B 檔案。A 檔案只能是單一個檔案。
- 【-f】參數：若已存在則強制覆蓋不詢問。
$ ssh "$USER"@master hadoop fs -mkdir -p "/user/$USER/wordcount/input"
$ ssh "$USER"@master hadoop fs -copyFromLocal -f "/opt/hadoop/LICENSE.txt" "/user/$USER/wordcount/input"- 【--master】：選擇 PySpark 要執行在哪一個模式下，這邊為 Hadoop Yarn 下進行運算。
$ ssh "$USER"@master
hadoop@master:~$ pyspark --master yarn[I 16:06:17.848 NotebookApp] Writing notebook server cookie secret to /home/hadoop/.local/share/jupyter/runtime/notebook_cookie_secret
[I 16:06:18.004 NotebookApp] JupyterLab alpha preview extension loaded from /opt/anaconda3-5.0.1/lib/python3.6/site-packages/jupyterlab
JupyterLab v0.27.0
Known labextensions:
[I 16:06:18.008 NotebookApp] Running the core application with no additional extensions or settings
[I 16:06:18.013 NotebookApp] Serving notebooks from local directory: /opt/workspace/anaconda
[I 16:06:18.013 NotebookApp] 0 active kernels
[I 16:06:18.013 NotebookApp] The Jupyter Notebook is running at: http://0.0.0.0:8888/?token=b30619e714a01bb56039d3c4dd81e8144122edeabba9d4f9
[I 16:06:18.013 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
[C 16:06:18.014 NotebookApp]
Copy/paste this URL into your browser when you connect for the first time,
to login with a token:
http://0.0.0.0:8888/?token=b30619e714a01bb56039d3c4dd81e8144122edeabba9d4f9
- 在這邊為例，使用 Windows 瀏覽器進入「http://192.168.133.10:8888/?token=b30619e714a01bb56039d3c4dd81e8144122edeabba9d4f9」進行認證
05-005、進入 Jupyter Notebook 後亦可至 Hadoop Yarn 頁面 http://192.168.133.139:8088 看到 Spark 執行狀況。
-
在「In [1]:」中輸入【sc.master】→按下【Shift】+【Enter】開始運算。
Out[1]: 'yarn'
-
在「In [2]:」中輸入【textFile=sc.textFile("hdfs://master:9000/user/hadoop/wordcount/input/LICENSE.txt")】→按下【Enter】。
-
接著輸入【textFile.count()】→按下【Shift】+【Enter】開始運算。
Out[2]: 1975
- 點選【File】→【Close and Halt】離開。
- 注意：請一定要使用此法關閉 Python Notebook，若只是將瀏覽器關閉，在將 Jupyter Notebook 關閉時會因為 Python Notebook 仍在運作而出現錯誤。
- 按下鍵盤的【Ctrl】+【c】→輸入【y】→按下【Enter】完成關閉。
- 【--num-executors】：設置執行程式的程序數量。
- 【--total-executor-cores】：設置執行程式的總核心數量。
- 【--executor-memory】：設置執行程式的總記憶體大小。
hadoop@master:~$ pyspark --master spark://master:7077 --num-executors 1 --total-executor-cores 3 --executor-memory 512m[I 16:15:53.618 NotebookApp] JupyterLab alpha preview extension loaded from /opt/anaconda3-5.0.1/lib/python3.6/site-packages/jupyterlab
JupyterLab v0.27.0
Known labextensions:
[I 16:15:53.620 NotebookApp] Running the core application with no additional extensions or settings
[I 16:15:53.626 NotebookApp] Serving notebooks from local directory: /opt/workspace/anaconda
[I 16:15:53.626 NotebookApp] 0 active kernels
[I 16:15:53.626 NotebookApp] The Jupyter Notebook is running at: http://0.0.0.0:8888/?token=f3eaa2386914c1fe2e252c9083bfde21aa2a31c28bc84b41
[I 16:15:53.626 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
[C 16:15:53.627 NotebookApp]
Copy/paste this URL into your browser when you connect for the first time,
to login with a token:
http://0.0.0.0:8888/?token=f3eaa2386914c1fe2e252c9083bfde21aa2a31c28bc84b41
- 在這邊為例，使用 Windows 瀏覽器進入「http://192.168.133.10:8888/?token=f3eaa2386914c1fe2e252c9083bfde21aa2a31c28bc84b41」進行認證
-
在「In [1]:」中輸入【sc.master】→按下【Shift】+【Enter】開始運算。
Out[1]: 'spark://master:7077'
-
在「In [2]:」中輸入【textFile=sc.textFile("hdfs://master:9000/user/hadoop/wordcount/input/LICENSE.txt")】→按下【Enter】。
-
接著輸入【textFile.count()】→按下【Shift】+【Enter】開始運算。
Out[2]: 1975
06-006、此時可以進入 Spark Stand Alone 頁面 http://192.168.133.139:8080 看到 Spark 執行狀況。
- 點選【File】→【Close and Halt】離開。
- 按下鍵盤的【Ctrl】+【c】→輸入【y】→按下【Enter】完成關閉。
hadoop@master:~$ exit$ ssh "$USER"@master /opt/spark/sbin/stop-all.sh && ssh "$USER"@master stop-yarn.sh && ssh "$USER"@master stop-dfs.shaster stop-yarn.sh && ssh "$USER"@master stop-dfs.sh
data3: stopping org.apache.spark.deploy.worker.Worker
data2: stopping org.apache.spark.deploy.worker.Worker
data1: stopping org.apache.spark.deploy.worker.Worker
stopping org.apache.spark.deploy.master.Master
stopping yarn daemons
stopping resourcemanager
data1: stopping nodemanager
data1: nodemanager did not stop gracefully after 5 seconds: killing with kill -9
data2: stopping nodemanager
data2: nodemanager did not stop gracefully after 5 seconds: killing with kill -9
data3: stopping nodemanager
data3: nodemanager did not stop gracefully after 5 seconds: killing with kill -9
no proxyserver to stop
Stopping namenodes on [master]
master: stopping namenode
data3: stopping datanode
data1: stopping datanode
data2: stopping datanode
Stopping secondary namenodes [master]
master: stopping secondarynamenode
$ /opt/bin/dkrstopContainer master is stopped.
Container data1 is stopped.
Container data2 is stopped.
Container data3 is stopped.
$ /opt/bin/dkrremoveContainer master is removed.
Container data1 is removed.
Container data2 is removed.
Container data3 is removed.
8d58a977748f327eca834d194fc163451aedf9fabf020255f90408457842dd45
906406c8970c690815ffe078fa5e4d68fe3ddd333c68827e20c684f6f14a5c74
bf6abc7ca1e798110e31749370e5253fae88c41c8d7fc4472aaea3277e9e5c5a
f28e08e0ea88ff75da31d1b900977915d9788dad26fa2b0af3f203561f872c7e
All of the Docker dangling volumes are deleted.
$ sudo sync; sudo sync; sudo sync; sudo sync; sudo sync; sudo sync; sudo sync; sudo shutdown -h now;- [01] 林大貴，「博碩出版社 - Python+Spark 2.0+Hadoop機器學習與大數據分析實戰」，ISBN-13：9789864341535
- [02] VMware Docs, "VMware Workstation 14 Pro Product Documentation", https://docs.vmware.com/en/VMware-Workstation-Pro/14.0/com.vmware.ws.using.doc/GUID-0EE752F8-C159-487A-9159-FE1F646EE4CA.html
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- [08] LIBFEIHU ，「ssh連接遠程主機執行腳本的環境變量問題」，http://feihu.me/blog/2014/env-problem-when-ssh-executing-command-on-remote/
- [09] Fenriswolf 程式筆記，「Hadoop 參數設定」，https://fenriswolf.me/
- [10] 楊保華、戴王劍、曹亞侖，「碁峰書局 - Docker入門與實戰」，http://www.books.com.tw/products/0010676115