hadoop 集群+kylin

作者: 大诗兄_zl | 来源:发表于2018-02-11 16:31 被阅读267次

    说明:不少读者反馈,想使用开源组件搭建Hadoop平台,然后再部署Kylin,但是遇到各种问题。这里我为读者部署一套环境,请朋友们参考一下。如果还有问题,再交流。

    系统环境以及各组件版本信息

    Linux操作系统:

    cat /etc/redhat-release

    CentOS Linux release 7.2.1511 (Core)

    JDK版本:

    java -version

    java version "1.8.0_111"

    Java(TM) SE Runtime Environment (build1.8.0_111-b14)

    Java HotSpot(TM) 64-Bit Server VM (build25.111-b14, mixed mode)

    Hadoop组件版本:

    Hive:apache-hive-1.2.1-bin

    Hadoop:hadoop-2.7.2

    HBase:hbase-1.1.9-bin

    Zookeeper:zookeeper-3.4.6

    Kylin版本:

    apache-kylin-1.5.4.1-hbase1.x-bin

    三个节点情况以及安装的组件(仅测试):

    192.168.1.129 ldvl-kyli-a01 ldvl-kyli-a01.idc.dream.com

    192.168.1.130 ldvl-kyli-a02 ldvl-kyli-a02.idc.dream.com

    192.168.1.131 ldvl-kyli-a03 ldvl-kyli-a03.idc.dream.com

    基础组件部署

    1.  JDK环境搭建(3个节点)
      

    rpm包安装:

    rpm -ivh jdk-8u111-linux-x64.rpm

    配置环境变量:

    vi /etc/profile

    export JAVA_HOME=/usr/java/default

    export JRE_HOME=/usr/java/default/jre

    exportCLASSPATH=.:$JAVA_HOME/lib:$JRE_HOME/lib:$CLASSPATH

    exportPATH=$JAVA_HOME/bin:$JRE_HOME/bin:$PATH

    source /etc/profile

    验证:

    java -version

    java version "1.8.0_111"

    Java(TM) SE Runtime Environment (build1.8.0_111-b14)

    Java HotSpot(TM) 64-Bit Server VM (build25.111-b14, mixed mode)

    1.  Zookeeper环境搭建(3个节点)
      

    安装:

    tar -zxvf zookeeper-3.4.6.tar.gz -C /usr/local/

    cd /usr/local/

    ln -s zookeeper-3.4.6 zookeeper

    创建数据和日志目录

    mkdir /usr/local/zookeeper/zkdata

    mkdir /usr/local/zookeeper/zkdatalog

    配置Zookeeper参数

    cd /usr/local/zookeeper/conf

    cp zoo_sample.cfg zoo.cfg

    修改好的配置文件如下:

    tickTime=2000

    initLimit=10

    syncLimit=5

    dataDir=/usr/local/zookeeper/zkdata

    dataLogDir=/usr/local/zookeeper/zkdatalog

    clientPort=2181

    server.1=ldvl-kyli-a01:2888:3888

    server.2=ldvl-kyli-a02:2888:3888

    server.3=ldvl-kyli-a03:2888:3888

    创建myid

    cd /usr/local/zookeeper/zkdata

    echo 1 > myid #每个节点根据上面的配置(server.x)创建对应的文件内容

    启动Zookeeper:

    zkServer.sh start

    查看状态:

    192.168.1.129节点:

    zkServer.sh status

    JMX enabled by default

    Using config:/usr/local/zookeeper/bin/../conf/zoo.cfg

    Mode: follower

    192.168.1.130节点:

    zkServer.sh status

    JMX enabled by default

    Using config:/usr/local/zookeeper/bin/../conf/zoo.cfg

    Mode: leader

    192.168.1.131节点:

    zkServer.sh status

    JMX enabled by default

    Using config:/usr/local/zookeeper/bin/../conf/zoo.cfg

    Mode: follower

    1.  MariaDB数据库
      

    安装:

    yum install MariaDB-server MariaDB-client

    启动:

    systemctl start mariadb

    设置root密码,安全加固等:

    mysql_secure_installation

    1.  关闭防火墙
      

    systemctl disable firewalld

    systemctl stop firewalld

    同时,也需要关闭SELinux,可修改 /etc/selinux/config 文件,将其中的 SELINUX=enforcing 改为 SELINUX=disabled即可。

    1.  三个节点保证时间同步
      

    可以通过ntp服务进行设置

    Hadoop组件部署

    1.  Hadoop
      

    创建组和用户:

    groupadd hadoop

    useradd -s /bin/bash -d /app/hadoop -m hadoop-g hadoop

    passwd hadoop

    下面所有的操作都是在hadoop用户下面操作

    切换到hadoop用户下面创建信任关系:

    ssh-keygen -t rsa

    ssh-copy-id -p 22 hadoop@192.168.1.129

    ssh-copy-id -p 22 hadoop@192.168.1.130

    ssh-copy-id -p 22 hadoop@192.168.1.131

    解压缩:

    $ tar -zxvf hadoop-2.7.2.tar.gz

    设置软链接:

    $ ln -s hadoop-2.7.2 hadoop

    配置:

    $ cd /app/hadoop/hadoop/etc/hadoop

    l core-site.xml

    <configuration>

    <property>

       <name>fs.defaultFS</name>
    
       <value>hdfs://ldvl-kyli-a01:9000</value>
    

    </property>

    <property>

       <name>hadoop.tmp.dir</name>
    
       <value>file:/app/hadoop/hadoop/tmp</value>
    

    </property>

    <property>

       <name>io.file.buffer.size</name>
    
       <value>131702</value>
    

    </property>

    </configuration>

    l hdfs-site.xml

    <configuration>

    <property>

       <name>dfs.namenode.name.dir</name>
    
       <value>file:/app/hadoop/hdfs/name</value>
    

    </property>

    <property>

       <name>dfs.datanode.data.dir</name>
    
       <value>file:/app/hadoop/hdfs/data</value>
    

    </property>

    <property>

       <name>dfs.replication</name>
    
       <value>3</value>
    

    </property>

    <property>

      <name>dfs.http.address</name> 
    
      <value>ldvl-kyli-a01:50070</value> 
    

    </property>

    <property>

       <name>dfs.namenode.secondary.http-address</name>
    
       <value>ldvl-kyli-a01:50090</value>
    

    </property>

    <property>

       <name>dfs.webhdfs.enabled</name>
    
       <value>true</value>
    

    </property>

    <property>

       <name>dfs.permissions</name>
    
       <value>false</value>
    

    </property>

    <property>

       <name>dfs.blocksize</name>
    
       <value>268435456</value>
    
       <description>HDFS blocksize of 256MB for largefile-systems.</description>
    

    </property>

    <property>

      <name>dfs.datanode.max.xcievers</name>
    
      <value>4096</value>
    

    </property>

    </configuration>

    l yarn-site.xml

    <configuration>

    <property>

    <name>yarn.resourcemanager.address</name>

    <value>ldvl-kyli-a01:8032</value>

    </property>

    <property>

    <name>yarn.resourcemanager.scheduler.address</name>

    <value>ldvl-kyli-a01:8030</value>

    </property>

    <property>

    <name>yarn.resourcemanager.resource-tracker.address</name>

    <value>ldvl-kyli-a01:8031</value>

    </property>

    <property>

    <name>yarn.resourcemanager.admin.address</name>

    <value>ldvl-kyli-a01:8033</value>

    </property>

    <property>

    <name>yarn.resourcemanager.webapp.address</name>

    <value>ldvl-kyli-a01:8088</value>

    </property>

    <property>

    <name>yarn.nodemanager.aux-services</name>

    <value>mapreduce_shuffle</value>

    <description>Configuration to enable or disable logaggregation.Shuffle service that needs to be set for Map Reduceapplications.</description>

    </property>

    <property>

    <name>yarn.nodemanager.aux-services.mapreduce_shuffle.class</name>

    <value>org.apache.hadoop.mapred.ShuffleHandler</value>

    </property>

    </configuration>

    l mapred-site.xml

    <configuration>

    <property>

       <name>mapreduce.framework.name</name>
    
       <value>yarn</value>
    

    </property>

    <property>

       <name>mapreduce.jobhistory.address</name>
    
       <value>ldvl-kyli-a01:10020</value>
    

    </property>

    <property>

       <name>mapreduce.jobhistory.webapp.address</name>
    
       <value>ldvl-kyli-a01:19888</value>
    

    </property>

    </configuration>

    l slaves

    ldvl-kyli-a01

    ldvl-kyli-a02

    ldvl-kyli-a03

    l hadoop-env.sh,mapred-env.sh和yarn-env.sh

    export JAVA_HOME=/usr/java/default

    环境变量配置(这里我将所有的组件的环境变量都配置好了,后面每个组件我就不再说明):

    $ cat .bashrc

    export JAVA_HOME=/usr/java/default

    export JRE_HOME=/usr/java/default/jre

    exportCLASSPATH=.:$JAVA_HOME/lib:$JRE_HOME/lib:$CLASSPATH

    export PATH=$JAVA_HOME/bin:$JRE_HOME/bin:$PATH

    export HIVE_HOME=/app/hadoop/hive

    export HADOOP_HOME=/app/hadoop/hadoop

    export HBASE_HOME=/app/hadoop/hbase

    added by HCAT

    export HCAT_HOME=/app/hadoop/hive/hcatalog

    added by Kylin

    export KYLIN_HOME=/app/hadoop/kylin

    export KYLIN_CONF=/app/hadoop/kylin/conf

    exportPATH=$HADOOP_HOME/bin:$HADOOP_HOME/sbin:$HIVE_HOME/bin:$HBASE_HOME/bin:${KYLIN_HOME}/bin:$PATH

    创建HDFS的数据目录

    $ mkdir -p /app/hadoop/hdfs/data

    $ mkdir -p /app/hadoop/hdfs/name

    $ mkdir -p /app/hadoop/tmp

    加入上面的hadoop所有配置都配置完成了,你也可以全部拷贝到其他节点。

    HDFS格式化:

    $ hdfs namenode -format

    $ start-dfs.sh

    $ start-yarn.sh

    $ mr-jobhistory-daemon.sh starthistoryserver

    然后进行验证操作,比如同通过jps查看进程,通过web页面服务hdfs和yarn,执行wordcount的测试程序等等

    Hive组件部署

    安装:

    $ tar -zxvf apache-hive-1.2.1-bin.tar.gz

    $ ln -s apache-hive-1.2.1-bin hive

    配置:

    $ cd /app/hadoop/hive/conf

    l hive-env.sh

    export HIVE_HOME=/app/hadoop/hive

    HADOOP_HOME=/app/hadoop/hadoop

    export HIVE_CONF_DIR=/app/hadoop/hive/conf

    l hive-site.xml

    <configuration>

    <property>

    <name>hive.metastore.warehouse.dir</name>

    <value>hdfs://ldvl-kyli-a01:9000/user/hive/warehouse</value>

    </property>

    <property>

    <name>hive.exec.scratchdir</name>

    <value>hdfs://ldvl-kyli-a01:9000/user/hive/scratchdir</value>

    </property>

    <property>

    <name>javax.jdo.option.ConnectionURL</name>

    <value>jdbc:mysql://ldvl-kyli-a01:3306/metastore?createDatabaseIfNotExist=true</value>

    </property>

    <property>

    <name>javax.jdo.option.ConnectionDriverName</name>

    <value>com.mysql.jdbc.Driver</value>

    </property>

    <property>

    <name>javax.jdo.option.ConnectionUserName</name>

    <value>hive</value>

    </property>

    <property>

    <name>javax.jdo.option.ConnectionPassword</name>

    <value>123456</value>

    </property>

    <property>

    <name>hive.metastore.local</name>

    <value>true</value>

    </property>

    <property>

    <name>hive.metastore.uris</name>

    <value>thrift://ldvl-kyli-a01:9083</value>

    </property>

    </configuration>

    l hive-log4j.properties

    hive.log.dir=/app/hadoop/hive/log

    hive.log.file=hive.log

    将mysql-connector-java-5.1.38-bin.jar放到Hive的lib目录下面:

    $ cp mysql-connector-java-5.1.38-bin.jar/app/hadoop/hive/lib/

    创建Hive元数据库:

    MariaDB [(none)]> create database metastore character set latin1;

    grant all on metastore.* to hive@"%" identified by "123456" with grant option;

    flush privileges;

    启动服务:

    nohup hive --service metastore -v &

    $ tailf nohup.out

    Starting Hive Metastore Server

    17/03/16 14:10:29 WARN conf.HiveConf:HiveConf of name hive.metastore.local does not exist

    Starting hive metastore on port 9083

    HBase组件部署

    安装:

    $ tar -zxvf hbase-1.1.9-bin.tar.gz

    $ ln -s hbase-1.1.9 hbase

    配置:

    l hbase-site.xml

    <configuration>

    <property>

       <name>hbase.rootdir</name>
    
       <value>hdfs://ldvl-kyli-a01:9000/hbaseforkylin</value>
    

    </property>

    <property>

       <name>hbase.cluster.distributed</name>
    
       <value>true</value>
    

    </property>

    <property>

       <name>hbase.master.port</name>
    
       <value>16000</value>
    
    </property>
    

    <property>

       <name>hbase.master.info.port</name>
    
       <value>16010</value>
    

    </property>

    <property>

       <name>hbase.zookeeper.quorum</name>
    
       <value>ldvl-kyli-a01,ldvl-kyli-a02,ldvl-kyli-a03</value>
    

    </property>

    <property>

       <name>hbase.zookeeper.property.clientPort</name>
    
       <value>2181</value>
    

    </property>

    <property>

       <name>hbase.zookeeper.property.dataDir</name>
    
       <value>/usr/local/zookeeper/zkdata</value>
    

    </property>

    </configuration>

    l regionservers

    ldvl-kyli-a02

    ldvl-kyli-a03

    l hbase-env.sh

    export JAVA_HOME=/usr/java/latest

    export HBASE_OPTS="-Xmx268435456-XX:+HeapDumpOnOutOfMemoryError -XX:+UseConcMarkSweepGC -XX:+CMSIncrementalMode-Djava.net.preferIPv4Stack=true $HBASE_OPTS"

    exportHBASE_MASTER_OPTS="$HBASE_MASTER_OPTS -XX:PermSize=128m-XX:MaxPermSize=128m"

    exportHBASE_REGIONSERVER_OPTS="$HBASE_REGIONSERVER_OPTS -XX:PermSize=128m-XX:MaxPermSize=128m"

    export HBASE_LOG_DIR=${HBASE_HOME}/logs

    export HBASE_PID_DIR=${HBASE_HOME}/logs

    export HBASE_MANAGES_ZK=false

    如果日志目录不存在,需要提前创建好。

    启动HBase服务:

    $ start-hbase.sh

    Kylin环境部署(我只选第一个节点安装,仅测试)

    安装:

    $ tar -zxvf apache-kylin-1.5.4.1-hbase1.x-bin.tar.gz

    $ ln -s apache-kylin-1.5.4.1-hbase1.x-bin kylin

    配置:

    $ cd kylin/conf/

    l kylin.properties # 基本默认值

    kyin.server.mode=all

    kylin.rest.servers=192.168.1.129:7070

    kylin.rest.timezone=GMT+8

    kylin.hive.client=cli

    kylin.hive.keep.flat.table=false

    kylin.metadata.url=kylin_metadata@hbase

    kylin.storage.url=hbase

    kylin.storage.cleanup.time.threshold=172800000

    kylin.hdfs.working.dir=/kylin

    kylin.hbase.default.compression.codec=none

    kylin.hbase.region.cut=5

    kylin.hbase.hfile.size.gb=2

    kylin.hbase.region.count.min=1

    kylin.hbase.region.count.max=50

    环境变量配置:

    $ cat .bashrc

    export JAVA_HOME=/usr/java/default

    export JRE_HOME=/usr/java/default/jre

    exportCLASSPATH=.:$JAVA_HOME/lib:$JRE_HOME/lib:$CLASSPATH

    exportPATH=$JAVA_HOME/bin:$JRE_HOME/bin:$PATH

    export HIVE_HOME=/app/hadoop/hive

    export HADOOP_HOME=/app/hadoop/hadoop

    export HBASE_HOME=/app/hadoop/hbase

    added by HCAT

    export HCAT_HOME=/app/hadoop/hive/hcatalog

    added by Kylin

    export KYLIN_HOME=/app/hadoop/kylin

    export KYLIN_CONF=/app/hadoop/kylin/conf

    exportPATH=$HADOOP_HOME/bin:$HADOOP_HOME/sbin:$HIVE_HOME/bin:$HBASE_HOME/bin:${KYLIN_HOME}/bin:$PATH

    检查Kylin用来的环境变量:

    $ ${KYLIN_HOME}/bin/check-env.sh

    KYLIN_HOME is set to /app/hadoop/kylin

    $ kylin/bin/find-hbase-dependency.sh

    hbase dependency: /app/hadoop/hbase/lib/hbase-common-1.1.9.jar

    $ kylin/bin/find-hive-dependency.sh

    Logging initialized using configuration infile:/app/hadoop/apache-hive-1.2.1-bin/conf/hive-log4j.properties

    HCAT_HOME is set to:/app/hadoop/hive/hcatalog, use it to find hcatalog path:

    hive dependency:/app/hadoop/hive/conf:/app/hadoop/hive/lib/jcommander-1.32.jar:/app/hadoop/hive/lib/stringtemplate-3.2.1.jar:/app/hadoop/hive/lib/hive-shims-0.23-1.2.1.jar:/app/hadoop/hive/lib/hive-jdbc-1.2.1-standalone.jar:/app/hadoop/hive/lib/hamcrest-core-1.1.jar:/app/hadoop/hive/lib/commons-compress-1.4.1.jar:/app/hadoop/hive/lib/xz-1.0.jar:/app/hadoop/hive/lib/hive-common-1.2.1.jar:/app/hadoop/hive/lib/guava-14.0.1.jar:/app/hadoop/hive/lib/commons-collections-3.2.1.jar:/app/hadoop/hive/lib/jta-1.1.jar:/app/hadoop/hive/lib/antlr-2.7.7.jar:/app/hadoop/hive/lib/maven-scm-provider-svn-commons-1.4.jar:/app/hadoop/hive/lib/hive-metastore-1.2.1.jar:/app/hadoop/hive/lib/hive-jdbc-1.2.1.jar:/app/hadoop/hive/lib/commons-httpclient-3.0.1.jar:/app/hadoop/hive/lib/ivy-2.4.0.jar:/app/hadoop/hive/lib/geronimo-annotation_1.0_spec-1.1.1.jar:/app/hadoop/hive/lib/commons-pool-1.5.4.jar:/app/hadoop/hive/lib/maven-scm-api-1.4.jar:/app/hadoop/hive/lib/mysql-connector-java-5.1.38-bin.jar:/app/hadoop/hive/lib/commons-configuration-1.6.jar:/app/hadoop/hive/lib/accumulo-start-1.6.0.jar:/app/hadoop/hive/lib/asm-commons-3.1.jar:/app/hadoop/hive/lib/libfb303-0.9.2.jar:/app/hadoop/hive/lib/commons-dbcp-1.4.jar:/app/hadoop/hive/lib/log4j-1.2.16.jar:/app/hadoop/hive/lib/hive-shims-common-1.2.1.jar:/app/hadoop/hive/lib/junit-4.11.jar:/app/hadoop/hive/lib/antlr-runtime-3.4.jar:/app/hadoop/hive/lib/commons-cli-1.2.jar:/app/hadoop/hive/lib/commons-logging-1.1.3.jar:/app/hadoop/hive/lib/ant-1.9.1.jar:/app/hadoop/hive/lib/hive-contrib-1.2.1.jar:/app/hadoop/hive/lib/httpcore-4.4.jar:/app/hadoop/hive/lib/datanucleus-api-jdo-3.2.6.jar:/app/hadoop/hive/lib/commons-beanutils-1.7.0.jar:/app/hadoop/hive/lib/curator-recipes-2.6.0.jar:/app/hadoop/hive/lib/netty-3.7.0.Final.jar:/app/hadoop/hive/lib/accumulo-trace-1.6.0.jar:/app/hadoop/hive/lib/jetty-all-server-7.6.0.v20120127.jar:/app/hadoop/hive/lib/servlet-api-2.5.jar:/app/hadoop/hive/lib/curator-client-2.6.0.jar:/app/hadoop/hive/lib/hive-shims-scheduler-1.2.1.jar:/app/hadoop/hive/lib/commons-lang-2.6.jar:/app/hadoop/hive/lib/geronimo-jaspic_1.0_spec-1.0.jar:/app/hadoop/hive/lib/curator-framework-2.6.0.jar:/app/hadoop/hive/lib/asm-tree-3.1.jar:/app/hadoop/hive/lib/hive-beeline-1.2.1.jar:/app/hadoop/hive/lib/velocity-1.5.jar:/app/hadoop/hive/lib/maven-scm-provider-svnexe-1.4.jar:/app/hadoop/hive/lib/commons-io-2.4.jar:/app/hadoop/hive/lib/ant-launcher-1.9.1.jar:/app/hadoop/hive/lib/mail-1.4.1.jar:/app/hadoop/hive/lib/accumulo-core-1.6.0.jar:/app/hadoop/hive/lib/geronimo-jta_1.1_spec-1.1.1.jar:/app/hadoop/hive/lib/oro-2.0.8.jar:/app/hadoop/hive/lib/eigenbase-properties-1.1.5.jar:/app/hadoop/hive/lib/commons-math-2.1.jar:/app/hadoop/hive/lib/apache-log4j-extras-1.2.17.jar:/app/hadoop/hive/lib/commons-compiler-2.7.6.jar:/app/hadoop/hive/lib/commons-digester-1.8.jar:/app/hadoop/hive/lib/ST4-4.0.4.jar:/app/hadoop/hive/lib/parquet-hadoop-bundle-1.6.0.jar:/app/hadoop/hive/lib/datanucleus-core-3.2.10.jar:/app/hadoop/hive/lib/json-20090211.jar:/app/hadoop/hive/lib/bonecp-0.8.0.RELEASE.jar:/app/hadoop/hive/lib/hive-service-1.2.1.jar:/app/hadoop/hive/lib/snappy-java-1.0.5.jar:/app/hadoop/hive/lib/stax-api-1.0.1.jar:/app/hadoop/hive/lib/jetty-all-7.6.0.v20120127.jar:/app/hadoop/hive/lib/jline-2.12.jar:/app/hadoop/hive/lib/libthrift-0.9.2.jar:/app/hadoop/hive/lib/hive-testutils-1.2.1.jar:/app/hadoop/hive/lib/accumulo-fate-1.6.0.jar:/app/hadoop/hive/lib/hive-cli-1.2.1.jar:/app/hadoop/hive/lib/hive-accumulo-handler-1.2.1.jar:/app/hadoop/hive/lib/jpam-1.1.jar:/app/hadoop/hive/lib/groovy-all-2.1.6.jar:/app/hadoop/hive/lib/httpclient-4.4.jar:/app/hadoop/hive/lib/avro-1.7.5.jar:/app/hadoop/hive/lib/zookeeper-3.4.6.jar:/app/hadoop/hive/lib/hive-hwi-1.2.1.jar:/app/hadoop/hive/lib/hive-exec-1.2.1.jar:/app/hadoop/hive/lib/hive-shims-0.20S-1.2.1.jar:/app/hadoop/hive/lib/super-csv-2.2.0.jar:/app/hadoop/hive/lib/opencsv-2.3.jar:/app/hadoop/hive/lib/commons-vfs2-2.0.jar:/app/hadoop/hive/lib/hive-serde-1.2.1.jar:/app/hadoop/hive/lib/commons-beanutils-core-1.8.0.jar:/app/hadoop/hive/lib/derby-10.10.2.0.jar:/app/hadoop/hive/lib/plexus-utils-1.5.6.jar:/app/hadoop/hive/lib/datanucleus-rdbms-3.2.9.jar:/app/hadoop/hive/lib/jdo-api-3.0.1.jar:/app/hadoop/hive/lib/joda-time-2.5.jar:/app/hadoop/hive/lib/activation-1.1.jar:/app/hadoop/hive/lib/janino-2.7.6.jar:/app/hadoop/hive/lib/regexp-1.3.jar:/app/hadoop/hive/lib/hive-shims-1.2.1.jar:/app/hadoop/hive/lib/paranamer-2.3.jar:/app/hadoop/hive/lib/hive-hbase-handler-1.2.1.jar:/app/hadoop/hive/lib/tempus-fugit-1.1.jar:/app/hadoop/hive/lib/commons-codec-1.4.jar:/app/hadoop/hive/lib/hive-ant-1.2.1.jar:/app/hadoop/hive/lib/jsr305-3.0.0.jar:/app/hadoop/hive/lib/pentaho-aggdesigner-algorithm-5.1.5-jhyde.jar:/app/hadoop/hive/hcatalog/share/hcatalog/hive-hcatalog-core-1.2.1.jar

    环境检查没有问题,开始启动Kylin服务:

    kylin.sh start

    导入样例:

    $ sample.sh

    然后通过Kylin的Web页面重新加载元数据,然后构建Cube就可以查询了:

    image.png

    查询:

    select part_dt, sum(price) as total_selled,count(distinct seller_id) as sellers from kylin_sales group by part_dt order bypart_dt

    image.png

    转自 http://blog.csdn.net/jiangshouzhuang/article/details/64151586

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