一. 数据准备

以Oracle scott下的emp表dept表为例

参考博客:
MySQL版本scott

根据MySQL版本的数据，同步到hive。

二. 创建项目

Kylin的架构是 :
项目->Model->Cube

1. 重新加载元数据
   

2. 创建project
   

三. 选择数据源

1. Model->Data Source-> Load Table From Tree
   

2. 选择我们需要的emp和dept表
   

四. 创建Model

1. 选择 Model->Models->New Model
   

2. 输入mode的名称
   

3. 选择事实表
   

4. 选择维度表
   选择Add Lookup Table
   

4. 选择维度
   

5. 选择度量
   

6. 没有分区，直接略过
   

五. 创建cube

1. 选择 Model->New Cube->
   

2. 选择Model的名称，并填写Cube名称
   

3. 选择维度
   需要注意的是，维度表的维度默认是 Dervied，最好改为Normal
   

4. 选择度量
   默认有一个count(*),此时我们再加一个sum(sal)
   

5. HBase刷新策略，选择默认
   

6. 高级配置先都略过
   

7. 查看最终的描述
   

8. 将新创建的cube进行build
   

9. 监控build的进度
   

10. 等待几分钟，知道build运行完成
    

六. Kylin对比Hive

sql脚本

select dname, sum(sal) from emp ejoin dept don e.deptno = d.deptnogroup by dname;

6.1 Hive端执行

hive> > select dname, sum(sal) >   from emp e>   join dept d>     on e.deptno = d.deptno>   group by dname;
Query ID = root_20211231110115_de250e84-2a0a-4c12-bca0-12c17c2de113
Total jobs = 1
SLF4J: Actual binding is of type [org.apache.logging.slf4j.Log4jLoggerFactory]
2021-12-31 11:01:22     Starting to launch local task to process map join;      maximum memory = 1908932608
2021-12-31 11:01:24     Dump the side-table for tag: 1 with group count: 4 into file: file:/tmp/root/5f296d47-5178-464c-b2e4-28fdba9439ec/hive_2021-12-31_11-01-15_353_7571014424349893653-1/-local-10004/HashTable-Stage-2/MapJoin-mapfile01--.hashtable
2021-12-31 11:01:24     Uploaded 1 File to: file:/tmp/root/5f296d47-5178-464c-b2e4-28fdba9439ec/hive_2021-12-31_11-01-15_353_7571014424349893653-1/-local-10004/HashTable-Stage-2/MapJoin-mapfile01--.hashtable (373 bytes)
2021-12-31 11:01:24     End of local task; Time Taken: 1.475 sec.
Execution completed successfully
MapredLocal task succeeded
Launching Job 1 out of 1
Number of reduce tasks not specified. Estimated from input data size: 1
In order to change the average load for a reducer (in bytes):set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:set mapreduce.job.reduces=<number>
21/12/31 11:01:26 INFO client.ConfiguredRMFailoverProxyProvider: Failing over to rm69
Starting Job = job_1640913930054_0012, Tracking URL = http://hp3:8088/proxy/application_1640913930054_0012/
Kill Command = /opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/lib/hadoop/bin/hadoop job  -kill job_1640913930054_0012
Hadoop job information for Stage-2: number of mappers: 2; number of reducers: 1
2021-12-31 11:01:33,600 Stage-2 map = 0%,  reduce = 0%
2021-12-31 11:01:40,867 Stage-2 map = 100%,  reduce = 0%, Cumulative CPU 7.36 sec
2021-12-31 11:01:46,037 Stage-2 map = 100%,  reduce = 100%, Cumulative CPU 9.91 sec
MapReduce Total cumulative CPU time: 9 seconds 910 msec
Ended Job = job_1640913930054_0012
MapReduce Jobs Launched: 
Stage-Stage-2: Map: 2  Reduce: 1   Cumulative CPU: 9.91 sec   HDFS Read: 21315 HDFS Write: 174 HDFS EC Read: 0 SUCCESS
Total MapReduce CPU Time Spent: 9 seconds 910 msec
OK
accounting      8750.00
research        10875.00
sales   9400.00
Time taken: 31.859 seconds, Fetched: 3 row(s)
hive> 

6.2 Kylin

第一次查询 1.79秒，速度比Hive的10秒快了很多

第二次就很快