My postgresql version is 10.6. I have created an index but that is not used for all where clause condition check. Below are more details :
Create index concurrently ticket_created_at_portal_id_created_by_id_assigned_group_id_idx on ticket(created_at, portal_id, created_by_id, assigned_group);
EXPLAIN (analyze true, verbose true, costs true, buffers true, timing true ) select * from ticket where status is not null
and (assigned_group in ('447') or created_by_id in ('39731566'))
and portal_id=8
and created_at>='2020-12-07T03:00:10.973'
and created_at<='2021-02-05T03:00:10.973'
order by updated_at DESC limit 10;
QUERY PLAN
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Limit (cost=18975.23..18975.25 rows=10 width=638) (actual time=278.340..278.345 rows=10 loops=1)
Output: id, action, assigned_agent, assigned_at, assigned_group, attachments, closed_at, created_at, created_by_email, created_by_id, description, first_response_time, parent_id, portal_id, priority, reopened_at, resolution_id, resolved_at, resolved_by_id, resource_id, resource_type, source, status, subject, tags, ticket_category, ticket_id, ticket_sub_category, ticket_sub_sub_category, type, updated_at, custom_fields, updated_by_id, first_assigned_at, mode, sla_breached, agent_assist_tags, comm_vendor, from_email
Buffers: shared hit=2280 read=3105
-> Sort (cost=18975.23..18975.45 rows=87 width=638) (actual time=278.338..278.339 rows=10 loops=1)
Output: id, action, assigned_agent, assigned_at, assigned_group, attachments, closed_at, created_at, created_by_email, created_by_id, description, first_response_time, parent_id, portal_id, priority, reopened_at, resolution_id, resolved_at, resolved_by_id, resource_id, resource_type, source, status, subject, tags, ticket_category, ticket_id, ticket_sub_category, ticket_sub_sub_category, type, updated_at, custom_fields, updated_by_id, first_assigned_at, mode, sla_breached, agent_assist_tags, comm_vendor, from_email
Sort Key: ticket.updated_at DESC
Sort Method: top-N heapsort Memory: 33kB
Buffers: shared hit=2280 read=3105
-> Bitmap Heap Scan on public.ticket (cost=17855.76..18973.35 rows=87 width=638) (actual time=111.871..275.835 rows=1256 loops=1)
Output: id, action, assigned_agent, assigned_at, assigned_group, attachments, closed_at, created_at, created_by_email, created_by_id, description, first_response_time, parent_id, portal_id, priority, reopened_at, resolution_id, resolved_at, resolved_by_id, resource_id, resource_type, source, status, subject, tags, ticket_category, ticket_id, ticket_sub_category, ticket_sub_sub_category, type, updated_at, custom_fields, updated_by_id, first_assigned_at, mode, sla_breached, agent_assist_tags, comm_vendor, from_email
Recheck Cond: (((ticket.assigned_group = '447'::bigint) AND (ticket.portal_id = 8)) OR ((ticket.created_at >= '2020-12-07 03:00:10.973'::timestamp without time zone) AND (ticket.created_at <= '2021-02-05 03:00:10.973'::timestamp without time zone) AND (ticket.portal_id = 8) AND (ticket.created_by_id = '39731566'::bigint)))
Filter: ((ticket.status IS NOT NULL) AND (ticket.created_at >= '2020-12-07 03:00:10.973'::timestamp without time zone) AND (ticket.created_at <= '2021-02-05 03:00:10.973'::timestamp without time zone))
Rows Removed by Filter: 1517
Heap Blocks: exact=2638
Buffers: shared hit=2277 read=3105
-> BitmapOr (cost=17855.76..17855.76 rows=291 width=0) (actual time=106.215..106.216 rows=0 loops=1)
Buffers: shared hit=336 read=2408
-> Bitmap Index Scan on ticket_assigned_group_portal_id_assigned_agent_idx (cost=0.00..11.25 rows=282 width=0) (actual time=10.661..10.661 rows=2776 loops=1)
Index Cond: ((ticket.assigned_group = '447'::bigint) AND (ticket.portal_id = 8))
Buffers: shared hit=4 read=15
-> Bitmap Index Scan on ticket_created_at_portal_id_created_by_id_assigned_group_id_idx (cost=0.00..17844.47 rows=9 width=0) (actual time=95.551..95.551 rows=2 loops=1)
Index Cond: ((ticket.created_at >= '2020-12-07 03:00:10.973'::timestamp without time zone) AND (ticket.created_at <= '2021-02-05 03:00:10.973'::timestamp without time zone) AND (ticket.portal_id = 8) AND (ticket.created_by_id = '39731566'::bigint))
Buffers: shared hit=332 read=2393
Planning time: 43.083 ms
Execution time: 278.556 ms
(25 rows)
ticket_created_at_portal_id_created_by_id_assigned_group_id_idx is having all the columns of where clause except status is not null, but still query is using separate index for Index Cond: ((ticket.assigned_group = '447'::bigint) AND (ticket.portal_id = 8)) which is already present in 2nd index ticket_created_at_portal_id_created_by_id_assigned_group_id_idx.
Why it is so? Even when I include status column as well in the index, still query was using 2 index and hen doing a heavy filter on index heap scan.
How we can optimize it?
Also I did experiment with indexes of table but still unable to remove indexes. It seems that same columns are repeated in multiple indexes for diff queries, Please help if we can reduce these no of indexes.
All indexes for table are:
"ticket_pkey" PRIMARY KEY, btree (id)
"ticket_ticket_id_idx" UNIQUE, btree (ticket_id)
"uk2uors84i0m8sjxc6oaocuy6oj" UNIQUE CONSTRAINT, btree (ticket_id)
"idx_resource_id" btree (resource_id)
"idx_ticket_created_at" btree (created_at)
"ticket_assigned_agent_idx" btree (assigned_agent)
"ticket_assigned_group_idx" btree (assigned_group)
"ticket_assigned_group_portal_id_assigned_agent_idx" btree (assigned_group, portal_id, assigned_agent)
"ticket_created_at_portal_id_created_by_id_assigned_group_id_idx" btree (created_at, portal_id, created_by_id, assigned_group)
"ticket_created_at_portal_id_status_idx" btree (created_at, portal_id, status)
"ticket_id_resolved_at_assigned_group_status_idx" btree (id, resolved_at, assigned_group, status)
How easy would it be to use a phone book (sorted by last-name then first-name) to find every one with a first name of "Francis" whose last name starts with a letter between K and T? Not very easy, because it is not sorted by first name. You would have to go through the entire middle half of the phone book, reading everyone's first name.
Same here. When the first column in your index is used in a range/inequality query rather than equality, it makes all the columns after that one much less efficient. You would want to put the columns used for equality and not in an OR first. Unfortunately, that is only portal_id. The best thing to put next would depend on how selective each of those other conditions are which we can't guess from the info provided.
In deciding this, status IS NULL would be the same thing as equality, but status IS NOT NULL is not as there are any number of values it could be while still being not null, so it is effectively the same as an inequality. If this condition is highly selective, the best way to incorporate it would be in the WHERE of a partial index.
Because of the OR, you might still be best off with 2 indexes which could be combined in a bitmap or.
...(portal_id, assigned_group, created_at) WHERE status IS NOT NULL;
...(portal_id, created_by_id, created_at) WHERE status IS NOT NULL;
Another approach would be to avoid fetching and sorting all of the matching rows, by walking rows in order by updated_at using an index and stopping after 10 of them are found. An index can be used for walking a column in order as long as only things tested for equality (and without ORs) occur before the ORDER BY column, so:
...(portal_id, updated_at) WHERE status IS NOT NULL;
Related
I have a table inside my Postgresql database, called consumer_actions. It contains all the actions done by consumers registered in my app. At the moment, this table has ~ 500 million records. What i'm trying to do is to get the maximum id, based on the system that the action came from.
The definition of the table is:
CREATE TABLE public.consumer_actions (
id int4 NOT NULL,
system_id int4 NOT NULL,
consumer_id int4 NOT NULL,
action_id int4 NOT NULL,
payload_json jsonb NULL,
external_system_date timestamptz NULL,
local_system_date timestamptz NULL,
CONSTRAINT consumer_actions_pkey PRIMARY KEY (id, system_id)
);
CREATE INDEX consumer_actions_ext_date ON public.consumer_actions USING btree (external_system_date);
CREATE INDEX consumer_actions_system_consumer_id ON public.consumer_actions USING btree (system_id, consumer_id);
when i'm trying
select max(id) from consumer_actions where system_id = 1
it takes less than one second, but if i try to use the same index (consumer_actions_system_consumer_id) to get the max(id) by system_id = 2, it takes more than an hour.
select max(id) from consumer_actions where system_id = 2
I have also checked the query planner, is looks similar for both queries; i also rerun vacuum analyze on the table and a reindex. Neither of them helped. Any idea what i can do to improve the second query time?
Here are the query planners for both tables, and the size at the moment of this table:
explain analyze
select max(id) from consumer_actions where system_id = 1;
Result (cost=1.49..1.50 rows=1 width=4) (actual time=0.062..0.063 rows=1 loops=1)
InitPlan 1 (returns $0)
-> Limit (cost=0.57..1.49 rows=1 width=4) (actual time=0.057..0.057 rows=1 loops=1)
-> Index Only Scan Backward using consumer_actions_pkey on consumer_actions ca (cost=0.57..524024735.49 rows=572451344 width=4) (actual time=0.055..0.055 rows=1 loops=1)
Index Cond: ((id IS NOT NULL) AND (system_id = 1))
Heap Fetches: 1
Planning Time: 0.173 ms
Execution Time: 0.092 ms
explain analyze
select max(id) from consumer_actions where system_id = 2;
Result (cost=6.46..6.47 rows=1 width=4) (actual time=7099484.855..7099484.858 rows=1 loops=1)
InitPlan 1 (returns $0)
-> Limit (cost=0.57..6.46 rows=1 width=4) (actual time=7099484.839..7099484.841 rows=1 loops=1)
-> Index Only Scan Backward using consumer_actions_pkey on consumer_actions ca (cost=0.57..20205843.58 rows=3436129 width=4) (actual time=7099484.833..7099484.834 rows=1 loops=1)
Index Cond: ((id IS NOT NULL) AND (system_id = 2))
Heap Fetches: 1
Planning Time: 3.078 ms
Execution Time: 7099484.992 ms
(8 rows)
select count(*) from consumer_actions; --result is 577408504
Instead of using an aggregation function like max() that has to potentially scan and aggregate large numbers of rows for a table like yours you could get similar results with a query designed to return the fewest rows possible:
SELECT id FROM consumer_actions WHERE system_id = ? ORDER BY id DESC LIMIT 1;
This should still benefit significantly in performance from the existing indices.
I think that you should create an index like this one
CREATE INDEX consumer_actions_system_system_id_id ON public.consumer_actions USING btree (system_id, id);
I migrated my database from MySQL to PostgreSQL with pgloader, it's globally much more efficient but a query with like condition is more slower on PostgreSQL.
MySQL : ~1ms
PostgreSQL : ~110 ms
Table info:
105 columns
23 indexes
1.6M records
Columns info:
name character varying(30) COLLATE pg_catalog."default" NOT NULL,
ratemax3v3 integer NOT NULL DEFAULT 0,
Query is :
SELECT name, realm, region, class, id
FROM personnages
WHERE blacklisted = 0 AND name LIKE 'Krok%' AND region = 'eu'
ORDER BY ratemax3v3 DESC LIMIT 5;
EXPLAIN ANALYSE (PostgreSQL)
Limit (cost=629.10..629.12 rows=5 width=34) (actual time=111.128..111.130 rows=5 loops=1)
-> Sort (cost=629.10..629.40 rows=117 width=34) (actual time=111.126..111.128 rows=5 loops=1)
Sort Key: ratemax3v3 DESC
Sort Method: top-N heapsort Memory: 25kB
-> Bitmap Heap Scan on personnages (cost=9.63..627.16 rows=117 width=34) (actual time=110.619..111.093 rows=75 loops=1)
Recheck Cond: ((name)::text ~~ 'Krok%'::text)
Rows Removed by Index Recheck: 1
Filter: ((blacklisted = 0) AND ((region)::text = 'eu'::text))
Rows Removed by Filter: 13
Heap Blocks: exact=88
-> Bitmap Index Scan on trgm_idx_name (cost=0.00..9.60 rows=158 width=0) (actual time=110.581..110.582 rows=89 loops=1)
Index Cond: ((name)::text ~~ 'Krok%'::text)
Planning Time: 0.268 ms
Execution Time: 111.174 ms
Pgloader have been created indexes on ratemax3v3 and name like:
CREATE INDEX idx_24683_ratemax3v3
ON wow.personnages USING btree
(ratemax3v3 ASC NULLS LAST)
TABLESPACE pg_default;
CREATE INDEX idx_24683_name
ON wow.personnages USING btree
(name COLLATE pg_catalog."default" ASC NULLS LAST)
TABLESPACE pg_default;
I created a new index on name :
CREATE INDEX trgm_idx_name ON wow.personnages USING GIST (name gist_trgm_ops);
I'm quite a beginner with postgresql at the moment.
Do you see anything I could do?
Don't hesitate to ask me if you need more information!
Antoine
To support a LIKE query like that (left anchored) you need to use a special "operator class":
CREATE INDEX ON wow.personnages(name varchar_pattern_ops);
But for your given query, an index on multiple columns would probably be more efficient:
CREATE INDEX ON wow.personnages(region, blacklisted, name varchar_pattern_ops);
Of maybe even a filtered index if e.g. the blacklisted = 0 is a static condition and there are relatively few rows matching that condition.
CREATE INDEX ON wow.personnages(region, name varchar_pattern_ops)
WHERE blacklisted = 0;
If the majority of the rows has blacklisted = 0 that won't really help (and adding the column to the index wouldn't help either). In that case just an index with (region, name varchar_pattern_ops) is probably more efficient.
If your pattern is anchored at the beginning, the following index would perform better:
CREATE INDEX ON personnages (name text_pattern_ops);
Besides, GIN indexes usually perform better than GiST indexes in a case like this. Try with a GIN index.
Finally, it is possible that the trigrams k, kr, kro, rok and ok occur very frequently, which would also make the index perform bad.
Say I have the following tables and indices:
create table inbound_messages(id int, user_id int, received_at timestamp);
create table outbound_messages(id int, user_id int, sent_at timestamp);
create index on inbound_messages(user_id, received_at);
create index on outbound_messages(user_id, sent_at);
Now I want to pull out the last 20 messages for a user, either inbound or outbound in a specific time range. I can do the following and from the explain it looks like PG walks back both indices in 'parallel' so it minimises the amount of rows it needs to scan.
explain select * from (select id, user_id, received_at as time from inbound_messages union all select id, user_id, sent_at as time from outbound_messages) x where user_id = 5 and time between '2018-01-01' and '2020-01-01' order by user_id,time desc limit 20;
Limit (cost=0.32..16.37 rows=2 width=16)
-> Merge Append (cost=0.32..16.37 rows=2 width=16)
Sort Key: inbound_messages.received_at DESC
-> Index Scan Backward using inbound_messages_user_id_received_at_idx on inbound_messages (cost=0.15..8.17 rows=1 width=16)
Index Cond: ((user_id = 5) AND (received_at >= '2018-01-01 00:00:00'::timestamp without time zone) AND (received_at <= '2020-01-01 00:00:00'::timestamp without time zone))
-> Index Scan Backward using outbound_messages_user_id_sent_at_idx on outbound_messages (cost=0.15..8.17 rows=1 width=16)
Index Cond: ((user_id = 5) AND (sent_at >= '2018-01-01 00:00:00'::timestamp without time zone) AND (sent_at <= '2020-01-01 00:00:00'::timestamp without time zone))
For example it could do something crazy like find all the matching rows in memory, and then sort the rows. Lets say there were millions of matching rows then this could take a long time. But because it walks the indices in the same order we want the results in this is a fast operation. It looks like the 'Merge Append' operation is done lazily and it doesn't actually materialize all the matching rows.
Now we can see postgres supports this operation for two distinct tables, however is it possible to force Postgres to use this optimisation for a single table.
Lets say I wanted the last 20 inbound messages for user_id = 5 or user_id = 6.
explain select * from inbound_messages where user_id in (6,7) order by received_at desc limit 20;
Then we get a query plan that does a bitmap heap scan, and then does an in-memory sort. So if there are millions of messages that match then it will look at millions of rows even though theoretically it could use the same Merge trick to only look at a few rows.
Limit (cost=15.04..15.09 rows=18 width=16)
-> Sort (cost=15.04..15.09 rows=18 width=16)
Sort Key: received_at DESC
-> Bitmap Heap Scan on inbound_messages (cost=4.44..14.67 rows=18 width=16)
Recheck Cond: (user_id = ANY ('{6,7}'::integer[]))
-> Bitmap Index Scan on inbound_messages_user_id_received_at_idx (cost=0.00..4.44 rows=18 width=0)
Index Cond: (user_id = ANY ('{6,7}'::integer[]))
We could think of just adding (received_at) as an index on the table and then it will do the same backwards scan. However, if we have a large number of users then we are missing out on a potentially large speedup because we are scanning lots of index entries that would not match the query.
The following approach should work as a way of forcing Postgres to use the "merge append" plan when you are interested in most recent messages for two users from the same table.
[Note: I tested this on YugabyteDB (which is based on Postgres)- so I expect the same to apply to Postgres also.]
explain select * from (
(select * from inbound_messages where user_id = 6 order by received_at DESC)
union all
(select * from inbound_messages where user_id = 7 order by received_at DESC)
) AS result order by received_at DESC limit 20;
which produces:
-------------------------------------------------------------------------------------------------------------------------------------------------------------
Limit (cost=0.01..3.88 rows=20 width=16)
-> Merge Append (cost=0.01..38.71 rows=200 width=16)
Sort Key: inbound_messages.received_at DESC
-> Index Scan Backward using inbound_messages_user_id_received_at_idx on inbound_messages (cost=0.00..17.35 rows=100 width=16)
Index Cond: (user_id = 6)
-> Index Scan Backward using inbound_messages_user_id_received_at_idx on inbound_messages inbound_messages_1 (cost=0.00..17.35 rows=100 width=16)
Index Cond: (user_id = 7)
I have a table
CREATE TABLE timedevent
(
id bigint NOT NULL,
eventdate timestamp with time zone NOT NULL,
newstateids character varying(255) NOT NULL,
sourceid character varying(255) NOT NULL,
CONSTRAINT timedevent_pkey PRIMARY KEY (id)
) WITH (OIDS=FALSE);
with PK id.
I have to query rows between two dates with certain newstate and source from a set of possible sources.
I created a btree indexes on eventdate and newstateids and one more (hash index) on sourceid. Only the index on date made the queries faster - it seems the other two are not used. Why is that so? How could I make my queries faster?
CREATE INDEX eventdate_index ON timedevent USING btree (eventdate);
CREATE INDEX newstateids_index ON timedevent USING btree (newstateids COLLATE pg_catalog."default");
CREATE INDEX sourceid_index_hash ON timedevent USING hash (sourceid COLLATE pg_catalog."default");
Here is the query as Hibernate generates it:
select this_.id as id1_0_0_, this_.description as descript2_0_0_, this_.eventDate as eventDat3_0_0_, this_.locationId as location4_0_0_, this_.newStateIds as newState5_0_0_, this_.oldStateIds as oldState6_0_0_, this_.sourceId as sourceId7_0_0_
from TimedEvent this_
where ((this_.newStateIds=? and this_.sourceId in (?, ?, ?, ?, ?, ?)))
and this_.eventDate between ? and ?
limit ?
EDIT:
Sorry for the misleading title but it seem postges uses all indexes. The problem is my query time still remains the same. Here ist the query plan I got:
Limit (cost=25130.29..33155.77 rows=321 width=161) (actual time=705.330..706.744 rows=279 loops=1)
Buffers: shared hit=6 read=8167 written=61
-> Bitmap Heap Scan on timedevent this_ (cost=25130.29..33155.77 rows=321 width=161) (actual time=705.330..706.728 rows=279 loops=1)
Recheck Cond: (((sourceid)::text = ANY ('{"root,kus-chemnitz,ize-159,Anwesend Bad","root,kus-chemnitz,ize-159,Alarmruf","root,kus-chemnitz,ize-159,Bett Alarm 1","root,kus-chemnitz,ize-159,Bett Alarm 2","root,kus-chemnitz,ize-159,Anwesend Zimmer" (...)
Filter: ((eventdate >= '2017-11-01 15:41:00+01'::timestamp with time zone) AND (eventdate <= '2018-03-20 14:58:16.724+01'::timestamp with time zone))
Buffers: shared hit=6 read=8167 written=61
-> BitmapAnd (cost=25130.29..25130.29 rows=2122 width=0) (actual time=232.990..232.990 rows=0 loops=1)
Buffers: shared hit=6 read=2152
-> Bitmap Index Scan on sourceid_index_hash (cost=0.00..1403.36 rows=39182 width=0) (actual time=1.195..1.195 rows=9308 loops=1)
Index Cond: ((sourceid)::text = ANY ('{"root,kus-chemnitz,ize-159,Anwesend Bad","root,kus-chemnitz,ize-159,Alarmruf","root,kus-chemnitz,ize-159,Bett Alarm 1","root,kus-chemnitz,ize-159,Bett Alarm 2","root,kus-chemnitz,ize-159,Anwesend Z (...)
Buffers: shared hit=6 read=26
-> Bitmap Index Scan on state_index (cost=0.00..23726.53 rows=777463 width=0) (actual time=231.160..231.160 rows=776520 loops=1)
Index Cond: ((newstateids)::text = 'ACTIV'::text)
Buffers: shared read=2126
Total runtime: 706.804 ms
After creating an index using btree on (sourceid, newstateids) as a_horse_with_no_name suggested, the cost reduced:
Limit (cost=125.03..8150.52 rows=321 width=161) (actual time=13.611..14.454 rows=279 loops=1)
Buffers: shared hit=18 read=4336
-> Bitmap Heap Scan on timedevent this_ (cost=125.03..8150.52 rows=321 width=161) (actual time=13.609..14.432 rows=279 loops=1)
Recheck Cond: (((sourceid)::text = ANY ('{"root,kus-chemnitz,ize-159,Anwesend Bad","root,kus-chemnitz,ize-159,Alarmruf","root,kus-chemnitz,ize-159,Bett Alarm 1","root,kus-chemnitz,ize-159,Bett Alarm 2","root,kus-chemnitz,ize-159,Anwesend Zimmer","r (...)
Filter: ((eventdate >= '2017-11-01 15:41:00+01'::timestamp with time zone) AND (eventdate <= '2018-03-20 14:58:16.724+01'::timestamp with time zone))
Buffers: shared hit=18 read=4336
-> Bitmap Index Scan on src_state_index (cost=0.00..124.95 rows=2122 width=0) (actual time=0.864..0.864 rows=4526 loops=1)
Index Cond: (((sourceid)::text = ANY ('{"root,kus-chemnitz,ize-159,Anwesend Bad","root,kus-chemnitz,ize-159,Alarmruf","root,kus-chemnitz,ize-159,Bett Alarm 1","root,kus-chemnitz,ize-159,Bett Alarm 2","root,kus-chemnitz,ize-159,Anwesend Zimmer (...)
Buffers: shared hit=18 read=44
Total runtime: 14.497 ms"
Basically only one index is used because database would have to combine your indexes into one so that they would be useful (or combine results from searches over more indexes) and doing this is so expensive, that it in this case chooses not to and use just one of the indexes relevant for one predicate and check the other predicates directly on values in found rows.
One B-tree index with several columns would work better, just as a_horse_with_no_name suggests in comments. Also note, that order of columns matter a lot (columns that are used for single value search should be first, the one for range search later, you want to limit range searches as much as possible).
Then databese will go through index, looking for rows that satisfy on predicate using the first column of index (hopefully narrowing the numbers of rows a lot) and the the second column and second predicate come to play, ...
Using separate B-tree indexes when predicates are combined using AND operator does not make sense to database, because it would have to use one index to choose all rows, which satisfy one predicate and then, it would have to use another index, read its blocks (where the index is stored) from disk again, only to get to rows which satisfy the condition relevant to this second index, but possibly not the other condition. And if they satisfy it, it's probably cheaper to just load the row after first using index and check the other predicates directly, not using index.
I have a table with about 7.5million records and am trying to implement an autocomplete form based on said table, but the performance is pretty bad.
The schema (irrelevant fields omitted) is as follows:
COMPANIES
---------
sid (integer primary key)
world_hq_sid (integer)
name (varchar(64))
marketing_alias (varchar(64))
address_country_code (char(4))
address_state (varchar(64))
sort_order integer
search_weight integer
annual_sales integer
The fields passed in are the optional country_code and state, along with a search term. What I want is for the search term to match (case insensitive) the beginning of either name or marketing_alias. I want the top ten results, with those results that also match country and state at the top, then country only, then no state/country match. After that, I want the results sorted by sort_order.
Also, I only want one match per world_hq_sid. Finally, when I have the top match per world_hq_sid, I want the final results to be sorted by search_weight.
I'm using a window query to achieve the world_hq_sid part. Here is the query:
SELECT * FROM (
SELECT ROW_NUMBER() OVER (PARTITION BY world_hq_sid ORDER BY CASE WHEN address_country_code = 'US' AND address_state = 'CA' THEN 2 WHEN address_country_code = 'US' THEN 1 ELSE 0 END desc, sort_order asc) AS r,
companies.*
FROM companies
WHERE ((upper(name) LIKE upper('co%')) OR (upper(marketing_alias) LIKE upper('co%')))
) x
WHERE x.r = 1
ORDER BY CASE WHEN address_country_code = 'US' AND address_state = 'CA' THEN 2 WHEN address_state = 'CA' THEN 1 ELSE 0 END desc, search_weight asc, annual_sales desc
LIMIT 10;
I have normal btree indexes on address_state, address_country_code, world_hq_sid, sort_order, and search_weight.
I have the following indexes on the name and marketing_alias fields:
CREATE INDEX companies_alias_pattern_upper_idx ON companies(upper(marketing_alias) varchar_pattern_ops);
CREATE INDEX companies_name_pattern_upper_idx ON companies(upper(name) varchar_pattern_ops)
And here is the explain analyze when I pass CA as the state and 'co' as the search term
Limit (cost=676523.01..676523.03 rows=10 width=939) (actual time=18695.686..18695.687 rows=10 loops=1)
-> Sort (cost=676523.01..676526.67 rows=1466 width=939) (actual time=18695.686..18695.687 rows=10 loops=1)
Sort Key: x.search_weight, x.annual_sales
Sort Method: top-N heapsort Memory: 30kB
-> Subquery Scan on x (cost=665492.58..676491.33 rows=1466 width=939) (actual time=18344.715..18546.830 rows=151527 loops=1)
Filter: (x.r = 1)
Rows Removed by Filter: 20672
-> WindowAgg (cost=665492.58..672825.08 rows=293300 width=931) (actual time=18344.710..18511.625 rows=172199 loops=1)
-> Sort (cost=665492.58..666225.83 rows=293300 width=931) (actual time=18344.702..18359.145 rows=172199 loops=1)
Sort Key: companies.world_hq_sid, (CASE WHEN ((companies.address_state)::text = 'CA'::text) THEN 1 ELSE 0 END), companies.sort_order
Sort Method: quicksort Memory: 108613kB
-> Bitmap Heap Scan on companies (cost=17236.64..518555.98 rows=293300 width=931) (actual time=1861.665..17999.806 rows=172199 loops=1)
Recheck Cond: ((upper((name)::text) ~~ 'CO%'::text) OR (upper((marketing_alias)::text) ~~ 'CO%'::text))
Filter: ((upper((name)::text) ~~ 'CO%'::text) OR (upper((marketing_alias)::text) ~~ 'CO%'::text))
-> BitmapOr (cost=17236.64..17236.64 rows=196219 width=0) (actual time=1829.061..1829.061 rows=0 loops=1)
-> Bitmap Index Scan on companies_name_pattern_upper_idx (cost=0.00..8987.98 rows=97772 width=0) (actual time=971.331..971.331 rows=169390 loops=1)
Index Cond: ((upper((name)::text) ~>=~ 'CO'::text) AND (upper((name)::text) ~<~ 'CP'::text))
-> Bitmap Index Scan on companies_alias_pattern_upper_idx (cost=0.00..8102.02 rows=98447 width=0) (actual time=857.728..857.728 rows=170616 loops=1)
Index Cond: ((upper((marketing_alias)::text) ~>=~ 'CO'::text) AND (upper((marketing_alias)::text) ~<~ 'CP'::text))
I've bumped work_mem and shared_buffers to 100M.
As you can see, this query returns in 18 seconds. What is odd is that the results are all over the board for different starting characters, from 400ms (acceptable) to 30 seconds (very not acceptable). Postgres gurus, my question is, am I just expecting too much of postgresql to perform such a query quickly consistently? Is there a way I can speed this up?
select *
from (
select distinct on (world_hq_sid)
world_hq_sid,
(address_country_code = 'US')::int + (address_state = 'CA')::int address_weight,
sort_order,
search_weight, annual_sales,
sid, name, marketing_alias,
address_country_code, address_state
from companies
where
upper(name) LIKE upper('co%')
OR upper(marketing_alias) LIKE upper('co%')
order by 1, 2 desc, 3
) s
order by
address_weight desc,
search_weight,
annual_sales desc
limit 10
For autocomplete it's possible to use trigram search.
pg_trgm module.
CREATE EXTENSION pg_trgm;
ALTER TABLE companies ADD COLUMN name_trgm TEXT NULL;
UPDATE companies SET name_trgm = UPPER(name);
CREATE INDEX companies_name_trgm_gin_idx ON companies USING GIN (name_trgm gin_trgm_ops);