我有用于存储用户网页视图的日志表。
表格结构：
CREATE TABLE page_views (
id integer NOT NULL,
user_id integer,
ip character varying(255),
url character varying,
title character varying(255),
user_agent character varying(255),
created_at timestamp(0) with time zone
);
CREATE SEQUENCE analytics_id_seq
START WITH 1
INCREMENT BY 1
NO MINVALUE
NO MAXVALUE
CACHE 1;
ALTER SEQUENCE analytics_id_seq OWNED BY page_views.id;
ALTER TABLE ONLY page_views ALTER COLUMN id SET DEFAULT nextval('analytics_id_seq'::regclass);
ALTER TABLE ONLY page_views ADD CONSTRAINT page_views_pk PRIMARY KEY (id);
CREATE INDEX page_views_url_title_index ON page_views USING btree (url, title);
CREATE INDEX page_views_user_id_index ON page_views USING btree (user_id);
我想提取有关每个页面浏览量的信息。
页面必须按网址和标题分组。
我写的查询如下：
SELECT count(page_views.id) AS "total_count", "page_views"."url", "page_views"."title", "current_week"."count" AS "current_week_count", "prev_week"."count" AS "prev_week_count", "current_month"."count" AS "current_month_count", "prev_month"."count" AS "prev_month_count" FROM "page_views"
LEFT JOIN (
SELECT count(id) AS "count", "url", "title" FROM "page_views"
WHERE (extract(year from page_views.created_at) = extract(year from current_date)) AND (extract(week from page_views.created_at) = extract(week from current_date))
GROUP BY "url", "title"
) "current_week" ON page_views.url = current_week.url AND page_views.title = current_week.title
LEFT JOIN (
SELECT count(id) AS "count", "url", "title" FROM "page_views"
WHERE (extract(year from page_views.created_at) = extract(year from current_date)) AND (extract(week from page_views.created_at) = extract(week from current_date - interval '1 week'))
GROUP BY "url", "title"
) "prev_week" ON page_views.url = prev_week.url AND page_views.title = prev_week.title
LEFT JOIN (
SELECT count(id) AS "count", "url", "title" FROM "page_views"
WHERE (extract(year from page_views.created_at) = extract(year from current_date)) AND (extract(month from page_views.created_at) = extract(month from current_date))
GROUP BY "url", "title"
) "current_month" ON page_views.url = current_month.url AND page_views.title = current_month.title
LEFT JOIN (
SELECT count(id) AS "count", "url", "title" FROM "page_views"
WHERE (extract(year from page_views.created_at) = extract(year from current_date)) AND (extract(month from page_views.created_at) = extract(month from current_date - interval '1 month'))
GROUP BY "url", "title"
) "prev_month" ON page_views.url = prev_month.url AND page_views.title = prev_month.title
GROUP BY "page_views"."url", "page_views"."title", "current_week_count", "prev_week_count", "current_month_count", "prev_month_count"
ORDER BY "total_count" DESC
LIMIT 25
首先，只需要总计数。向url和title对添加索引会增加查询性能。但是现在有了这个额外的计数，执行时间又变大了。
平均查询执行时间：17秒。
使用该查询加载页面大约需要45秒（该查询再次执行一次以获取记录总数）。
我认为左连接就是这个原因，因为5个单一查询的执行速度非常快。
EXPLAIN ANALYZE提供以下输出：
QUERY PLAN
Limit (cost=93923.09..93923.16 rows=25 width=136) (actual time=18779.707..18779.722 rows=25 loops=1)
-> Sort (cost=93923.09..93961.56 rows=15387 width=136) (actual time=18779.701..18779.707 rows=25 loops=1)
Sort Key: (count(page_views.id))
Sort Method: top-N heapsort Memory: 21kB
-> GroupAggregate (cost=90257.65..93488.88 rows=15387 width=136) (actual time=16884.156..18768.617 rows=17778 loops=1)
-> Sort (cost=90257.65..90642.32 rows=153868 width=136) (actual time=16711.488..18631.405 rows=153879 loops=1)
Sort Key: page_views.url, page_views.title, (count(page_views_1.id)), (count(page_views_2.id)), (count(page_views_3.id)), (count(page_views_4.id))
Sort Method: external merge Disk: 20968kB
-> Hash Left Join (cost=44767.14..55958.90 rows=153868 width=136) (actual time=3531.133..4874.422 rows=153879 loops=1)
Hash Cond: (((page_views.url)::text = (page_views_4.url)::text) AND ((page_views.title)::text = (page_views_4.title)::text))
-> Hash Left Join (cost=33575.36..43613.10 rows=153868 width=128) (actual time=2730.681..3609.524 rows=153879 loops=1)
Hash Cond: (((page_views.url)::text = (page_views_3.url)::text) AND ((page_views.title)::text = (page_views_3.title)::text))
-> Hash Left Join (cost=22383.57..31267.29 rows=153868 width=120) (actual time=2103.744..2738.826 rows=153879 loops=1)
Hash Cond: (((page_views.url)::text = (page_views_2.url)::text) AND ((page_views.title)::text = (page_views_2.title)::text))
-> Hash Left Join (cost=11191.79..18921.49 rows=153868 width=112) (actual time=1157.999..1538.455 rows=153879 loops=1)
Hash Cond: (((page_views.url)::text = (page_views_1.url)::text) AND ((page_views.title)::text = (page_views_1.title)::text))
-> Seq Scan on page_views (cost=0.00..6575.68 rows=153868 width=104) (actual time=0.027..91.389 rows=153879 loops=1)
-> Hash (cost=11191.77..11191.77 rows=1 width=108) (actual time=1157.958..1157.958 rows=3072 loops=1)
Buckets: 1024 Batches: 1 Memory Usage: 581kB
-> HashAggregate (cost=11191.75..11191.76 rows=1 width=104) (actual time=1150.583..1153.481 rows=3072 loops=1)
-> Seq Scan on page_views page_views_1 (cost=0.00..11191.72 rows=4 width=104) (actual time=573.709..1105.539 rows=21945 loops=1)
Filter: ((date_part('year'::text, created_at) = date_part('year'::text, (('now'::cstring)::date)::timestamp without time zone)) AND (date_part('week'::text, created_at) = date_part('week'::text, (('now'::cstring)::date)::timestamp without time zone)))
Rows Removed by Filter: 131934
-> Hash (cost=11191.77..11191.77 rows=1 width=108) (actual time=945.707..945.707 rows=4093 loops=1)
Buckets: 1024 Batches: 1 Memory Usage: 756kB
-> HashAggregate (cost=11191.75..11191.76 rows=1 width=104) (actual time=938.578..941.428 rows=4093 loops=1)
-> Seq Scan on page_views page_views_2 (cost=0.00..11191.72 rows=4 width=104) (actual time=291.257..889.204 rows=29781 loops=1)
Filter: ((date_part('year'::text, created_at) = date_part('year'::text, (('now'::cstring)::date)::timestamp without time zone)) AND (date_part('week'::text, created_at) = date_part('week'::text, (('now'::cstring)::date - '7 days'::interval))))
Rows Removed by Filter: 124098
-> Hash (cost=11191.77..11191.77 rows=1 width=108) (actual time=626.909..626.909 rows=3072 loops=1)
Buckets: 1024 Batches: 1 Memory Usage: 581kB
-> HashAggregate (cost=11191.75..11191.76 rows=1 width=104) (actual time=621.925..623.915 rows=3072 loops=1)
-> Seq Scan on page_views page_views_3 (cost=0.00..11191.72 rows=4 width=104) (actual time=284.954..598.724 rows=21945 loops=1)
Filter: ((date_part('year'::text, created_at) = date_part('year'::text, (('now'::cstring)::date)::timestamp without time zone)) AND (date_part('month'::text, created_at) = date_part('month'::text, (('now'::cstring)::date)::timestamp without time zone)))
Rows Removed by Filter: 131934
-> Hash (cost=11191.77..11191.77 rows=1 width=108) (actual time=800.412..800.412 rows=10871 loops=1)
Buckets: 1024 Batches: 4 (originally 1) Memory Usage: 1025kB
-> HashAggregate (cost=11191.75..11191.76 rows=1 width=104) (actual time=776.933..785.074 rows=10871 loops=1)
-> Seq Scan on page_views page_views_4 (cost=0.00..11191.72 rows=4 width=104) (actual time=0.028..680.576 rows=84245 loops=1)
Filter: ((date_part('year'::text, created_at) = date_part('year'::text, (('now'::cstring)::date)::timestamp without time zone)) AND (date_part('month'::text, created_at) = date_part('month'::text, (('now'::cstring)::date - '1 mon'::interval))))
Rows Removed by Filter: 69634
Total runtime: 19112.027 ms
目前生产服务器上有153 868条记录。 在我的开发服务器上，只有3 463条记录，查询执行得非常快。
答案 0 :(得分：1)
我认为你可以在一个查询中做到这一点：
SELECT url,
title,
count(*) as total_count,
count(case when date_trunc('week', current_date) = date_trunc('week', created_at) then 1 else null end) as current_week_count,
count(case when date_trunc('week', current_date - interval '1 week') = date_trunc('week', created_at) then 1 else null end) as prev_week_count,
count(case when date_trunc('month', current_date) = date_trunc('month', created_at) then 1 else null end) as this_month_count,
count(case when date_trunc('month', current_date - interval '1' month) = date_trunc('month', created_at) then 1 else null end) as prev_month_count
FROM page_views
GROUP BY url, title