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：
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：
- 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：
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：
- 可以看到每个Pod对外提供的端口：
- 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：
- 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：
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
$HADOOP_PREFIX/sbin/mr-jobhistory-daemon.sh start historyserver
#exec nohup /etc/init.d/ssh start &
/etc/init.d/ssh start
#exec /bin/bash
while true; do sleep 1000; done
docker-entrypoint-datanode.sh:
#!/bin/bash
source /etc/environment
source ~/.bashrc
source /etc/profile
export PATH=$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; done
hadoop-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/logs021
hadoop-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）
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
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