助力工业物联网，工业大数据之其他维度：组织机构【十五】

01：其他维度：组织机构

• 目标：实现组织机构维度的设计及构建

• 路径

  • step1：需求
  • step2：设计
  • step3：实现

• 实施

  • 需求：实现组织机构维度表的构建，得到每个工程师对应的组织机构信息

    

    • 统计不同服务人员的工单数、核销数等

  • 设计

    • org_employee：员工信息表【员工id、员工编码、员工名称、用户系统id】

      select empid,empcode,empname,userid from org_employee;
      

    • org_empposition：员工岗位信息表【员工id、岗位id】

      select empid,positionid from org_empposition;
      

    • org_position：岗位信息表【岗位id、岗位编码、岗位名称、部门id】

      select positionid,posicode,posiname,orgid from org_position;
      

    • org_organization：部门信息表【部门id、部门编码、部门名称】

      select orgid,orgcode,orgname from org_organization;
      

    

  • 实现

    • 建维度表

      -- 创建组织机构维度表，组织机构人员是经常变动的，所以按照日期分区
      create external table if not exists one_make_dws.dim_emporg(
          empid string comment '人员id'   
          , empcode string comment '人员编码(erp对应的账号id)'
          , empname string comment '人员姓名'
          , userid string comment '用户系统id（登录用户名）'
          , posid string comment '岗位id'
          , posicode string comment '岗位编码'
          , posiname string comment '岗位名称'
          , orgid string comment '部门id'
          , orgcode string comment '部门编码'
          , orgname string comment '部门名称'
      ) comment '组织机构维度表'
      partitioned by (dt string)
      stored as orc
      location '/data/dw/dws/one_make/dim_emporg';
      

    • 抽取数据

      -- 先根据dwd层的表进行关联，然后分别把数据取出来
      insert overwrite table one_make_dws.dim_emporg partition(dt='20210101')
      select
          emp.empid as empid
          , emp.empcode as empcode
          , emp.empname as empname
          , emp.userid as userid
          , pos.positionid as posid
          , pos.posicode as posicode
          , pos.posiname as posiname
          , org.orgid as orgid
          , org.orgcode as orgcode
          , org.orgname as orgname
      from  one_make_dwd.org_employee emp
      left join one_make_dwd.org_empposition emppos
          on emp.empid = emppos.empid and emp.dt = '20210101' and emppos.dt = '20210101'
      left join one_make_dwd.org_position pos
          on emppos.positionid = pos.positionid and pos.dt = '20210101'
      left join one_make_dwd.org_organization org
          on pos.orgid = org.orgid and org.dt = '20210101';
      

• 小结**

  • 实现组织机构维度的设计及构建

02：其他维度：仓库、物流

• 目标：实现仓库维度、物流维度的构建

• 路径

  • step1：仓库维度
  • step2：物流维度

• 实施

  • 仓库维度

    • 建表

      -- 仓库维度表
      create external table if not exists one_make_dws.dim_warehouse(
          code string comment '仓库编码'
          , name string comment '仓库名称'
          , company_id string comment '所属公司'
          , company string comment '公司名称'
          , srv_station_id string comment '所属服务网点ID'
          , srv_station_name string comment '所属服务网点名称'
      )comment '仓库维度表'
      partitioned by (dt string)
      stored as orc
      location '/data/dw/dws/one_make/dim_warehouse';
      

    • 加载

      insert overwrite table one_make_dws.dim_warehouse partition(dt='20210101')
      select
          warehouse.code as code
          , warehouse.name as name
          , warehouse.company as company_id
          , cmp.compmay as compmay
          , station.id as srv_station_id
          , station.name as srv_station_name
      from
          one_make_dwd.ciss_base_warehouse warehouse
      -- 关联公司信息表
      left join (
           select
                 ygcode as company_id, max(companyname) as compmay
           from one_make_dwd.ciss_base_baseinfo where dt='20210101'
           -- 需要对company信息进行分组去重，里面有一些重复数据 
           group by ygcode) cmp
           on warehouse.dt = '20210101' and cmp.company_id = warehouse.company
      -- 关联服务网点和仓库关系表
      left join one_make_dwd.ciss_r_serstation_warehouse station_r_warehouse
           on station_r_warehouse.dt = '20210101' and station_r_warehouse.warehouse_code = warehouse.code
      -- 关联服务网点表 
      left join one_make_dwd.ciss_base_servicestation station
           on station.dt = '20210101' and station.id = station_r_warehouse.service_station_id;
      

  • 物流维度

    • 建表

      -- 物流维度表(和服务属性表类似)
      create external table if not exists one_make_dws.dim_logistics(
          prop_name string comment '字典名称'
          , type_id string comment '属性id'
          , type_name string comment '属性名称'
      )comment '物流维度表'
      partitioned by (dt string)
      stored as orc
      location '/data/dw/dws/one_make/dim_logistics';
      

    • 加载

      insert overwrite table one_make_dws.dim_logistics partition(dt = '20210101')
      select
          dict_t.dicttypename as prop_name
          , dict_e.dictid as type_id
          , dict_e.dictname as type_name
      from  one_make_dwd.eos_dict_type dict_t
      inner join one_make_dwd.eos_dict_entry dict_e
          on dict_t.dt = '20210101'
              and dict_e.dt = '20210101'
              and dict_t.dicttypeid = dict_e.dicttypeid
              and dict_t.dicttypename in (
                  '物流公司'
                  , '物流类型'
              )
      order by dict_t.dicttypename, dict_e.dictid;
      

    • 使用如下写法会好一些

      insert overwrite table one_make_dws.dim_logistics partition (dt = '20210101')
      select dict_t.dicttypename as prop_name
           , dict_e.dictid       as type_id
           , dict_e.dictname     as type_name
      from one_make_dwd.eos_dict_type dict_t
              inner join one_make_dwd.eos_dict_entry dict_e on dict_t.dt = '20210101'
              and dict_e.dt = '20210101'
              and dict_t.dicttypeid = dict_e.dicttypeid   -- 通过状态字符串进行关联
              and dict_t.dicttypename in ('物流公司', '物流类型') -- 通过和物流相关的字样进行过滤
      order by prop_name, type_id;
      

• 小结**

  • 实现仓库维度、物流维度的构建

附录一：常见问题

1.错误：没有开启Cross Join

Exception in thread "main" org.apache.spark.sql.AnalysisException: Detected implicit cartesian product for INNER join between logical plans.Use the CROSS JOIN syntax to allow cartesian products between these relations

• Spark2.x默认不允许执行笛卡尔积，除非显示申明cross join或者开启属性：spark.sql.crossJoin.enabled true

2.错误：Unable to move source

Error: org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: Unable to move source hdfs://hadoop.bigdata.cn:9000/data/dw/dws/one_make/dim_warehouse/.hive-staging_hive_2020-12-23_04-26-01_363_5663538019799519260-16/-ext-10000/part-00000-63069107-6405-4e31-a55a-6bdeefcd7d9b-c000 to destination hdfs://hadoop.bigdata.cn:9000/data/dw/dws/one_make/dim_warehouse/dt=20210101/part-00000-63069107-6405-4e31-a55a-6bdeefcd7d9b-c000; (state=,code=0)

• 重启SparkSQL的ThriftServer，与MetaStore构建新的会话连接