kylin-learn_kylin表结构解读

基于kylin-3.0

背景

学习kylin，当搭建好kylin平台后，无论是单节点的还是集群模式的，接下来就是需要进行kylin官方提供的样例cube进行学习；
脚本位置:${KYLIN_HOME}/bin/sample.sh，直接执行即可;
执行结束后，默认是在default库下，会生成5张表；暂命名为销售模型，具体如下：
一定要耐着性子、仔细的理解一下这几张表，对后面的model、cube的创建的理解有很大的帮助；

# 用户账户表
kylin_account
# 日期维度
kylin_cal_dt
# 商品类别表
kylin_category_groupings
# 地理位置国家表
kylin_country
# Sales order table, fact table
kylin_sales

数仓模型

典型的星形模型（start model）。其中，事实表为:kylin_sales(此表为非分区表,这一点需要注意)；维度表(或查找表Lookup table)：kylin_account , kylin_cal_dt，kylin_category_groupings，kylin_country。
下面具体学习一下各个表的结构;

事实表 kylin_sales

表结构如下：

CREATE TABLE `kylin_sales`(
  `trans_id` bigint, 
  `part_dt` date COMMENT 'Order Date', 
  `lstg_format_name` string COMMENT 'Order Transaction Type', 
  `leaf_categ_id` bigint COMMENT 'Category ID', 
  `lstg_site_id` int COMMENT 'Site ID', 
  `slr_segment_cd` smallint, 
  `price` decimal(19,4) COMMENT 'Order Price', 
  `item_count` bigint COMMENT 'Number of Purchased Goods', 
  `seller_id` bigint COMMENT 'Seller ID', 
  `buyer_id` bigint COMMENT 'Buyer ID', 
  `ops_user_id` string COMMENT 'System User ID', 
  `ops_region` string COMMENT 'System User Region')
COMMENT 'Sales order table, fact table'
ROW FORMAT SERDE 
  'org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe' 
WITH SERDEPROPERTIES ( 
  'field.delim'=',', 
  'serialization.format'=',') 
STORED AS INPUTFORMAT 
  'org.apache.hadoop.mapred.TextInputFormat' 
OUTPUTFORMAT 
  'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION
  'hdfs://bigdata/user/hive/warehouse/kylin_sales'
TBLPROPERTIES (
  'transient_lastDdlTime'='1580892437')

在cube的设计中，采用字段part_dt作为时间字段;
个人觉得事实表应该设计成以dt为分区字段的分区表；

维度表 kylin_account

表结构如下：

CREATE TABLE `kylin_account`(
  `account_id` bigint, 
  `account_buyer_level` int COMMENT 'Account Buyer Level', 
  `account_seller_level` int COMMENT 'Account Seller Level', 
  `account_country` string COMMENT 'Account Country', 
  `account_contact` string COMMENT 'Account Contact Info')
ROW FORMAT SERDE 
  'org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe' 
WITH SERDEPROPERTIES ( 
  'field.delim'=',', 
  'serialization.format'=',') 
STORED AS INPUTFORMAT 
  'org.apache.hadoop.mapred.TextInputFormat' 
OUTPUTFORMAT 
  'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION
  'hdfs://bigdata/user/hive/warehouse/kylin_account'
TBLPROPERTIES (
  'transient_lastDdlTime'='1580892437')

维度表为非分区表；
KYLIN_SALES.BUYER_ID = BUYER_ACCOUNT.ACCOUNT_ID
KYLIN_SALES.SELLER_ID = SELLER_ACCOUNT.ACCOUNT_ID

维度表 kylin_cal_dt

表结构如下：

CREATE TABLE `kylin_cal_dt`(
  `cal_dt` date COMMENT 'Date, PK', 
  `year_beg_dt` date COMMENT 'YEAR Begin Date', 
  `qtr_beg_dt` date COMMENT 'Quarter Begin Date', 
  `month_beg_dt` date COMMENT 'Month Begin Date', 
  `week_beg_dt` date COMMENT 'Week Begin Date', 
  `age_for_year_id` smallint, 
  `age_for_qtr_id` smallint, 
  `age_for_month_id` smallint, 
  `age_for_week_id` smallint, 
  `age_for_dt_id` smallint, 
  `age_for_rtl_year_id` smallint, 
  `age_for_rtl_qtr_id` smallint, 
  `age_for_rtl_month_id` smallint, 
  `age_for_rtl_week_id` smallint, 
  `age_for_cs_week_id` smallint, 
  `day_of_cal_id` int, 
  `day_of_year_id` smallint, 
  `day_of_qtr_id` smallint, 
  `day_of_month_id` smallint, 
  `day_of_week_id` int, 
  `week_of_year_id` tinyint, 
  `week_of_cal_id` int, 
  `month_of_qtr_id` tinyint, 
  `month_of_year_id` tinyint, 
  `month_of_cal_id` smallint, 
  `qtr_of_year_id` tinyint, 
  `qtr_of_cal_id` smallint, 
  `year_of_cal_id` smallint, 
  `year_end_dt` string, 
  `qtr_end_dt` string, 
  `month_end_dt` string, 
  `week_end_dt` string, 
  `cal_dt_name` string, 
  `cal_dt_desc` string, 
  `cal_dt_short_name` string, 
  `ytd_yn_id` tinyint, 
  `qtd_yn_id` tinyint, 
  `mtd_yn_id` tinyint, 
  `wtd_yn_id` tinyint, 
  `season_beg_dt` string, 
  `day_in_year_count` smallint, 
  `day_in_qtr_count` tinyint, 
  `day_in_month_count` tinyint, 
  `day_in_week_count` tinyint, 
  `rtl_year_beg_dt` string, 
  `rtl_qtr_beg_dt` string, 
  `rtl_month_beg_dt` string, 
  `rtl_week_beg_dt` string, 
  `cs_week_beg_dt` string, 
  `cal_date` string, 
  `day_of_week` string, 
  `month_id` string, 
  `prd_desc` string, 
  `prd_flag` string, 
  `prd_id` string, 
  `prd_ind` string, 
  `qtr_desc` string, 
  `qtr_id` string, 
  `qtr_ind` string, 
  `retail_week` string, 
  `retail_year` string, 
  `retail_start_date` string, 
  `retail_wk_end_date` string, 
  `week_ind` string, 
  `week_num_desc` string, 
  `week_beg_date` string, 
  `week_end_date` string, 
  `week_in_year_id` string, 
  `week_id` string, 
  `week_beg_end_desc_mdy` string, 
  `week_beg_end_desc_md` string, 
  `year_id` string, 
  `year_ind` string, 
  `cal_dt_mns_1year_dt` string, 
  `cal_dt_mns_2year_dt` string, 
  `cal_dt_mns_1qtr_dt` string, 
  `cal_dt_mns_2qtr_dt` string, 
  `cal_dt_mns_1month_dt` string, 
  `cal_dt_mns_2month_dt` string, 
  `cal_dt_mns_1week_dt` string, 
  `cal_dt_mns_2week_dt` string, 
  `curr_cal_dt_mns_1year_yn_id` tinyint, 
  `curr_cal_dt_mns_2year_yn_id` tinyint, 
  `curr_cal_dt_mns_1qtr_yn_id` tinyint, 
  `curr_cal_dt_mns_2qtr_yn_id` tinyint, 
  `curr_cal_dt_mns_1month_yn_id` tinyint, 
  `curr_cal_dt_mns_2month_yn_id` tinyint, 
  `curr_cal_dt_mns_1week_yn_ind` tinyint, 
  `curr_cal_dt_mns_2week_yn_ind` tinyint, 
  `rtl_month_of_rtl_year_id` string, 
  `rtl_qtr_of_rtl_year_id` tinyint, 
  `rtl_week_of_rtl_year_id` tinyint, 
  `season_of_year_id` tinyint, 
  `ytm_yn_id` tinyint, 
  `ytq_yn_id` tinyint, 
  `ytw_yn_id` tinyint, 
  `kylin_cal_dt_cre_date` string, 
  `kylin_cal_dt_cre_user` string, 
  `kylin_cal_dt_upd_date` string, 
  `kylin_cal_dt_upd_user` string)
COMMENT 'Date Dimension Table'
ROW FORMAT SERDE 
  'org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe' 
WITH SERDEPROPERTIES ( 
  'field.delim'=',', 
  'serialization.format'=',') 
STORED AS INPUTFORMAT 
  'org.apache.hadoop.mapred.TextInputFormat' 
OUTPUTFORMAT 
  'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION
  'hdfs://bigdata/user/hive/warehouse/kylin_cal_dt'
TBLPROPERTIES (
  'transient_lastDdlTime'='1580892438')

这个日期维度的表结构，可以适配绝大多数的日期维度；
KYLIN_SALES.PART_DT = KYLIN_CAL_DT.CAL_DT

维度表 kylin_category_groupings

表结构

CREATE TABLE `kylin_category_groupings`(
  `leaf_categ_id` bigint COMMENT 'Category ID, PK', 
  `leaf_categ_name` string, 
  `site_id` int COMMENT 'Site ID, PK', 
  `categ_busn_mgr` string, 
  `categ_busn_unit` string, 
  `regn_categ` string, 
  `user_defined_field1` string COMMENT 'User Defined Field1', 
  `user_defined_field3` string COMMENT 'User Defined Field3', 
  `kylin_groupings_cre_date` string, 
  `kylin_groupings_upd_date` string COMMENT 'Last Updated Date', 
  `kylin_groupings_cre_user` string, 
  `kylin_groupings_upd_user` string COMMENT 'Last Updated User', 
  `meta_categ_id` decimal(10,0), 
  `meta_categ_name` string COMMENT 'Level1 Category', 
  `categ_lvl2_id` decimal(10,0), 
  `categ_lvl3_id` decimal(10,0), 
  `categ_lvl4_id` decimal(10,0), 
  `categ_lvl5_id` decimal(10,0), 
  `categ_lvl6_id` decimal(10,0), 
  `categ_lvl7_id` decimal(10,0), 
  `categ_lvl2_name` string COMMENT 'Level2 Category', 
  `categ_lvl3_name` string COMMENT 'Level3 Category', 
  `categ_lvl4_name` string, 
  `categ_lvl5_name` string, 
  `categ_lvl6_name` string, 
  `categ_lvl7_name` string, 
  `categ_flags` decimal(10,0), 
  `adult_categ_yn` string, 
  `domain_id` decimal(10,0), 
  `user_defined_field5` string, 
  `vcs_id` decimal(10,0), 
  `gcs_id` decimal(10,0), 
  `move_to` decimal(10,0), 
  `sap_category_id` decimal(10,0), 
  `src_id` tinyint, 
  `bsns_vrtcl_name` string)
COMMENT 'Detail category inforamtion, Dimension Table'
ROW FORMAT SERDE 
  'org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe' 
WITH SERDEPROPERTIES ( 
  'field.delim'=',', 
  'serialization.format'=',') 
STORED AS INPUTFORMAT 
  'org.apache.hadoop.mapred.TextInputFormat' 
OUTPUTFORMAT 
  'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION
  'hdfs://bigdata/user/hive/warehouse/kylin_category_groupings'
TBLPROPERTIES (
  'transient_lastDdlTime'='1580892439')

表结构也不短；
KYLIN_SALES.LEAF_CATEG_ID = KYLIN_CATEGORY_GROUPINGS.LEAF_CATEG_ID
KYLIN_SALES.LSTG_SITE_ID = KYLIN_CATEGORY_GROUPINGS.SITE_ID

维度表 kylin_country

表结构

CREATE TABLE `kylin_country`(
  `country` string, 
  `latitude` double, 
  `longitude` double, 
  `name` string)
ROW FORMAT SERDE 
  'org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe' 
WITH SERDEPROPERTIES ( 
  'field.delim'=',', 
  'serialization.format'=',') 
STORED AS INPUTFORMAT 
  'org.apache.hadoop.mapred.TextInputFormat' 
OUTPUTFORMAT 
  'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION
  'hdfs://bigdata/user/hive/warehouse/kylin_country'
TBLPROPERTIES (
  'transient_lastDdlTime'='1580892438')

源码中设计这个国家级别，有点牵强。
在实际中，我们需要考虑到地区、城市、城市分区等信息；
BUYER_ACCOUNT.ACCOUNT_COUNTRY = BUYER_COUNTRY.COUNTRY
SELLER_ACCOUNT.ACCOUNT_COUNTRY = SELLER_COUNTRY.COUNTRY

模型关系图

在官网给定的learn_kylin中，model:kylin_sales_model中表结构关系如下：

kylin_sales_model

kylin-learn_kylin表结构解读

背景

数仓模型

事实表 kylin_sales

维度表 kylin_account

维度表 kylin_cal_dt

维度表 kylin_category_groupings

维度表 kylin_country

模型关系图

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