How to re-write SQL query to be more efficient? - sql

Question

I've got a query that's decently sized on it's own, but there's one section of it that turns it into something ridiculously large (billions of rows returned type thing).
There must be a better way to write it than what I have done.
To simplify the section of the query in question, it takes the client details from one table and tries to find the most recent transaction dates in their savings and spending accounts (not the actual situation, but close enough).
I've joined it with left joins because if someone (for example) doesn't have a savings account, I still want the client details to pop up. But when there's hundreds of thousands of clients with tens of thousands of transactions, it's a little slow to run.
select client_id, max(e.transation_date), max(s.transaction_date)
from client_table c
left join everyday_account e
on c.client_id = e.client_id
left join savings_account s
on c.client_id = s.client_id
group by client_id
I'm still new to this so I'm not great at knowing how to optimise things, so is there any thing I should be looking at? Perhaps different joins, or something other than max()?
I've probably missed some key details while trying to simplify it, let me know if so!

Answer 1

Sometimes aggregating first, then joining to the aggregated result is faster. But this depends on the actual DBMS being used and several other factors.
select client_id, e.max_everyday_transaction_date, s.max_savings_transaction_date
from client_table c
left join (
select client_id, max(transaction_date) as max_everyday_transaction_date
from everyday_account
group by client_id
) e on c.client_id = e.client_id
left join (
select client_id, max(transaction_date) as max_savings_transaction_date
from savings_account
) s on c.client_id = s.client_id
The indexes suggested by Tim Biegeleisen should help in this case as well.
But as the query has to process all rows from all tables there no good way to speed up this query, other than throwing more hardware at it. If your database supports it, make sure parallel query is enabled (which will distribute the total work over multiple threads in the backend which can substantially improve query performance if the I/O system can keep up)

Answer 2

There are no WHERE or HAVING clauses, which basically means there is no explicit filtering in your SQL query. However, we can still try to optimize the joins using appropriate indices. Consider:
CREATE INDEX idx1 ON everyday_account (client_id, transation_date);
CREATE INDEX idx2 ON savings_account (client_id, transation_date);
These two indices, if chosen for use, should speed up the two left joins in your query. I also cover the transaction_date in both cases, in case that might help.
Side note: You might want to also consider just having a single table containing all customer accounts. Include a separate column which distinguishes between everyday and savings accounts.

Answer 3

I would suggest correlated subqueries:
select client_id,
(select max(e.transation_date)
from everyday_account e
where c.client_id = e.client_id
),
(select max(s.transaction_date)
from savings_account s
where c.client_id = s.client_id
)
from client_table c;
Along with indexes on everyday_account(client_id, transaction_date desc) and savings_account(client_id, transaction_date desc).
The subqueries should basically be index lookups (or very limited index scans), with no additional joining needed.