kylin2.4.1订单案例详细构建流程
一、Hive订单数据仓库构建：
hive表创建可以在命令行中直接完成，也可以在Hue中完成，本文在Hue中的完成，如下图：
下文的样例文本文件下载地址：https://files-cdn.cnblogs.com/files/qqflying/KylinData.zip
1. 创建事实表并插入数据
执行1： DROP TABLE IF EXISTS default.fact_order ;
执行2：
create table default.fact_order (
time_key string,
product_key string,
salesperson_key string,
custom_key string,
quantity_ordered bigint,
order_dollars bigint,
cost_dollars bigint
)
ROW FORMAT DELIMITED FIELDS TERMINATED BY ','
STORED AS TEXTFILE;
执行3：load data local inpath '/data/fact_order.txt' overwrite into table default.fact_order;
fact_order.txt
2016-05-01,pd001,sp001,ct001,100,2000,1000
2016-05-01,pd001,sp002,ct002,100,2000,1000
2016-05-01,pd001,sp003,ct002,100,2000,1000
2016-05-01,pd002,sp002,ct002,100,2000,1000
2016-05-01,pd003,sp003,ct001,100,2000,1000
2016-05-01,pd001,sp003,ct001,100,2000,1000
2016-05-01,pd001,sp002,ct001,100,2000,1000
2016-05-01,pd001,sp003,ct002,100,2000,1000
2016-05-01,pd002,sp001,ct001,100,2000,1000
2016-05-01,pd003,sp001,ct001,100,2000,1000
2016-05-01,pd004,sp001,ct001,50,1000,600
2016-05-02,pd001,sp001,ct001,50,1000,600
2016-05-02,pd001,sp002,ct002,100,2000,1000
2016-05-02,pd001,sp003,ct002,100,2000,1000
2016-05-02,pd002,sp001,ct001,50,1000,600
2016-05-02,pd003,sp001,ct001,50,1000,600
2016-05-02,pd004,sp001,ct001,50,1000,600
2016-05-03,pd001,sp001,ct001,50,1000,600
2016-05-03,pd001,sp002,ct002,100,2000,1000
2016-05-03,pd001,sp003,ct002,100,2000,1000
2016-05-04,pd002,sp001,ct001,700,14000,10000
2016-05-04,pd003,sp001,ct001,700,14000,10000
2016-05-04,pd004,sp001,ct001,100,2000,1000
2016-05-05,pd001,sp001,ct001,100,2000,1000
2016-05-05,pd001,sp002,ct002,700,14000,10000
2016-05-05,pd001,sp003,ct002,700,14000,10000
2016-05-05,pd002,sp001,ct001,100,2000,1000
2016-05-05,pd003,sp001,ct001,100,2000,1000
2016-05-05,pd004,sp001,ct001,100,2000,1000
2016-05-06,pd001,sp001,ct001,100,2000,1000
2016-05-06,pd001,sp002,ct002,100,2000,1000
2016-05-06,pd001,sp003,ct002,100,2000,1000
2016-05-07,pd002,sp001,ct001,100,2000,1000
2016-05-07,pd003,sp001,ct001,100,2000,1000
2016-05-07,pd004,sp001,ct001,50,1000,600
2016-05-07,pd002,sp001,ct001,100,2000,1000
2016-05-07,pd003,sp001,ct001,100,2000,1000
2016-05-07,pd004,sp001,ct001,50,1000,600
2016-05-08,pd001,sp001,ct001,50,1000,600
2016-05-08,pd001,sp002,ct002,100,2000,1000
2016-05-08,pd001,sp003,ct002,100,2000,1000
2016-05-08,pd001,sp001,ct001,50,1000,600
2016-05-08,pd001,sp002,ct002,100,2000,1000
2016-05-08,pd001,sp003,ct002,100,2000,1000
2016-05-08,pd001,sp001,ct001,50,1000,600
2016-05-08,pd001,sp002,ct002,100,2000,1000
2016-05-08,pd001,sp003,ct002,100,2000,1000
2016-05-09,pd002,sp001,ct001,50,1000,600
2016-05-09,pd003,sp001,ct001,50,1000,600
2016-05-09,pd004,sp001,ct001,50,1000,600
2016-05-09,pd001,sp001,ct001,50,1000,600
2016-05-09,pd002,sp001,ct001,50,1000,600
2016-05-09,pd003,sp001,ct001,50,1000,600
2016-05-09,pd004,sp001,ct001,50,1000,600
2016-05-09,pd001,sp001,ct001,50,1000,600
2016-05-09,pd001,sp002,ct002,100,2000,1000
2016-05-09,pd004,sp003,ct002,100,2000,1000
2016-05-09,pd002,sp001,ct001,700,14000,10000
2016-05-09,pd003,sp003,ct001,700,14000,10000
2016-05-09,pd004,sp003,ct001,100,2000,1000
2016-05-10,pd001,sp001,ct001,100,2000,1000
2016-05-10,pd001,sp002,ct002,700,14000,10000
2016-05-10,pd001,sp003,ct002,700,14000,10000
2016-05-10,pd002,sp001,ct001,100,2000,1000
2016-05-11,pd003,sp003,ct001,100,2000,1000
2016-05-11,pd004,sp001,ct001,100,2000,1000
2016-05-12,pd001,sp001,ct001,100,2000,1000
2016-05-12,pd004,sp002,ct002,100,2000,1000
2016-05-12,pd001,sp003,ct002,100,2000,1000
2016-05-12,pd001,sp001,ct001,100,2000,1000
2016-05-12,pd004,sp002,ct002,100,2000,1000
2016-05-12,pd001,sp003,ct002,100,2000,1000
2016-05-13,pd002,sp001,ct001,100,2000,1000
2016-05-13,pd003,sp001,ct001,100,2000,1000
2016-05-13,pd004,sp001,ct001,50,1000,600
2016-05-14,pd001,sp001,ct001,50,1000,600
2016-05-14,pd001,sp002,ct002,100,2000,1000
2016-05-14,pd001,sp003,ct002,100,2000,1000
2016-05-15,pd002,sp001,ct001,50,1000,600
2016-05-15,pd003,sp001,ct001,50,1000,600
2016-05-15,pd004,sp001,ct001,50,1000,600
2016-05-15,pd002,sp001,ct001,50,1000,600
2016-05-15,pd003,sp001,ct001,50,1000,600
2016-05-15,pd004,sp001,ct001,50,1000,600
2016-05-15,pd002,sp001,ct001,50,1000,600
2016-05-15,pd003,sp001,ct001,50,1000,600
2016-05-15,pd004,sp001,ct001,50,1000,600
2016-05-16,pd001,sp001,ct001,50,1000,600
2016-05-16,pd001,sp002,ct002,100,2000,1000
2016-05-16,pd001,sp003,ct002,100,2000,1000
2016-05-16,pd001,sp001,ct001,50,1000,600
2016-05-16,pd001,sp002,ct002,100,2000,1000
2016-05-16,pd001,sp003,ct002,100,2000,1000
2016-05-17,pd002,sp001,ct001,700,14000,10000
2016-05-17,pd003,sp001,ct001,700,14000,10000
2016-05-17,pd004,sp001,ct001,100,2000,1000
2016-05-17,pd002,sp001,ct001,700,14000,10000
2016-05-17,pd003,sp001,ct001,700,14000,10000
2016-05-17,pd004,sp001,ct001,100,2000,1000
2016-05-18,pd001,sp001,ct001,100,2000,1000
2016-05-18,pd003,sp002,ct001,700,14000,10000
2016-05-18,pd001,sp003,ct002,700,14000,10000
2016-05-19,pd002,sp001,ct001,100,2000,1000
2016-05-19,pd003,sp001,ct002,100,2000,1000
2016-05-20,pd001,sp001,ct001,100,2000,1000
2016-05-20,pd002,sp002,ct002,100,2000,1000
2016-05-20,pd003,sp003,ct001,100,2000,1000
2016-05-20,pd004,sp001,ct001,100,2000,1000
2016-05-20,pd001,sp002,ct002,100,2000,1000
2016-05-20,pd002,sp001,ct002,100,2000,1000
2. 创建天维度表dim_day（同样也分三步执行）
DROP TABLE IF EXISTS default.dim_day ;
create table default.dim_day (
day_key string,
full_day string,
month_name string,
quarter string,
year string
)
ROW FORMAT DELIMITED FIELDS TERMINATED BY ','
STORED AS TEXTFILE;
load data local inpath '/data/dim_day.txt' overwrite into table default.dim_day;
dim_day.txt
2016-05-01,2016-05-01,201605,2016q2,2016
2016-05-02,2016-05-02,201605,2016q2,2016
2016-05-03,2016-05-03,201605,2016q2,2016
2016-05-04,2016-05-04,201605,2016q2,2016
2016-05-05,2016-05-05,201605,2016q2,2016
2016-05-06,2016-05-06,201605,2016q2,2016
2016-05-07,2016-05-07,201605,2016q2,2016
2016-05-08,2016-05-08,201605,2016q2,2016
2016-05-09,2016-05-09,201605,2016q2,2016
2016-05-10,2016-05-10,201605,2016q2,2016
2016-05-11,2016-05-11,201605,2016q2,2016
2016-05-12,2016-05-12,201605,2016q2,2016
2016-05-13,2016-05-13,201605,2016q2,2016
2016-05-14,2016-05-14,201605,2016q2,2016
2016-05-15,2016-05-15,201605,2016q2,2016
2016-05-16,2016-05-16,201605,2016q2,2016
2016-05-17,2016-05-17,201605,2016q2,2016
2016-05-18,2016-05-18,201605,2016q2,2016
2016-05-19,2016-05-19,201605,2016q2,2016
2016-05-20,2016-05-20,201605,2016q2,2016
3. 创建售卖员的维度表salesperson_dim
DROP TABLE IF EXISTS default.dim_salesperson ;
create table default.dim_salesperson (
salesperson_key string,
salesperson string,
salesperson_id string,
region string,
region_code string
)
ROW FORMAT DELIMITED FIELDS TERMINATED BY ','
STORED AS TEXTFILE;
load data local inpath '/data/dim_salesperson.txt' overwrite into table default.dim_salesperson;
dim_salesperson.txt
sp001,hongbin,sp001,beijing,10086
sp002,hongming,sp002,beijing,10086
sp003,hongmei,sp003,beijing,10086
4. 创建客户维度 custom_dim
DROP TABLE IF EXISTS default.dim_custom ;
create table default.dim_custom (
custom_key string,
custom_name string,
custorm_id string,
headquarter_states string,
billing_address string,
billing_city string,
billing_state string,
industry_name string
)
ROW FORMAT DELIMITED FIELDS TERMINATED BY ','
STORED AS TEXTFILE;
load data local inpath '/data/dim_custom.txt' overwrite into table default.dim_custom;
dim_custom.txt
ct001,custom_john,ct001,beijing,zgx-beijing,beijing,beijing,internet
ct002,custom_herry,ct002,henan,shlinjie,shangdang,henan,internet
5. 创建产品维度表并插入数据
DROP TABLE IF EXISTS default.dim_product ;
create table default.dim_product (
product_key string,
product_name string,
product_id string,
product_desc string,
sku string,
brand string,
brand_code string,
brand_manager string,
category string,
category_code string
)
ROW FORMAT DELIMITED FIELDS TERMINATED BY ','
STORED AS TEXTFILE;
load data local inpath '/data/dim_product.txt' overwrite into table default.dim_product;
dim_product.txt
pd001,Box-Large,pd001,Box-Large-des,large1.0,brand001,brandcode001,brandmanager001,Packing,cate001
pd002,Box-Medium,pd001,Box-Medium-des,medium1.0,brand001,brandcode001,brandmanager001,Packing,cate001
pd003,Box-small,pd001,Box-small-des,small1.0,brand001,brandcode001,brandmanager001,Packing,cate001
pd004,Evelope,pd001,Evelope_des,large3.0,brand001,brandcode001,brandmanager001,Pens,cate002
这样一个星型的结构表在hive中创建完毕, 实际上一个离线的数据仓库已经完成, 它包含一个主题, 即商品订单.
三.Kylin的Project创建与数据同步
1.单击"Manage Project"
2.单击"New Project"
3.输入"Project Name", WareHouse_01
4.Submit
1.选择WareHouse_01,选择"Data Source" tab页
2.单击"Load Hive Table"
3.输入需要同步的表
"DEFAULT.FACT_ORDER,DEFAULT.DIM_DAY,DEFAULT.DIM_PRODUCT,DEFAULT.DIM_SALESPERSON,DEFAULT.DIM_CUSTOM"
4.Sync
四.Kylin的Model创建
1.选择"Models" tab页,单击"New Model"
2."Model Name"输入,WareHouse_01_Model
3.选择"Fact Table"为 DEFAULT.FACT_ORDER;再 添加Lookup Table;
4.选取每张表的哪些列字段作为Dimensions
ID Table Name Columns
1 DEFAULT.FACT_ORDER TIME_KEY PRODUCT_KEY SALESPERSON_KEY CUSTOM_KEY
2 DEFAULT.DIM_DAY FULL_DAY
3 DEFAULT.DIM_PRODUCT PRODUCT_NAME
4 DEFAULT.DIM_SALESPERSON SALESPERSON
5 DEFAULT.DIM_CUSTOM CUSTOM_NAME
5.选取DEFAULT.FACT_ORDER表的哪些列字段作为measures
QUANTITY_ORDERED ORDER_DOLLARS COST_DOLLARS
6.a.选取 "Partition Date Column"为DEFAULT.FACT_ORDER.TIME_KEY,格式 yyyy-MM-dd
b.对于"Filter"条件,由于没有要过滤的条件,故不填写
7.Save
五.Kylin的Cube创建
1.选择"Models" tab页,单击"New Cube“
2.Cube Info:
"Model Name"选择,WareHouse_01_Model
"Cube Name"输入,cube01
3.Dismensions:
单击"Auto Generator",依据情况选择维度的列,全选
4.Measures:
a.单击"+Measure",添加要聚合计算的度量,添加： sum(QUANTITY_ORDERED),sum(ORDER_DOLLARS)
b.Expression: SUM/MIN/MAX/COUNT/COUNT_DISTINCT/TOP_N/RAW
5.Refresh Setting:
a.Auto Merge Thresholds,自动合并阈值,7~28 days
b.Retention Threshold,保留天数,60
c.Partition Start Date,非常重要,是后面build cube的开始日期
6.Advanced Setting:
--Aggregation Groups:
a.Includes: TIME_KEY ,PRODUCT_KEY ,SALESPERSON_KEY , CUSTOM_KEY
b.Mandatory Dimensions: TIME_KEY
c.Hierarchy Dimensions: PRODUCT_KEY ,SALESPERSON_KEY ,CUSTOM_KEY
d.Joint Dimensions: 无
--Rowkeys:
TIME_KEY ,PRODUCT_KEY ,SALESPERSON_KEY ,CUSTOM_KEY 4个字段为dict字典编码
7.Configuration Overwrites: 无
8.Overview:
保存cube
五.Cube Build
1.选择 cube01,单击”Action”,选择Build
2.填写End Date,Submit
3.单击”Monitor”,观察Job
4.等待Process100% (Any Errors)
5.返回cube01,查看 cube size 和 Source Records等字段更新
六.Hive* kyin 查询对比
点击(此处)折叠或打开
- 1.2016-05-01到2016-05-15期间的每天的订单数量,订单金额,订单成本
- Hive: 65.816 s
select fact.time_key, sum(fact.quantity_ordered), sum(fact.order_dollars) from fact_order as fact
where fact.time_key >= "2016-05-01" and fact.time_key <= "2016-05-15"
group by fact.time_key order by fact.time_key;- Kylin: 0.32s-->0.27s
select fact.time_key, sum(fact.quantity_ordered), sum(fact.order_dollars) from fact_order as fact
where fact.time_key between '2016-05-01' and '2016-05-15'
group by fact.time_key order by fact.time_key
- 2.2016-05-01到2016-05-15期间的每天的产品的订单量
- Hive: 100.336s
select dday.full_day,dsp.product_name, sum(fact.quantity_ordered) from fact_order as fact
inner join dim_day as dday on fact.time_key = dday.day_key
inner join dim_product as dsp on fact.product_key = dsp.product_key
where dday.full_day >= "2016-05-01" and dday.full_day <= "2016-05-15"
group by dday.full_day,dsp.product_name
order by dday.full_day,dsp.product_name;- Kylin:0.93s-->0.39s
select dday.full_day,dsp.product_name, sum(fact.quantity_ordered) from fact_order as fact
inner join dim_day as dday on fact.time_key = dday.day_key
inner join dim_product as dsp on fact.product_key = dsp.product_key
where dday.full_day >= '2016-05-01' and dday.full_day <= '2016-05-15'
group by dday.full_day,dsp.product_name
order by dday.full_day,dsp.product_name
本文参考：
http://blog.itpub.net/30089851/viewspace-2122586/
http://www.mamicode.com/info-detail-2332910.html
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