通过hadoop + hive搭建离线式的分析系统之快速搭建一览
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最近有个需求，需要整合所有店铺的数据做一个离线式分析系统，曾经都是按照店铺分库分表来给各自商家通过highchart多维度展示自家的店铺经营
状况，我们知道这是一个以店铺为维度的切分，非常适合目前的在线业务，这回老板提需求了，曾经也是一位数据分析师，sql自然就溜溜的，所以就来了
一个以买家维度展示用户画像，从而更好的做数据推送和用户行为分析，因为是离线式分析，目前还没研究spark，impala，drill了。
一：搭建hadoop集群
hadoop的搭建是一个比较繁琐的过程，采用3台Centos，废话不过多，一图胜千言。。。
二： 基础配置
1. 关闭防火墙
[root@localhost ~]# systemctl stop firewalld.service #关闭防火墙 [root@localhost ~]# systemctl disable firewalld.service #禁止开机启动 [root@localhost ~]# firewall-cmd --state #查看防火墙状态 not running [root@localhost ~]#
2. 配置SSH免登录
不管在开启还是关闭hadoop的时候，hadoop内部都要通过ssh进行通讯，所以需要配置一个ssh公钥免登陆，做法就是将一个centos的公钥copy到另一
台centos的authorized_keys文件中。
<1>： 在196上生成公钥私钥 ，从下图中可以看到通过ssh-keygen之后会生成 id_rsa 和 id_rsa.pub 两个文件，这里我们
关心的是公钥id_rsa.pub。
[root@localhost ~]# ssh-keygen -t rsa -P \'\' Generating public/private rsa key pair. Enter file in which to save the key (/root/.ssh/id_rsa): Created directory \'/root/.ssh\'. Your identification has been saved in /root/.ssh/id_rsa. Your public key has been saved in /root/.ssh/id_rsa.pub. The key fingerprint is: 40:72:cc:f4:c3:e7:15:c9:9f:ee:f8:48:ec:22:be:a1 root@localhost.localdomain The key\'s randomart image is: +--[ RSA 2048]----+ | .++ ... | | +oo o. | | . + . .. . | | . + . o | | S . . | | . . | | . oo | | ....o... | | E.oo .o.. | +-----------------+ [root@localhost ~]# ls /root/.ssh/id_rsa /root/.ssh/id_rsa [root@localhost ~]# ls /root/.ssh id_rsa id_rsa.pub
<2> 通过scp复制命令 将公钥copy到 146 和 150主机，以及将id_ras.pub 追加到本机中
[root@master ~]# scp /root/.ssh/id_rsa.pub root@192.168.23.146:/root/.ssh/authorized_keys root@192.168.23.146\'s password: id_rsa.pub 100% 408 0.4KB/s 00:00 [root@master ~]# scp /root/.ssh/id_rsa.pub root@192.168.23.150:/root/.ssh/authorized_keys root@192.168.23.150\'s password: id_rsa.pub 100% 408 0.4KB/s 00:00 [root@master ~]# cat /root/.ssh/id_rsa.pub >> /root/.ssh/authorized_keys
<3> 做host映射，主要给几台机器做别名映射，方便管理。
[root@master ~]# cat /etc/hosts 127.0.0.1 localhost localhost.localdomain localhost4 localhost4.localdomain4 ::1 localhost localhost.localdomain localhost6 localhost6.localdomain6 192.168.23.196 master 192.168.23.150 slave1 192.168.23.146 slave2 [root@master ~]#
<4> java安装环境
hadoop是java写的，所以需要安装java环境，具体怎么安装，大家可以网上搜一下，先把centos自带的openjdk卸载掉，最后在profile中配置一下。
[root@master ~]# cat /etc/profile # /etc/profile # System wide environment and startup programs, for login setup # Functions and aliases go in /etc/bashrc # It\'s NOT a good idea to change this file unless you know what you # are doing. It\'s much better to create a custom.sh shell script in # /etc/profile.d/ to make custom changes to your environment, as this # will prevent the need for merging in future updates. pathmunge () { case ":${PATH}:" in *:"$1":*) ;; *) if [ "$2" = "after" ] ; then PATH=$PATH:$1 else PATH=$1:$PATH fi esac } if [ -x /usr/bin/id ]; then if [ -z "$EUID" ]; then # ksh workaround EUID=`id -u` UID=`id -ru` fi USER="`id -un`" LOGNAME=$USER MAIL="/var/spool/mail/$USER" fi # Path manipulation if [ "$EUID" = "0" ]; then pathmunge /usr/sbin pathmunge /usr/local/sbin else pathmunge /usr/local/sbin after pathmunge /usr/sbin after fi HOSTNAME=`/usr/bin/hostname 2>/dev/null` HISTSIZE=1000 if [ "$HISTCONTROL" = "ignorespace" ] ; then export HISTCONTROL=ignoreboth else export HISTCONTROL=ignoredups fi export PATH USER LOGNAME MAIL HOSTNAME HISTSIZE HISTCONTROL # By default, we want umask to get set. This sets it for login shell # Current threshold for system reserved uid/gids is 200 # You could check uidgid reservation validity in # /usr/share/doc/setup-*/uidgid file if [ $UID -gt 199 ] && [ "`id -gn`" = "`id -un`" ]; then umask 002 else umask 022 fi for i in /etc/profile.d/*.sh ; do if [ -r "$i" ]; then if [ "${-#*i}" != "$-" ]; then . "$i" else . "$i" >/dev/null fi fi done unset i unset -f pathmunge export JAVA_HOME=/usr/big/jdk1.8 export HADOOP_HOME=/usr/big/hadoop export PATH=$JAVA_HOME/bin:$JAVA_HOME/jre/bin:$HADOOP_HOME/sbin:$HADOOP_HOME/bin:$PATH [root@master ~]#
二： hadoop安装包
1. 大家可以到官网上找一下安装链接：http://hadoop.apache.org/releases.html， 我这里选择的是最新版的2.9.0，binary安装。
2. 然后就是一路命令安装【看清楚目录哦。。。没有的话自己mkdir】
[root@localhost big]# pwd /usr/big [root@localhost big]# ls hadoop-2.9.0 hadoop-2.9.0.tar.gz [root@localhost big]# tar -xvzf hadoop-2.9.0.tar.gz
3. 对core-site.xml ,hdfs-site.xml，mapred-site.xml,yarn-site.xml，slaves，hadoop-env.sh的配置，路径都在etc目录下，
这也是最麻烦的。。。
[root@master hadoop]# pwd
/usr/big/hadoop/etc/hadoop
[root@master hadoop]# ls
capacity-scheduler.xml hadoop-policy.xml kms-log4j.properties slaves
configuration.xsl hdfs-site.xml kms-site.xml ssl-client.xml.example
container-executor.cfg httpfs-env.sh log4j.properties ssl-server.xml.example
core-site.xml httpfs-log4j.properties mapred-env.cmd yarn-env.cmd
hadoop-env.cmd httpfs-signature.secret mapred-env.sh yarn-env.sh
hadoop-env.sh httpfs-site.xml mapred-queues.xml.template yarn-site.xml
hadoop-metrics2.properties kms-acls.xml mapred-site.xml
hadoop-metrics.properties kms-env.sh mapred-site.xml.template
[root@master hadoop]#
<1> core-site.xml 下的配置中，我指定了hadoop的基地址，namenode的端口号，namenode的地址。
<configuration> <property> <name>hadoop.tmp.dir</name> <value>/usr/myapp/hadoop/data</value> <description>A base for other temporary directories.</description> </property> <property> <name>fs.defaultFS</name> <value>hdfs://master:9000</value> </property> </configuration>
<2> hdfs-site.xml 这个文件主要用来配置datanode以及datanode的副本。
<configuration> <property> <name>dfs.replication</name> <value>1</value> </property> </configuration>
3. mapred-site.xml 这里配置一下启用yarn框架
<configuration> <property> <name>mapreduce.framework.name</name> <value>yarn</value> </property> </configuration>
4. yarn-site.xml文件配置
<configuration> <!-- Site specific YARN configuration properties --> <property> <name>yarn.nodemanager.aux-services</name> <value>mapreduce_shuffle</value> </property> <property> <name>yarn.resourcemanager.address</name> <value>master:8032</value> </property> <property> <name>yarn.resourcemanager.scheduler.address</name> <value>master:8030</value> </property> <property> <name>yarn.resourcemanager.resource-tracker.address</name> <value>master:8031</value> </property> </configuration>
5. 在etc的slaves文件中，追加我们在host中配置的salve1和slave2，这样启动的时候，hadoop才能知道slave的位置。
[root@master hadoop]# cat slaves
slave1
slave2
[root@master hadoop]# pwd
/usr/big/hadoop/etc/hadoop
[root@master hadoop]#
6. 在hadoop-env.sh中配置java的路径，其实就是把 /etc/profile的配置copy一下，追加到文件末尾。
[root@master hadoop]# vim hadoop-env.sh
export JAVA_HOME=/usr/big/jdk1.8
不过这里还有一个坑，hadoop在计算时，默认的heap-size是512M，这就容易导致在大数据计算时，堆栈溢出，这里将512改成2048。
export HADOOP_NFS3_OPTS="$HADOOP_NFS3_OPTS"
export HADOOP_PORTMAP_OPTS="-Xmx2048m $HADOOP_PORTMAP_OPTS"
# The following applies to multiple commands (fs, dfs, fsck, distcp etc)
export HADOOP_CLIENT_OPTS="$HADOOP_CLIENT_OPTS"
# set heap args when HADOOP_HEAPSIZE is empty
if [ "$HADOOP_HEAPSIZE" = "" ]; then
export HADOOP_CLIENT_OPTS="-Xmx2048m $HADOOP_CLIENT_OPTS"
fi
7. 不要忘了在/usr目录下创建文件夹哦，然后在/etc/profile中配置hadoop的路径。
/usr/hadoop
/usr/hadoop/namenode
/usr/hadoop/datanode
export JAVA_HOME=/usr/big/jdk1.8
export HADOOP_HOME=/usr/big/hadoop
export PATH=$JAVA_HOME/bin:$JAVA_HOME/jre/bin:$HADOOP_HOME/sbin:$HADOOP_HOME/bin:$PATH
8. 将196上配置好的整个hadoop文件夹通过scp到 146 和150 服务器上的/usr/big目录下，后期大家也可以通过svn进行hadoop文件夹的
管理，这样比较方便。
scp -r /usr/big/hadoop root@192.168.23.146:/usr/big
scp -r /usr/big/hadoop root@192.168.23.150:/usr/big
三：启动hadoop
1. 启动之前通过hadoop namede -format 格式化一下hadoop dfs。
[root@master hadoop]# hadoop namenode -format
DEPRECATED: Use of this script to execute hdfs command is deprecated.
Instead use the hdfs command for it.
17/11/24 20:13:19 INFO namenode.NameNode: STARTUP_MSG:
/************************************************************
STARTUP_MSG: Starting NameNode
STARTUP_MSG: host = master/192.168.23.196
STARTUP_MSG: args = [-format]
STARTUP_MSG: version = 2.9.0
2. 在master机器上start-all.sh 启动hadoop集群。
[root@master hadoop]# start-all.sh
This script is Deprecated. Instead use start-dfs.sh and start-yarn.sh
Starting namenodes on [master]
root@master\'s password:
master: starting namenode, logging to /usr/big/hadoop/logs/hadoop-root-namenode-master.out
slave1: starting datanode, logging to /usr/big/hadoop/logs/hadoop-root-datanode-slave1.out
slave2: starting datanode, logging to /usr/big/hadoop/logs/hadoop-root-datanode-slave2.out
Starting secondary namenodes [0.0.0.0]
root@0.0.0.0\'s password:
0.0.0.0: starting secondarynamenode, logging to /usr/big/hadoop/logs/hadoop-root-secondarynamenode-master.out
starting yarn daemons
starting resourcemanager, logging to /usr/big/hadoop/logs/yarn-root-resourcemanager-master.out
slave1: starting nodemanager, logging to /usr/big/hadoop/logs/yarn-root-nodemanager-slave1.out
slave2: starting nodemanager, logging to /usr/big/hadoop/logs/yarn-root-nodemanager-slave2.out
[root@master hadoop]# jps
8851 NameNode
9395 ResourceManager
9655 Jps
9146 SecondaryNameNode
[root@master hadoop]#
通过jps可以看到，在master中已经开启了NameNode 和 ResouceManager，那么接下来，大家也可以到slave1和slave2机器上看一下是不是把NodeManager
和 DataNode都开起来了。。。
[root@slave1 hadoop]# jps
7112 NodeManager
7354 Jps
6892 DataNode
[root@slave1 hadoop]#
[root@slave2 hadoop]# jps
7553 NodeManager
7803 Jps
7340 DataNode
[root@slave2 hadoop]#
四：搭建完成，查看结果
通过下面的tlnp命令，可以看到50070端口和8088端口打开，一个是查看datanode，一个是查看mapreduce任务。
[root@master hadoop]# netstat -tlnp
五：最后通过hadoop自带的wordcount来结束本篇的搭建过程。
在hadoop的share目录下有一个wordcount的测试程序，主要用来统计单词的个数，hadoop/share/hadoop/mapreduce/hadoop-mapreduce-
examples-2.9.0.jar。
1. 我在/usr/soft下通过程序生成了一个39M的2.txt文件（全是随机汉字哦。。。）
[root@master soft]# ls -lsh 2.txt
39M -rw-r--r--. 1 root root 39M Nov 24 00:32 2.txt
[root@master soft]#
2. 在hadoop中创建一个input文件夹，然后在把2.txt上传过去
[root@master soft]# hadoop fs -mkdir /input
[root@master soft]# hadoop fs -put /usr/soft/2.txt /input
[root@master soft]# hadoop fs -ls /
Found 1 items
drwxr-xr-x - root supergroup 0 2017-11-24 20:30 /input
3. 执行wordcount的mapreduce任务
[root@master soft]# hadoop jar /usr/big/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.9.0.jar wordcount /input/2.txt /output/v1
17/11/24 20:32:21 INFO Configuration.deprecation: session.id is deprecated. Instead, use dfs.metrics.session-id
17/11/24 20:32:21 INFO jvm.JvmMetrics: Initializing JVM Metrics with processName=JobTracker, sessionId=
17/11/24 20:32:21 INFO input.FileInputFormat: Total input files to process : 1
17/11/24 20:32:21 INFO mapreduce.JobSubmitter: number of splits:1
17/11/24 20:32:21 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_local1430356259_0001
17/11/24 20:32:22 INFO mapreduce.Job: The url to track the job: http://localhost:8080/
17/11/24 20:32:22 INFO mapreduce.Job: Running job: job_local1430356259_0001
17/11/24 20:32:22 INFO mapred.LocalJobRunner: OutputCommitter set in config null
17/11/24 20:32:22 INFO output.FileOutputCommitter: File Output Committer Algorithm version is 1
17/11/24 20:32:22 INFO output.FileOutputCommitter: FileOutputCommitter skip cleanup _temporary folders under output directory:false, ignore cleanup failures: false
17/11/24 20:32:22 INFO mapred.LocalJobRunner: OutputCommitter is org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter
17/11/24 20:32:22 INFO mapred.LocalJobRunner: Waiting for map tasks
17/11/24 20:32:22 INFO mapred.LocalJobRunner: Starting task: attempt_local1430356259_0001_m_000000_0
17/11/24 20:32:22 INFO output.FileOutputCommitter: File Output Committer Algorithm version is 1
17/11/24 20:32:22 INFO output.FileOutputCommitter: FileOutputCommitter skip cleanup _temporary folders under output directory:false, ignore cleanup failures: false
17/11/24 20:32:22 INFO mapred.Task: Using ResourceCalculatorProcessTree : [ ]
17/11/24 20:32:22 INFO mapred.MapTask: Processing split: hdfs://192.168.23.196:9000/input/2.txt:0+40000002
17/11/24 20:32:22 INFO mapred.MapTask: (EQUATOR) 0 kvi 26214396(104857584)
17/11/24 20:32:22 INFO mapred.MapTask: mapreduce.task.io.sort.mb: 100
17/11/24 20:32:22 INFO mapred.MapTask: soft limit at 83886080
17/11/24 20:32:22 INFO mapred.MapTask: bufstart = 0; bufvoid = 104857600
17/11/24 20:32:22 INFO mapred.MapTask: kvstart = 26214396; length = 6553600
17/11/24 20:32:22 INFO mapred.MapTask: Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer
17/11/24 20:32:23 INFO mapreduce.Job: Job job_local1430356259_0001 running in uber mode : false
17/11/24 20:32:23 INFO mapreduce.Job: map 0% reduce 0%
17/11/24 20:32:23 INFO input.LineRecordReader: Found UTF-8 BOM and skipped it
17/11/24 20:32:27 INFO mapred.MapTask: Spilling map output
17/11/24 20:32:27 INFO mapred.MapTask: bufstart = 0; bufend = 27962024; bufvoid = 104857600
17/11/24 20:32:27 INFO mapred.MapTask: kvstart = 26214396(104857584); kvend = 12233388(48933552); length = 13981009/6553600
17/11/24 20:32:27 INFO mapred.MapTask: (EQUATOR) 38447780 kvi 9611940(38447760)
17/11/24 20:32:32 INFO mapred.MapTask: Finished spill 0
17/11/24 20:32:32 INFO mapred.MapTask: (RESET) equator 38447780 kv 9611940(38447760) kvi 6990512(27962048)
17/11/24 20:32:33 INFO mapred.MapTask: Spilling map output
17/11/24 20:32:33 INFO mapred.MapTask: bufstart = 38447780; bufend = 66409804; bufvoid = 104857600
17/11/24 20:32:33 INFO mapred.MapTask: kvstart = 9611940(38447760); kvend = 21845332(87381328); length = 13981009/6553600
17/11/24 20:32:33 INFO mapred.MapTask: (EQUATOR) 76895558 kvi 19223884(76895536)
17/11/24 20:32:34 INFO mapred.LocalJobRunner: map > map
17/11/24 20:32:34 INFO mapreduce.Job: map 67% reduce 0%
17/11/24 20:32:38 INFO mapred.MapTask: Finished spill 1
17/11/24 20:32:38 INFO mapred.MapTask: (RESET) equator 76895558 kv 19223884(76895536) kvi 16602456(66409824)
17/11/24 20:32:39 INFO mapred.LocalJobRunner: map > map
17/11/24 20:32:39 INFO mapred.MapTask: Starting flush of map output
17/11/24 20:32:39 INFO mapred.MapTask: Spilling map output
17/11/24 20:32:39 INFO mapred.MapTask: bufstart = 76895558; bufend = 100971510; bufvoid = 104857600
17/11/24 20:32:39 INFO mapred.MapTask: kvstart = 19223884(76895536); kvend = 7185912(28743648); length = 12037973/6553600
17/11/24 20:32:40 INFO mapred.LocalJobRunner: map > sort
17/11/24 20:32:43 INFO mapred.MapTask: Finished spill 2
17/11/24 20:32:43 INFO mapred.Merger: Merging 3 sorted segments
17/11/24 20:32:43 INFO mapred.Merger: Down to the last merge-pass, with 3 segments left of total size: 180000 bytes
17/11/24 20:32:43 INFO mapred.Task: Task:attempt_local1430356259_0001_m_000000_0 is done. And is in the process of committing
17/11/24 20:32:43 INFO mapred.LocalJobRunner: map > sort
17/11/24 20:32:43 INFO mapred.Task: Task \'attempt_local1430356259_0001_m_000000_0\' done.
17/11/24 20:32:43 INFO mapred.LocalJobRunner: Finishing task: attempt_local1430356259_0001_m_000000_0
17/11/24 20:32:43 INFO mapred.LocalJobRunner: map task executor complete.
17/11/24 20:32:43 INFO mapred.LocalJobRunner: Waiting for reduce tasks
17/11/24 20:32:43 INFO mapred.LocalJobRunner: Starting task: attempt_local1430356259_0001_r_000000_0
17/11/24 20:32:43 INFO output.FileOutputCommitter: File Output Committer Algorithm version is 1
17/11/24 20:32:43 INFO output.FileOutputCommitter: FileOutputCommitter skip cleanup _temporary folders under output directory:false, ignore cleanup failures: false
17/11/24 20:32:43 INFO mapred.Task: Using ResourceCalculatorProcessTree : [ ]
17/11/24 20:32:43 INFO mapred.ReduceTask: Using ShuffleConsumerPlugin: org.apache.hadoop.mapreduce.task.reduce.Shuffle@f8eab6f
17/11/24 20:32:43 INFO mapreduce.Job: map 100% reduce 0%
17/11/24 20:32:43 INFO reduce.MergeManagerImpl: MergerManager: memoryLimit=1336252800, maxSingleShuffleLimit=334063200, mergeThreshold=881926912, iosortFactor=10, memToMemMergeOutputsThreshold=10
17/11/24 20:32:43 INFO reduce.EventFetcher: attempt_local1430356259_0001_r_000000_0 Thread started: EventFetcher for fetching Map Completion Events
17/11/24 20:32:43 INFO reduce.LocalFetcher: localfetcher#1 about to shuffle output of map attempt_local1430356259_0001_m_000000_0 decomp: 60002 len: 60006 to MEMORY
17/11/24 20:32:43 INFO reduce.InMemoryMapOutput: Read 60002 bytes from map-output for attempt_local1430356259_0001_m_000000_0
17/11/24 20:32:43 INFO reduce.MergeManagerImpl: closeInMemoryFile -> map-output of size: 60002, inMemoryMapOutputs.size() -> 1, commitMemory -> 0, usedMemory ->60002
17/11/24 20:32:43 INFO reduce.EventFetcher: EventFetcher is interrupted.. Returning
17/11/24 20:32:43 INFO mapred.LocalJobRunner: 1 / 1 copied.
17/11/24 20:32:43 INFO reduce.MergeManagerImpl: finalMerge called with 1 in-memory map-outputs and 0 on-disk map-outputs
17/11/24 20:32:43 INFO mapred.Merger: Merging 1 sorted segments
17/11/24 20:32:43 INFO mapred.Merger: Down to the last merge-pass, with 1 segments left of total size: 59996 bytes
17/11/24 20:32:43 INFO reduce.MergeManagerImpl: Merged 1 segments, 60002 bytes to disk to satisfy reduce memory limit
17/11/24 20:32:43 INFO reduce.MergeManagerImpl: Merging 1 files, 60006 bytes from disk
17/11/24 20:32:43 INFO reduce.MergeManagerImpl: Merging 0 segments, 0 bytes from memory into reduce
17/11/24 20:32:43 INFO mapred.Merger: Merging 1 sorted segments
17/11/24 20:32:43 INFO mapred.Merger: Down to the last merge-pass, with 1 segments left of total size: 59996 bytes
17/11/24 20:32:43 INFO mapred.LocalJobRunner: 1 / 1 copied.
17/11/24 20:32:43 INFO Configuration.deprecation: mapred.skip.on is deprecated. Instead, use mapreduce.job.skiprecords
17/11/24 20:32:44 INFO mapred.Task: Task:attempt_local1430356259_0001_r_000000_0 is done. And is in the process of committing
17/11/24 20:32:44 INFO mapred.LocalJobRunner: 1 / 1 copied.
17/11/24 20:32:44 INFO mapred.Task: Task attempt_local1430356259_0001_r_000000_0 is allowed to commit now
17/11/24 20:32:44 INFO output.FileOutputCommitter: Saved output of task \'attempt_local1430356259_0001_r_000000_0\' to hdfs://192.168.23.196:9000/output/v1/_temporary/0/task_local1430356259_0001_r_000000
17/11/24 20:32:44 INFO mapred.LocalJobRunner: reduce > reduce
17/11/24 20:32:44 INFO mapred.Task: Task \'attempt_local1430356259_0001_r_000000_0\' done.
17/11/24 20:32:44 INFO mapred.LocalJobRunner: Finishing task: attempt_local1430356259_0001_r_000000_0
17/11/24 20:32:44 INFO mapred.LocalJobRunner: reduce task executor complete.
17/11/24 20:32:44 INFO mapreduce.Job: map 100% reduce 100%
17/11/24 20:32:44 INFO mapreduce.Job: Job job_local1430356259_0001 completed successfully
17/11/24 20:32:44 INFO mapreduce.Job: Counters: 35
File System Counters
FILE: Number of bytes read=1087044
FILE: Number of bytes written=2084932
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=80000004
HDFS: Number of bytes written=54000
HDFS: Number of read operations=13
HDFS: Number of large read operations=0
HDFS: Number of write operations=4
Map-Reduce Framework
Map input records=1
Map output records=10000000
Map output bytes=80000000
Map output materialized bytes=60006
Input split bytes=103
Combine input records=10018000
Combine output records=24000
Reduce input groups=6000
Reduce shuffle bytes=60006
Reduce input records=6000
Reduce output records=6000
Spilled Records=30000
Shuffled Maps =1
Failed Shuffles=0
Merged Map outputs=1
GC time elapsed (ms)=1770
Total committed heap usage (bytes)=1776287744
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=40000002
File Output Format Counters
Bytes Written=54000
4. 最后我们到/output/v1下面去看一下最终生成的结果，由于生成的汉字太多，我这里只输出了一部分
[root@master soft]# hadoop fs -ls /output/v1
Found 2 items
-rw-r--r-- 2 root supergroup 0 2017-11-24 20:32 /output/v1/_SUCCESS
-rw-r--r-- 2 root supergroup 54000 2017-11-24 20:32 /output/v1/part-r-00000
[root@master soft]# hadoop fs -ls /output/v1/part-r-00000
-rw-r--r-- 2 root supergroup 54000 2017-11-24 20:32 /output/v1/part-r-00000
[root@master soft]# hadoop fs -tail /output/v1/part-r-00000
1609
攟 1685
攠 1636
攡 1682
攢 1657
攣 1685
攤 1611
攥 1724
攦 1732
攧 1657
攨 1767
攩 1768
攪 1624
好了，搭建的过程确实是麻烦，关于hive的搭建，我们放到后面的博文中去说吧。。。希望本篇对你有帮助。
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