單機安裝hadoop-2.9.2+apache-hive-2.3.4-bin
centos7.5
hostname:
hadoop1
soft:
hadoop-2.9.2
apache-hive-2.3.4-bin
jdk-8u201-linux-x64
mysql5.7《安裝略》
設置靜態ip地址
略
添加主機與ip映射
[[email protected] ~]# cat /etc/hosts
127.0.0.1 localhost localhost.localdomain localhost4 localhost4.localdomain4
::1 localhost localhost.localdomain localhost6 localhost6.localdomain6
關閉防火墻：
[[email protected] ~]# systemctl stop firewalld
[[email protected] ~]# systemctl disable firewalld
關閉seLinux
[[email protected] ~]# egrep -v "^#|^$" /etc/selinux/config
SELINUX=disabled
其它參數設置：
[[email protected] ~]# sysctl -w vm.max_map_count=262144
vm.max_map_count = 262144
[[email protected] ~]# egrep -v "^#|^$" /etc/security/limits.conf
* soft nofile 65536
* hard nofile 131072
* soft nproc 65536
* hard nproc 65536安裝java：
[[email protected] opt]# ls
jdk-8u201-linux-x64.rpm
[[email protected] opt]# rpm -ih jdk-8u201-linux-x64.rpm
################################# [100%]
Updating / installing...
################################# [100%]
Unpacking JAR files...
tools.jar...
plugin.jar...
javaws.jar...
deploy.jar...
rt.jar...
jsse.jar...
charsets.jar...
localedata.jar...
[[email protected] opt]# java -version
java version "1.8.0_201"
Java(TM) SE Runtime Environment (build 1.8.0_201-b09)
Java HotSpot(TM) 64-Bit Server VM (build 25.201-b09, mixed mode) 建立hadoop賬戶《987654321》：
[[email protected] opt]# useradd hadoop
[[email protected] opt]# passwd hadoop
設置sudo權限
略
重啟系統：
[[email protected] ~]# reboot
以下是有hadoop操作：
| 設置ssh免密碼登錄： [[email protected] ~]$ ssh-keygen Generating public/private rsa key pair. Enter file in which to save the key (/home/hadoop/.ssh/id_rsa): Enter passphrase (empty for no passphrase): Enter same passphrase again: Your identification has been saved in /home/hadoop/.ssh/id_rsa. Your public key has been saved in /home/hadoop/.ssh/id_rsa.pub. The key fingerprint is: 6e:d0:62:99:3c:02:10:cf:00:5a:71:3c:6f:82:67:94 [email protected] The key‘s randomart image is: +--[ RSA 2048]----+ |
*.oo.. | .* .E | . +o o | ..+.o+ | o.oO S | o = | o | . |
|---|
+-----------------+
[[email protected] ~]$ ssh-copy-id -i .ssh/id_rsa.pub [email protected]hadoop搭建：
[[email protected] hadoop]# su - hadoop
Last login: Fri Apr 19 20:56:04 CST 2019 on pts/0
[[email protected] ~]$ ls
hadoop-2.9.2.tar.gz
[[email protected] ~]$ tar -zxf hadoop-2.9.2.tar.gz
[[email protected] ~]$ ls
hadoop-2.9.2 hadoop-2.9.2.tar.gz
配置hadoop-env.sh
[[email protected] ~]$ vim hadoop-2.9.2/etc/hadoop/hadoop-env.sh
#export JAVA_HOME=${JAVA_HOME}
export JAVA_HOME=/usr/java/jdk1.8.0_201-amd64
配置core-site.xml
vim hadoop-2.9.2/etc/hadoop/core-site.xml
<configuration>
<!-- 指定HADOOP所使用的文件系統schema（URI），HDFS的老大（NameNode）的地址 -->
<property>
<name>fs.defaultFS</name>
<value>hdfs://192.168.3.76:9000</value>
</property>
<!-- 指定hadoop運行時產生臨時文件的存儲目錄 -->
<property>
<name>hadoop.tmp.dir</name>
<value>/home/hadoop/tmp</value>
</property>
[[email protected] ~]$ mkdir tmp配置hdfs-site.xml
[[email protected] ~]$ vim hadoop-2.9.2/etc/hadoop/hdfs-site.xml
<property>
<name>dfs.namenode.name.dir</name>
<value>/home/hadoop/dfs/namenode</value>
<final>true</final>
</property>
<property>
<name>dfs.datanode.data.dir</name>
<value>/home/hadoop/dfs/datanode</value>
<final>true</final>
</property>
<property>
<name>dfs.http.address</name>
<value>192.168.3.76:50070</value>
<description>The address and the base port where the dfs namenode web ui will listen on.If the port is 0 then the server will start on a free port</description>
</property>
<!-- 指定HDFS副本的數量 -->
<property>
<name>dfs.replication</name>
<value>1</value>
</property>
<property>
<name>dfs.permissions</name>
<value>false</value>
</property>
建目錄：
[[email protected] ~]$ mkdir dfs/datanode -p
[[email protected] ~]$ mkdir dfs/namenode -p
配置mapred-site.xml
[[email protected] ~]$ cp hadoop-2.9.2/etc/hadoop/mapred-site.xml.template hadoop-2.9.2/etc/hadoop/mapred-site.xml
[[email protected] ~]$ vim hadoop-2.9.2/etc/hadoop/mapred-site.xml
<!-- 指定mr運行在yarn上 -->
<property>
<name>mapreduce.framework.name</name>
<value>yarn</value>
</property>
<property>
<name>mapred.job.tracker</name>
<value>hdfs://192.168.3.76:9001</value>
</property>
<property>
<name>mapred.system.dir</name>
<value>file:/home/hadoop/mapred/system</value>
<final>true</final>
</property>
<property>
<name>mapred.local.dir</name>
<value>file:/home/hadoop/mapred/local</value>
<final>true</final>
</property>
建立目錄：
[[email protected] ~]$ mkdir mapred/local -p
[[email protected] ~]$ mkdir mapred/system -p
配置yarn-site.xml
[[email protected] ~]$ vim hadoop-2.9.2/etc/hadoop/yarn-site.xml
<!-- 指定YARN的老大（ResourceManager）的地址 -->
<property>
<name>yarn.resourcemanager.hostname</name>
<value>192.168.3.76</value>
</property>
<!-- reducer獲取數據的方式 -->
<property>
<name>yarn.nodemanager.aux-services</name>
<value>mapreduce_shuffle</value>
</property>
格式化hdfs
[[email protected] ~]$ hadoop-2.9.2/bin/hdfs namenode -format
出現下面第二行的successfully，那麽表名成功
19/04/19 21:24:20 INFO namenode.FSImage: Allocated new BlockPoolId: BP-1608387477-192.168.3.76-1555680260442
19/04/19 21:24:20 INFO common.Storage: Storage directory /home/hadoop/tmp/dfs/name has been successfully formatted.
19/04/19 21:24:20 INFO namenode.FSImageFormatProtobuf: Saving image file /home/hadoop/tmp/dfs/name/current/fsimage.ckpt_0000000000000000000 using no compression
19/04/19 21:24:20 INFO namenode.FSImageFormatProtobuf: Image file /home/hadoop/tmp/dfs/name/current/fsimage.ckpt_0000000000000000000 of size 325 bytes saved in 0 seconds .
19/04/19 21:24:20 INFO namenode.NNStorageRetentionManager: Going to retain 1 images with txid >= 0
19/04/19 21:24:20 INFO namenode.NameNode: SHUTDOWN_MSG:
/****
SHUTDOWN_MSG: Shutting down NameNode at hadoop1/192.168.3.76
****/
啟動並測試hdfs：
[[email protected] ~]$ hadoop-2.9.2/sbin/start-all.sh
This script is Deprecated. Instead use start-dfs.sh and start-yarn.sh
Starting namenodes on [hadoop1]
hadoop1: starting namenode, logging to /home/hadoop/hadoop-2.9.2/logs/hadoop-hadoop-namenode-hadoop1.out
localhost: starting datanode, logging to /home/hadoop/hadoop-2.9.2/logs/hadoop-hadoop-datanode-hadoop1.out
Starting secondary namenodes [0.0.0.0]
0.0.0.0: starting secondarynamenode, logging to /home/hadoop/hadoop-2.9.2/logs/hadoop-hadoop-secondarynamenode-hadoop1.out
starting yarn daemons
starting resourcemanager, logging to /home/hadoop/hadoop-2.9.2/logs/yarn-hadoop-resourcemanager-hadoop1.out
localhost: starting nodemanager, logging to /home/hadoop/hadoop-2.9.2/logs/yarn-hadoop-nodemanager-hadoop1.out
檢測：
[[email protected] ~]$ jps
4705 SecondaryNameNode
4865 ResourceManager
4386 NameNode
5157 NodeManager
5318 Jps
4488 DataNode
實例測試：
[[email protected] ~]$ ll
total 357872
drwxrwxr-x 4 hadoop hadoop 36 Apr 19 22:03 dfs
drwxr-xr-x 10 hadoop hadoop 150 Apr 19 21:27 hadoop-2.9.2
-rw-r--r-- 1 hadoop hadoop 366447449 Apr 19 20:56 hadoop-2.9.2.tar.gz
drwxrwxr-x 4 hadoop hadoop 31 Apr 19 21:52 mapred
-rw-r--r-- 1 hadoop hadoop 11323 Apr 19 22:11 qqqq.xlsx
drwxrwxr-x 4 hadoop hadoop 35 Apr 19 22:06 tmp
這裏的文件名必須要以‘/’開頭，暫時只了解是hdfs是以絕對路徑為基礎，因為沒有 ‘-cd’這樣的命令支持
[[email protected] ~]$ hadoop-2.9.2/bin/hdfs dfs -mkdir /input
[[email protected] ~]$ hadoop-2.9.2/bin/hdfs dfs -put qqqq.xlsx /input
也可以查看此時新建的input目錄裏面有什麽
[[email protected] ~]$ hadoop-2.9.2/bin/hdfs dfs -ls /
Found 1 items
drwxr-xr-x - hadoop supergroup 0 2019-04-19 22:14 /input
[[email protected] ~]$ hadoop-2.9.2/bin/hdfs dfs -ls /input
Found 1 items
-rw-r--r-- 1 hadoop supergroup 11323 2019-04-19 22:14 /input/qqqq.xlsx
[[email protected] ~]$ hadoop-2.9.2/bin/hadoop jar hadoop-2.9.2/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.9.2.jar grep /input /output ‘dfs[a-z.]+‘
19/04/19 22:23:05 INFO mapreduce.Job: Job job_1555682785585_0001 completed successfully
19/04/19 22:23:23 INFO mapreduce.Job: Job job_1555682785585_0002 completed successfully
結果：
[[email protected] ~]$ hadoop-2.9.2/bin/hdfs dfs -ls /
Found 4 items
drwxr-xr-x - hadoop supergroup 0 2019-04-19 22:14 /input
drwxr-xr-x - hadoop supergroup 0 2019-04-19 22:23 /output
drwx------ - hadoop supergroup 0 2019-04-19 22:22 /tmp
drwxr-xr-x - hadoop supergroup 0 2019-04-19 22:22 /user
[[email protected] ~]$ hadoop-2.9.2/bin/hdfs dfs -ls /output
Found 2 items
-rw-r--r-- 1 hadoop supergroup 0 2019-04-19 22:23 /output/_SUCCESS
-rw-r--r-- 1 hadoop supergroup 0 2019-04-19 22:23 /output/part-r-00000
檢測二：
[[email protected] ~]$ ll
total 357876
drwxrwxr-x 4 hadoop hadoop 36 Apr 19 22:03 dfs
drwxr-xr-x 10 hadoop hadoop 150 Apr 19 21:27 hadoop-2.9.2
-rw-r--r-- 1 hadoop hadoop 366447449 Apr 19 20:56 hadoop-2.9.2.tar.gz
drwxrwxr-x 4 hadoop hadoop 31 Apr 19 21:52 mapred
-rw-r--r-- 1 hadoop hadoop 11323 Apr 19 22:11 qqqq.xlsx
drwxrwxr-x 4 hadoop hadoop 35 Apr 19 22:06 tmp
-rw-rw-r-- 1 hadoop hadoop 213 Apr 19 22:30 www.text
[[email protected] ~]$ cat www.text
http://blog.csdn.net/u012342408/article/details/50520696
http://blog.csdn.net/hitwengqi/article/details/8008203
http://blog.csdn.net/zl007700/article/details/50533675
https://www.cnblogs.com/yanglf/p/4020555.html
[[email protected] ~]$ hadoop-2.9.2/bin/hdfs dfs -put www.text /input
[[email protected] ~]$ hadoop-2.9.2/bin/hdfs dfs -ls /input
Found 2 items
-rw-r--r-- 1 hadoop supergroup 11323 2019-04-19 22:14 /input/qqqq.xlsx
-rw-r--r-- 1 hadoop supergroup 213 2019-04-19 22:31 /input/www.text
[[email protected] ~]$ hadoop-2.9.2/bin/hadoop jar hadoop-2.9.2/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.9.2.jar grep /input/www.text /www ‘dfs[a-z.]+‘
19/04/19 22:33:33 INFO mapreduce.Job: Job job_1555682785585_0004 completed successfully
19/04/19 22:33:51 INFO mapreduce.Job: Job job_1555682785585_0005 completed successfully
結果：
[[email protected] ~]$ hadoop-2.9.2/bin/hdfs dfs -ls /www
Found 2 items
-rw-r--r-- 1 hadoop supergroup 0 2019-04-19 22:33 /www/_SUCCESS
-rw-r--r-- 1 hadoop supergroup 0 2019-04-19 22:33 /www/part-r-00000
查看安裝包：
[[email protected] hive]# su - hadoop
Last login: Sat Apr 20 15:46:10 CST 2019 on pts/1
[[email protected] ~]$ ll
total 589016
-rw-r--r-- 1 hadoop hadoop 232234292 Apr 20 15:53 apache-hive-2.3.4-bin.tar.gz
drwxrwxr-x 4 hadoop hadoop 36 Apr 19 22:03 dfs
drwxr-xr-x 10 hadoop hadoop 150 Apr 19 21:27 hadoop-2.9.2
-rw-r--r-- 1 hadoop hadoop 366447449 Apr 19 20:56 hadoop-2.9.2.tar.gz
drwxrwxr-x 4 hadoop hadoop 31 Apr 19 21:52 mapred
-rw-r--r-- 1 hadoop hadoop 4452049 Apr 20 15:53 mysql-connector-java-5.1.47.tar.gz
-rw-r--r-- 1 hadoop hadoop 11323 Apr 19 22:11 qqqq.xlsx
drwxrwxr-x 4 hadoop hadoop 35 Apr 19 22:06 tmp
-rw-rw-r-- 1 hadoop hadoop 213 Apr 19 22:30 www.text
[[email protected] ~]$ tar -zxf apache-hive-2.3.4-bin.tar.gz
[[email protected] ~]$ tar -zxf mysql-connector-java-5.1.47.tar.gz
設置hive變量：
[[email protected] ~]$ egrep -v "^#|^$" .bashrc
if [ -f /etc/bashrc ]; then
. /etc/bashrc
fi
export HIVE_HOME=/home/hadoop/apache-hive-2.3.4-bin
export PATH=$PATH:$HIVE_HOME/bin
[[email protected] ~]$ source .bashrc建立配置文件：
[[email protected] conf]$ pwd
/home/hadoop/apache-hive-2.3.4-bin/conf
[[email protected] conf]$ cp hive-env.sh.template hive-env.sh
[[email protected] conf]$ cp hive-default.xml.template hive-site.xml
[[email protected] conf]$ cp hive-log4j2.properties.template hive-log4j2.properties
[[email protected] conf]$ cp hive-exec-log4j2.properties.template hive-exec-log4j2.properties
配置hive-site.xml，修改一下幾行：
<property>
<name>hive.exec.scratchdir</name>
<value>/home/hadoop/tmp/hive-${user.name}</value>
<description>HDFS root scratch dir for Hive jobs which gets created with write all (733) permission. For each connecting user, an HDFS scratch dir: ${hive.exec.scratchdir}/<username> is created, with ${hive.scratch.dir.permission}.</description>
</property>
<property>
<name>hive.exec.local.scratchdir</name>
<value>/home/hadoop/tmp/${user.name}</value>
<description>Local scratch space for Hive jobs</description>
</property>
<property>
<name>hive.downloaded.resources.dir</name>
<value>/home/hadoop/tmp/hive/resources</value>
<description>Temporary local directory for added resources in the remote file system.</description>
</property>
<property>
<name>hive.server2.logging.operation.log.location</name>
<value>/home/hadoop/tmp/${user.name}/operation_logs</value>
<description>Top level directory where operation logs are stored if logging functionality is enabled</description>
</property>
<property>
<name>hive.querylog.location</name>
<value>/home/hadoop/tmp/${user.name}</value>
<description>Location of Hive run time structured log file</description>
</property>
<property>
<name>javax.jdo.option.ConnectionURL</name>
<value>jdbc:mysql://192.168.3.76:3306/hive_metadata?&createDatabaseIfNotExist=true&characterEncoding=UTF-8&useSSL=false</value>
<description>
JDBC connect string for a JDBC metastore.
To use SSL to encrypt/authenticate the connection, provide database-specific SSL flag in the connection URL.
For example, jdbc:postgresql://myhost/db?ssl=true for postgres database.
</description>
</property>
<property>
<name>javax.jdo.option.ConnectionUserName</name>
<value>root</value>
<description>Username to use against metastore database</description>
</property>
<property>
<name>javax.jdo.option.ConnectionPassword</name>
<value>Wd#GDrf142D</value>
<description>password to use against metastore database</description>
</property>
<property>
<name>javax.jdo.option.ConnectionDriverName</name>
<value>com.mysql.jdbc.Driver</value>
<description>Driver class name for a JDBC metastore</description>
</property>
<property>
<name>datanucleus.schema.autoCreateAll</name>
<value>true</value>
<description>Auto creates necessary schema on a startup if one doesn‘t exist. Set this to false, after creating it once.To enable auto create also set hive.metastore.schema.verification=false. Auto creation is not recommended for production use cases, run schematool command instead.</description>
</property>
<property>
<name>hive.metastore.schema.verification</name>
<value>false</value>
<description>
Enforce metastore schema version consistency.
True: Verify that version information stored in is compatible with one from Hive jars. Also disable automatic
schema migration attempt. Users are required to manually migrate schema after Hive upgrade which ensures
proper metastore schema migration. (Default)
False: Warn if the version information stored in metastore doesn‘t match with one from in Hive jars.
</description>
</property>
配置hive-env.sh：
Set HADOOP_HOME to point to a specific hadoop install directory
# HADOOP_HOME=${bin}/../../hadoop
HADOOP_HOME=/home/hadoop/hadoop-2.9.2
# Hive Configuration Directory can be controlled by:
# export HIVE_CONF_DIR=
export HIVE_CONF_DIR=/home/hive/apache-hive-2.3.4-bin/conf加載mysql驅動
[[email protected] ~]$ ll
total 231144
drwxrwxr-x 10 hive hive 4096 Apr 20 14:21 apache-hive-2.3.4-bin
-rw-r--r-- 1 hive hive 232234292 Apr 20 14:16 apache-hive-2.3.4-bin.tar.gz
-rw-r--r-- 1 hive hive 4452049 Apr 20 15:23 mysql-connector-java-5.1.47.tar.gz
drwxrwxr-x 3 hive hive 17 Apr 20 15:07 tmp
[[email protected] ~]$ tar -zxf mysql-connector-java-5.1.47.tar.gz
[[email protected] ~]$ cp mysql-connector-java-5.1.47/mysql-connector-java-5.1.47.jar apache-hive-2.3.4-bin/lib/
為Hive創建HDFS目錄
在 Hive 中創建表之前需要使用以下 HDFS 命令創建 /tmp 和 /user/hive/warehouse (hive-site.xml 配置文件中屬性項 hive.metastore.warehouse.dir 的默認值) 目錄並給它們賦寫權限
[[email protected] ~]$ hadoop-2.9.2/bin/hdfs dfs -mkdir tmp
[[email protected] ~]$ hadoop-2.9.2/bin/hdfs dfs -mkdir -p /user/hive/warehouse
[[email protected] ~]$ hadoop-2.9.2/bin/hdfs dfs -chmod g+w /user/hive/warehouse
[[email protected] ~]$ hadoop-2.9.2/bin/hdfs dfs -chmod g+w tmp/
給mysql創建用戶hive/密碼hive<在沒有使用mysql數據庫root賬戶的情況下使用>：
$ mysql -u root -p #密碼已設為123456
mysql> CREATE USER ‘hive‘@‘localhost‘ IDENTIFIED BY "hive";
mysql> grant all privileges on . to [email protected] identified by ‘hive‘;
更改root遠程訪問：
mysql> use mysql;
mysql> update user set host = ‘%‘ where user = ‘root‘;
mysql> flush privileges;
mysql> select host, user from user;
+-----------+---------------+
| host | user |
+-----------+---------------+
| % | root |
| localhost | mysql.session |
| localhost | mysql.sys |
+-----------+---------------+
運行Hive
在命令行運行 hive 命令時必須保證 HDFS 已經啟動。可以使用 start-dfs.sh 來啟動 HDFS。
從 Hive 2.1 版本開始, 我們需要先運行 schematool 命令來執行初始化操作。
[[email protected] ~]$ apache-hive-2.3.4-bin/bin/schematool -dbType mysql -initSchema
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/home/hadoop/apache-hive-2.3.4-bin/lib/log4j-slf4j-impl-2.6.2.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/home/hadoop/hadoop-2.9.2/share/hadoop/common/lib/slf4j-log4j12-1.7.25.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
SLF4J: Actual binding is of type [org.apache.logging.slf4j.Log4jLoggerFactory]
Metastore connection URL: jdbc:mysql://192.168.3.76:3306/hive_metadata?&createDatabaseIfNotExist=true&characterEncoding=UTF-8&useSSL=false
Metastore Connection Driver : com.mysql.jdbc.Driver
Metastore connection User: root
Starting metastore schema initialization to 2.3.0
Initialization script hive-schema-2.3.0.mysql.sql
Initialization script completed
schemaTool completed啟動hive並測試：
[[email protected] ~]$ apache-hive-2.3.4-bin/bin/hive
which: no hbase in (/usr/local/bin:/bin:/usr/bin:/usr/local/sbin:/usr/sbin:/home/hadoop/.local/bin:/home/hadoop/bin:/home/hadoop/apache-hive-2.3.4-bin/bin)
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/home/hadoop/apache-hive-2.3.4-bin/lib/log4j-slf4j-impl-2.6.2.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/home/hadoop/hadoop-2.9.2/share/hadoop/common/lib/slf4j-log4j12-1.7.25.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
SLF4J: Actual binding is of type [org.apache.logging.slf4j.Log4jLoggerFactory]
Logging initialized using configuration in file:/home/hadoop/apache-hive-2.3.4-bin/conf/hive-log4j2.properties Async: true
Hive-on-MR is deprecated in Hive 2 and may not be available in the future versions. Consider using a different execution engine (i.e. spark, tez) or using Hive 1.X releases.
hive> show tables;
OK
Time taken: 3.236 seconds
hive> show databases;
OK
default
Time taken: 0.055 seconds, Fetched: 1 row(s)
hive> 簡答的hive語句測試：
建表：
hive> CREATE TABLE IF NOT EXISTS test (id INT,name STRING)ROW FORMAT DELIMITED FIELDS TERMINATED BY " " LINES TERMINATED BY "\n";
OK
Time taken: 0.524 seconds
hive> insert into test values(1,‘張三‘);
WARNING: Hive-on-MR is deprecated in Hive 2 and may not be available in the future versions. Consider using a different execution engine (i.e. spark, tez) or using Hive 1.X releases.
Query ID = hadoop_20190420163725_0be10015-72ae-4642-b2c4-311aaeaacaa8
Total jobs = 3
Launching Job 1 out of 3
Number of reduce tasks is set to 0 since there‘s no reduce operator
Starting Job = job_1555738609578_0001, Tracking URL = http://hadoop1:8088/proxy/application_1555738609578_0001/
Kill Command = /home/hadoop/hadoop-2.9.2/bin/hadoop job -kill job_1555738609578_0001
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0
2019-04-20 16:37:38,182 Stage-1 map = 0%, reduce = 0%
2019-04-20 16:37:43,443 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 2.28 sec
MapReduce Total cumulative CPU time: 2 seconds 280 msec
Ended Job = job_1555738609578_0001
Stage-4 is selected by condition resolver.
Stage-3 is filtered out by condition resolver.
Stage-5 is filtered out by condition resolver.
Moving data to directory hdfs://192.168.3.76:9000/user/hive/warehouse/test/.hive-staging_hive_2019-04-20_16-37-25_672_7073846121967206245-1/-ext-10000
Loading data to table default.test
MapReduce Jobs Launched:
Stage-Stage-1: Map: 1 Cumulative CPU: 2.28 sec HDFS Read: 4249 HDFS Write: 77 SUCCESS
Total MapReduce CPU Time Spent: 2 seconds 280 msec
OK
Time taken: 19.356 seconds
hive> select * from test;
OK
1 張三
Time taken: 0.352 seconds, Fetched: 1 row(s)
單機安裝hadoop-2.9.2+apache-hive-2.3.4-bin