Sql 在使用CTE时理解解释-尝试获取要计算的查询
我一直在努力处理一个查询，并尝试各种变化以达到我想要的结果。但我失败了。我希望，如果我与explain语句输出共享我尝试过的变体，那么任何人都可能有一个指针 博士后11.6 对于下面的代码块，dimension1是存在于我所引用的所有表上的字段。日期只出现在sessions表中，所以为了获取特定日期的数据，我创建了一个cte filter_sessions，只获取在给定日期出现的维度1，然后连接到我的其他表。这允许我的查询选择特定日期的数据，在本例中为2月6日 这是我最初的尝试。它使用了一个CTE，我更喜欢它的可读性，如果它只是运行，我可以编写更少的代码，但它没有：Sql 在使用CTE时理解解释-尝试获取要计算的查询,sql,postgresql,Sql,Postgresql,我一直在努力处理一个查询，并尝试各种变化以达到我想要的结果。但我失败了。我希望，如果我与explain语句输出共享我尝试过的变体，那么任何人都可能有一个指针 博士后11.6 对于下面的代码块，dimension1是存在于我所引用的所有表上的字段。日期只出现在sessions表中，所以为了获取特定日期的数据，我创建了一个cte filter_sessions，只获取在给定日期出现的维度1，然后连接到我的其他表。这允许我的查询选择特定日期的数据，在本例中为2月6日 这是我最初的尝试。它使用了一个CT
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;
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)
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)
GroupAggregate（成本=222818.33..222818.38行=1宽=188）
组键：s.date，（lower（（s.channel_分组）：：text）），（lower（（s.device_类别）：：text）），（lower（（s.user_类型）：：text）），（lower（（ev.event_动作）：：text）），（lower（（合并（ev.event_标签，'na'：字符变化）），ev.zero_val_产品，ev.download
CTE过滤器会话
->在会话上使用会话\日期\ idx进行索引扫描（成本=0.56..2.78行=1宽度=76）
索引条件：（（日期>='2020-02-06'：：日期）和（日期嵌套循环（成本=0.56..47603.39行=48宽度=66）
->过滤器上的CTE扫描（成本=0.00..0.02行=1宽度=32）
->在ecom e上使用ecom_维度1_idx进行索引扫描（成本=0.56..47602.77行=48宽度=51）
索引条件：（（维度1）：：text=（f.dimension1）：：text）
CTE ee
->HashAggregate（成本=1.68..2.40行=48宽度=48）
组键：ee_base.dimension1，ee_base.dimension3
->ee_基座上的CTE扫描（成本=0.00..0.96行=48宽度=76）
CTE ecom_活动
->散列联接（成本=1.68..175209.67行=1宽度=60）
散列条件：（（ev_1.dimension1）：：text=（ee.dimension1）：：text）和（ev_1.dimension3=ee.dimension3））
->事件ev_1的顺序扫描（成本=0.00..150210.69行=3332973宽度=52）
筛选器：（（事件类别）：：text='ecom'：：text）
->散列（成本=0.96..0.96行=48宽度=48）
->ee上的CTE扫描（成本=0.00..0.96行=48宽度=48）
->排序（成本=0.08..0.08行=1宽度=236）
排序键：s.date，（lower（（s.channel_分组）：：text）），（lower（（s.device_类别）：：text）），（lower（（s.user_类型）：：text）），（lower（（ev.event_动作）：：text）），（lower（（合并（ev.event_标签，'na'：字符变化）），ev.zero_val_产品，ev.download
->嵌套循环（成本=0.00..0.07行=1宽度=236）
联接筛选器：（（s.dimension1）：：text=（ev.dimension1）：：text）
->过滤器上的CTE扫描（成本=0.00..0.02行=1宽度=164）
->ecom_事件ev上的CTE扫描（成本=0.00..0.02行=1宽度=104）
这里还有什么我可以做的吗？您可以简单地将CTE重写到临时视图中，临时视图包含在主查询计划中
将临时视图筛选器会话创建为
选择
尺寸1，
尺寸2，
zdate，
信道分组，
设备类别，
用户类型
来自ga_旗舰会议
其中zdate>='2020-02-06'
zdate 0，然后1，否则0作为零值积结束，-上卷到事件级别
--用于推断产品是否已下载并因此看到所有签出步骤的近似值
求和时的大小写（小写时的大小写（产品名称）~'digital | download | file'然后1 else 0 end）>0然后1 else 0作为下载结束
来自ga_旗舰公司ecom.ecom e
在f.dimension1=e.dimension1上加入筛选会话f
按1,2分组
;
创建临时视图ecom_事件作为
选择
ev.1，
ev.3，
ev.事件和行动，
ev.event_标签，
ee.zero_val_产品，
下载
来自ga_旗舰_ecom.events ev
在ee.dimension1=ev.dimension1和ee.dimension3=ev.dimension3上加入ee
其中ev.event_category='ecom'
;
选择
s、 zdate，
较低（s.channel_分组）作为channel_分组，
较低（s.设备类别）为设备类别，
较低（s.user_类型）为user_类型，
降低（电动事件动作）作为事件动作，
下部（合并（ev.event_标签，'na'））作为事件标签，
ev.zero_val_产品，
下载，
将（不同的s.1）计数为会话，
将（不同的s.2）计数为每日用户
从筛选器会话
在ev上加入ecom_事件ev.dimension1=s.dimension1
按1,2,3,4,5,6,7,8分组；
将临时视图筛选器会话创建为
选择
尺寸1，
尺寸2，
zdate，
信道分组，
设备类别，
用户类型
来自ga_旗舰会议
其中zdate>='2020-02-06'
zdate 0，然后1，否则0作为零值积结束，-上卷到事件级别
--用于推断产品是否已下载并因此看到所有签出步骤的近似值
求和时的大小写（小写时的大小写（产品名称）~'digital | download | file'然后1 else 0 end）>0然后1 else 0作为下载结束
来自ga_旗舰公司ecom.ecom e
在f.dimension1=e.dimension1上加入筛选会话f
按1,2分组
;
创建临时视图ecom_事件作为
选择
ev.1，
ev.3，
ev.事件和行动，
ev.event_标签，
ee.zero_val_产品，
下载
来自ga_旗舰_ecom.events ev
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)
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
),
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)
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
),
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)
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;