Sql 如何使用大数据优化此查询
-===============================Sql 如何使用大数据优化此查询,sql,postgresql,optimization,greatest-n-per-group,Sql,Postgresql,Optimization,Greatest N Per Group,-=============================== CREATE TABLE ticket ( id serial NOT NULL, source integer NOT NULL, status integer NOT NULL, ticket_type integer NOT NULL, remaining_uses integer NOT NULL, expiry_date timestamp with time zone NOT NULL, p
CREATE TABLE ticket
(
id serial NOT NULL,
source integer NOT NULL,
status integer NOT NULL,
ticket_type integer NOT NULL,
remaining_uses integer NOT NULL,
expiry_date timestamp with time zone NOT NULL,
price numeric(20,2) NOT NULL,
created_date timestamp with time zone NOT NULL,
pax_type integer NOT NULL,
last_updated timestamp with time zone NOT NULL,
service integer,
client_id character varying(50),
CONSTRAINT skybus_ticket_pkey PRIMARY KEY (id),
CONSTRAINT skybus_ticket_sale_id_fkey FOREIGN KEY (sale_id)
REFERENCES skybus_sale (id) MATCH SIMPLE
ON UPDATE NO ACTION ON DELETE NO ACTION DEFERRABLE INITIALLY DEFERRED
)
WITH (
OIDS=FALSE
);
ALTER TABLE ticket
OWNER TO umd;
-- Index: ticket_client_id_idx
-- DROP INDEX ticket_client_id_idx;
CREATE INDEX ticket_client_id_idx
ON ticket
USING btree
(client_id COLLATE pg_catalog."default");
-- Index: ticket_profile_id_idx
-- DROP INDEX ticket_profile_id_idx;
CREATE INDEX ticket_profile_id_idx
ON ticket
USING btree
(profile_id);
-- Index: ticket_sale_id
-- DROP INDEX ticket_sale_id;
CREATE INDEX skybus_ticket_sale_id
ON ticket
USING btree
(sale_id);
-- Index: ticket_ticket_number
-- DROP INDEX ticket_ticket_number;
CREATE INDEX ticket_ticket_number
ON ticket
USING btree
(ticket_number COLLATE pg_catalog."default");
-- Index: ticket_ticket_number_like
-- DROP INDEX ticket_ticket_number_like;
CREATE INDEX ticket_ticket_number_like
ON ticket
USING btree
(ticket_number COLLATE pg_catalog."default" varchar_pattern_ops);
-- Index: ticket_topup_for_idx
-- DROP INDEX ticket_topup_for_idx;
CREATE INDEX ticket_topup_for_idx
ON ticket
USING btree
(topup_for COLLATE pg_catalog."default");
-====执行计划-这是行号更改
CREATE TABLE tickethistory
(
id serial NOT NULL,
ticket_id integer,
action integer NOT NULL,
action_result integer NOT NULL,
initial_status integer NOT NULL,
final_status integer NOT NULL,
final_remaining_uses integer NOT NULL,
ticket_type integer NOT NULL,
action_when timestamp with time zone NOT NULL,
last_updated timestamp with time zone NOT NULL,
service integer,
CONSTRAINT tickethistory_pkey PRIMARY KEY (id),
CONSTRAINT tickethistory_ticket_id_fkey FOREIGN KEY (ticket_id)
REFERENCES ticket (id) MATCH SIMPLE
ON UPDATE NO ACTION ON DELETE NO ACTION DEFERRABLE INITIALLY DEFERRED
)
WITH (
OIDS=FALSE
);
ALTER TABLE tickethistory
OWNER TO umd;
-- Index: tickethistory_ticket_id
-- DROP INDEX tickethistory_ticket_id;
CREATE INDEX tickethistory_ticket_id
ON tickethistory
USING btree
(ticket_id);
您可以使用row_number在一次通行中获取每张票的最新一行:
"HashAggregate (cost=4526158.63..4526158.64 rows=1 width=16) (actual time=382849.323..382849.376 rows=41 loops=1)"
" -> Nested Loop (cost=3880592.94..4526158.62 rows=1 width=16) (actual time=380338.613..382825.688 rows=11745 loops=1)"
" -> Subquery Scan on sub (cost=3880592.94..4463424.47 rows=6563 width=8) (actual time=126346.043..258837.523 rows=293717 loops=1)"
" Filter: ((sub.remaining_uses > 0) AND (sub.rn = 1) AND (sub.status = 1))"
" Rows Removed by Filter: 15244064"
" -> WindowAgg (cost=3880592.94..4191436.42 rows=15542174 width=203) (actual time=126345.775..237172.180 rows=15537781 loops=1)"
" -> Sort (cost=3880592.94..3919448.38 rows=15542174 width=203) (actual time=126345.757..180461.191 rows=15537781 loops=1)"
" Sort Key: th.ticket_id, th.*"
" Sort Method: external merge Disk: 3050616kB"
" -> Seq Scan on skybus_tickethistory th (cost=0.00..483544.74 rows=15542174 width=203) (actual time=14.091..53312.782 rows=15537781 loops=1)"
" -> Index Scan using skybus_ticket_pkey on skybus_ticket t (cost=0.00..9.55 rows=1 width=12) (actual time=0.418..0.418 rows=0 loops=293717)"
" Index Cond: (id = sub.ticket_id)"
" Filter: ((source = ANY ('{0,1,2,6,7,8}'::integer[])) AND (created_date < ('now'::cstring)::date) AND (expiry_date >= (('now'::cstring)::date - 1)) AND (created_date > (('now'::cstring)::date - 30)) AND (ticket_type = ANY ('{2,3,4,5,6,7,16,17, (...)"
" Rows Removed by Filter: 1"
"Total runtime: 383045.381 ms"
distinct on通常是解决博士后问题的最快方法:
with last_history as
(
select *
from (
select row_number() over (partition by ticket_id
order by th desc) rn
, *
from TicketHistory
) sub
where rn = 1 -- Latest history row only
)
select *
from ticket t
join th
on t.id = th.ticket_id
where remaining_uses > 0
and <... other conditions ...>
distinct on与order by一起返回每个票证id的tickethistory.id值最高的行
在tickethistory ticket\u id,id desc上建立索引可能会有所帮助。甚至可能还有一个关于tickethistory ticket\u id、id desc、final\u剩余使用、final\u状态、action\u何时启用仅索引扫描
但是,存储创建时刻的时间戳列可能更准确。如果tickethistory.id是通过序列生成的id,因为它是串行的,那么这些值可能不会反映实际的插入顺序 这当然会缩短查询时间。那么我需要使用distinct还是这种方法?我的印象是,我现在做的方式更快捷。我会选择一个有没有名字的马的答案,这比行号更可读:@a有没有名字的马,我编辑了这个问题,这不是你查询的执行计划-执行计划有一个WindowAgg步骤,但你的查询没有窗口函数。原始查询的计划可能更有用,也可能是针对distinct on解决方案的计划
with last_history as
(
select *
from (
select row_number() over (partition by ticket_id
order by th desc) rn
, *
from TicketHistory
) sub
where rn = 1 -- Latest history row only
)
select *
from ticket t
join th
on t.id = th.ticket_id
where remaining_uses > 0
and <... other conditions ...>
select ticket_type,f_rows.remaining_uses,t.source,count(t.id) as total
FROM (
-- Filter rows to get those where remaining_uses > 0 and status = 1
SELECT *
FROM (
--Get all the latest rows for each ticket
SELECT distinct on (ticket_id)
ticket_id,
final_remaining_uses AS remaining_uses,
final_status AS status, action_when
FROM TicketHistory th
order by ticket_id, id desc
) latest_rows
WHERE remaining_uses > 0
AND status = 1 --and (action_when current_date -30)
) f_rows
JOIN Ticket t ON f_rows.ticket_id = t.id
WHERE t.expiry_date >= current_date -1
and source in (0,1,2,6,7,8)
and created_date current_date - 30
GROUP BY ticket_type, f_rows.remaining_uses, t.source
order by source, ticket_type, remaining_uses;