【黑马甄选离线数仓day10_会员主题域开发_DWS和ADS层】

day10_会员主题域开发

会员主题_DWS和ADS层

DWS层开发

门店会员分类天表:
维度指标:
指标：新增注册会员数、累计注册会员数、新增消费会员数、累计消费会员数、新增复购会员数、累计复购会员数、活跃会员数、沉睡会员数、会员消费金额
维度: 时间维度（天、周、月）
​
涉及表: 门店会员分类天表
​
表字段的组成: 维度字段 + 指标结果字段

建表语句:
CREATE TABLE IF NOT EXISTS dws.dws_mem_store_member_classify_day_i(trade_date                   STRING COMMENT '统计时间',week_trade_date             STRING COMMENT '周一日期',month_trade_date            STRING COMMENT '月一日期',
​store_no                    STRING COMMENT '店铺编码',store_name                  STRING COMMENT '店铺名称',store_sale_type             BIGINT COMMENT '店铺销售类型',store_type_code             BIGINT COMMENT '分店类型',city_id                     BIGINT COMMENT '城市ID',city_name                   STRING COMMENT '城市名称',region_code                 STRING COMMENT '区域编码',region_name                 STRING COMMENT '区域名称',is_day_clear                BIGINT COMMENT '是否日清:0否,1是',
​reg_num_add                 BIGINT COMMENT '新增注册会员数',reg_num_sum                 BIGINT COMMENT '累计注册会员数',consume_num_add             BIGINT COMMENT '新增消费会员数',consume_num_sum             BIGINT COMMENT '累计消费会员数',repurchase_num_add          BIGINT COMMENT '新增复购会员数',repurchase_num_sum          BIGINT COMMENT '累计复购会员数',active_member_num           BIGINT COMMENT '活跃会员数',sleep_member_num            BIGINT COMMENT '沉睡会员数',sale_amount_bind            DECIMAL(27, 2) COMMENT '会员消费金额'
)
comment '门店会员分类天表'
partitioned by (dt STRING COMMENT '统计日期')
row format delimited fields terminated by ','
stored as orc
tblproperties ('orc.compress'='SNAPPY');
数据导入:

活跃会员：30天内有消费

沉睡会员：90天内有消费，30天内没有消费

这个需求的难点在于计算累计值。思路是 使用天进行聚合，得到每天的值，然后使用sum () over 窗口，得到累加值，对于每天的累积情况，这里需要使用拉链表的思想，即构造一个生效日期，这里使用lead() over 窗口函数，取到下一个日期，然后再用这个当日时间去卡，即可得到当日对应的累计值。

-- DWS层: 门店会员分类天
-- 注意: 以下内容仅仅以2023-11-14为例,实际需要把14-20日的所有数据都要导入对应表中
with t1 as (selecttrade_date as start_date,store_no,reg_num_add,  -- 新增注册会员数sum(reg_num_add) over(partition by store_no order by trade_date) as reg_num_sum,  -- 累计注册会员数lead(trade_date,1,'9999-99-99') over (partition by store_no order by  trade_date) as end_datefrom(  -- 先统计每天注册会员数selecttrade_date,reg_md as store_no,count(1) as reg_num_addfrom dwm.dwm_mem_member_behavior_day_iwhere is_register = 1group bytrade_date, reg_md) temp1
),
t2 as (selecttrade_date as start_date,store_no,consume_num_add, -- 新增消费会员数sum(consume_num_add) over(partition by store_no order by trade_date) as consume_num_sum,  -- 累计消费会员数lead(trade_date,1,'9999-99-99') over (partition by store_no order by  trade_date) as end_datefrom (selecttrade_date,store_no,count(1) as consume_num_addfrom dwm.dwm_mem_first_buy_igroup by  trade_date, store_no) temp2
),
t3 as (selecttrade_date as start_date,store_no,repurchase_num_add, -- 新增充值会员数sum(repurchase_num_add) over(partition by store_no order by trade_date) as repurchase_num_sum,  -- 累计充值会员数lead(trade_date,1,'9999-99-99') over (partition by store_no order by  trade_date) as end_datefrom (selecttrade_date,store_no,count(1) as repurchase_num_addfrom dwm.dwm_mem_second_buy_igroup by  trade_date, store_no) temp2
),
t4 as (-- 活跃会员数(最近30天有消费)  2023-11-14select'2023-11-14' as trade_date,bind_md as store_no,count(distinct zt_id) as  active_member_numfrom dwm.dwm_mem_member_behavior_day_iwhere trade_date <= '2023-11-14' and trade_date >= date_sub('2023-11-14',30) and is_consume = 1group by bind_md
),
t5 as (-- 沉睡会员数:  最近90天有消费 , 但是最近30天无消费select'2023-11-14' as trade_date,temp3.bind_md as store_no,count(temp3.zt_id) as sleep_member_numfrom(selectbind_md,zt_idfrom dwm.dwm_mem_member_behavior_day_iwhere trade_date <= '2023-11-14' and trade_date >= date_sub('2023-11-14',90) and is_consume = 1group by bind_md,zt_id) temp3LEFT JOIN(selectbind_md,zt_idfrom dwm.dwm_mem_member_behavior_day_iwhere trade_date <= '2023-11-14' and trade_date >= date_sub('2023-11-14',30) and is_consume = 1group by  bind_md,zt_id) temp4  on  temp3.bind_md = temp4.bind_md and temp3.zt_id =  temp4.zt_idwhere temp4.zt_id is nullgroup by  temp3.bind_md
),
t6 as (selecttrade_date,store_no,sum(real_paid_amount) as sale_amount_bindfrom dwm.dwm_mem_sell_order_iwhere trade_date = '2023-11-14'group by trade_date,store_no
),
t7 as (select'2023-11-14' as trade_date,store_no,if(start_date = '2023-11-14',reg_num_add,0) as reg_num_add,reg_num_sum,0 as consume_num_add,0 as consume_num_sum,0 as repurchase_num_add,0 as repurchase_num_sum,0 as active_member_num,0 as sleep_member_num,0 as sale_amount_bindfrom t1where start_date <= '2023-11-14' and end_date >= '2023-11-14'union allselect'2023-11-14' as trade_date,store_no,0 reg_num_add,0 as reg_num_sum,if( start_date = '2023-11-14',consume_num_add,0) as consume_num_add,consume_num_sum,0 as repurchase_num_add,0 as repurchase_num_sum,0 as active_member_num,0 as sleep_member_num,0 as sale_amount_bindfrom t2where start_date <= '2023-11-14' and end_date >= '2023-11-14'union allselect'2023-11-14' as trade_date,store_no,0 reg_num_add,0 as reg_num_sum,0 as consume_num_add,0 as consume_num_sum,if(start_date = '2023-11-14',repurchase_num_add,0) as repurchase_num_add,repurchase_num_sum,0 as active_member_num,0 as sleep_member_num,0 as sale_amount_bindfrom t3where start_date <= '2023-11-14' and end_date >= '2023-11-14'union allselecttrade_date,store_no,0 reg_num_add,0 as reg_num_sum,0 as consume_num_add,0 as consume_num_sum,0 as repurchase_num_add,0 as repurchase_num_sum,active_member_num,0 as sleep_member_num,0 as sale_amount_bindfrom t4union allselecttrade_date,store_no,0 reg_num_add,0 as reg_num_sum,0 as consume_num_add,0 as consume_num_sum,0 as repurchase_num_add,0 as repurchase_num_sum,0 as active_member_num,sleep_member_num,0 as sale_amount_bindfrom t5union allselecttrade_date,store_no,0 reg_num_add,0 as reg_num_sum,0 as consume_num_add,0 as consume_num_sum,0 as repurchase_num_add,0 as repurchase_num_sum,0 as active_member_num,0 as sleep_member_num,sale_amount_bindfrom t6
)
-- insert overwrite table dws.dws_mem_store_member_classify_day_i partition (dt)
selectt7.trade_date,t8.week_trade_date,t8.month_trade_date,t7.store_no,t9.store_name,t9.store_sale_type,t9.store_type_code,t9.city_id,t9.city_name,t9.region_code,t9.region_name,t9.is_day_clear,sum(t7.reg_num_add) as reg_num_add,sum(t7.reg_num_sum) as reg_num_sum,sum(t7.consume_num_add) as consume_num_add,sum(t7.consume_num_sum) as consume_num_sum,sum(t7.repurchase_num_add) as repurchase_num_add,sum(t7.repurchase_num_sum) as repurchase_num_sum,sum(t7.active_member_num) as active_member_num,sum(t7.sleep_member_num) as sleep_member_num,sum(t7.sale_amount_bind) as sale_amount_bind,t7.trade_date as dt
from t7left join dim.dwd_dim_date_f t8 on t7.trade_date = t8.trade_date-- 注意: 一定要检查自己的dwd_dim_store_i分区目录,此处填写自己的分区目录时间left join dim.dwd_dim_store_i t9 on t7.store_no = t9.store_no and t9.dt ='2023-11-23'
group byt7.trade_date,t8.week_trade_date,t8.month_trade_date,t7.store_no,t9.store_name,t9.store_sale_type,t9.store_type_code,t9.city_id,t9.city_name,t9.region_code,t9.region_name,t9.is_day_clear;
门店会员统计天表:
维度指标:
指标: 门店销售额、门店总订单量、当日注册人数、累计注册会员数、当日注册且充值会员数、当日注册且充值且消费会员数、当日注册且消费会员数、充值会员数、充值金额、累计会员充值金额、当日有余额的会员人数、当日会员余额、余额消费人数/单量、余额支付金额、余额消费金额、会员消费人数/单量、会员消费金额、会员首单人数/订单量/销售额、会员非首单人数/订单量/销售额
维度: 时间维度（天、周、月）涉及表:门店会员统计天表表字段的组成: 维度字段 + 指标结果字段

建表语句:
CREATE TABLE IF NOT EXISTS dws.dws_mem_store_member_statistics_day_i(trade_date                  STRING COMMENT '统计时间',week_trade_date             STRING COMMENT '周一日期',month_trade_date            STRING COMMENT '月一日期',store_no                    STRING COMMENT '店铺编码',store_name                  STRING COMMENT '店铺名称',store_sale_type             BIGINT COMMENT '店铺销售类型',store_type_code             BIGINT COMMENT '分店类型',city_id                     BIGINT COMMENT '城市ID',city_name                   STRING COMMENT '城市名称',region_code                 STRING COMMENT '区域编码',region_name                 STRING COMMENT '区域名称',is_day_clear                BIGINT COMMENT '是否日清:0否,1是',store_sale_amount           DECIMAL(27, 2) COMMENT '门店销售金额',store_orders_number         BIGINT COMMENT '门店总订单量',register_member_num         BIGINT COMMENT '当日注册人数',register_member_num_all     BIGINT COMMENT '累计注册会员数',register_recharge_num       BIGINT COMMENT '当日注册且充值会员数',rg_rc_td_num                BIGINT COMMENT '当日注册且充值且消费会员数',register_trade_num          BIGINT COMMENT '当日注册且消费会员数',recharge_member_num         BIGINT COMMENT '充值会员数',recharge_amount             DECIMAL(27, 2) COMMENT '充值金额',recharge_amount_all         DECIMAL(27, 2) COMMENT '累计会员充值金额',remain_member_num           BIGINT COMMENT '当日有余额的会员人数',remain_member_amount        DECIMAL(27, 2) COMMENT '当日会员余额',balance_member_num          BIGINT COMMENT '余额消费人数',balance_member_order_num    BIGINT COMMENT '余额消费单量',balance_pay_amount          DECIMAL(27, 2) COMMENT '余额支付金额',balance_member_amount       DECIMAL(27, 2) COMMENT '余额消费金额',member_num                  BIGINT COMMENT '会员消费人数',member_order_num            BIGINT COMMENT '会员消费单量',member_amount               DECIMAL(27, 2) COMMENT '会员消费金额',member_first_num            BIGINT COMMENT '会员首单人数',member_first_order_num      BIGINT COMMENT '会员首单订单量',member_first_amount         DECIMAL(27, 2) COMMENT '会员首单销售额',member_nofirst_num          BIGINT COMMENT '会员非首单人数',member_nofirst_order_num    BIGINT COMMENT '会员非首单订单量',member_nofirst_amount       DECIMAL(27, 2) COMMENT '会员非首单销售额'
) 
comment '门店会员统计日表'
partitioned by (dt STRING COMMENT '统计日期')
row format delimited fields terminated by ','
stored as orc
tblproperties ('orc.compress'='SNAPPY');
数据导入:

门店的消费情况可以从dwm_sell_o2o_order_i表中出，

注册、充值、消费这些数据可以从dwm_mem_member_behavior_day_i中出，

余额数据可以从dwd_mem_balance_online_i中出。

需要注意的是，这里有新增的指标还有累计的指标，为了方便计算，可以分开求解。

新增指标可以大部分从dwm_mem_member_behavior_day_i中出，因为 dwm_mem_member_behavior_day_i是会员粒度的表，记录了会员的各种行为。在计算会员指标的时候，很多需要count()来计算的指标，可以转化成sum（1），根据条件进行判断即可。

-- DWS 门店会员统计宽表
-- 注意: 以下内容仅仅以2023-11-14为例,实际需要把14-20日的所有数据都要导入对应表中
with t1 as (selecttrade_date,store_no,sum(real_paid_amount) as store_sale_amount,count(if(trade_type = 0,parent_order_no,NULL)) - count(if(trade_type = 5,parent_order_no,NULL)) as store_orders_number,0 as register_member_num,0 as register_member_num_all,0 as register_recharge_num,0 as rg_rc_td_num,0 as register_trade_num,0 as recharge_member_num,0 as recharge_amount,0 as recharge_amount_all,0 as remain_member_num,0 as remain_member_amount,0 as balance_member_num,0 as balance_member_order_num,0 as balance_pay_amount,0 as balance_member_amount,0 as member_num,0 as member_order_num,0 as member_amount,0 as member_first_num,0 as member_first_order_num,0 as member_first_amount,0 as member_nofirst_num,0 as member_nofirst_order_num,0 as member_nofirst_amountfrom dwm.dwm_sell_o2o_order_i where dt = '2023-11-14'group by trade_date,store_nounion allselecttrade_date,bind_md as store_no,0 as store_sale_amount, -- 门店销售额0 as store_orders_number, -- 门店总订单量sum(is_register) as register_member_num, -- 当日注册人数0 as register_member_num_all, -- 累计注册人数sum(if(is_register = 1 and  is_recharge = 1, 1,0)) as register_recharge_num, -- 当日注册且充值会员数sum(if(is_register = 1 and  is_recharge = 1 and is_consume = 1, 1,0)) as rg_rc_td_num, -- 当日注册 且充值且消费会员数sum(if(is_register = 1  and is_consume = 1, 1,0)) as register_trade_num, -- 当日注册且消费会员数sum(is_recharge) as recharge_member_num, -- 充值会员数sum(if( is_recharge = 1,recharge_amount,0) ) as recharge_amount, -- 充值金额0 as recharge_amount_all, -- 累计会员充值金额0 as remain_member_num, -- 当日有余额的会员人数0 as remain_member_amount, -- 当日会员余额sum(is_balance_consume) as balance_member_num,  --余额消费人数sum(if(is_balance_consume = 1, balance_consume_times, 0)) as balance_member_order_num, --余额消费单量sum(if(is_balance_consume = 1, balance_pay_amount, 0))  as balance_pay_amount,  -- 余额支付金额sum(if(is_balance_consume = 1, balance_consume_amount, 0))  as balance_member_amount, -- 余额消费金额sum(is_consume) as member_num, -- 会员消费人数sum(if(is_consume = 1, consume_times, 0)) as member_order_num, -- 会员消费单量sum(if(is_consume = 1, consume_amount, 0))  as member_amount,  -- 会员消费金额sum(is_first_consume) as member_first_num, -- 会员首单人数sum(is_first_consume) as member_first_order_num, -- 会员首单订单量sum(if(is_first_consume = 1, first_consume_amount,0)) as member_first_amount, -- 会员首单销售额sum(is_consume) - sum(is_first_consume) as member_nofirst_num, -- 会员非首单人数sum(if(is_consume = 1, consume_times, 0)) -  sum(is_first_consume) as member_nofirst_order_num, -- 会员非首单订单量sum(if(is_consume = 1, consume_amount, 0)) - sum(if(is_first_consume = 1, first_consume_amount,0)) as member_nofirst_amount -- 会员非首单销售额from dwm.dwm_mem_member_behavior_day_i where dt = '2023-11-14'group by trade_date,bind_mdunion allselecttrade_date,store_no,0 as store_sale_amount,0 as store_orders_number,0 as register_member_num,0 as register_member_num_all,0 as register_recharge_num,0 as rg_rc_td_num,0 as register_trade_num,0 as recharge_member_num,0 as recharge_amount,0 as recharge_amount_all,count(1) as remain_member_num,sum(balance_amount) as remain_member_amount,0 as balance_member_num,0 as balance_member_order_num,0 as balance_pay_amount,0 as balance_member_amount,0 as member_num,0 as member_order_num,0 as member_amount,0 as member_first_num,0 as member_first_order_num,0 as member_first_amount,0 as member_nofirst_num,0 as member_nofirst_order_num,0 as member_nofirst_amountfrom dwd.dwd_mem_balance_online_i where dt = '2023-11-14'group by trade_date,store_nounion allselectstart_date as trade_date,store_no,0 as store_sale_amount,0 as store_orders_number,0 as register_member_num,register_member_num_all,0 as register_recharge_num,0 as rg_rc_td_num,0 as register_trade_num,0 as recharge_member_num,0 as recharge_amount,recharge_amount_all,0 as remain_member_num,0 as remain_member_amount,0 as balance_member_num,0 as balance_member_order_num,0 as balance_pay_amount,0 as balance_member_amount,0 as member_num,0 as member_order_num,0 as member_amount,0 as member_first_num,0 as member_first_order_num,0 as member_first_amount,0 as member_nofirst_num,0 as member_nofirst_order_num,0 as member_nofirst_amountfrom(selecttrade_date as start_date,store_no,sum(reg_num_add) over(partition by store_no order by trade_date) as register_member_num_all,  -- 累计注册会员数sum(recharge_amount) over(partition by store_no order by trade_date) as recharge_amount_all,  -- 累计充值金额lead(trade_date,1,'9999-99-99') over (partition by store_no order by  trade_date) as end_datefrom(  -- 先统计每天注册会员数selecttrade_date,bind_md as store_no,sum(is_register) as reg_num_add,sum(if(is_recharge = 1,recharge_amount,0)) as recharge_amountfrom dwm.dwm_mem_member_behavior_day_igroup bytrade_date, bind_md) temp1) twhere start_date <= '2023-11-14' and end_date >= '2023-11-14'
)
insert overwrite table dws.dws_mem_store_member_statistics_day_i partition(dt)
selectt1.trade_date,t2.week_trade_date,t2.month_trade_date,t1.store_no,t3.store_name,t3.store_sale_type,t3.store_type_code,t3.city_id,t3.city_name,t3.region_code,t3.region_name,t3.is_day_clear,sum(t1.store_sale_amount) as store_sale_amount,sum(t1.store_orders_number) as store_orders_number,sum(t1.register_member_num) as register_member_num,sum(t1.register_member_num_all) as register_member_num_all,sum(t1.register_recharge_num) as register_recharge_num,sum(t1.rg_rc_td_num) as rg_rc_td_num,sum(t1.register_trade_num) as register_trade_num,sum(t1.recharge_member_num) as recharge_member_num,sum(t1.recharge_amount) as recharge_amount,sum(t1.recharge_amount_all) as recharge_amount_all,sum(t1.remain_member_num) as remain_member_num,sum(t1.remain_member_amount) as remain_member_amount,sum(t1.balance_member_num) as balance_member_num,sum(t1.balance_member_order_num) as balance_member_order_num,sum(t1.balance_pay_amount) as balance_pay_amount,sum(t1.balance_member_amount) as balance_member_amount,sum(t1.member_num) as member_num,sum(t1.member_order_num) as member_order_num,sum(t1.member_amount) as member_amount,sum(t1.member_first_num) as member_first_num,sum(t1.member_first_order_num) as member_first_order_num,sum(t1.member_first_amount) as member_first_amount,sum(t1.member_nofirst_num) as member_nofirst_num,sum(t1.member_nofirst_order_num) as member_nofirst_order_num,sum(t1.member_nofirst_amount) as member_nofirst_amount,t1.trade_date as dt
from t1left join dim.dwd_dim_date_f t2 on t1.trade_date = t2.trade_date-- 注意: 一定要检查自己的dwd_dim_store_i分区目录,此处填写自己的分区目录时间left join dim.dwd_dim_store_i t3 on t1.store_no = t3.store_no and t3.dt ='2023-11-23'
group byt1.trade_date,t2.week_trade_date,t2.month_trade_date,t1.store_no,t3.store_name,t3.store_sale_type,t3.store_type_code,t3.city_id,t3.city_name,t3.region_code,t3.region_name,t3.is_day_clear;

ADS层开发

回顾dayofweek函数
-- dayofweek
-- 注意: dayofweek是老外从周日算,所以返回的结果和咱们中国人思路差1天
select dayofweek('2023-12-7');
-- 需求: 获取到2023-12-7所在的周中的周一日期
select date_sub('2023-12-7',if(dayofweek('2023-12-7')=1,6,dayofweek('2023-12-7')-2));-- 需求: 获取到2023-12-7所在的周中的周日日期
select date_sub('2023-12-8',if(dayofweek('2023-12-8')=1,0,dayofweek('2023-12-8')-8));-- day0fmonth
select dayofmonth('2023-12-07');
-- 需求: 获取2023-12-7所在月的第一天的日期
select date_sub('2023-12-07',dayofmonth('2023-12-07')-1);
-- 需求: 获取2023-12-7所在月的最后一天的日期
select last_day('2023-12-07');

各类会员数量统计分析
维度指标:
指标：新增注册会员数、累计注册会员数、新增消费会员数、累计消费会员数、新增复购会员数、累计复购会员数、活跃会员数、沉睡会员数、会员消费金额
维度: 时间维度（天、周、月）涉及ADS表:门店会员分类月表  和  门店会员分类周表表字段的组成: 维度字段 + 指标结果字段

门店会员分类周表
建表语句:
CREATE TABLE IF NOT EXISTS ads.ads_mem_store_member_classify_week_i(trade_date                  STRING COMMENT '周一日期',store_no                    STRING COMMENT '店铺编码',store_name                  STRING COMMENT '店铺名称',store_sale_type             BIGINT COMMENT '店铺销售类型',store_type_code             BIGINT COMMENT '分店类型',city_id                     BIGINT COMMENT '城市ID',city_name                   STRING COMMENT '城市名称',region_code                 STRING COMMENT '区域编码',region_name                 STRING COMMENT '区域名称',is_day_clear                BIGINT COMMENT '是否日清:0否,1是',reg_num_add                 BIGINT COMMENT '新增注册会员数',reg_num_sum                 BIGINT COMMENT '累计注册会员数',consume_num_add             BIGINT COMMENT '新增消费会员数',consume_num_sum             BIGINT COMMENT '累计消费会员数',repurchase_num_add          BIGINT COMMENT '新增复购会员数',repurchase_num_sum          BIGINT COMMENT '累计复购会员数',active_member_num           BIGINT COMMENT '活跃会员数',sleep_member_num            BIGINT COMMENT '沉睡会员数',sale_amount_bind            DECIMAL(27, 2) COMMENT '会员消费金额'
)
comment '门店会员分类周表'
partitioned by (dt STRING COMMENT '统计日期')
row format delimited fields terminated by ','
stored as orc
tblproperties ('orc.compress'='SNAPPY');
数据导入:

指标分为累计值和新增值，累计值可以取当周最后一天的数值、新增值可以进行聚合得到。 ​ 需要注意的是，这里计算的是一张周表，所以当考虑到数据的场景时，需要取到当周所有的数据进行聚合，以及取到当周最后一天进行取累加值。

思考: 当计算某一天对应这一周的指标, 如果获取这一周相关的数据呢?

where t.dt in (select max(dt) from dws.dws_mem_store_member_classify_day_iwhere dt>=date_sub('${inputdate}', if (dayofweek('${inputdate}') = 1, 6, dayofweek('${inputdate}') - 2)) and dt<=date_sub('${inputdate}', if (dayofweek('${inputdate}') = 1, 0, dayofweek('${inputdate}') - 8))
)

代码实现:

with t1 as (
-- 计算非累加值
selectweek_trade_date,store_no,sum(reg_num_add) as reg_num_add,sum(consume_num_add) as consume_num_add,sum(repurchase_num_add) as repurchase_num_add,sum(sale_amount_bind) as sale_amount_bind
from dws.dws_mem_store_member_classify_day_i
where dt >= date_sub('2023-11-14',if(dayofweek('2023-11-14') = 1,6, dayofweek('2023-11-14')-2 ))and dt <= date_add('2023-11-14',if(dayofweek('2023-11-14') = 1,0,-dayofweek('2023-11-14')+8))
group by week_trade_date,store_no
),
t2 as (
-- 计算 累计值
-- 如果获取这一周的最后一天呢?selectweek_trade_date as trade_date,store_no,store_name,store_sale_type,store_type_code,city_id,city_name,region_code,region_name,is_day_clear,reg_num_sum,consume_num_sum,repurchase_num_sum,active_member_num,sleep_member_numfrom dws.dws_mem_store_member_classify_day_i where dt in (selectmax(dt) as c1from dws.dws_mem_store_member_classify_day_i as twhere dt >= date_sub('2023-11-14',if(dayofweek('2023-11-14') = 1,6, dayofweek('2023-11-14')-2 ))and dt <= date_add('2023-11-14',if(dayofweek('2023-11-14') = 1,0,-dayofweek('2023-11-14')+8)))
)
insert overwrite table ads.ads_mem_store_member_classify_week_i partition (dt)
selectt2.trade_date,t2.store_no,t2.store_name,t2.store_sale_type,t2.store_type_code,t2.city_id,t2.city_name,t2.region_code,t2.region_name,t2.is_day_clear,t1.reg_num_add,t2.reg_num_sum,t1.consume_num_add,t2.consume_num_sum,t1.repurchase_num_add,t2.repurchase_num_sum,t2.active_member_num,t2.sleep_member_num,t1.sale_amount_bind,t2.trade_date as dt
from t2 left join  t1 on t2.trade_date = t1.week_trade_date and t2.store_no = t1.store_no;

门店会员分类月表
建表语句:
CREATE TABLE IF NOT EXISTS ads.ads_mem_store_member_classify_month_i(trade_date                  STRING COMMENT '月一日期',store_no                    STRING COMMENT '店铺编码',store_name                  STRING COMMENT '店铺名称',store_sale_type             BIGINT COMMENT '店铺销售类型',store_type_code             BIGINT COMMENT '分店类型',city_id                     BIGINT COMMENT '城市ID',city_name                   STRING COMMENT '城市名称',region_code                 STRING COMMENT '区域编码',region_name                 STRING COMMENT '区域名称',is_day_clear                BIGINT COMMENT '是否日清:0否,1是',reg_num_add                 BIGINT COMMENT '新增注册会员数',reg_num_sum                 BIGINT COMMENT '累计注册会员数',consume_num_add             BIGINT COMMENT '新增消费会员数',consume_num_sum             BIGINT COMMENT '累计消费会员数',repurchase_num_add          BIGINT COMMENT '新增复购会员数',repurchase_num_sum          BIGINT COMMENT '累计复购会员数',active_member_num           BIGINT COMMENT '活跃会员数',sleep_member_num            BIGINT COMMENT '沉睡会员数',sale_amount_bind            DECIMAL(27, 2) COMMENT '会员消费金额'
) 
comment '门店会员分类月表'
partitioned by (dt STRING COMMENT '统计日期')
row format delimited fields terminated by ','
stored as orc
tblproperties ('orc.compress'='SNAPPY');
数据导入:

处理思路: 同周表ads_mem_store_member_classify_week_i,改变下范围即可

思考: 如果获取一个月范围的数据呢?

select date_sub('2023-09-30',dayofmonth('2023-09-30')-1), last_day('2023-09-30')

门店会员分析
维度指标:
指标: 门店销售额、门店总订单量、当日注册人数、累计注册会员数、当日注册且充值会员数、当日注册且充值且消费会员数、当日注册且消费会员数、充值会员数、充值金额、累计会员充值金额、当日有余额的会员人数、当日会员余额、余额消费人数/单量、余额支付金额、余额消费金额、会员消费人数/单量、会员消费金额、会员首单人数/订单量/销售额、会员非首单人数/订单量/销售额
维度: 时间维度（天、周、月）涉及表: 门店会员统计周表 和 门店会员统计月表涉及表字段: 维度字段 + 指标结果字段

门店会员统计周表
建表语句:
CREATE TABLE IF NOT EXISTS ads.ads_mem_store_member_statistics_week_i(trade_date                  STRING COMMENT '周一日期',store_no                    STRING COMMENT '店铺编码',store_name                  STRING COMMENT '店铺名称',store_sale_type             BIGINT COMMENT '店铺销售类型',store_type_code             BIGINT COMMENT '分店类型',city_id                     BIGINT COMMENT '城市ID',city_name                   STRING COMMENT '城市名称',region_code                 STRING COMMENT '区域编码',region_name                 STRING COMMENT '区域名称',is_day_clear                BIGINT COMMENT '是否日清:0否,1是',store_sale_amount           DECIMAL(27, 2) COMMENT '门店销售金额',store_orders_number         BIGINT COMMENT '门店总订单量',register_member_num         BIGINT COMMENT '当日注册人数',register_member_num_all     BIGINT COMMENT '累计注册会员数',register_recharge_num       BIGINT COMMENT '当日注册且充值会员数',rg_rc_td_num                BIGINT COMMENT '当日注册且充值且消费会员数',register_trade_num          BIGINT COMMENT '当日注册且消费会员数',recharge_member_num         BIGINT COMMENT '充值会员数',recharge_amount             DECIMAL(27, 2) COMMENT '充值金额',recharge_amount_all         DECIMAL(27, 2) COMMENT '累计会员充值金额',remain_member_num           BIGINT COMMENT '当周最后一天有余额的会员人数',remain_member_amount        DECIMAL(27, 2) COMMENT '当周最后一天会员余额',balance_member_num          BIGINT COMMENT '余额消费人数',balance_member_order_num    BIGINT COMMENT '余额消费单量',balance_pay_amount          DECIMAL(27, 2) COMMENT '余额支付金额',balance_member_amount       DECIMAL(27, 2) COMMENT '余额消费金额',member_num                  BIGINT COMMENT '会员消费人数',member_order_num            BIGINT COMMENT '会员消费单量',member_amount               DECIMAL(27, 2) COMMENT '会员消费金额',member_first_num            BIGINT COMMENT '会员首单人数',member_first_order_num      BIGINT COMMENT '会员首单订单量',member_first_amount         DECIMAL(27, 2) COMMENT '会员首单销售额',member_nofirst_num          BIGINT COMMENT '会员非首单人数',member_nofirst_order_num    BIGINT COMMENT '会员非首单订单量',member_nofirst_amount       DECIMAL(27, 2) COMMENT '会员非首单销售额'
) 
comment '门店会员统计周表'
partitioned by (dt STRING COMMENT '统计日期')
row format delimited fields terminated by ','
stored as orc
tblproperties ('orc.compress'='SNAPPY');
数据导入:
指标分为三种情况：一种是状态值，比如说累计指标，register_member_num_all等，还有状态指标，remain_member_num等；另一种情况是可累加的指标，比如金额和单量等；还有一种情况是不可累积指标，比如人数。状态值可以从最新的天表中dws_mem_store_member_statistics_day_i获取。
然后以这张表作为主表，关联其他表。
可累加的指标直接从dws_mem_store_member_statistics_day_i中进行聚合得到。
不可累加的指标从dwm_mem_member_behavior_day_i中进行计算得到。

代码实现:

-- ads 门店会员统计周表
with t1 as (
-- 第一部分:　基于ＤＷＳ层门店会员统计天表 获取指定天的对应这一周的数据, 对这一周进行聚合统计
selectweek_trade_date as trade_date,store_no,sum(store_sale_amount) as store_sale_amount,sum(store_orders_number) as store_orders_number,sum(register_member_num) as register_member_num,sum(register_recharge_num) as register_recharge_num,sum(rg_rc_td_num) as rg_rc_td_num,sum(register_trade_num) as register_trade_num,sum(recharge_amount) as recharge_amount,sum(balance_member_order_num) as balance_member_order_num,sum(balance_pay_amount) as balance_pay_amount,sum(balance_member_amount) as balance_member_amount,sum(member_order_num) as member_order_num,sum(member_amount) as member_amount,sum(member_first_num) as member_first_num,sum(member_first_order_num) as member_first_order_num,sum(member_first_amount) as member_first_amount,sum(member_nofirst_order_num) as member_nofirst_order_num,sum(member_nofirst_amount) as member_nofirst_amountfrom dws.dws_mem_store_member_statistics_day_i
where dt >= date_sub('2023-11-14',if(dayofweek('2023-11-14') = 1,6, dayofweek('2023-11-14')-2 ))and dt <= date_add('2023-11-14',if(dayofweek('2023-11-14') = 1,0,-dayofweek('2023-11-14')+8))
group by week_trade_date,store_no
),t2 as (selectweek_trade_date as trade_date,store_no,store_name,store_sale_type,store_type_code,city_id,city_name,region_code,region_name,is_day_clear,register_member_num_all,recharge_amount_all,remain_member_num,remain_member_amountfrom dws.dws_mem_store_member_statistics_day_i where dt in (selectmax(dt)from dws.dws_mem_store_member_statistics_day_i twhere dt >= date_sub('2023-11-14',if(dayofweek('2023-11-14') = 1,6, dayofweek('2023-11-14')-2 ))and dt <= date_add('2023-11-14',if(dayofweek('2023-11-14') = 1,0,-dayofweek('2023-11-14')+8)))
),t3 as (selectweek_trade_date as trade_date,bind_md as store_no,count( DISTINCT  if(is_recharge = 1,zt_id,NULL) ) AS recharge_member_num,count( DISTINCT  if(is_balance_consume = 1,zt_id,NULL) ) AS balance_member_num,count( DISTINCT  if(is_consume = 1,zt_id,NULL) ) AS member_num,count( DISTINCT  if(is_first_consume = 0 and consume_times > 0,zt_id,NULL) ) AS member_nofirst_numfrom dwm.dwm_mem_member_behavior_day_iwhere dt >= date_sub('2023-11-14',if(dayofweek('2023-11-14') = 1,6, dayofweek('2023-11-14')-2 ))and dt <= date_add('2023-11-14',if(dayofweek('2023-11-14') = 1,0,-dayofweek('2023-11-14')+8))group by week_trade_date,bind_md
)insert overwrite table ads.ads_mem_store_member_statistics_week_i partition (dt)
selectt2.trade_date,t2.store_no,t2.store_name,t2.store_sale_type,t2.store_type_code,t2.city_id,t2.city_name,t2.region_code,t2.region_name,t2.is_day_clear,t1.store_sale_amount,t1.store_orders_number,t1.register_member_num,t2.register_member_num_all,t1.register_recharge_num,t1.rg_rc_td_num,t1.register_trade_num,t3.recharge_member_num,t1.recharge_amount,t2.recharge_amount_all,t2.remain_member_num,t2.remain_member_amount,t3.balance_member_num,t1.balance_member_order_num,t1.balance_pay_amount,t1.balance_member_amount,t3.member_num,t1.member_order_num,t1.member_amount,t1.member_first_num,t1.member_first_order_num,t1.member_first_amount,t3.member_nofirst_num,t1.member_nofirst_order_num,t1.member_nofirst_amount,t2.trade_date as dt
from t2 left join t1 on t2.trade_date = t1.trade_date and t2.store_no = t1.store_noleft join  t3 on t2.trade_date = t3.trade_date and t2.store_no = t3.store_no;
门店会员统计月表
建表语句:
CREATE TABLE IF NOT EXISTS ads.ads_mem_store_member_statistics_month_i(trade_date                  STRING COMMENT '月一日期',store_no                    STRING COMMENT '店铺编码',store_name                  STRING COMMENT '店铺名称',store_sale_type             BIGINT COMMENT '店铺销售类型',store_type_code             BIGINT COMMENT '分店类型',city_id                     BIGINT COMMENT '城市ID',city_name                   STRING COMMENT '城市名称',region_code                 STRING COMMENT '区域编码',region_name                 STRING COMMENT '区域名称',is_day_clear                BIGINT COMMENT '是否日清:0否,1是',store_sale_amount           DECIMAL(27, 2) COMMENT '门店销售金额',store_orders_number         BIGINT COMMENT '门店总订单量',register_member_num         BIGINT COMMENT '当日注册人数',register_member_num_all     BIGINT COMMENT '累计注册会员数',register_recharge_num       BIGINT COMMENT '当日注册且充值会员数',rg_rc_td_num                BIGINT COMMENT '当日注册且充值且消费会员数',register_trade_num          BIGINT COMMENT '当日注册且消费会员数',recharge_member_num         BIGINT COMMENT '充值会员数',recharge_amount             DECIMAL(27, 2) COMMENT '充值金额',recharge_amount_all         DECIMAL(27, 2) COMMENT '累计会员充值金额',remain_member_num           BIGINT COMMENT '当月最后一天有余额的会员人数',remain_member_amount        DECIMAL(27, 2) COMMENT '当月最后一天会员余额',balance_member_num          BIGINT COMMENT '余额消费人数',balance_member_order_num    BIGINT COMMENT '余额消费单量',balance_pay_amount          DECIMAL(27, 2) COMMENT '余额支付金额',balance_member_amount       DECIMAL(27, 2) COMMENT '余额消费金额',member_num                  BIGINT COMMENT '会员消费人数',member_order_num            BIGINT COMMENT '会员消费单量',member_amount               DECIMAL(27, 2) COMMENT '会员消费金额',member_first_num            BIGINT COMMENT '会员首单人数',member_first_order_num      BIGINT COMMENT '会员首单订单量',member_first_amount         DECIMAL(27, 2) COMMENT '会员首单销售额',member_nofirst_num          BIGINT COMMENT '会员非首单人数（非去重）',member_nofirst_order_num    BIGINT COMMENT '会员非首单订单量',member_nofirst_amount       DECIMAL(27, 2) COMMENT '会员非首单销售额'
) 
comment '门店会员统计月表'
partitioned by (dt STRING COMMENT '统计日期')
row format delimited fields terminated by ','
stored as orc
tblproperties ('orc.compress'='SNAPPY');
数据导入:

同 ads_mem_store_member_statistics_week_i,改变下范围即可

ADS层其他需求(基于Presto实现)

Presto--分布式SQL查询引擎

Presto-简介

  • 背景

    大数据分析类软件发展历程。

    • ==Apache Hadoop-MapReduce==

      • 优点：统一、通用、简单的编程模型，分而治之思想处理海量数据。

      • 缺点：java学习成本高、MR执行慢、内部过程繁琐

    • ==Apache Hive==

      • 优点：SQL on Hadoop。sql语言上手方便。学习成本低。

      • 缺点：底层默认还是MapReduce引擎、慢、延迟高

    • 各种SQL类计算引擎开始出现，主要追求的就是一个问题：==计算如何更快，延迟如何降低==。

      • ==Presto/trino==

      • Spark On Hive、Spark SQL

      • Flink

      • .......

    FaceBook维护的原始版本: presto, 也叫prestoDBPresto创始人团队离职后研发并维护的: PrestoSQL 因为版权更名为Trino已经给大家整理好了对应网址如下:FaceBook维护的, Presto的官网: https://prestodb.io/    创始人团队维护的, Trino的官网: https://trino.io/	Presto创始人团队维护的, 因为版权更名为Trino: http://github.com/trinodb/trino	
相关文章如下:Presto在有赞的实践之路: https://cloud.tencent.com/developer/news/606849 Presto更名为-Trino: https://www.sohu.com/a/441836081_106784

  • 介绍

    Presto是一个开源的==分布式SQL查询引擎==，适用于==交互式查询==，数据量支持GB到PB字节。

    Presto的设计和编写完全是为了解决==Facebook==这样规模的商业数据仓库交互式分析和处理速度的问题。

    presto简介: 一条Presto查询可以将多个数据源进行合并，可以跨越整个组织进行分析;presto特点: Presto以分析师的需求作为目标，他们期望响应速度小于1秒到几分钟;

  • 优缺点

    # 优点
1）Presto与Hive对比，都能够处理PB级别的海量数据分析，但Presto是基于内存运算，减少没必要的硬盘IO，所以更快。2）能够连接多个数据源，跨数据源连表查，如从Hive查询大量网站访问记录，然后从Mysql中匹配出设备信息。3）部署也比Hive简单，因为Hive是基于HDFS的，需要先部署HDFS。# 缺点
1）虽然能够处理PB级别的海量数据分析，但不是代表Presto把PB级别都放在内存中计算的。而是根据场景，如count，avg等聚合运算，是边读数据边计算，再清内存，再读数据再计算，这种耗的内存并不高。但是连表查，就可能产生大量的临时数据，因此速度会变慢，反而Hive此时会更擅长。2）为了达到实时查询，可能会想到用它直连MySql来操作查询，这效率并不会提升，瓶颈依然在MySql，此时还引入网络瓶颈，所以会比原本直接操作数据库要慢。
Presto-架构、相关术语

  • 架构图

    Presto是一个运行在多台服务器上的分布式系统。 完整安装包括==一个coordinator和多个worker==。 由客户端提交查询，从Presto命令行CLI提交到coordinator; coordinator进行解析，分析并执行查询计划，然后分发处理队列到worker。

    整个presto是一个 M-S架构 (主从架构):coordinator: 主节点  作用: 负责接收客户端发送的SQL, 对SQL进行编译, 形成执行计划, 根据执行计划, 分发给各个从节点进行执行操作
discovery service: 附属节点作用: 一般内嵌在主节点中, 主要负责维护从节点列表, 当从节点启动后, 都需要到 discovery 节点进行注册操作
worker节点: 从节点作用: 负责接收coordinator传递过来任务, 对任务进行具体处理工作(读取数据, 处理数据, 将处理后结果数据返回给coordinator)

  • ==Connector== 连接器

    1、Presto通过Connector连接器来连接访问不同数据源，例如Hive或mysql。连接器功能类似于数据库的驱动程序。允许Presto使用标准API与资源进行交互。2、Presto包含几个内置连接器：JMX连接器，可访问内置系统表的System连接器，Hive连接器和旨在提供TPC-H基准数据的TPCH连接器。许多第三方开发人员都贡献了连接器，因此Presto可以访问各种数据源中的数据，比如：ES、Kafka、MongoDB、Redis、Postgre、Druid、Cassandra等。

  • ==Catalog== 连接目录: hive或者mysql等数据源

    1、Presto Catalog是数据源schema的上一级，并通过连接器访问数据源。2、例如，可以配置Hive Catalog以通过Hive Connector连接器提供对Hive信息的访问。3、在Presto中使用表时，标准表名始终是被支持的。
例如，hive.test_data.test的标准表名将引用hive catalog中test_data schema中的test table。
Catalog需要在Presto的配置文件中进行配置。

  • ==schema== 库

    Schema是组织表的一种方式。Catalog和Schema共同定义了一组可以查询的表。当使用Presto访问Hive或关系数据库（例如MySQL）时，Schema会转换为目标数据库中的对应Schema(database)。= schema通俗理解就是我们所讲的database.
= 想一下在hive中，下面这两个sql是否相等。
show databases; -- presto不支持
show schemas;

  • ==table== 表

    ...

Presto-集群启停
[root@hadoop01 ~]# /export/server/presto/bin/launcher start
Started as 89560# 可以使用jps 配合kill -9命令 关闭进程

  • web UI页面

    链接: http://192.168.88.80:8090/ui/

Presto-Datagrip连接使用

  • JDBC 驱动：==presto-jdbc-0.245.1.jar==

  • JDBC 地址：==jdbc:presto://192.168.88.80:8090/hive==

  • step1：创建连接

    由于驱动比较大,好多人经常下载失败,可以按照下图关联资料中提供的包: presto-jdbc-0.245.1.jar

Presto--时间日期类型注意事项

  • ==date_format==(timestamp, format) ==> varchar

    • 作用: 将指定的日期对象转换为字符串操作

  • ==date_parse==(string, format) → timestamp

    • 作用: 用于将字符串的日期数据转换为日期对象

    select date_format( timestamp '2020-10-10 12:50:50' , '%Y/%m/%d %H:%i:%s');
select date_format( date_parse('2020:10:10 12-50-50','%Y:%m:%d %H-%i-%s') ,'%Y/%m/%d %H:%i:%s');----
注意: 参数一必须是日期对象所以如果传递的是字符串, 必须将先转换为日期对象:  方式一:  标识为日期对象, 但是格式必须为标准日期格式timestamp '2020-10-10 12:50:50'date '2020-10-10'方式二: 如果不标准，先用date_parse解析成为标准date_parse('2020-10-10 12:50:50','%Y-%m-%d %H:%i:%s')  扩展说明: 日期format格式说明年：%Y月：%m日：%d时：%H分：%i 秒：%s周几：%w(0..6)

  • ==date_add==(unit, value, timestamp) → [same as input]

    • 作用: 用于对日期数据进行 加 减 操作

  • ==date_diff==(unit, timestamp1, timestamp2) → bigint

    • 作用: 用于比对两个日期之间差值

    select  date_add('hour',3,timestamp '2021-09-02 15:59:50');
select  date_add('day',-1,timestamp '2021-09-02 15:59:50');
select  date_add('month',-1,timestamp '2021-09-02 15:59:50');select date_diff('year',timestamp '2020-09-02 06:30:30',timestamp '2021-09-02 15:59:50')
select date_diff('month',timestamp '2021-06-02 06:30:30',timestamp '2021-09-02 15:59:50')
select date_diff('day',timestamp '2021-08-02 06:30:30',timestamp '2021-09-02 15:59:50')
Presto-常规优化

  • 数据存储优化

    --1）合理设置分区与Hive类似，Presto会根据元信息读取分区数据，合理的分区能减少Presto数据读取量，提升查询性能。--2）使用列式存储Presto对ORC文件读取做了特定优化，因此在Hive中创建Presto使用的表时，建议采用ORC格式存储。相对于Parquet，Presto对ORC支持更好。Parquet和ORC一样都支持列式存储，但是Presto对ORC支持更好，而Impala对Parquet支持更好。在数仓设计时，要根据后续可能的查询引擎合理设置数据存储格式。--3）使用压缩数据压缩可以减少节点间数据传输对IO带宽压力，对于需要快速解压的，建议采用Snappy压缩。--4）预先排序对于已经排序的数据，在查询的数据过滤阶段，ORC格式支持跳过读取不必要的数据。比如对于经常需要过滤的字段可以预先排序。

  • SQL优化

    • 列裁剪

    • 分区裁剪

    • group by优化

      • 按照数据量大小降序排列

    • order by使用limit

    • ==join时候大表放置在左边==

      ...

  • 替换非ORC格式的Hive表

Presto-内存调优

  • 内存管理机制--内存分类

    Presto管理的内存分为两大类：==user memory==和==system memory==

    • user memory用户内存

      跟用户数据相关的，比如读取用户输入数据会占据相应的内存，这种内存的占用量跟用户底层数据量大小是强相关的

    • system memory系统内存

      执行过程中衍生出的副产品，比如tablescan表扫描，write buffers写入缓冲区，跟查询输入的数据本身不强相关的内存。

  • 内存管理机制--内存池

    ==内存池中来实现分配user memory和system memory==。

    内存池为常规内存池GENERAL_POOL、预留内存池RESERVED_POOL。

    1、GENERAL_POOL:在一般情况下，一个查询执行所需要的user/system内存都是从general pool中分配的，reserved pool在一般情况下是空闲不用的。2、RESERVED_POOL:大部分时间里是不参与计算的，但是当集群中某个Worker节点的general pool消耗殆尽之后，coordinator会选择集群中内存占用最多的查询，把这个查询分配到reserved pool，这样这个大查询自己可以继续执行，而腾出来的内存也使得其它的查询可以继续执行，从而避免整个系统阻塞。注意:
reserved pool到底多大呢？这个是没有直接的配置可以设置的，他的大小上限就是集群允许的最大的查询的大小(query.total-max-memory-per-node)。reserved pool也有缺点，一个是在普通模式下这块内存会被浪费掉了，二是大查询可以用Hive来替代。因此也可以禁用掉reserved pool（experimental.reserved-pool-enabled设置为false），那系统内存耗尽的时候没有reserved pool怎么办呢？它有一个OOM Killer的机制，对于超出内存限制的大查询SQL将会被系统Kill掉，从而避免影响整个presto。

  • 内存相关参数

    1、user memory用户内存参数
query.max-memory-per-node:单个query操作在单个worker上user memory能用的最大值
query.max-memory:单个query在整个集群中允许占用的最大user memory2、user+system总内存参数
query.max-total-memory-per-node:单个query操作可在单个worker上使用的最大(user + system)内存
query.max-total-memory:单个query在整个集群中允许占用的最大(user + system) memory当这些阈值被突破的时候，query会以insufficient memory（内存不足）的错误被终结。3、协助阻止机制
在高内存压力下保持系统稳定。当general pool常规内存池已满时，操作会被置为blocked阻塞状态，直到通用池中的内存可用为止。此机制可防止激进的查询填满JVM堆并引起可靠性问题。4、其他参数
memory.heap-headroom-per-node:这个内存是JVM堆中预留给第三方库的内存分配，presto无法跟踪统计，默认值是-Xmx * 0.35、结论
GeneralPool = 服务器总内存 - ReservedPool - memory.heap-headroom-per-node - Linux系统内存常规内存池内存大小=服务器物理总内存-服务器linux操作系统内存-预留内存池大小-预留给第三方库内存

  • 内存优化建议

    • 常见的报错解决

      1、Query exceeded per-node total memory limit of xx
适当增加query.max-total-memory-per-node。2、Query exceeded distributed user memory limit of xx
适当增加query.max-memory。3、Could not communicate with the remote task. The node may have crashed or be under too much load
内存不够，导致节点crash，可以查看/var/log/message。

    • 建议参数设置

      1、query.max-memory-per-node和query.max-total-memory-per-node是query操作使用的主要内存配置，因此这两个配置可以适当加大。
memory.heap-headroom-per-node是三方库的内存，默认值是JVM-Xmx * 0.3，可以手动改小一些。1) 各节点JVM内存推荐大小: 当前节点剩余内存*80%2) 对于heap-headroom-pre-node第三方库的内存配置: 建议jvm内存的%15左右3) 在配置的时候, 不要正正好好, 建议预留一点点, 以免出现问题数据量在35TB , presto节点数量大约在30台左右 (128GB内存 + 8核CPU)   注意：
1、query.max-memory-per-node小于query.max-total-memory-per-node。
2、query.max-memory小于query.max-total-memory。
3、query.max-total-memory-per-node 与memory.heap-headroom-per-node 之和必须小于 jvm max memory，也就是jvm.config 中配置的-Xmx。

ADS层开发_其他需求(Presto实现)

维度指标
需求一: 会员首次充值(统计每个会员首次充值的时间, 交易单ID以及对应门店和充值金额)
需求二: 门店新老会员消费(统计每个门店每个月新会员、老会员、全部会员、非会员的数量、消费金额、消费单量(新会员指的首次消费后30天内, 老会员指的首次消费后大于30天))
需求三: 会员复购统计(留存)(统计的指标为统计日期用户量、一日后用户量、二日后用户量、三日后用户量、四日后用户量、五日后用户量、六日后用户量)
需求四: 会员贡献(统计各个会员每天在各个门店消费单量、消费金额、消费成本、线上订单量、线上消费金额、线上消费成本、线下订单量、线下消费金额、线下消费成本)涉及表:会员首次充值表 和  门店新老会员消费月表 和 会员复购统计天表 以及 会员贡献天表

会员首次充值表
建表语句:
CREATE TABLE IF NOT EXISTS ads.ads_mem_member_first_recharge_i(trade_date_time     STRING COMMENT '交易时间',trade_date          STRING COMMENT '日期',trade_order_id      STRING COMMENT '对应的交易单id',zt_id               BIGINT COMMENT '中台 会员id',store_no            STRING COMMENT '门店编号',city_id             BIGINT COMMENT '城市ID',recharge_amount     DECIMAL(27, 2) COMMENT '充值金额'
) 
comment '会员首次充值表'
partitioned by (dt STRING COMMENT '统计日期')
row format delimited fields terminated by ','
stored as orc
tblproperties ('orc.compress'='SNAPPY');
数据导入:

说明: 同dwm_mem_first_buy_i

insert into  hive.ads.ads_mem_member_first_recharge_i
with t1 as (selectdate_format(trade_date,'%Y-%m-%d %H:%i:%s') as trade_date_time,date_format(trade_date,'%Y-%m-%d') as trade_date,trade_order_id,zt_id,store_no,city_id,amount as recharge_amount,row_number() over(partition by zt_id order by trade_date) as rnfrom hive.ods.ods_mem_store_amount_record_i where record_type = 2 and date_format(trade_date,'%Y-%m-%d') = '2023-11-20'
)
selectt1.trade_date_time,t1.trade_date,t1.trade_order_id,t1.zt_id,t1.store_no,t1.city_id,t1.recharge_amount,'2023-11-20' as dt
from t1left join hive.ads.ads_mem_member_first_recharge_i ton t1.zt_id = t.zt_id and t1.store_no = t.store_no and t.dt < '2023-11-20'
where rn = 1 and t.zt_id is null;
门店新老会员消费月表
建表语句:
CREATE TABLE IF NOT EXISTS ads.ads_mem_store_new_old_member_month_i(trade_date                  STRING COMMENT '月一时间',store_no                    STRING COMMENT '店铺编码',store_name                  STRING COMMENT '店铺名称',store_sale_type             BIGINT COMMENT '店铺销售类型',store_type_code             BIGINT COMMENT '分店类型',city_id                     BIGINT COMMENT '城市ID',city_name                   STRING COMMENT '城市名称',region_code                 STRING COMMENT '区域编码',region_name                 STRING COMMENT '区域名称',is_day_clear                BIGINT COMMENT '是否日清:0否,1是',member_type                 BIGINT COMMENT '会员类型:1新会员,2老会员,3会员,4非会员',member_num                  BIGINT COMMENT '消费会员数',sale_amount                 DECIMAL(27, 2) COMMENT '消费金额',order_num                   BIGINT COMMENT '消费单量'
) 
comment '门店新老会员消费月表'
partitioned by (dt STRING COMMENT '消费日期')
row format delimited fields terminated by ','
stored as orc
tblproperties ('orc.compress'='SNAPPY');

数据导入:
新会员：首次消费后30天内的；
老会员：首次消费后大于30天；需要统计每个门店每个月新会员、老会员、全部会员、非会员的数量、消费金额、消费单量。
注意：这里是一个月表，在判断新老会员的时候，按照当月最后一天为标准，往前推30天，30天内的为新会员。比如今天是5月28日，在计算5月份的数据时，4月29日——5月28日这30天的都是新会员。而在计算4月份数据时，因为4月份已经过去了，所以以4月30日为最后一天，4月1日——4月30日为4月份的新会员。

1）在计算月表时，需要取到当月最后一天，然后以最后一天为标准，取到前30天

selecta.trade_date,date_sub(a.trade_date, 30) as day30 -- 前30天,a.month_trade_date -- 对应的月一时间
from  dim.dwd_dim_date_f a
inner join(select max(dt) mdt from dws.dws_mem_store_member_statistics_day_i -- 取到最大的分区where dt>=date_sub('${inputdate}', dayofmonth('${inputdate}') - 1)and dt<=last_day('${inputdate}') ) b
on a.trade_date = b.mdt

2）新会员：30天内首次消费的会员

取新会员，可以使用首次消费表，取前30天到当月最大一天的会员即可。

因为在hive的where语句中不能使用子查询，所以这里使用join的方式解决

with dtt as (selecta.trade_date,date_sub(a.trade_date, 30) as day30 -- 前30天,a.month_trade_date -- 对应的月一时间from  dim.dwd_dim_date_f ainner join(select max(dt) mdt from dws.dws_mem_store_member_statistics_day_i -- 取到最大的分区where dt>=date_sub('${inputdate}', dayofmonth('${inputdate}') - 1)and dt<=last_day('${inputdate}') ) bon a.trade_date = b.mdt),
zt as (select s.zt_id from dwm.dwm_mem_first_buy_i s -- 取到最大分区与其前30天的数据cross join dttwhere s.dt >= dtt.day30 and s.dt <= dtt.trade_date)

代码实现:

insert into hive.ads.ads_mem_store_new_old_member_month_i
with t1 as (selecttrade_date,date_format(date_add('day',-30,date '2023-11-20'),'%Y-%m-%d') as day30,month_trade_date,month_end_datefrom hive.dim.dwd_dim_date_f where trade_date = (selectmax(dt)from hive.dws.dws_mem_store_member_statistics_day_iwhere  dt >= date_format(date_add('day', -day(date '2023-11-20') + 1 ,date '2023-11-20'),'%Y-%m-%d')and  dt < date_format(date_add('month',1,date_add('day', -day(date '2023-11-20') + 1 ,date '2023-11-20')),'%Y-%m-%d'))
),
t2 as (-- 获取最近30天有过消费的新用户selecttemp1.trade_date,temp1.zt_id,temp1.store_nofrom hive.dwm.dwm_mem_first_buy_i temp1cross join t1where temp1.dt >= t1.day30 and temp1.dt <= t1.trade_date
),
t3 as (-- 获取 最近30天新用户的消费select1 as member_type,temp2.bind_md as store_no,count(distinct temp2.zt_id) as member_num,sum(consume_amount) as sale_amount,sum(consume_times) as order_numfrom hive.dwm.dwm_mem_member_behavior_day_i temp2join t2 on temp2.zt_id = t2.zt_id and temp2.bind_md = t2.store_nowhere dt >= date_format(date_add('day', -day(date '2023-11-20') + 1 ,date '2023-11-20'),'%Y-%m-%d')and  dt < date_format(date_add('month',1,date_add('day', -day(date '2023-11-20') + 1 ,date '2023-11-20')),'%Y-%m-%d')and consume_times > 0group bytemp2.bind_mdunion all-- 获取 老会员select2 as member_type,temp2.bind_md as store_no,count(distinct temp2.zt_id) as member_num,sum(consume_amount) as sale_amount,sum(consume_times) as order_numfrom hive.dwm.dwm_mem_member_behavior_day_i temp2left join t2 on temp2.zt_id = t2.zt_id and temp2.bind_md = t2.store_nowhere dt >= date_format(date_add('day', -day(date '2023-11-20') + 1 ,date '2023-11-20'),'%Y-%m-%d')and  dt < date_format(date_add('month',1,date_add('day', -day(date '2023-11-20') + 1 ,date '2023-11-20')),'%Y-%m-%d')and consume_times > 0 and t2.zt_id is nullgroup bytemp2.bind_mdunion all-- 获取 全部会员select3 as member_type,temp2.bind_md as store_no,count(distinct temp2.zt_id) as member_num,sum(consume_amount) as sale_amount,sum(consume_times) as order_numfrom hive.dwm.dwm_mem_member_behavior_day_i temp2where dt >= date_format(date_add('day', -day(date '2023-11-20') + 1 ,date '2023-11-20'),'%Y-%m-%d')and  dt < date_format(date_add('month',1,date_add('day', -day(date '2023-11-20') + 1 ,date '2023-11-20')),'%Y-%m-%d')and consume_times > 0group bytemp2.bind_mdunion all-- 非会员数据select4 as member_type,store_no,0 as  member_num,sum(real_paid_amount) as sale_amount,count(if(trade_type = 0,parent_order_no,NULL)) - count(if(trade_type = 5,parent_order_no,NULL)) as order_numfrom hive.dwm.dwm_sell_o2o_order_iwhere dt >= date_format(date_add('day', -day(date '2023-11-20') + 1 ,date '2023-11-20'),'%Y-%m-%d')and  dt < date_format(date_add('month',1,date_add('day', -day(date '2023-11-20') + 1 ,date '2023-11-20')),'%Y-%m-%d')and  member_type = 0group by store_no
)
selectt1.month_trade_date as trade_date,t3.store_no,t4.store_name,t4.store_sale_type,t4.store_type_code,t4.city_id,t4.city_name,t4.region_code,t4.region_name,t4.is_day_clear,t3.member_type,t3.member_num,cast(t3.sale_amount as decimal(27,2)),t3.order_num,t1.month_trade_date as dt
from t3 cross join t1-- 注意: 一定要检查自己的dwd_dim_store_i分区目录,此处填写自己的分区目录时间left join hive.dim.dwd_dim_store_i t4 on t3.store_no = t4.store_no and t4.dt = '2023-11-23';
会员复购统计天表
建表语句:
CREATE TABLE IF NOT EXISTS ads.ads_mem_repurchase_day_i(trade_date                  STRING COMMENT '统计时间',store_no                    STRING COMMENT '店铺编码',store_name                  STRING COMMENT '店铺名称',store_sale_type             BIGINT COMMENT '店铺销售类型',store_type_code             BIGINT COMMENT '分店类型',city_id                     BIGINT COMMENT '城市ID',city_name                   STRING COMMENT '城市名称',region_code                 STRING COMMENT '区域编码',region_name                 STRING COMMENT '区域名称',is_day_clear                BIGINT COMMENT '是否日清:0否,1是',member_count                BIGINT COMMENT '统计日期用户量',next_member_count_1         BIGINT COMMENT '一日后用户量',next_member_count_2         BIGINT COMMENT '二日后用户量',next_member_count_3         BIGINT COMMENT '三日后用户量',next_member_count_4         BIGINT COMMENT '四日后用户量',next_member_count_5         BIGINT COMMENT '五日后用户量',next_member_count_6         BIGINT COMMENT '六日后用户量'
)
comment '会员复购统计天表'
partitioned by (dt STRING COMMENT '消费日期')
row format delimited fields terminated by ','
stored as orc
tblproperties ('orc.compress'='SNAPPY');

数据导入:

复购是一个非常重要的指标，用来衡量客户的粘性。这个需求需要统计当天下单的用户，一日、二日到六日的复购情况，为了方便后续使用，这里不直接统计复购率，而是统计人数。所以，这个需求需要统计的指标为统计日期用户量、一日后用户量、二日后用户量、三日后用户量、四日后用户量、五日后用户量、六日后用户量。本需求类似于计算留存，也就是统计当天的用户，在1日、2日、3日。。。之后是否再次购买。

	使用dwm_mem_member_behavior_day_i表进行计算。因为需求中最多需要计算六日后的用户量，所以当天中的这些消费用户，需要6天之后，才能拿到所有的数据（1日后，2日后...6日后）。换个角度看，只有6天前的数据才会稳定，6天内的分区对应的数据每天都要进行更新，所以，每天要更新6个分区的数据。如果计算n天后的复购人数，其实就是用第一天的会员与第n天的会员进行关联，这里使用左关联，关联条件为会员id以及日期 能关联上的，即是复购的用户，然后再count()则可得到相应数值。

代码实现:

-- 六天前消费用户和往后每一天的复购情况--会员主题: ADS层  会员复购天表
-- 需求: 计算某一天及相对于第一天往后六天每天的复购的人数
-- 思路: 首先知道6天前的那一天的所有的消费用户  基于这个结果 left join 往后1天的所有消费用户 left join  往后2天的所有消费用户 ...往后6天的所有消费用户
selectdate_format(date_add('day',-6,date '2023-09-20'),'%Y-%m-%d') as trade_date,s.store_no,s.store_name,s.store_sale_type,s.store_type_code,s.city_id,s.city_name,s.region_code,s.region_name,s.is_day_clear,count(day0.zt_id) as member_count,count(day1.zt_id) as next_member_count_1,count(day2.zt_id) as next_member_count_2,count(day3.zt_id) as next_member_count_3,count(day4.zt_id) as next_member_count_4,count(day5.zt_id) as next_member_count_5,count(day6.zt_id) as next_member_count_6,date_format(date_add('day',-6,date '2023-09-20'),'%Y-%m-%d') as dt
from (selectt.zt_id,t.bind_md,t.dt as after,tt.days_after1,tt.days_after2,tt.days_after3,tt.days_after4,tt.days_after5,tt.days_after6from hive.dwm.dwm_mem_member_behavior_day_i t left join hive.dim.dwd_dim_date_f tt on t.trade_date = tt.trade_datewhere dt = date_format(date_add('day',-6,date '2023-09-20'),'%Y-%m-%d') and consume_times>0
) as day0
left join (selectzt_id,bind_md,dt as after1from hive.dwm.dwm_mem_member_behavior_day_iwhere dt = date_format(date_add('day',-5,date '2023-09-20'),'%Y-%m-%d') and consume_times>0
) day1 on day0.days_after1 = day1.after1 and day0.zt_id = day1.zt_id
left join (selectzt_id,bind_md,dt as after2from hive.dwm.dwm_mem_member_behavior_day_iwhere dt = date_format(date_add('day',-4,date '2023-09-20'),'%Y-%m-%d') and consume_times>0
) day2 on day0.days_after2 = day2.after2 and day0.zt_id = day2.zt_id
left join (selectzt_id,bind_md,dt as after3from hive.dwm.dwm_mem_member_behavior_day_iwhere dt = date_format(date_add('day',-3,date '2023-09-20'),'%Y-%m-%d') and consume_times>0
) day3 on day0.days_after3 = day3.after3 and day0.zt_id = day3.zt_id
left join (selectzt_id,bind_md,dt as after4from hive.dwm.dwm_mem_member_behavior_day_iwhere dt = date_format(date_add('day',-2,date '2023-09-20'),'%Y-%m-%d') and consume_times>0
) day4 on day0.days_after4 = day4.after4 and day0.zt_id = day4.zt_id
left join (selectzt_id,bind_md,dt as after5from hive.dwm.dwm_mem_member_behavior_day_iwhere dt = date_format(date_add('day',-1,date '2023-09-20'),'%Y-%m-%d') and consume_times>0
) day5 on day0.days_after5 = day5.after5 and day0.zt_id = day5.zt_id
left join (selectzt_id,bind_md,dt as after6from hive.dwm.dwm_mem_member_behavior_day_iwhere dt = '2023-09-20' and consume_times>0
) day6 on day0.days_after6 = day6.after6 and day0.zt_id = day6.zt_id
join hive.dim.dwd_dim_store_i s on day0.bind_md = s.store_no and s.dt = '2023-09-24'
group bys.store_no,s.store_name,s.store_sale_type,s.store_type_code,s.city_id,s.city_name,s.region_code,s.region_name,s.is_day_clear;另一种写法: 直接计算出 6天 及其每一天和后面六天的数据
--会员主题: ADS层  会员复购天表
-- 需求: 计算某一天及相对于第一天往后六天每天的复购的人数
-- 思路: 首先知道6天前的那一天的所有的消费用户  基于这个结果 left join 往后1天的所有消费用户 left join  往后2天的所有消费用户 ...往后6天的所有消费用户
selectday0.after as trade_date,s.store_no,s.store_name,s.store_sale_type,s.store_type_code,s.city_id,s.city_name,s.region_code,s.region_name,s.is_day_clear,count(day0.zt_id) as member_count,count(day1.zt_id) as next_member_count_1,count(day2.zt_id) as next_member_count_2,count(day3.zt_id) as next_member_count_3,count(day4.zt_id) as next_member_count_4,count(day5.zt_id) as next_member_count_5,count(day6.zt_id) as next_member_count_6,date_format(date_add('day',-6,date '2023-09-20'),'%Y-%m-%d') as dt
from (-- 获取 统计日期前6天的所有的消费数据selectt.zt_id,t.bind_md,t.dt as after,tt.days_after1,tt.days_after2,tt.days_after3,tt.days_after4,tt.days_after5,tt.days_after6from hive.dwm.dwm_mem_member_behavior_day_i t left join hive.dim.dwd_dim_date_f tt on t.trade_date = tt.trade_datewhere dt >= date_format(date_add('day',-6,date '2023-09-20'),'%Y-%m-%d')and dt <= '2023-09-20'and consume_times>0
) as day0
left join (-- 获取 统计日期前5天和 后1天的的所有的消费数据selectzt_id,bind_md,dt as after1from hive.dwm.dwm_mem_member_behavior_day_iwhere dt >= date_format(date_add('day',-5,date '2023-09-20'),'%Y-%m-%d')and dt <= date_format(date_add('day',1,date '2023-09-20'),'%Y-%m-%d')and consume_times>0
) day1 on day0.days_after1 = day1.after1 and day0.zt_id = day1.zt_id
left join (-- 获取 统计日期前4天和 后2天的的所有的消费数据selectzt_id,bind_md,dt as after2from hive.dwm.dwm_mem_member_behavior_day_iwhere dt >= date_format(date_add('day',-4,date '2023-09-20'),'%Y-%m-%d')and dt <= date_format(date_add('day',2,date '2023-09-20'),'%Y-%m-%d')and consume_times>0
) day2 on day0.days_after2 = day2.after2 and day0.zt_id = day2.zt_id
left join (-- 获取 统计日期前3天和 后3天的的所有的消费数据selectzt_id,bind_md,dt as after3from hive.dwm.dwm_mem_member_behavior_day_iwhere dt >= date_format(date_add('day',-3,date '2023-09-20'),'%Y-%m-%d')and dt <= date_format(date_add('day',3,date '2023-09-20'),'%Y-%m-%d')and consume_times>0
) day3 on day0.days_after3 = day3.after3 and day0.zt_id = day3.zt_id
left join (-- 获取 统计日期前2天和 后4天的的所有的消费数据selectzt_id,bind_md,dt as after4from hive.dwm.dwm_mem_member_behavior_day_iwhere dt >= date_format(date_add('day',-2,date '2023-09-20'),'%Y-%m-%d')and dt <= date_format(date_add('day',4,date '2023-09-20'),'%Y-%m-%d')and consume_times>0
) day4 on day0.days_after4 = day4.after4 and day0.zt_id = day4.zt_id
left join (-- 获取 统计日期前1天和 后5天的的所有的消费数据selectzt_id,bind_md,dt as after5from hive.dwm.dwm_mem_member_behavior_day_iwhere dt >= date_format(date_add('day',-1,date '2023-09-20'),'%Y-%m-%d')and dt <= date_format(date_add('day',5,date '2023-09-20'),'%Y-%m-%d')and consume_times>0
) day5 on day0.days_after5 = day5.after5 and day0.zt_id = day5.zt_id
left join (-- 获取 统计日期后6天的的所有的消费数据selectzt_id,bind_md,dt as after6from hive.dwm.dwm_mem_member_behavior_day_iwhere dt >= '2023-09-20'and dt <= date_format(date_add('day',6,date '2023-09-20'),'%Y-%m-%d')and consume_times>0
) day6 on day0.days_after6 = day6.after6 and day0.zt_id = day6.zt_id
join hive.dim.dwd_dim_store_i s on day0.bind_md = s.store_no
group byday0.after,s.store_no,s.store_name,s.store_sale_type,s.store_type_code,s.city_id,s.city_name,s.region_code,s.region_name,s.is_day_clear;

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