Hi
this plan looks wellPavel
2015-01-25 6:45 GMT+01:00 Joe Van Dyk <joe@xxxxxxxxx>:
Oops, didn't run vacuum analyze after deleting the events. Here is another 'explain analyze': http://explain.depesz.com/s/AviNOn Sat, Jan 24, 2015 at 9:43 PM, Joe Van Dyk <joe@xxxxxxxxx> wrote:On Sat, Jan 24, 2015 at 9:41 PM, Joe Van Dyk <joe@xxxxxxxxx> wrote:I have an events table that records page views and purchases (type = 'viewed' or type='purchased'). I have a query that figures out "people who bought/viewed this also bought/viewed that".It worked fine, taking about 0.1 seconds to complete, until a few hours ago when it started taking hours to complete. Vacuum/analyze didn't help. Turned out there was one session_id that had 400k rows in the system. Deleting that made the query performant again.Is there anything I can do to make the query work better in cases like that? Missing index, or better query?This is on 9.3.5.The below is reproduced at the following URL if it's not formatted correctly in the email. https://gist.githubusercontent.com/joevandyk/cb8f4afdb6c1b178c606/raw/9940bbe033ebd56d38caa46e33c1ddfd9df36eda/gistfile1.txtexplain select e1.product_id, e2.site_id, e2.product_id, count(nullif(e2.type='viewed', false)) view_count, count(nullif(e2.type='purchased', false)) purchase_count from events e1 join events e2 on e1.session_id = e2.session_id and e1.type = e2.type where e1.product_id = '82503' and e1.product_id != e2.product_id group by e1.product_id, e2.product_id, e2.site_id; QUERY PLAN ---------------------------------------------------------------------------------------------------------------------------- GroupAggregate (cost=828395.67..945838.90 rows=22110 width=19) -> Sort (cost=828395.67..840117.89 rows=4688885 width=19) Sort Key: e1.product_id, e2.product_id, e2.site_id -> Nested Loop (cost=11.85..20371.14 rows=4688885 width=19) -> Bitmap Heap Scan on events e1 (cost=11.29..1404.31 rows=369 width=49) Recheck Cond: (product_id = '82503'::citext) -> Bitmap Index Scan on events_product_id_site_id_idx (cost=0.00..11.20 rows=369 width=0) Index Cond: (product_id = '82503'::citext) -> Index Scan using events_session_id_type_product_id_idx on events e2 (cost=0.56..51.28 rows=12 width=51) Index Cond: ((session_id = e1.session_id) AND (type = e1.type)) Filter: (e1.product_id <> product_id) (11 rows) recommender_production=> \d events Table "public.events" Column | Type | Modifiers -------------+--------------------------+----------------------------------------------------- id | bigint | not null default nextval('events_id_seq'::regclass) user_id | citext | session_id | citext | not null product_id | citext | not null site_id | citext | not null type | text | not null happened_at | timestamp with time zone | not null created_at | timestamp with time zone | not null Indexes: "events_pkey" PRIMARY KEY, btree (id) "events_product_id_site_id_idx" btree (product_id, site_id) "events_session_id_type_product_id_idx" btree (session_id, type, product_id) Check constraints: "events_session_id_check" CHECK (length(session_id::text) < 255) "events_type_check" CHECK (type = ANY (ARRAY['purchased'::text, 'viewed'::text])) "events_user_id_check" CHECK (length(user_id::text) < 255)After removing the session with 400k events, I was able to do an explain analyze, here is one of them:
