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)
Related
When joining on a table and then filtering (LIMIT 30 for instance), Postgres will apply a JOIN operation on all rows, even if the columns from those rows is only used in the returned column, and not as a filtering predicate.
This would be understandable for an INNER JOIN (PG has to know if the row will be returned or not) or for a LEFT JOIN without a unique constraint (PG has to know if more than one row will be returned or not), but for a LEFT JOIN on a UNIQUE column, this seems wasteful: if the query matches 10k rows, then 10k joins will be performed, and then only 30 will be returned.
It would seem more efficient to "delay", or defer, the join, as much as possible, and this is something that I've seen happen on some other queries.
Splitting this into a subquery (SELECT * FROM (SELECT * FROM main WHERE x LIMIT 30) LEFT JOIN secondary) works, by ensuring that only 30 items are returned from the main table before joining them, but it feels like I'm missing something, and the "standard" form of the query should also apply the same optimization.
Looking at the EXPLAIN plans, however, I can see that the number of rows joined is always the total number of rows, without "early bailing out" as you could see when, for instance, running a Seq Scan with a LIMIT 5.
Example schema, with a main table and a secondary one: secondary columns will only be returned, never filtered on.
drop table if exists secondary;
drop table if exists main;
create table main(id int primary key not null, main_column int);
create index main_column on main(main_column);
insert into main(id, main_column) SELECT i, i % 3000 from generate_series( 1, 1000000, 1) i;
create table secondary(id serial primary key not null, main_id int references main(id) not null, secondary_column int);
create unique index secondary_main_id on secondary(main_id);
insert into secondary(main_id, secondary_column) SELECT i, (i + 17) % 113 from generate_series( 1, 1000000, 1) i;
analyze main;
analyze secondary;
Example query:
explain analyze verbose select main.id, main_column, secondary_column
from main
left join secondary on main.id = secondary.main_id
where main_column = 5
order by main.id
limit 50;
This is the most "obvious" way of writing the query, takes on average around 5ms on my computer.
Explain:
Limit (cost=3742.93..3743.05 rows=50 width=12) (actual time=5.010..5.322 rows=50 loops=1)
Output: main.id, main.main_column, secondary.secondary_column
-> Sort (cost=3742.93..3743.76 rows=332 width=12) (actual time=5.006..5.094 rows=50 loops=1)
Output: main.id, main.main_column, secondary.secondary_column
Sort Key: main.id
Sort Method: top-N heapsort Memory: 27kB
-> Nested Loop Left Join (cost=11.42..3731.90 rows=332 width=12) (actual time=0.123..4.446 rows=334 loops=1)
Output: main.id, main.main_column, secondary.secondary_column
Inner Unique: true
-> Bitmap Heap Scan on public.main (cost=11.00..1036.99 rows=332 width=8) (actual time=0.106..1.021 rows=334 loops=1)
Output: main.id, main.main_column
Recheck Cond: (main.main_column = 5)
Heap Blocks: exact=334
-> Bitmap Index Scan on main_column (cost=0.00..10.92 rows=332 width=0) (actual time=0.056..0.057 rows=334 loops=1)
Index Cond: (main.main_column = 5)
-> Index Scan using secondary_main_id on public.secondary (cost=0.42..8.12 rows=1 width=8) (actual time=0.006..0.006 rows=1 loops=334)
Output: secondary.id, secondary.main_id, secondary.secondary_column
Index Cond: (secondary.main_id = main.id)
Planning Time: 0.761 ms
Execution Time: 5.423 ms
explain analyze verbose select m.id, main_column, secondary_column
from (
select main.id, main_column
from main
where main_column = 5
order by main.id
limit 50
) m
left join secondary on m.id = secondary.main_id
where main_column = 5
order by m.id
limit 50
This returns the same results, in 2ms.
The total EXPLAIN cost is also three times higher, in line with the performance gain we're seeing.
Limit (cost=1048.44..1057.21 rows=1 width=12) (actual time=1.219..2.027 rows=50 loops=1)
Output: m.id, m.main_column, secondary.secondary_column
-> Nested Loop Left Join (cost=1048.44..1057.21 rows=1 width=12) (actual time=1.216..1.900 rows=50 loops=1)
Output: m.id, m.main_column, secondary.secondary_column
Inner Unique: true
-> Subquery Scan on m (cost=1048.02..1048.77 rows=1 width=8) (actual time=1.201..1.515 rows=50 loops=1)
Output: m.id, m.main_column
Filter: (m.main_column = 5)
-> Limit (cost=1048.02..1048.14 rows=50 width=8) (actual time=1.196..1.384 rows=50 loops=1)
Output: main.id, main.main_column
-> Sort (cost=1048.02..1048.85 rows=332 width=8) (actual time=1.194..1.260 rows=50 loops=1)
Output: main.id, main.main_column
Sort Key: main.id
Sort Method: top-N heapsort Memory: 27kB
-> Bitmap Heap Scan on public.main (cost=11.00..1036.99 rows=332 width=8) (actual time=0.054..0.753 rows=334 loops=1)
Output: main.id, main.main_column
Recheck Cond: (main.main_column = 5)
Heap Blocks: exact=334
-> Bitmap Index Scan on main_column (cost=0.00..10.92 rows=332 width=0) (actual time=0.029..0.030 rows=334 loops=1)
Index Cond: (main.main_column = 5)
-> Index Scan using secondary_main_id on public.secondary (cost=0.42..8.44 rows=1 width=8) (actual time=0.004..0.004 rows=1 loops=50)
Output: secondary.id, secondary.main_id, secondary.secondary_column
Index Cond: (secondary.main_id = m.id)
Planning Time: 0.161 ms
Execution Time: 2.115 ms
This is a toy dataset here, but on a real DB, the IO difference is significant (no need to fetch 1000 rows when 30 are enough), and the timing difference also quickly adds up (up to an order of magnitude slower).
So my question: is there any way to get the planner to understand that the JOIN can be applied much later in the process?
It seems like something that could be applied automatically to gain a sizeable performance boost.
Deferred joins are good. It's usually helpful to run the limit operation on a subquery that yields only the id values. The order by....limit operation has to sort less data just to discard it.
select main.id, main.main_column, secondary.secondary_column
from main
join (
select id
from main
where main_column = 5
order by id
limit 50
) selection on main.id = selection.id
left join secondary on main.id = secondary.main_id
order by main.id
limit 50
It's also possible adding id to your main_column index will help. With a BTREE index the query planner knows it can get the id values in ascending order from the index, so it may be able to skip the sort step entirely and just scan the first 50 values.
create index main_column on main(main_column, id);
Edit In a large table, the heavy lifting of your query will be the selection of the 50 main.id values to process. To get those 50 id values as cheaply as possible you can use a scan of the covering index I proposed with the subquery I proposed. Once you've got your 50 id values, looking up 50 rows' worth of details from your various tables by main.id and secondary.main_id is trivial; you have the correct indexes in place and it's a limited number of rows. Because it's a limited number of rows it won't take much time.
It looks like your table sizes are too small for various optimizations to have much effect, though. Query plans change a lot when tables are larger.
Alternative query, using row_number() instead of LIMIT (I think you could even omit LIMIT here):
-- prepare q3 AS
select m.id, main_column, secondary_column
from (
select id, main_column
, row_number() OVER (ORDER BY id, main_column) AS rn
from main
where main_column = 5
) m
left join secondary on m.id = secondary.main_id
WHERE m.rn <= 50
ORDER BY m.id
LIMIT 50
;
Puttting the subsetting into a CTE can avoid it to be merged into the main query:
PREPARE q6 AS
WITH
-- MATERIALIZED -- not needed before version 12
xxx AS (
SELECT DISTINCT x.id
FROM main x
WHERE x.main_column = 5
ORDER BY x.id
LIMIT 50
)
select m.id, m.main_column, s.secondary_column
from main m
left join secondary s on m.id = s.main_id
WHERE EXISTS (
SELECT *
FROM xxx x WHERE x.id = m.id
)
order by m.id
-- limit 50
;
I'm new to the concept of indexes but I'm trying to figure out how its working.
I have the following query that I want to improve the performance of.
explain analyze select to_char(rental_date, 'month') as month, count(*) count
from rental
join instrument on rental.instrument_id = instrument.instrument_id
where extract(year from rental_date) = 2020
group by month, extract(month from rental_date)
order by extract(month from rental_date) asc
;
Execution plan
QUERY PLAN
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------
GroupAggregate (cost=75.10..75.55 rows=15 width=48) (actual time=14.204..14.821 rows=12 loops=1)
Group Key: (date_part('month'::text, (rental.rental_date)::timestamp without time zone)), (to_char((rental.rental_date)::timestamp with time zone, 'month'::text))
-> Sort (cost=75.10..75.14 rows=15 width=40) (actual time=14.121..14.298 rows=1540 loops=1)
Sort Key: (date_part('month'::text, (rental.rental_date)::timestamp without time zone)), (to_char((rental.rental_date)::timestamp with time zone, 'month'::text))
Sort Method: quicksort Memory: 169kB
-> Hash Join (cost=1.20..74.81 rows=15 width=40) (actual time=7.912..13.166 rows=1540 loops=1)
Hash Cond: (rental.instrument_id = instrument.instrument_id)
-> Seq Scan on rental (cost=0.00..73.39 rows=15 width=8) (actual time=0.061..2.027 rows=1540 loops=1)
Filter: (date_part('year'::text, (rental_date)::timestamp without time zone) = '2020'::double precision)
Rows Removed by Filter: 1511
-> Hash (cost=1.09..1.09 rows=9 width=4) (actual time=0.046..0.047 rows=9 loops=1)
Buckets: 1024 Batches: 1 Memory Usage: 9kB
-> Seq Scan on instrument (cost=0.00..1.09 rows=9 width=4) (actual time=0.012..0.016 rows=9 loops=1)
Planning Time: 3.908 ms
Execution Time: 15.072 ms
(15 rows)
My idea was to have indexes on instrument_id and on rental_date because intrument_id is a foreign key and rental_date is in the where clause.
create index isx_rental ON rental(instrument_id);
create index isx_date ON rental(rental_date);
But this doesn't affect the runtime at all.
Why doesn't this help me with the performance?
The condition extract(year from rental_date) = 2020 can't use an index on rental_date as the index doesn't store the result of the expression, only the original column value.
You need to at least change that condition to:
where rental_date >= date '2020-01-01'
and rental_date < date '2021-01-01'
in order for the index to be considered at all. Whether that improves the performance is hard to say without seeing the execution plan.
To begin with extract(year from rental_date) = 2020 cannot use an index, because the function need to be applied for each row before the comparison can be done. You should whenever possible not extract a portion of a date but use a range instead. In this case you could use rental_date >= '2020-01-01' AND rental_date < '2021-01-01'.
Then it's a little weird that you group by the alias month, which is the month (as name) and then again by extract(month from rental_date) which is also the month (yet numerical). Those can be bijectively mapped. I don't know if the optimizer gets that, so you better only group by one of them. It might also help, if you grouped by the same expression you order by. So GROUP BY extract(month from rental_date) seems more promising.
In short, you can try to rewrite the query to:
SELECT to_char(rental_date, 'month') month,
count(*) count
FROM rental
INNER JOIN instrument
ON rental.instrument_id = instrument.instrument_id
WHERE rental_date >= '2020-01-01'
AND rental_date < '2021-01-01'
GROUP BY extract(month from rental_date)
ORDER BY extract(month from rental_date) ASC;
For instrument, there's only one column involved, instrument_id, and that might be a primary key, i.e. it is already indexed. If not, index it, it might help the JOIN.
The next thing is, that only one index can be used on a table in a SELECT (unless it appears more often in the FROM clause, but that's not the case here). So your two indexes on rental are one too many. You need a composite index.
Now for rental you could try an index on (rental_date, instrument_id) to support the WHERE and the JOIN. To also support the GROUP BY and ORDER BY you can try to add extract(month from rental_date) to the index as well, making it an index on (rental_date, instrument_id, extract(month from rental_date)).
Approximately every 10 min I insert ~50 records with the same timestamp.
It means ~600 records per hour or 7.200 records per day or 2.592.000 records per year.
User wants to retrieve all records for the timestamp closest to the asked time.
Design #1 - one table with index on timestamp column:
CREATE TABLE A (t timestamp, value int);
CREATE a_idx ON A (t);
Single insert statement creates ~50 records with the same timestamp:
INSERT INTO A VALUES (
(‘2019-01-02 10:00’, 5),
(‘2019-01-02 10:00’, 12),
(‘2019-01-02 10:00’, 7),
….
)
Get all records which are closest to the asked time
(I use the function greatest() available in PostgreSQL):
SELECT * FROM A WHERE t =
(SELECT t FROM A ORDER BY greatest(t - asked_time, asked_time - t) LIMIT 1)
I think this query is not efficient because it requires the full table scan.
I plan to partition the A table by timestamp to have 1 partition per year, but the approximate match above still will be slow.
Design #2 - create 2 tables:
1st table: to keep the unique timestamps and auto-incremented PK,
2nd table: to keep data and the foreign key on 1st table PK
CREATE TABLE UNIQ_TIMESTAMP (id SERIAL PRIMARY KEY, t timestamp);
CREATE TABLE DATA (id INTEGER REFERENCES UNIQ_TIMESTAMP (id), value int);
CREATE INDEX data_time_idx ON DATA (id);
Get all records which are closest to the asked time:
SELECT * FROM DATA WHERE id =
(SELECT id FROM UNIQ_TIMESTAMP ORDER BY greatest(t - asked_time, asked_time - t) LIMIT 1)
It should run faster compared to Design #1 because the nested select scans the smaller table.
Disadvantage of this approach:
- I have to insert into 2 tables instead just one
- I lost the ability to partition the DATA table by timestamp
What you could recommend?
I'd go with tje single table approach, perhaps partitioned by year so that it becomes easy to get rid of old data.
Create an index like
CREATE INDEX ON a (date_trunc('hour', t + INTERVAL '30 minutes'));
Then use your query like you wrote it, but add
AND date_trunc('hour', t + INTERVAL '30 minutes')
= date_trunc('hour', asked_time + INTERVAL '30 minutes')
The additional condition acts as a filter and can use the index.
You can use a UNION of two queries to find all timestamps closest to a given one:
(
select t
from a
where t >= timestamp '2019-03-01 17:00:00'
order by t
limit 1
)
union all
(
select t
from a
where t <= timestamp '2019-03-01 17:00:00'
order by t desc
limit 1
)
That will efficiently make use of an index on t. On a table with 10 million rows (~3 years of data), I get the following execution plan:
Append (cost=0.57..1.16 rows=2 width=8) (actual time=0.381..0.407 rows=2 loops=1)
Buffers: shared hit=6 read=4
I/O Timings: read=0.050
-> Limit (cost=0.57..0.58 rows=1 width=8) (actual time=0.380..0.381 rows=1 loops=1)
Output: a.t
Buffers: shared hit=1 read=4
I/O Timings: read=0.050
-> Index Only Scan using a_t_idx on stuff.a (cost=0.57..253023.35 rows=30699415 width=8) (actual time=0.380..0.380 rows=1 loops=1)
Output: a.t
Index Cond: (a.t >= '2019-03-01 17:00:00'::timestamp without time zone)
Heap Fetches: 0
Buffers: shared hit=1 read=4
I/O Timings: read=0.050
-> Limit (cost=0.57..0.58 rows=1 width=8) (actual time=0.024..0.025 rows=1 loops=1)
Output: a_1.t
Buffers: shared hit=5
-> Index Only Scan Backward using a_t_idx on stuff.a a_1 (cost=0.57..649469.88 rows=78800603 width=8) (actual time=0.024..0.024 rows=1 loops=1)
Output: a_1.t
Index Cond: (a_1.t <= '2019-03-01 17:00:00'::timestamp without time zone)
Heap Fetches: 0
Buffers: shared hit=5
Planning Time: 1.823 ms
Execution Time: 0.425 ms
As you can see it only requires very few I/O operations and that is pretty much independent of the table size.
The above can be used for an IN condition:
select *
from a
where t in (
(select t
from a
where t >= timestamp '2019-03-01 17:00:00'
order by t
limit 1)
union all
(select t
from a
where t <= timestamp '2019-03-01 17:00:00'
order by t desc
limit 1)
);
If you know you will never have more than 100 values close to that requested timestamp, you could remove the IN query completely and simply use a limit 100 in both parts of the union. That makes the query a bit more efficient as there is no second step for evaluating the IN condition, but might return more rows than you want.
If you always look for timestamps in the same year, then partitioning by year will indeed help with this.
You can put that into a function if it is too complicated as a query:
create or replace function get_closest(p_tocheck timestamp)
returns timestamp
as
$$
select *
from (
(select t
from a
where t >= p_tocheck
order by t
limit 1)
union all
(select t
from a
where t <= p_tocheck
order by t desc
limit 1)
) x
order by greatest(t - p_tocheck, p_tocheck - t)
limit 1;
$$
language sql stable;
The the query gets as simple as:
select *
from a
where t = get_closest(timestamp '2019-03-01 17:00:00');
Another solution is to use the btree_gist extension which provides a "distance" operator <->
Then you can create a GiST index on the timestamp:
create index on a using gist (t) ;
and use the following query:
select *
from a where t in (select t
from a
order by t <-> timestamp '2019-03-01 17:00:00'
limit 1);
I have a very simple SQL:
select * from email.email_task where acquire_time < now() and state IN ('CREATED', 'RELEASED') order by creation_time asc limit 1;
I have 2 indexes created:
Index of state
Index of state, acquire_time, creation_time
Ideally I think Postgres should pick the 2nd one since it matches every column required in this SQL:
However the execution plan shows differently, it uses neither of the indexes:
Limit (cost=187404.36..187404.36 rows=1 width=743)
-> Sort (cost=187404.36..190753.58 rows=1339690 width=743)
Sort Key: creation_time
-> Seq Scan on email_task (cost=0.00..180705.91 rows=1339690 width=743)
Filter: (((state)::text = 'CREATED'::text) AND (acquire_time < now()))
I understand that if the number of rows returned arrives like 10% of total, then it would pick Seq Scan over Index Scan. (As explained at Why does PostgreSQL perform sequential scan on indexed column?
) So that's why index1 is not picked.
What I don't understand is why index2 is not picked since matches all the columns?
Then I tried a 3rd index:
Index of create_time, acquire_time, state
And this time it uses the index3 (I add the index using another smaller database
perf_1 because the original one has 2 million rows and it takes too much time)
Limit (cost=0.29..0.36 rows=1 width=75) (actual time=0.043..0.043 rows=1 loops=1)
-> Index Scan using perf_1 on email_task (cost=0.29..763.76 rows=9998 width=75) (actual time=0.042..0.042 rows=1 loops=1)
Index Cond: (acquire_time < now())
Filter: ((state)::text = ANY ('{CREATED,RELEASED}'::text[]))
It seems that, Postgres execution planner is picking the order by clause first then the where clause which is a little bit counter-intuitive.
Is my understanding correct or there are some other factors that impact the Postgres planner?
Thanks in advance.
I'm using a partitioned postgres table following the documentation using rules, using a partitioning scheme based on date ranges (my date column is an epoch integer)
The problem is that a simple query to select the row with the maximum value of the sharded column is not using indices:
First, some settings to coerce postgres to do what I want:
SET constraint_exclusion = on;
SET enable_seqscan = off;
The query on a single partition works:
explain (SELECT * FROM urls_0 ORDER BY date_created ASC LIMIT 1);
Limit (cost=0.00..0.05 rows=1 width=38)
-> Index Scan using urls_date_created_idx_0 on urls_0 (cost=0.00..436.68 rows=8099 width=38)
However, the same query on the entire table is seq scanning:
explain (SELECT * FROM urls ORDER BY date_created ASC LIMIT 1);
Limit (cost=50000000274.88..50000000274.89 rows=1 width=51)
-> Sort (cost=50000000274.88..50000000302.03 rows=10859 width=51)
Sort Key: public.urls.date_created
-> Result (cost=10000000000.00..50000000220.59 rows=10859 width=51)
-> Append (cost=10000000000.00..50000000220.59 rows=10859 width=51)
-> Seq Scan on urls (cost=10000000000.00..10000000016.90 rows=690 width=88)
-> Seq Scan on urls_15133 urls (cost=10000000000.00..10000000016.90 rows=690 width=88)
-> Seq Scan on urls_15132 urls (cost=10000000000.00..10000000016.90 rows=690 width=88)
-> Seq Scan on urls_15131 urls (cost=10000000000.00..10000000016.90 rows=690 width=88)
-> Seq Scan on urls_0 urls (cost=10000000000.00..10000000152.99 rows=8099 width=38)
Finally, a lookup by date_created does work correctly with contraint exclusions and index scans:
explain (SELECT * FROM urls where date_created = 1212)
Result (cost=10000000000.00..10000000052.75 rows=23 width=45)
-> Append (cost=10000000000.00..10000000052.75 rows=23 width=45)
-> Seq Scan on urls (cost=10000000000.00..10000000018.62 rows=3 width=88)
Filter: (date_created = 1212)
-> Index Scan using urls_date_created_idx_0 on urls_0 urls (cost=0.00..34.12 rows=20 width=38)
Index Cond: (date_created = 1212)
Does anyone know how to use partitioning so that this type of query will use an index scan?
Postgresql 9.1 knows how to optimize this out of the box.
In 9.0 or earlier, you need to decompose the query manually, by unioning each of the subqueries individually with their own order by/limit statement.