Hi. We have 400,000,000 records in a table (soon to be 800,000,000), here is the schema (\d+) https://gist.github.com/ioquatix/bddda36d9e4ffaceb7a62d7b62259121 We want the following kinds of query to be fast: SELECT DISTINCT "user_event"."user_id" FROM "user_event" WHERE "user_event"."what" IN ('poll', 'location_change', 'midnight_location') AND ("user_event"."created_at" >= '2016-04-19 01:23:55') AND (latitude > -37.03079375089291 AND latitude < -36.67086424910709 AND longitude > 174.6307139779924 AND longitude < 175.0805140220076); We have a btree index and it appears to be working. However, it's still pretty slow. EXPLAIN ANALYZE gives the following: https://gist.github.com/ioquatix/bddda36d9e4ffaceb7a62d7b62259121#gistcomment-2065314 I'm thinking that I need to do the following to help: CLUSTER user_event ON index_user_event_for_visits_3 followed by analyze... Our data is mostly time series but sometimes we get some dumps with historical records. Perhaps add a BRIN index on created_at I'm wondering if... we can use an index to cache, all user_ids seen on a given day. If we change our index to be more discrete, e.g. created_at::date, would this help? The set union of user_ids for 365 days should be pretty fast? I'm open to any ideas or suggestions, ideally we can keep optimisations within the database, rather than adding a layer of caching on top. Kind regards, Samuel -- Sent via pgsql-general mailing list (pgsql-general@xxxxxxxxxxxxxx) To make changes to your subscription: http://www.postgresql.org/mailpref/pgsql-general
