我一直在努力查询，并尝试了各种变体以获得所需的结果。但是我失败了。我希望，如果我将与try语句输出一起尝试的变体共享，那么任何人都可能会有一个指针。
Postgres 11.6。
对于下面的代码块，Dimension1是我引用的所有表上都存在的字段。日期仅显示在会话表中，因此，为了提取特定日期的数据，我创建了一个cte filter_sessions以仅获取出现在给定日期的Dimension1，然后加入其他表。这使我的查询可以选择特定日期的数据，在这种情况下为2月6日。
这是我最初的尝试。它使用CTE，出于可读性考虑，我更喜欢该CTE，并且如果它可以运行，我可以省去编写更少代码的麻烦，但是它不会：
with
filter_sessions as (
select
dimension1,
dimension2,
date,
channel_grouping,
device_category,
user_type
from ga_flagship_ecom.sessions
where date >= '2020-02-06'
and date <= '2020-02-06'
),
ee as (
select
e.dimension1,
e.dimension3,
case when sum(case when e.metric1 = 0 then 1 else 0 end) > 0 then 1 else 0 end as zero_val_product, -- roll up to event level
-- approximation for inferring if the product i a download and hence sees all the checkout steps
case when sum(case when lower(product_name) ~ 'digital|download|file' then 1 else 0 end) > 0 then 1 else 0 end as download
from ga_flagship_ecom.ecom e
join filter_sessions f on f.dimension1 = e.dimension1
group by 1,2
),
ecom_events as (
select
ev.dimension1,
ev.dimension3,
ev.event_action,
ev.event_label,
ee.zero_val_product,
ee.download
from ga_flagship_ecom.events ev
join ee on ee.dimension1 = ev.dimension1 and ee.dimension3 = ev.dimension3
where ev.event_category = 'ecom'
)
select
s.date,
lower(s.channel_grouping) as channel_grouping,
lower(s.device_category) as device_category,
lower(s.user_type) as user_type,
lower(ev.event_action) as event_action,
lower(coalesce(ev.event_label, 'na')) as event_label,
ev.zero_val_product,
ev.download,
count(distinct s.dimension1) as sessions,
count(distinct s.dimension2) as daily_users
from filter_sessions s
join ecom_events ev on ev.dimension1 = s.dimension1
group by 1,2,3,4,5,6,7,8;
这是该查询的解释输出如下：
GroupAggregate (cost=222818.83..222818.88 rows=1 width=188)
Group Key: s.date, (lower((s.channel_grouping)::text)), (lower((s.device_category)::text)), (lower((s.user_type)::text)), (lower((ev.event_action)::text)), (lower((COALESCE(ev.event_label, 'na'::character varying))::text)), ev.zero_val_product, ev.download
CTE filter_sessions
-> Index Scan using sessions_date_idx on sessions (cost=0.56..2.78 rows=1 width=76)
Index Cond: ((date >= '2020-02-06'::date) AND (date <= '2020-02-06'::date))
CTE ee
-> GroupAggregate (cost=47604.61..47606.29 rows=48 width=38)
Group Key: e.dimension1, e.dimension3
-> Sort (cost=47604.61..47604.73 rows=48 width=51)
Sort Key: e.dimension1, e.dimension3
-> Nested Loop (cost=0.56..47603.27 rows=48 width=51)
-> CTE Scan on filter_sessions f (cost=0.00..0.02 rows=1 width=32)
-> Index Scan using ecom_dimension1_idx on ecom e (cost=0.56..47602.77 rows=48 width=51)
Index Cond: ((dimension1)::text = (f.dimension1)::text)
CTE ecom_events
-> Hash Join (cost=1.68..175209.67 rows=1 width=60)
Hash Cond: (((ev_1.dimension1)::text = (ee.dimension1)::text) AND (ev_1.dimension3 = ee.dimension3))
-> Seq Scan on events ev_1 (cost=0.00..150210.69 rows=3332973 width=52)
Filter: ((event_category)::text = 'ecom'::text)
-> Hash (cost=0.96..0.96 rows=48 width=48)
-> CTE Scan on ee (cost=0.00..0.96 rows=48 width=48)
-> Sort (cost=0.08..0.08 rows=1 width=236)
Sort Key: s.date, (lower((s.channel_grouping)::text)), (lower((s.device_category)::text)), (lower((s.user_type)::text)), (lower((ev.event_action)::text)), (lower((COALESCE(ev.event_label, 'na'::character varying))::text)), ev.zero_val_product, ev.download
-> Nested Loop (cost=0.00..0.07 rows=1 width=236)
Join Filter: ((s.dimension1)::text = (ev.dimension1)::text)
-> CTE Scan on filter_sessions s (cost=0.00..0.02 rows=1 width=164)
-> CTE Scan on ecom_events ev (cost=0.00..0.02 rows=1 width=104)
有人建议，cetee是我的瓶颈，我应该集中精力。我在cte ee上尝试了子查询，而不是引用cte filter_sessions。因此更改：
ee as (
select
e.dimension1,
e.dimension3,
case when sum(case when e.metric1 = 0 then 1 else 0 end) > 0 then 1 else 0 end as zero_val_product, -- roll up to event level
-- approximation for inferring if the product i a download and hence sees all the checkout steps
case when sum(case when lower(product_name) ~ 'digital|download|file' then 1 else 0 end) > 0 then 1 else 0 end as download
from ga_flagship_ecom.ecom e
--join filter_sessions f on f.dimension1 = e.dimension1
join (select dimension1 from ga_flagship_ecom.sessions where date >= '2020-02-06' and date <= '2020-02-06') f
on f.dimension1 = e.dimension1
group by 1,2
),
这里有个小小的改动：
GroupAggregate (cost=107619.19..107619.24 rows=1 width=188)
Group Key: s.date, (lower((s.channel_grouping)::text)), (lower((s.device_category)::text)), (lower((s.user_type)::text)), (lower((ev.event_action)::text)), (lower((COALESCE(ev.event_label, 'na'::character varying))::text)), ev.zero_val_product, ev.download
CTE filter_sessions
-> Index Scan using sessions_date_idx on sessions (cost=0.56..2.78 rows=1 width=76)
Index Cond: ((date >= '2020-02-06'::date) AND (date <= '2020-02-06'::date))
CTE ee
-> GroupAggregate (cost=47606.05..47606.08 rows=1 width=38)
Group Key: e.dimension1, e.dimension3
-> Sort (cost=47606.05..47606.05 rows=1 width=51)
Sort Key: e.dimension1, e.dimension3
-> Nested Loop (cost=1.12..47606.04 rows=1 width=51)
-> Index Only Scan using sessions_date_idx on sessions sessions_1 (cost=0.56..2.78 rows=1 width=22)
Index Cond: ((date >= '2020-02-06'::date) AND (date <= '2020-02-06'::date))
-> Index Scan using ecom_dimension1_idx on ecom e (cost=0.56..47602.77 rows=48 width=51)
Index Cond: ((dimension1)::text = (sessions_1.dimension1)::text)
CTE ecom_events
-> Nested Loop (cost=0.56..60010.25 rows=1 width=60)
-> CTE Scan on ee (cost=0.00..0.02 rows=1 width=48)
-> Index Scan using events_pk on events ev_1 (cost=0.56..60010.22 rows=1 width=52)
Index Cond: (((dimension1)::text = (ee.dimension1)::text) AND (dimension3 = ee.dimension3))
Filter: ((event_category)::text = 'ecom'::text)
-> Sort (cost=0.08..0.08 rows=1 width=236)
Sort Key: s.date, (lower((s.channel_grouping)::text)), (lower((s.device_category)::text)), (lower((s.user_type)::text)), (lower((ev.event_action)::text)), (lower((COALESCE(ev.event_label, 'na'::character varying))::text)), ev.zero_val_product, ev.download
-> Nested Loop (cost=0.00..0.07 rows=1 width=236)
Join Filter: ((s.dimension1)::text = (ev.dimension1)::text)
-> CTE Scan on filter_sessions s (cost=0.00..0.02 rows=1 width=164)
-> CTE Scan on ecom_events ev (cost=0.00..0.02 rows=1 width=104)
我不确定如何在解释输出中解释这些数字，但是对于cte ee，这些数字实际上是相同的，因此我认为更改没有太大的不同？ CTE ee-> GroupAggregate (cost=47606.05..47606.08 rows=1 width=38)
不管哪种方式，查询仍然不会完成。我尝试过的其他操作（所有操作均失败，查询将无限期运行）：
而不是内部联接，而是像这样的where过滤器：
ee as (
select
e.dimension1,
e.dimension3,
case when sum(case when e.metric1 = 0 then 1 else 0 end) > 0 then 1 else 0 end as zero_val_product, -- roll up to event level
-- approximation for inferring if the product i a download and hence sees all the checkout steps
case when sum(case when lower(product_name) ~ 'digital|download|file' then 1 else 0 end) > 0 then 1 else 0 end as download
from ga_flagship_ecom.ecom e
--join filter_sessions f on f.dimension1 = e.dimension1
where e.dimension1 in (select dimension1 from filter_sessions)
group by 1,2
),
这是基于使用where过滤器而不是内部联接的说明输出：
GroupAggregate (cost=222818.84..222818.89 rows=1 width=188)
Group Key: s.date, (lower((s.channel_grouping)::text)), (lower((s.device_category)::text)), (lower((s.user_type)::text)), (lower((ev.event_action)::text)), (lower((COALESCE(ev.event_label, 'na'::character varying))::text)), ev.zero_val_product, ev.download
CTE filter_sessions
-> Index Scan using sessions_date_idx on sessions (cost=0.56..2.78 rows=1 width=76)
Index Cond: ((date >= '2020-02-06'::date) AND (date <= '2020-02-06'::date))
CTE ee
-> GroupAggregate (cost=47604.63..47606.31 rows=48 width=38)
Group Key: e.dimension1, e.dimension3
-> Sort (cost=47604.63..47604.75 rows=48 width=51)
Sort Key: e.dimension1, e.dimension3
-> Nested Loop (cost=0.58..47603.29 rows=48 width=51)
-> HashAggregate (cost=0.02..0.03 rows=1 width=32)
Group Key: (filter_sessions.dimension1)::text
-> CTE Scan on filter_sessions (cost=0.00..0.02 rows=1 width=32)
-> Index Scan using ecom_dimension1_idx on ecom e (cost=0.56..47602.77 rows=48 width=51)
Index Cond: ((dimension1)::text = (filter_sessions.dimension1)::text)
CTE ecom_events
-> Hash Join (cost=1.68..175209.67 rows=1 width=60)
Hash Cond: (((ev_1.dimension1)::text = (ee.dimension1)::text) AND (ev_1.dimension3 = ee.dimension3))
-> Seq Scan on events ev_1 (cost=0.00..150210.69 rows=3332973 width=52)
Filter: ((event_category)::text = 'ecom'::text)
-> Hash (cost=0.96..0.96 rows=48 width=48)
-> CTE Scan on ee (cost=0.00..0.96 rows=48 width=48)
-> Sort (cost=0.08..0.08 rows=1 width=236)
Sort Key: s.date, (lower((s.channel_grouping)::text)), (lower((s.device_category)::text)), (lower((s.user_type)::text)), (lower((ev.event_action)::text)), (lower((COALESCE(ev.event_label, 'na'::character varying))::text)), ev.zero_val_product, ev.download
-> Nested Loop (cost=0.00..0.07 rows=1 width=236)
Join Filter: ((s.dimension1)::text = (ev.dimension1)::text)
-> CTE Scan on filter_sessions s (cost=0.00..0.02 rows=1 width=164)
-> CTE Scan on ecom_events ev (cost=0.00..0.02 rows=1 width=104)
然后我尝试将cte ee分成两部分，如下所示：
ee_base as (
select
e.dimension1,
e.dimension3,
e.metric1,
lower(product_name) as product_name
from ga_flagship_ecom.ecom e
join filter_sessions f on f.dimension1 = e.dimension1
),
ee as (
select
dimension1,
dimension3,
case when sum(case when metric1 = 0 then 1 else 0 end) > 0 then 1 else 0 end as zero_val_product, -- roll up to event level
-- approximation for inferring if the product i a download and hence sees all the checkout steps
case when sum(case when product_name ~ 'digital|download|file' then 1 else 0 end) > 0 then 1 else 0 end as download
from ee_base
group by 1,2
),
这也失败了（我真的很乐观这将会起作用）。这是此尝试的解释输出：
GroupAggregate (cost=222818.33..222818.38 rows=1 width=188)
Group Key: s.date, (lower((s.channel_grouping)::text)), (lower((s.device_category)::text)), (lower((s.user_type)::text)), (lower((ev.event_action)::text)), (lower((COALESCE(ev.event_label, 'na'::character varying))::text)), ev.zero_val_product, ev.download
CTE filter_sessions
-> Index Scan using sessions_date_idx on sessions (cost=0.56..2.78 rows=1 width=76)
Index Cond: ((date >= '2020-02-06'::date) AND (date <= '2020-02-06'::date))
CTE ee_base
-> Nested Loop (cost=0.56..47603.39 rows=48 width=66)
-> CTE Scan on filter_sessions f (cost=0.00..0.02 rows=1 width=32)
-> Index Scan using ecom_dimension1_idx on ecom e (cost=0.56..47602.77 rows=48 width=51)
Index Cond: ((dimension1)::text = (f.dimension1)::text)
CTE ee
-> HashAggregate (cost=1.68..2.40 rows=48 width=48)
Group Key: ee_base.dimension1, ee_base.dimension3
-> CTE Scan on ee_base (cost=0.00..0.96 rows=48 width=76)
CTE ecom_events
-> Hash Join (cost=1.68..175209.67 rows=1 width=60)
Hash Cond: (((ev_1.dimension1)::text = (ee.dimension1)::text) AND (ev_1.dimension3 = ee.dimension3))
-> Seq Scan on events ev_1 (cost=0.00..150210.69 rows=3332973 width=52)
Filter: ((event_category)::text = 'ecom'::text)
-> Hash (cost=0.96..0.96 rows=48 width=48)
-> CTE Scan on ee (cost=0.00..0.96 rows=48 width=48)
-> Sort (cost=0.08..0.08 rows=1 width=236)
Sort Key: s.date, (lower((s.channel_grouping)::text)), (lower((s.device_category)::text)), (lower((s.user_type)::text)), (lower((ev.event_action)::text)), (lower((COALESCE(ev.event_label, 'na'::character varying))::text)), ev.zero_val_product, ev.download
-> Nested Loop (cost=0.00..0.07 rows=1 width=236)
Join Filter: ((s.dimension1)::text = (ev.dimension1)::text)
-> CTE Scan on filter_sessions s (cost=0.00..0.02 rows=1 width=164)
-> CTE Scan on ecom_events ev (cost=0.00..0.02 rows=1 width=104)
有效的方法是创建临时表。但是我真的很想找到一种解决方法，并按照偏好的顺序解决：
我还能在这里做其他事情吗？
您可以简单地将CTE重写为临时视图，这些视图包含在主查询计划中。
CREATE TEMP VIEW filter_sessions as
select
dimension1,
dimension2,
zdate,
channel_grouping,
device_category,
user_type
from ga_flagship_ecom.sessions
where zdate >= '2020-02-06'
and zdate <= '2020-02-06'
;
CREATE TEMP VIEW ee as
select
e.dimension1,
e.dimension3,
case when sum(case when e.metric1 = 0 then 1 else 0 end) > 0 then 1 else 0 end as zero_val_product, -- roll up to event level
-- approximation for inferring if the product i a download and hence sees all the checkout steps
case when sum(case when lower(product_name) ~ 'digital|download|file' then 1 else 0 end) > 0 then 1 else 0 end as download
from ga_flagship_ecom.ecom e
join filter_sessions f on f.dimension1 = e.dimension1
group by 1,2
;
CREATE TEMP VIEW ecom_events as
select
ev.dimension1,
ev.dimension3,
ev.event_action,
ev.event_label,
ee.zero_val_product,
ee.download
from ga_flagship_ecom.events ev
join ee on ee.dimension1 = ev.dimension1 and ee.dimension3 = ev.dimension3
where ev.event_category = 'ecom'
;
select
s.zdate,
lower(s.channel_grouping) as channel_grouping,
lower(s.device_category) as device_category,
lower(s.user_type) as user_type,
lower(ev.event_action) as event_action,
lower(coalesce(ev.event_label, 'na')) as event_label,
ev.zero_val_product,
ev.download,
count(distinct s.dimension1) as sessions,
count(distinct s.dimension2) as daily_users
from filter_sessions s
join ecom_events ev on ev.dimension1 = s.dimension1
group by 1,2,3,4,5,6,7,8;