星型数据仓库olap工具kylin介绍

2022-11-20 17:03:25

星型数据仓库olap工具kylin介绍

数据仓库是目前企业级BI分析的重要平台,尤其在互联网公司,每天都会产生数以百G的日志,如何从这些日志中发现数据的规律很重要. 数据仓库是数据分析的重要工具, 每个大公司都花费数百万每年的资金进行数据仓库的运维.

本文介绍一个基于hadoop的数据仓库, 它基于hadoop(HIVE, HBASE)水平扩展的特性, 客服传统olap受限于关系型数据库数据容量的问题. Kylin是ebay推出的olap星型数据仓库的开源实现.

首先请安装Kylin, 和它的运行环境(Hadoop, yarn, hive, hbase). 如果安装成功, 登陆(http://<KYLIN_HOST>:7070/), 用户名:ADMIN, 密码(KYLIN). 安装过程请参考(http://kylin.incubator.apache.org/download/, 注意下载编译后的二进制包, 免去很多编译烦恼).

在创建数据仓库前, 我们先聊一下, 什么是数据仓库.

从业务过程的角度考虑, 信息系统可以划分为两个主要类别, 一类用于支持业务过程的执行, 代表作品是mysql; 另一类用于支持业务过程的分析, 代表作品是hive, 还有就是今天的主角kylin.

首先, 数据仓库的设计

下图展示了一个简单的基于订单流程中事实和维度的星型模型.

这是一个典型的星型结构, 订单的事实表有3个度量值(messures)(订单数量, 订单金额, 和订单成本); 另外有4个度量维度(dimession), 分别是时间, 产品, 销售员, 客户. 这里时间以天为单位, 这里注意day_key必须是(YYYY-MM-DD)格式(这是kylin的规定).

其次, 根据数据仓库的设计创建hive表

1. 创建事实表并插入数据

DROP TABLE IF EXISTS DEFAULT.fact_order ;

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;

load data local inpath 'fact_order.csv' overwrite into table DEFAULT.fact_order;

fact_order.csv

2015-05-01,pd001,sp001,ct001,100,101,51

2015-05-01,pd001,sp002,ct002,100,101,51

2015-05-01,pd001,sp003,ct002,100,101,51

2015-05-01,pd002,sp001,ct001,100,101,51

2015-05-01,pd003,sp001,ct001,100,101,51

2015-05-01,pd004,sp001,ct001,100,101,51

2015-05-02,pd001,sp001,ct001,100,101,51

2015-05-02,pd001,sp002,ct002,100,101,51

2015-05-02,pd001,sp003,ct002,100,101,51

2015-05-02,pd002,sp001,ct001,100,101,51

2015-05-02,pd003,sp001,ct001,100,101,51

2015-05-02,pd004,sp001,ct001,100,101,51

2. 创建天维度表day_dim

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 'dim_day.csv' overwrite into table DEFAULT.dim_day;

dim_day.csv

2015-05-01,2015-05-01,201505,2015q2,2015

2015-05-02,2015-05-02,201505,2015q2,2015

2015-05-03,2015-05-03,201505,2015q2,2015

2015-05-04,2015-05-04,201505,2015q2,2015

2015-05-05,2015-05-05,201505,2015q2,2015

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 'dim_salesperson.csv' overwrite into table DEFAULT.dim_salesperson;

dim_salesperson.csv

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 'dim_custom.csv' overwrite into table DEFAULT.dim_custom;

dim_custom.csv

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 'dim_product.csv' overwrite into table DEFAULT.dim_product;

dim_product.csv

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.

关于商品订单的统计需求可以使用hive命令产生. 比如:

1. 统计20150501到20150502所有的订单数.

Hive> select dday.full_day, sum(quantity_ordered) from fact_order as fact inner join dim_day as dday on fact.time_key == dday.day_key and dday.full_day >= "2015-05-01" and dday.full_day <= "2015-05-02" group by dday.full_day order by dday.full_day;

2015-05-01 600

2015-05-02 600

2. 统计20150501到20150502各个销售员的销售订单数

select dday.full_day, dsp.salesperson_key, sum(quantity_ordered) from fact_order as fact

inner join dim_day as dday on fact.time_key == dday.day_key

inner join dim_salesperson as dsp on fact.salesperson_key == dsp.salesperson_key

where dday.full_day >= "2015-05-01" and dday.full_day <= "2015-05-02"

group by dday.full_day, dsp.salesperson_key

order by dday.full_day;

2015-05-01 sp003 100

2015-05-01 sp002 100

2015-05-01 sp001 400

2015-05-02 sp003 100

2015-05-02 sp002 100

2015-05-02 sp001 400

然后,导入kylin数据仓库中

kylin在hive的基础上仓库olap数据cube, 完成实时数据仓库服务的任务. kylin在hive的基础上完成:

1. 将星型数据库部署在hbase上实现实时的查询服务

2. 提供restful查询接口

3. 集成BI

首先, 创建一个数据仓库工程(kylin_test_project)

其次, 点击tables标签,点击"load hive table"按钮, 同步上述的所有hive表

完成hive表和kylin的同步.

接着, 简历kylin的数据cube

点击cube 和新增cube按钮.

1. 命名cube order_cube

2. 增加fact 和 dim 表

3. 增加维度

4. 增加mesure值

5. 不用选filter条件

6. 选择开始开始时间

7. 完成

然后, build cube

可以在jobs中查看build状态. build过程实际上是把cube存到hbase中, 方便快速检索.

码农公寓

首先, 数据仓库的设计

其次, 根据数据仓库的设计创建hive表

然后,导入kylin数据仓库中

相关文章