在新建一张分区表或者对老分区表更改表结构后希望能保留老的分区的数据，因此就需要对新建的分区表进行初始化重刷历史分区数据操作。
一、初始化刷新方法1
事实表和维表均取最新分区数据，以事实表的业务动作事实发生日期作为历史分区的分区字段值。参考下面这段hive脚本
1 insert overwrite table edw_agents.adm_xf_edw_agents_performance_daily_report_edw_001_di_new partition(dt) 2 select 3 t1.statistic_date as statistic_date 4 ,t1.xf_agent_id as xf_agent_id 5 ,nvl(t3.agent_true_name,'?') as xf_agent_true_name 6 ,nvl(t3.xf_agent_cellphone_text,'?') as xf_agent_cellphone_text 7 ,nvl(t1.xf_agent_office_id,0) as xf_agent_office_id 8 ,nvl(t2.xf_agent_office_name,'?') as xf_agent_office_name 9 ,nvl(t2.xf_agent_office_broker_user_id,0) as xf_agent_office_broker_user_id 10 ,nvl(t2.xf_agent_office_broker_user_name,'?') as xf_agent_office_broker_user_name 11 ,nvl(t2.xf_agent_office_organization_id,0) as xf_agent_office_organization_id 12 ,nvl(t2.xf_agent_office_organization_short_name,'?') as xf_agent_office_organization_short_name 13 ,nvl(t1.xf_trade_order_record_count_number,0) as xf_trade_order_record_count_number 14 ,nvl(t1.xf_trade_order_guide_count_number,0) as xf_trade_order_guide_count_number 15 ,nvl(t1.xf_trade_order_appointment_net_increase_count_number,0) as xf_trade_order_appointment_net_increase_count_number 16 ,nvl(t1.xf_trade_order_deal_net_increase_count_number,0) as xf_trade_order_deal_net_increase_count_number 17 ,nvl(t1.xf_trade_order_received_total_amount,0) as xf_trade_order_received_total_amount 18 ,nvl(t1.xf_trade_order_gross_merchandise_volume_total_amount,0) as xf_trade_order_gross_merchandise_volume_total_amount 19 ,nvl(t1.xf_trade_order_payable_commission_total_amount,0) as xf_trade_order_payable_commission_total_amount 20 ,'${wf:id()}' as load_job_number 21 ,'${wf:name()}' as load_job_name 22 ,current_timestamp as insert_timestamp 23 ,2 as source_system_code 24 ,regexp_replace(t1.statistic_date,'-','') as dt -- 分区字段 25 from 26 ( 27 select 28 agent_id xf_agent_id 29 ,statistic_date 30 ,max(agent_office_id) xf_agent_office_id 31 ,max(agent_office_organization_id) xf_agent_office_organization_id 32 ,sum(record) xf_trade_order_record_count_number 33 ,sum(guide) xf_trade_order_guide_count_number 34 ,sum(pre_netorder) xf_trade_order_appointment_net_increase_count_number 35 ,sum(bargain_netorder) xf_trade_order_deal_net_increase_count_number 36 ,sum(totalincome) xf_trade_order_received_total_amount 37 ,sum(GMV) xf_trade_order_gross_merchandise_volume_total_amount 38 ,sum(shouldagent) xf_trade_order_payable_commission_total_amount 39 40 from 41 ( 42 -- 报备带看事实 43 select 44 agent_id 45 ,agent_business_date as statistic_date 46 ,max(agent_office_id) agent_office_id 47 ,max(agent_office_organization_id) agent_office_organization_id 48 ,count(distinct case when agent_business_code=1 then agent_business_id end ) record 49 ,count(distinct case when agent_business_code=2 then agent_business_id end ) guide 50 ,0 pre_netorder 51 ,0 bargain_netorder 52 ,0 totalincome 53 ,0 GMV 54 ,0 shouldagent 55 from 56 edw_agents.dws_xf_edw_agent_business_df 57 where 58 dt = '${dt}' 59 and agent_business_date between '2017-01-01' and '2018-04-11' 60 group by agent_id,agent_business_date 61 union all 62 -- 认购成交单事实 63 select 64 xf_trade_order_belong_agent_id agent_id 65 ,statistics_date as statistic_date 66 ,max(xf_trade_order_belong_agent_office_id) agent_office_id 67 ,max(xf_trade_order_house_project_organization_id) agent_office_organization_id 68 ,0 record 69 ,0 guide 70 ,sum(xf_trade_order_appointment_net_increase_count_number) pre_netorder 71 ,sum(xf_trade_order_deal_net_increase_count_number) bargain_netorder 72 ,sum(xf_trade_order_received_amount) totalincome 73 ,sum(xf_trade_order_gross_merchandise_volume_amount) GMV 74 ,sum(xf_trade_order_payable_commission_amount) shouldagent 75 from 76 edw_trade.adm_xf_edw_trade_order_report_daac_001_df 77 where 78 dt = '${dt}' 79 and statistics_date between '2017-01-01' and '2018-04-11' 80 group by xf_trade_order_belong_agent_id,statistics_date 81 ) t11 82 group by t11.agent_id,t11.statistic_date 83 84 ) t1 85 left join 86 (-- 门店、城市分公司、经服 87 88 select 89 t21.agent_office_id xf_agent_office_id, 90 t21.agent_office_name xf_agent_office_name, 91 t21.agent_office_organization_id xf_agent_office_organization_id, 92 t21.agent_office_service_user_id xf_agent_office_broker_user_id, 93 t22.organization_short_name xf_agent_office_organization_short_name, 94 t23.username xf_agent_office_broker_user_name 95 96 from 97 98 (-- 门店维表 99 select 100 agent_office_id, 101 agent_office_name, 102 agent_office_organization_id, 103 agent_office_service_user_id 104 from 105 edw_public.dim_edw_pub_agent_office_base_info 106 where 107 dt = '${dt}' 108 and agent_office_type_code=1 109 )t21 110 left join 111 (-- 城市分公司维表 112 select 113 organization_id, 114 organization_short_name 115 from 116 edw_public.dim_xf_edw_pub_organization_base_info 117 where 118 dt = '${dt}' 119 )t22 120 on (t21.agent_office_organization_id = t22.organization_id) 121 left join 122 (-- 经服维表 123 select 124 user_uc_id as user_id, 125 max(user_real_name) as username 126 from edw_public.dim_edw_pub_internal_staff_base_info 127 where dt='${dt}' 128 group by user_uc_id 129 )t23 130 on (t21.agent_office_service_user_id = t23.user_id) 131 ) t2 132 on (t1.xf_agent_office_id=t2.xf_agent_office_id) 133 left join 134 (-- 经纪人维表 135 136 select agent_id, 137 agent_true_name, 138 agent_phone_number_text as xf_agent_cellphone_text 139 from 140 edw_public.dim_edw_pub_agent_base_info 141 where dt = '${dt}' 142 ) t3 143 on (t1.xf_agent_id=t3.agent_id) 144 ;