SQL Aggregation Join and Subquery Optimisation - sql

Question

I am trying to get aggregate values by time periods of two relations (buys and uses) and join them so that I can get the results in one report and also draw a ratio on them. I am using PostgreSQL. The end report required is: dateTime, u.sum, b.sum, b.sum/u.sum
The following query works but scales very poorly with larger table sizes.
SELECT b2.datetime AS dateTime, b2.sum AS BUY_VOLUME, u1.sum AS USE_VOLUME,
CASE u1.sum
WHEN 0 THEN 0
ELSE (b2.sum / u1.sum)
END AS buyToUseRatio
FROM(
SELECT SUM(b.total / 100.0) AS sum, date_trunc('week', (b.datetime + INTERVAL '1 day')) - INTERVAL '1 day' as datetime
FROM buys AS b
WHERE
datetime > date_trunc('month', CURRENT_DATE) - INTERVAL '1 year'
GROUP BY datetime) AS b2
INNER JOIN (SELECT SUM(u.amount) / 100.00 AS sum, date_trunc('week', (u.datetime + INTERVAL '1 day')) - INTERVAL '1 day' AS datetime
FROM uses AS u
WHERE
datetime > date_trunc('month', CURRENT_DATE) - INTERVAL '1 year'
GROUP BY datetime) AS u1 ON b2.datetime = u1.datetime
ORDER BY b2.datetime ASC;
I was wondering if anyone could help me by providing an alternative query that would get the end result required and is faster to execute.
I appreciate any help on this :-) My junior level SQL is a little rusty and I can't think of another way of doing this without creating indexes. Thanks in advance.

Answer 1

At least, these indexes can help your query:
create index idx_buys_datetime on buys(datetime);
create index idx_uses_datetime on uses(datetime);
Your query seems fine. However, you could use full join (instead of inner) to have all rows, where at least one of your tables have data. You could even use generate_series() to always have 1 year of results, even when there is no data in either of your tables, but I'm not sure if that's what you need. Also, some other things can be written more easily; your query could look like this:
select dt, buy_volume, use_volume, buy_volume / nullif(use_volume, 0.0) buy_to_use_ratio
from (select sum(total / 100.0) buy_volume, date_trunc('week', (datetime + interval '1 day')) - interval '1 day' dt
from buys
where datetime > date_trunc('month', current_timestamp - interval '1 year')
group by 2) b
full join (select sum(amount) / 100.0 use_volume, date_trunc('week', (datetime + interval '1 day')) - interval '1 day' dt
from uses
where datetime > date_trunc('month', current_timestamp - interval '1 year')
group by 2) u using (dt)
order by 1
http://rextester.com/YVASV92568

Answer 2

So the answer depends on how large your tables are, but if it was me, I would create one or two new "summary" tables based on your query and make sure to keep them updated (run a batch job once a day to update them or once an hour with all the data that has changed recently).
Then, I would be able to query those tables and do so, much faster.
If however, your tables are very small, then just keep going the way you are and play around with indexes till you get some timing which is acceptable.