Kubernetes部署大数据组件系列二：一键部署Hadoop集群
系列一中忘了说明，用Kubernetes部署大数据容器平台，有一个很大的好处是压根不用操心容器间的网络通信，不管是同一物理服务器内，还是跨物理服务器间的网络通信，你都不用操心，只需要把容器间互相关心的端口暴露好，把容器间的service name映射好，就OK了。本篇教大家部署Hadoop 2.7.3集群，暂时没有做HA和联邦：Docker适合封装无状态的、单进程的应用程序，如果用它来部署Ha
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系列一中忘了说明，用Kubernetes部署大数据容器平台，有一个很大的好处是压根不用操心容器间的网络通信，不管是同一物理服务器内，还是跨物理服务器间的网络通信，你都不用操心，只需要把容器间互相关心的端口暴露好，把容器间的service name映射好，就OK了。
本篇教大家部署Hadoop 2.7.3集群，暂时没有做HA和联邦： Docker适合封装无状态的、单进程的应用程序，如果用它来部署Hadoop分布式集群，还是比较复杂的，主要考虑几个问题：
- 1，需要制作几个镜像？
- 2，哪些配置属于通用的，可以直接打包到镜像中？
- 3，会变更的配置，如何在容器启动时自动的、灵活的替换？
- 4，哪些服务端口需要暴露？
- 5，哪些目录需要持久化？
- 6，NameNode的Format怎么实现和如何避免重复Format？
- 7，用哪些脚本来启动哪些服务？
- 8，需要提前设置哪些环境变量？
- 9，Pod间的依赖和启动顺序如何控制？ 等等等。
物理拓扑及Hadoop集群角色如下：
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服务器 地址 角色 yuyan2 10.0.8.182 Docker镜像制作、K8s yaml脚本编辑、kubectl客户端 HARBOR 10.10.4.57 私有镜像库 ksp-1 10.10.4.56 NameNode、DataNode、ResourceManager、NodeManager ksp-2 10.10.4.57 DataNode、NodeManager ksp-5 10.10.4.60 DataNode、NodeManager 1，先制作一个基础镜像： 包含Ubuntu 16.04 LTS、jdk1.8.0_111、各类常用工具（net-tools、iputils、vim、wget）、SSH免密及开机启动、时钟与宿主机同步、无防火墙等。 该镜像PUSH到HARBOR私有镜像库，以便后面制作Hadoop容器时引用它。 制作过程略过，想用的话直接从链接下载即可: Ubuntu基础镜像
2，鉴于Hadoop2.x版本有YARN的存在，所以制作两个镜像，称为: 主镜像，包含：NameNode、ResourceManager守护进程； 从镜像，包含：DataNode、NodeManager守护进程。
新建一个hadoop2.7.3目录，结构如下：
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- 从官网下载hadoop-2.7.3.tar.gz；
- 取出其/etc/hadoop中的配置，放入到conf文件中，修改：core-site.xml、hdfs-site.xml、mapred-site.xml、yarn-site.xml、并将slaves清空；
- docker-entrypoint-namenode.sh 制作主镜像时，重命名为docker-entrypoint.sh，启动相关服务进程；
- docker-entrypoint-datanode.sh 制作从镜像时，重命名为docker-entrypoint.sh，启动相关服务进程；
- Dockerfile，定义环境变量、申明端口、拷贝及替换文件等操作。
如下几个目录是需要持久化的，在后面的yaml文件中体现： core-site.xml：hadoop.tmp.dir hdfs-site.xml：dfs.namenode.name.dir hdfs-site.xml：dfs.namenode.data.dir
哪些配置文件作为通用文件，哪些配置文件的内容需要动态替换，NameNode 什么情况下做Format，主、从镜像各启动什么服务等，请自行参考链接中的脚本： Hadoop的两个镜像及相关脚本
制作好镜像，并推送到HARBOR私有镜像库中，以便K8s编排容器时使用：
➜ hadoop2.7.3 mv docker-entrypoint-namenode.sh docker-entrypoint.sh ➜ hadoop2.7.3 sudo docker build -t hadoop-2.7.3-namenode-resourcemanager:0.0.1 . ➜ hadoop2.7.3 sudo docker tag hadoop-2.7.3-namenode-resourcemanager:0.0.1 registry.k8s.intra.knownsec.com/bigdata/hadoop-2.7.3-namenode-resourcemanager:0.0.1 ➜ hadoop2.7.3 sudo docker push registry.k8s.intra.knownsec.com/bigdata/hadoop-2.7.3-namenode-resourcemanager:0.0.1 ➜ hadoop2.7.3 mv docker-entrypoint-datanode.sh docker-entrypoint.sh ➜ hadoop2.7.3 sudo docker build -t hadoop-2.7.3-datanode-nodemanager:0.0.1 . ➜ hadoop2.7.3 sudo docker tag hadoop-2.7.3-datanode-nodemanager:0.0.1 registry.k8s.intra.knownsec.com/bigdata/hadoop-2.7.3-datanode-nodemanager:0.0.1 ➜ hadoop2.7.3 sudo docker push registry.k8s.intra.knownsec.com/bigdata/hadoop-2.7.3-datanode-nodemanager:0.0.1相关镜像已经推送到私有镜像库：
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3，经过上述的步骤，主、从两个镜像就制作好了，下面，新生成一个
hadoop文件夹，用来编写yaml文件，并执行K8s编排任务：![]()
由于容器间需要相互通信，且需要对外提供服务，所以我们仍然采用Deployment方式来编排，每个Pod启动一个容器，Pod间暴露相关接口，每个Pod都可以对外提供服务，启动主镜像的Pod使用0.0.0.0监听自己的端口，启动从镜像的其他Pod使用主Pod的service name，作为目的hostname，持久化的 细节及动态替换hostname的细节，请参考yaml文件：
K8s编排Hadoop集群的yaml文件按照顺序启动Pod（这个过程可以做成脚本，一键执行）：
➜ hadoop kubectl create -f hadoop-namenode-resourcemanager.yaml --validate=false ➜ hadoop kubectl create -f hadoop-datanode-nodemanager01.yaml --validate=false ➜ hadoop kubectl create -f hadoop-datanode-nodemanager02.yaml --validate=false ➜ hadoop kubectl create -f hadoop-datanode-nodemanager05.yaml --validate=false当主镜像的Pod要更换service name时，只需要替换从镜像Pod yaml中的ConfigMap hostname值即可
--- apiVersion: v1 kind: ConfigMap metadata: name: hdp-2-cm data: hostname: "hdp-1-svc" ---验证：
- 可以看到4个Pod分配到了指定的物理服务器上：
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- 可以看到每个Pod对外提供的端口：
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- 使用ksp-1主机的IP加hdp-1-2386450527-9x6b9暴露的50070:30980/TCP，可以看到HDFS Web中DataNode的情况：
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- 使用ksp-1主机的IP加hdp-1-2386450527-9x6b9暴露的8088:32326/TCP，可以看到YARN Web中NodeManager的情况：
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补充：
对上述操作部署好的hadoop集群进行简单验证，hdfs的基本功能正常，执行一个mapreduce wordcount用例时，出现错误提示：hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.3.jar wordcount file:///hadoop-2.7.3/NOTICE.txt file:///hadoop-2.7.3/output2 17/07/03 05:32:10 INFO client.RMProxy: Connecting to ResourceManager at hdp-1-svc/12.0.112.23:8032 17/07/03 05:32:11 INFO input.FileInputFormat: Total input paths to process : 1 17/07/03 05:32:12 INFO mapreduce.JobSubmitter: number of splits:1 17/07/03 05:32:12 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1498909283586_0006 17/07/03 05:32:12 INFO impl.YarnClientImpl: Submitted application application_1498909283586_0006 17/07/03 05:32:12 INFO mapreduce.Job: The url to track the job: http://hdp-1-2386450527-9x6b9:8088/proxy/application_1498909283586_0006/ 17/07/03 05:32:12 INFO mapreduce.Job: Running job: job_1498909283586_0006 17/07/03 05:32:18 INFO mapreduce.Job: Job job_1498909283586_0006 running in uber mode : false 17/07/03 05:32:18 INFO mapreduce.Job: map 0% reduce 0% 17/07/03 05:32:19 INFO mapreduce.Job: Task Id : attempt_1498909283586_0006_m_000000_0, Status : FAILED Container launch failed for container_1498909283586_0006_01_000002 : java.lang.IllegalArgumentException: java.net.UnknownHostException: hdp-2-1789154958-g0njg at org.apache.hadoop.security.SecurityUtil.buildTokenService(SecurityUtil.java:377) at org.apache.hadoop.security.SecurityUtil.setTokenService(SecurityUtil.java:356) at org.apache.hadoop.yarn.util.ConverterUtils.convertFromYarn(ConverterUtils.java:238) at org.apache.hadoop.yarn.client.api.impl.ContainerManagementProtocolProxy$ContainerManagementProtocolProxyData.newProxy(ContainerManagementProtocolProxy.java:266) at org.apache.hadoop.yarn.client.api.impl.ContainerManagementProtocolProxy$ContainerManagementProtocolProxyData.<init>(ContainerManagementProtocolProxy.java:244) at org.apache.hadoop.yarn.client.api.impl.ContainerManagementProtocolProxy.getProxy(ContainerManagementProtocolProxy.java:129) at org.apache.hadoop.mapreduce.v2.app.launcher.ContainerLauncherImpl.getCMProxy(ContainerLauncherImpl.java:409) at org.apache.hadoop.mapreduce.v2.app.launcher.ContainerLauncherImpl$Container.launch(ContainerLauncherImpl.java:138) at org.apache.hadoop.mapreduce.v2.app.launcher.ContainerLauncherImpl$EventProcessor.run(ContainerLauncherImpl.java:375) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) at java.lang.Thread.run(Thread.java:745) Caused by: java.net.UnknownHostException: hdp-2-1789154958-g0njg ... 12 more这个错误的原因：
因为创建Pod时，会给每个容器随机分配一个hostname，我们前面是使用svc-name来保证服务间的通信，但是mapreduce执行时，使用的是hostname，而每台容器启动后，/etc/hosts中是仅有自己的主机名和ip的映射，是不包含其他容器的。而且，也无法动态的向/etc/hosts里面增加其他容器的主机名和ip的映射，所以分配ApplicationMaster时，无法响应客户端的请求。修复起来较麻烦：
需要修改主、从镜像的dockerfile，docker-entrypoint.sh，重新制作主，从镜像，然后修改yaml，之后按照原有流程启动即可。
修改方案就是使用Headless Service，不再使用cluster ip，而是在 Pod之间直接使用各容器的私有IP，并在yaml指定Pod启动的容器的主机名，和svc-name配置成一致的。不过，这种方式下，外网不能直接访问集群了（比如外网的一个用户想登陆HDFS的web ui），需要启动一个Nginx容器，由它做一个DNS代理，能够让外网访问集群IP和端口即可，请自行搜索解决方案，这里不再描述。相关修改如下，修改之后如何重新搭建集群，请参考前文。另，yaml我只修改了2个，其他节点的yaml照着修改就行了：
Dockerfile:FROM registry.k8s.intra.knownsec.com/bigdata/ubuntu16.04_jdk1.8.0_111:0.0.2 MAINTAINER Wang Liang <wangl8@knownsec.com> ARG DISTRO_NAME=hadoop-2.7.3 ARG DISTRO_NAME_DIR=/hadoop-2.7.3 ENV HADOOP_HOME=$DISTRO_NAME_DIR ENV HADOOP_PREFIX=$DISTRO_NAME_DIR ENV HADOOP_CONF_DIR=$HADOOP_PREFIX/etc/hadoop ENV YARN_CONF_DIR=$HADOOP_PREFIX/etc/hadoop ENV HADOOP_TMP_DIR=$HADOOP_HOME/tmp ENV HADOOP_DFS_DIR=$HADOOP_HOME/dfs ENV HADOOP_DFS_NAME_DIR=$HADOOP_DFS_DIR/name ENV HADOOP_DFS_DATA_DIR=$HADOOP_DFS_DIR/data ENV HADOOP_LOGS=$HADOOP_HOME/logs ENV Master=localhost ENV USER=root USER root # Hdfs ports EXPOSE 9000 50010 50020 50070 50075 50090 31010 8020 # Mapred ports EXPOSE 19888 10020 #Yarn ports EXPOSE 8030 8031 8032 8033 8040 8042 8088 #Other ports EXPOSE 49707 2122 ADD hadoop-2.7.3.tar.gz / WORKDIR $DISTRO_NAME_DIR #ENV ZOO_USER=zookeeper \ # ZOO_CONF_DIR=/conf \ # ZOO_DATA_DIR=/data \ # ZOO_UI_DIR=/zkui \ # ZOO_DATA_LOG_DIR=/datalog \ # ZOO_PORT=2181 \ # ZOO_TICK_TIME=2000 \ # ZOO_INIT_LIMIT=5 \ # ZOO_SYNC_LIMIT=2 # Add a user and make dirs #RUN set -x \ # && adduser -D "$ZOO_USER" \ # && mkdir -p "$ZOO_DATA_LOG_DIR" "$ZOO_DATA_DIR" "$ZOO_CONF_DIR" \ # && chown "$ZOO_USER:$ZOO_USER" "$ZOO_DATA_LOG_DIR" "$ZOO_DATA_DIR" "$ZOO_CONF_DIR" #ARG DISTRO_NAME=hadoop-2.7.3 RUN rm -r -f $HADOOP_CONF_DIR RUN mkdir -p "$HADOOP_TMP_DIR" "$HADOOP_DFS_NAME_DIR" "$HADOOP_DFS_DATA_DIR" "$HADOOP_LOGS" ADD conf $HADOOP_CONF_DIR COPY conf /conf_tmp ENV PATH=$PATH:$HADOOP_HOME/bin:$HADOOP_HOME/sbin # Download Apache Zookeeper, verify its PGP signature, untar and clean up #RUN set -x \ # && tar -xzf "$DISTRO_NAME.tar.gz" #WORKDIR $DISTRO_NAME #VOLUME ["$ZOO_DATA_DIR", "$ZOO_DATA_LOG_DIR"] #EXPOSE $ZOO_PORT 2888 3888 #ENV PATH=$PATH:/$DISTRO_NAME/bin:$ZOO_UI_DIR \ # ZOOCFGDIR=$ZOO_CONF_DIR COPY docker-entrypoint.sh / ENTRYPOINT ["/docker-entrypoint.sh"]docker-entrypoint-namenode.sh:
#!/bin/bash source /etc/environment source ~/.bashrc source /etc/profile export PATH=$PATH:$HADOOP_HOME/bin:$HADOOP_HOME/sbin sed "s/HOSTNAME/"$Master"/g" /conf_tmp/core-site.xml > $HADOOP_CONF_DIR/core-site.xml sed "s/HOSTNAME/"$Master"/g" /conf_tmp/mapred-site.xml > $HADOOP_CONF_DIR/mapred-site.xml sed "s/HOSTNAME/"$Master"/g" /conf_tmp/yarn-site.xml > $HADOOP_CONF_DIR/yarn-site.xml if [ "`ls -A $HADOOP_DFS_NAME_DIR`" = "" ]; then echo "$DIRECTORY is indeed empty" $HADOOP_PREFIX/bin/hdfs namenode -format else echo "$DIRECTORY is not empty" fi $HADOOP_PREFIX/sbin/hadoop-daemon.sh --config $HADOOP_CONF_DIR start namenode #$HADOOP_PREFIX/sbin/hadoop-daemon.sh --config $HADOOP_CONF_DIR start datanode $HADOOP_PREFIX/sbin/yarn-daemon.sh --config $HADOOP_CONF_DIR start resourcemanager #$HADOOP_PREFIX/sbin/yarn-daemon.sh --config $HADOOP_CONF_DIR start nodemanager $HADOOP_PREFIX/sbin/mr-jobhistory-daemon.sh start historyserver #exec nohup /etc/init.d/ssh start & /etc/init.d/ssh start #exec /bin/bash while true; do sleep 1000; donedocker-entrypoint-datanode.sh:
#!/bin/bash source /etc/environment source ~/.bashrc source /etc/profile export PATH=$PATH:$HADOOP_HOME/bin:$HADOOP_HOME/sbin sed "s/HOSTNAME/"$Master"/g" /conf_tmp/core-site.xml > $HADOOP_CONF_DIR/core-site.xml sed "s/HOSTNAME/"$Master"/g" /conf_tmp/mapred-site.xml > $HADOOP_CONF_DIR/mapred-site.xml sed "s/HOSTNAME/"$Master"/g" /conf_tmp/yarn-site.xml > $HADOOP_CONF_DIR/yarn-site.xml #if [ "`ls -A $HADOOP_DFS_NAME_DIR`" = "" ]; then #echo "$DIRECTORY is indeed empty" #$HADOOP_PREFIX/bin/hdfs namenode -format #else #echo "$DIRECTORY is not empty" #fi #$HADOOP_PREFIX/sbin/hadoop-daemon.sh --config $HADOOP_CONF_DIR start namenode $HADOOP_PREFIX/sbin/hadoop-daemon.sh --config $HADOOP_CONF_DIR start datanode #$HADOOP_PREFIX/sbin/yarn-daemon.sh --config $HADOOP_CONF_DIR start resourcemanager $HADOOP_PREFIX/sbin/yarn-daemon.sh --config $HADOOP_CONF_DIR start nodemanager #$HADOOP_PREFIX/sbin/mr-jobhistory-daemon.sh start historyserver #exec nohup /etc/init.d/ssh start & /etc/init.d/ssh start #exec /bin/bash while true; do sleep 1000; donehadoop-namenode-resourcemanager.yaml :
--- apiVersion: v1 kind: Service metadata: name: hdp-1-svc labels: app: hdp-1-svc spec: clusterIP: None #注意这里，要写成None，才能使用headless service ports: - port: 9000 name: hdfs - port: 50070 name: hdfsweb - port: 19888 name: jobhistory - port: 8088 name: yarn - port: 50010 name: hdfs2 - port: 50020 name: hdfs3 - port: 50075 name: hdfs5 - port: 50090 name: hdfs6 - port: 10020 name: mapred2 - port: 8030 name: yarn1 - port: 8031 name: yarn2 - port: 8032 name: yarn3 - port: 8033 name: yarn4 - port: 8040 name: yarn5 - port: 8042 name: yarn6 - port: 49707 name: other1 - port: 2122 name: other2 - port: 31010 name: hdfs7 - port: 8020 name: hdfs8 selector: app: hdp-1 type: NodePort --- apiVersion: v1 kind: ConfigMap metadata: name: hdp-1-cm data: master: "0.0.0.0" hostname: "hdp-1-svc" --- apiVersion: extensions/v1beta1 kind: Deployment metadata: name: hdp-1 spec: replicas: 1 template: metadata: labels: app: hdp-1 spec: hostname: hdp-1-svc #指定容器启动时的主机名，配置成svc-name即可 nodeSelector: zk: zk-1 containers: - name: myhadoop-nn-rm imagePullPolicy: Always image: registry.k8s.intra.knownsec.com/bigdata/hadoop-2.7.3-namenode-resourcemanager:0.0.1 securityContext: privileged: true resources: requests: memory: "2Gi" cpu: "500m" ports: - containerPort: 9000 name: hdfs - containerPort: 50010 name: hdfs2 - containerPort: 50020 name: hdfs3 - containerPort: 50070 name: hdfsweb - containerPort: 50075 name: hdfs5 - containerPort: 50090 name: hdfs6 - containerPort: 19888 name: jobhistory - containerPort: 10020 name: mapred2 - containerPort: 8030 name: yarn1 - containerPort: 8031 name: yarn2 - containerPort: 8032 name: yarn3 - containerPort: 8033 name: yarn4 - containerPort: 8040 name: yarn5 - containerPort: 8042 name: yarn6 - containerPort: 8088 name: yarn - containerPort: 49707 name: other1 - containerPort: 2122 name: other2 - containerPort: 31010 name: hdfs7 - containerPort: 8020 name: hdfs8 env: - name: Master valueFrom: configMapKeyRef: name: hdp-1-cm key: master - name: HOSTNAME valueFrom: configMapKeyRef: name: hdp-1-cm key: hostname # readinessProbe: # exec: # command: # - "zkok.sh" # initialDelaySeconds: 10 # timeoutSeconds: 5 # livenessProbe: # exec: # command: # - "zkok.sh" # initialDelaySeconds: 10 # timeoutSeconds: 5 volumeMounts: - name: name mountPath: /hadoop-2.7.3/dfs/name - name: data mountPath: /hadoop-2.7.3/dfs/data - name: tmp mountPath: /hadoop-2.7.3/tmp - name: logs mountPath: /hadoop-2.7.3/logs volumes: - name: name hostPath: path: /home/data/bjrddata/hadoop/name021 - name: data hostPath: path: /home/data/bjrddata/hadoop/data021 - name: tmp hostPath: path: /home/data/bjrddata/hadoop/tmp021 - name: logs hostPath: path: /home/data/bjrddata/hadoop/logs021hadoop-datanode-nodemanager01.yaml :
--- apiVersion: v1 kind: Service metadata: name: hdp-2-svc labels: app: hdp-2-svc spec: clusterIP: None ports: - port: 9000 name: hdfs - port: 50070 name: hdfsweb - port: 19888 name: jobhistory - port: 8088 name: yarn - port: 50010 name: hdfs2 - port: 50020 name: hdfs3 - port: 50075 name: hdfs5 - port: 50090 name: hdfs6 - port: 10020 name: mapred2 - port: 8030 name: yarn1 - port: 8031 name: yarn2 - port: 8032 name: yarn3 - port: 8033 name: yarn4 - port: 8040 name: yarn5 - port: 8042 name: yarn6 - port: 49707 name: other1 - port: 2122 name: other2 - port: 31010 name: hdfs7 - port: 8020 name: hdfs8 selector: app: hdp-2 type: NodePort --- apiVersion: v1 kind: ConfigMap metadata: name: hdp-2-cm data: master: "hdp-1-svc" hostname: "hdp-2-svc" --- apiVersion: extensions/v1beta1 kind: Deployment metadata: name: hdp-2 spec: replicas: 1 template: metadata: labels: app: hdp-2 spec: hostname: hdp-2-svc nodeSelector: zk: zk-1 containers: - name: myhadoop-dn-nm imagePullPolicy: Always image: registry.k8s.intra.knownsec.com/bigdata/hadoop-2.7.3-datanode-nodemanager:0.0.1 securityContext: privileged: true resources: requests: memory: "2Gi" cpu: "500m" ports: - containerPort: 9000 name: hdfs - containerPort: 50010 name: hdfs2 - containerPort: 50020 name: hdfs3 - containerPort: 50070 name: hdfsweb - containerPort: 50075 name: hdfs5 - containerPort: 50090 name: hdfs6 - containerPort: 19888 name: jobhistory - containerPort: 10020 name: mapred2 - containerPort: 8030 name: yarn1 - containerPort: 8031 name: yarn2 - containerPort: 8032 name: yarn3 - containerPort: 8033 name: yarn4 - containerPort: 8040 name: yarn5 - containerPort: 8042 name: yarn6 - containerPort: 8088 name: yarn - containerPort: 49707 name: other1 - containerPort: 2122 name: other2 - containerPort: 31010 name: hdfs7 - containerPort: 8020 name: hdfs8 env: - name: Master valueFrom: configMapKeyRef: name: hdp-2-cm key: master - name: HOSTNAME valueFrom: configMapKeyRef: name: hdp-2-cm key: hostname # readinessProbe: # exec: # command: # - "zkok.sh" # initialDelaySeconds: 10 # timeoutSeconds: 5 # livenessProbe: # exec: # command: # - "zkok.sh" # initialDelaySeconds: 10 # timeoutSeconds: 5 volumeMounts: - name: name mountPath: /hadoop-2.7.3/dfs/name - name: data mountPath: /hadoop-2.7.3/dfs/data - name: tmp mountPath: /hadoop-2.7.3/tmp - name: logs mountPath: /hadoop-2.7.3/logs volumes: - name: name hostPath: path: /home/data/bjrddata/hadoop/name022 - name: data hostPath: path: /home/data/bjrddata/hadoop/data022 - name: tmp hostPath: path: /home/data/bjrddata/hadoop/tmp022 - name: logs hostPath: path: /home/data/bjrddata/hadoop/logs022重新部署集群：（可以看到Cluster ip已经没有了）
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wordcount运行结果：
root@hdp-5-svc:/hadoop-2.7.3# hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.3.jar wordcount hdfs://hdp-1-svc:9000/test/NOTICE.txt hdfs://hdp-1-svc:9000/test/output01 17/07/03 09:53:57 INFO client.RMProxy: Connecting to ResourceManager at hdp-1-svc/192.168.25.14:8032 17/07/03 09:53:57 INFO input.FileInputFormat: Total input paths to process : 1 17/07/03 09:53:57 INFO mapreduce.JobSubmitter: number of splits:1 17/07/03 09:53:58 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1499075310680_0001 17/07/03 09:53:58 INFO impl.YarnClientImpl: Submitted application application_1499075310680_0001 17/07/03 09:53:58 INFO mapreduce.Job: The url to track the job: http://hdp-1-svc:8088/proxy/application_1499075310680_0001/ 17/07/03 09:53:58 INFO mapreduce.Job: Running job: job_1499075310680_0001 17/07/03 09:54:04 INFO mapreduce.Job: Job job_1499075310680_0001 running in uber mode : false 17/07/03 09:54:04 INFO mapreduce.Job: map 0% reduce 0% 17/07/03 09:54:09 INFO mapreduce.Job: map 100% reduce 0% 17/07/03 09:54:14 INFO mapreduce.Job: map 100% reduce 100% 17/07/03 09:54:15 INFO mapreduce.Job: Job job_1499075310680_0001 completed successfully 17/07/03 09:54:15 INFO mapreduce.Job: Counters: 49 File System Counters FILE: Number of bytes read=11392 FILE: Number of bytes written=261045 FILE: Number of read operations=0 FILE: Number of large read operations=0 FILE: Number of write operations=0 HDFS: Number of bytes read=15080 HDFS: Number of bytes written=8969 HDFS: Number of read operations=6 HDFS: Number of large read operations=0 HDFS: Number of write operations=2 Job Counters Launched map tasks=1 Launched reduce tasks=1 Data-local map tasks=1 Total time spent by all maps in occupied slots (ms)=2404 Total time spent by all reduces in occupied slots (ms)=2690 Total time spent by all map tasks (ms)=2404 Total time spent by all reduce tasks (ms)=2690 Total vcore-milliseconds taken by all map tasks=2404 Total vcore-milliseconds taken by all reduce tasks=2690 Total megabyte-milliseconds taken by all map tasks=2461696 Total megabyte-milliseconds taken by all reduce tasks=2754560 Map-Reduce Framework Map input records=437 Map output records=1682 Map output bytes=20803 Map output materialized bytes=11392 Input split bytes=102 Combine input records=1682 Combine output records=614 Reduce input groups=614 Reduce shuffle bytes=11392 Reduce input records=614 Reduce output records=614 Spilled Records=1228 Shuffled Maps =1 Failed Shuffles=0 Merged Map outputs=1 GC time elapsed (ms)=173 CPU time spent (ms)=1800 Physical memory (bytes) snapshot=470913024 Virtual memory (bytes) snapshot=4067495936 Total committed heap usage (bytes)=342360064 Shuffle Errors BAD_ID=0 CONNECTION=0 IO_ERROR=0 WRONG_LENGTH=0 WRONG_MAP=0 WRONG_REDUCE=0 File Input Format Counters Bytes Read=14978 File Output Format Counters![]()
执行成功
作者：俺是亮哥
链接：http://www.jianshu.com/p/38606bbe138b
來源：简书
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