通過hadoop + hive搭建離線式的分析系統之快速搭建一覽

来源:http://www.cnblogs.com/huangxincheng/archive/2017/11/25/7895019.html
-Advertisement-
Play Games

最近有個需求，需要整合所有店鋪的數據做一個離線式分析系統，曾經都是按照店鋪分庫分表來給各自商家通過highchart多維度展示自家的店鋪經營 狀況，我們知道這是一個以店鋪為維度的切分，非常適合目前的線上業務，這回老闆提需求了，曾經也是一位數據分析師，sql自然就溜溜的，所以就來了 一個以買家維度展示 ...


　　最近有個需求，需要整合所有店鋪的數據做一個離線式分析系統，曾經都是按照店鋪分庫分表來給各自商家通過highchart多維度展示自家的店鋪經營

狀況，我們知道這是一個以店鋪為維度的切分，非常適合目前的線上業務，這回老闆提需求了，曾經也是一位數據分析師，sql自然就溜溜的，所以就來了

一個以買家維度展示用戶畫像，從而更好的做數據推送和用戶行為分析，因為是離線式分析，目前還沒研究spark，impala，drill了。

 

一：搭建hadoop集群

　     hadoop的搭建是一個比較繁瑣的過程，採用3台Centos，廢話不過多，一圖勝千言。。。

二： 基礎配置

 1. 關閉防火牆

[root@localhost ~]# systemctl stop firewalld.service    #關閉防火牆
[root@localhost ~]# systemctl disable firewalld.service #禁止開機啟動
[root@localhost ~]# firewall-cmd --state                #查看防火牆狀態
not running
[root@localhost ~]#

 

2. 配置SSH免登錄

　　 不管在開啟還是關閉hadoop的時候，hadoop內部都要通過ssh進行通訊，所以需要配置一個ssh公鑰免登陸，做法就是將一個centos的公鑰copy到另一

台centos的authorized_keys文件中。

     <1>： 在196上生成公鑰私鑰 ，從下圖中可以看到通過ssh-keygen之後會生成 id_rsa 和  id_rsa.pub 兩個文件，這裡我們

                關心的是公鑰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 [email protected]
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> 通過scp複製命令 將公鑰copy到 146 和 150主機。

[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 ~]#

 

<3> 做host映射，主要給幾台機器做別名映射，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安裝環境 

         hadoop是java寫的，所以需要安裝java環境，具體怎麼安裝，大家可以網上搜一下，先把centos自帶的openjdk卸載掉，最後在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安裝包

   1.  大家可以到官網上找一下安裝鏈接：http://hadoop.apache.org/releases.html， 我這裡選擇的是最新版的2.9.0，binary安裝。

  

    2.  然後就是一路命令安裝【看清楚目錄哦。。。沒有的話自己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. 對core-site.xml ,hdfs-site.xml，mapred-site.xml,yarn-site.xml，slaves，hadoop-env.sh的配置，路徑都在etc目錄下，

      這也是最麻煩的。。。

[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的埠號，namenode的地址。

<?xml version="1.0" encoding="UTF-8"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<!--
  Licensed under the Apache License, Version 2.0 (the "License");
  you may not use this file except in compliance with the License.
  You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

  Unless required by applicable law or agreed to in writing, software
  distributed under the License is distributed on an "AS IS" BASIS,
  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  See the License for the specific language governing permissions and
  limitations under the License. See accompanying LICENSE file.
-->

<!-- Put site-specific property overrides in this file. -->

<configuration>
    <property>
        <name>hadoop.tmp.dir</name>
        <value>/usr/hadoop</value>
        <description>A base for other temporary directories.</description>
    </property>
      <!-- file system properties -->
    <property>
        <name>fs.default.name</name>
        <value>hdfs://192.168.23.196:9000</value>
    </property>
    <property>
        <name>dfs.name.dir</name>
        <value>/usr/hadoop/namenode</value>
        <description>A base for other temporary directories.</description>
    </property>
</configuration>

 

   <2>  hdfs-site.xml  這個文件主要用來配置datanode的存放路徑，以及datanode的副本。

<?xml version="1.0" encoding="UTF-8"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<!--
  Licensed under the Apache License, Version 2.0 (the "License");
  you may not use this file except in compliance with the License.
  You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

  Unless required by applicable law or agreed to in writing, software
  distributed under the License is distributed on an "AS IS" BASIS,
  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  See the License for the specific language governing permissions and
  limitations under the License. See accompanying LICENSE file.
-->

<!-- Put site-specific property overrides in this file. -->

<configuration>
    <property>
        <name>dfs.replication</name>
        <value>2</value>
    </property>
        <property>
        <name>dfs.data.dir</name>
        <value>/usr/hadoop/datanode</value>
    </property>
</configuration>

 

3. 這裡配置一下jobtrace埠號

<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<!--
  Licensed under the Apache License, Version 2.0 (the "License");
  you may not use this file except in compliance with the License.
  You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

  Unless required by applicable law or agreed to in writing, software
  distributed under the License is distributed on an "AS IS" BASIS,
  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  See the License for the specific language governing permissions and
  limitations under the License. See accompanying LICENSE file.
-->

<!-- Put site-specific property overrides in this file. -->

<configuration>
    <property>
      <name>mapreduce.job.tracker</name>
      <value>hdfs://192.168.23.196:8001</value>
      <final>true</final>
    </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.nodemanager.aux-services.mapreduce.shuffle.class</name>
<value>org.apache.hadoop.mapred.ShuffleHandler</value>
</property>
<property>
    <name>yarn.nodemanager.resource.memory-mb</name>
    <value>20480</value>
</property>
<property>
   <name>yarn.scheduler.minimum-allocation-mb</name>
   <value>2048</value>
</property>
<property>
    <name>yarn.nodemanager.vmem-pmem-ratio</name>
    <value>2.1</value>
</property>

</configuration>

 

5. 在etc的slaves文件中，追加我們在host中配置的salve1和slave2，這樣啟動的時候，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的路徑，其實就是把 /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

   

    不過這裡還有一個坑，hadoop在計算時，預設的heap-size是512M，這就容易導致在大數據計算時，堆棧溢出，這裡將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目錄下創建文件夾哦，然後在/etc/profile中配置hadoop的路徑。

/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.  將196上配置好的整個hadoop文件夾通過scp到 146 和150 伺服器上的/usr/big目錄下，後期大家也可以通過svn進行hadoop文件夾的

      管理，這樣比較方便。

scp -r /usr/big/hadoop [email protected]:/usr/big
scp -r /usr/big/hadoop [email protected]:/usr/big

 

三：啟動hadoop

1.  啟動之前通過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機器上start-all.sh 啟動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]
[email protected]'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]#

  

    通過jps可以看到，在master中已經開啟了NameNode 和 ResouceManager，那麼接下來，大家也可以到slave1和slave2機器上看一下是不是把NodeManager

和 DataNode都開起來了。。。

[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，查看結果

      通過下麵的tlnp命令，可以看到50070埠和8088埠打開，一個是查看datanode，一個是查看mapreduce任務。

[root@master hadoop]# netstat -tlnp

 

五：最後通過hadoop自帶的wordcount來結束本篇的搭建過程。

      在hadoop的share目錄下有一個wordcount的測試程式，主要用來統計單詞的個數，hadoop/share/hadoop/mapreduce/hadoop-mapreduce-

examples-2.9.0.jar。

 

1. 我在/usr/soft下通過程式生成了一個39M的2.txt文件（全是隨機漢字哦。。。）

[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中創建一個input文件夾，然後在把2.txt上傳過去

[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. 執行wordcount的mapreduce任務

[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. 最後我們到/output/v1下麵去看一下最終生成的結果，由於生成的漢字太多，我這裡只輸出了一部分

[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，搭建的過程確實是麻煩，關於hive的搭建，我們放到後面的博文中去說吧。。。希望本篇對你有幫助。

 


您的分享是我們最大的動力!

-Advertisement-
Play Games
更多相關文章
  • 寫給小白看的 JavaScript 非同步
    某天突然寫了個方法要從後臺調用數據，顯示在前臺頁面，但是輸出結果總是空 undefined，得不到數據。多方找資料才發現，原來是入了 JS 非同步的 “坑”。 我們常常聽到單線程、多線程、同步、非同步這些概念，那麼這些東西到底是什麼呢？ 那麼我們先從上面那幾個概念說起 o(￣▽￣) ブ 單線程、多線程、 ...
  • 高性能分散式執行框架——Ray
    Ray是UC Berkeley RISELab新推出的高性能分散式執行框架，它使用了和傳統分散式計算系統不一樣的架構和對分散式計算的抽象方式，具有比Spark更優異的計算性能。 Ray目前還處於實驗室階段，最新版本為 "0.2.2版本" 。雖然Ray自稱是面向AI應用的分散式計算框架，但是它的架構具 ...
  • redis的持久化機制
    redis簡介 redis是一個基於記憶體的nosql資料庫，和傳統關係數據最大的區別就是數據是存在記憶體中而不是硬碟上，從而帶來了tps的巨大提升。但是基於記憶體的資料庫最大的缺陷就是機器斷電或者系統崩潰後數據會全部丟失。但是redis可以有一套自己的持久化機制，可以讓他在系統斷電或者崩潰後儘可能的少丟 ...
  • mongoose查詢資料庫步驟
    建立dumall資料庫，創建goods集合，導入數據文件，也可以自己手動插入。 mongodb安裝與環境搭建： http://www.cnblogs.com/ccyinghua/p/7887713.html 建立資料庫和插入數據，可以是進入mongo操作資料庫，以命令行形式插入操作，即成功啟動Mon ...
  • Windows下mysql的基礎操作
    1.資料庫表操作： - 首先啟動mysql伺服器，在安裝mysql的目錄下打開cmd視窗，運行mysql：'mysql.exe -hlocalhost -p3306 -uroot -p123456'; - 查看所有的資料庫: show databases; -- create database my ...
  • Oracle 同義詞 synonym
    同義詞： 對另一個數據對象而言同義詞是一個別名，同義詞對其目標對象進行依賴，如果目標對象被更改或刪除，則該同義詞將變為無效。 同義詞不能代替資料庫對象的特權，即先用對模式的對象有查詢、執行、刪除等許可權。 許可權： 要在自己的模式中創建私有同義詞，您必須具有CREATE SYNONYM系統特權。 要在另 ...
  • mongo 3.4分片集群系列之六：詳解配置資料庫
    這個系列大致想跟大家分享以下篇章（我會持續更新的↖(^ω^)↗）： 1、mongo 3.4分片集群系列之一：淺談分片集群 2、mongo 3.4分片集群系列之二：搭建分片集群--哈希分片 3、mongo 3.4分片集群系列之三：搭建分片集群--哈希分片 + 安全 4、mongo 3.4分片集群系列之 ...
  • 資料庫系統簡介
    什麼是資料庫系統？ 資料庫系統=資料庫（DataBase（DB））+資料庫管理系統（Database Management System （DBMS））+資料庫應用程式（Database Application （DBA）） 資料庫：為了滿足一個機構的信息需求而設計的邏輯上相關的可共用的數據及數據描 ...
一周排行
    -Advertisement-
    Play Games
  • C# + WPF 音頻播放器 界面優雅，體驗良好
    前言 本文介紹一款使用 C# 與 WPF 開發的音頻播放器，其界面簡潔大方，操作體驗流暢。該播放器支持多種音頻格式（如 MP4、WMA、OGG、FLAC 等），並具備標記、實時歌詞顯示等功能。 另外，還支持換膚及多語言（中英文）切換。核心音頻處理採用 FFmpeg 組件，獲得了廣泛認可，目前 Git ...
  • OAuth2.0授權-gitee授權碼模式
    OAuth2.0授權驗證-gitee授權碼模式 本文主要介紹如何筆者自己是如何使用gitee提供的OAuth2.0協議完成授權驗證並登錄到自己的系統，完整模式如圖 1、創建應用 打開gitee個人中心->第三方應用->創建應用 創建應用後在我的應用界面，查看已創建應用的Client ID和Clien ...
  • FastReport.OpenSource .Net下開源免費報表列印組件
    解決了這個問題：《winForm下，fastReport.net 從.net framework 升級到.net5遇到的錯誤“Operation is not supported on this platform.”》 本文內容轉載自：https://www.fcnsoft.com/Home/Sho ...
  • 如何在 ASP.NET Core Web API 方法執行前後 “偷偷“ 作一些 “壞“ 事？初識 ActionFilterAttribute
    國內文章 WPF 從裸 Win 32 的 WM_Pointer 消息獲取觸摸點繪製筆跡 https://www.cnblogs.com/lindexi/p/18390983 本文將告訴大家如何在 WPF 裡面，接收裸 Win 32 的 WM_Pointer 消息，從消息裡面獲取觸摸點信息，使用觸摸點 ...
  • 在 ASP.NET Core Web API 中使用異常篩選器捕獲和統一處理異常
    前言 給大家推薦一個專為新零售快消行業打造了一套高效的進銷存管理系統。 系統不僅具備強大的庫存管理功能，還集成了高性能的輕量級 POS 解決方案，確保頁面載入速度極快，提供良好的用戶體驗。 項目介紹 Dorisoy.POS 是一款基於 .NET 7 和 Angular 4 開發的新零售快消進銷存管理 ...
  • AbpHelper CLI積累與實戰分享
    ABP CLI常用的代碼分享 一、確保環境配置正確 安裝.NET CLI： ABP CLI是基於.NET Core或.NET 5/6/7等更高版本構建的，因此首先需要在你的開發環境中安裝.NET CLI。這可以通過訪問Microsoft官網下載並安裝相應版本的.NET SDK來實現。 安裝ABP ...
  • 記一次HttpClient使用問題分析
    問題 問題是這樣的：第三方的webapi，需要先調用登陸介面獲取Cookie，訪問其它介面時攜帶Cookie信息。 但使用HttpClient類調用登陸介面，返回的Headers中沒有找到Cookie信息。 分析 首先，使用Postman測試該登陸介面，正常返回Cookie信息，說明是HttpCli ...
  • .NET周刊【9月第3期 2024-09-15】
    國內文章 關於.NET在中國為什麼工資低的分析 https://www.cnblogs.com/thinkingmore/p/18406244 .NET在中國開發者的薪資偏低，主要因市場需求、技術棧選擇和企業文化等因素所致。歷史上，.NET曾因微軟的閉源策略發展受限，儘管後來推出了跨平臺的.NET ...
  • 我的網站集成ElasticSearch初體驗
    在WPF開發應用中，動畫不僅可以引起用戶的註意與興趣，而且還使軟體更加便於使用。前面幾篇文章講解了畫筆（Brush），形狀（Shape），幾何圖形（Geometry），變換（Transform）等相關內容，今天繼續講解動畫相關內容和知識點，僅供學習分享使用，如有不足之處，還請指正。 ...
  • 第23篇 委托的概述
    什麼是委托？ 委托可以說是把一個方法代入另一個方法執行，相當於指向函數的指針；事件就相當於保存委托的數組； 1.實例化委托的方式： 方式1：通過new創建實例： public delegate void ShowDelegate(); 或者 public delegate string ShowDe ...
所有分類