I have a large set of data for a company’s sales and payments received.

How can I quickly find the latest sale that cannot be covered by the total payments for each customer?

Eg. Suppose the data table looks like this

For Customer 1, it would be 2024-01-15, since total payment amount is 150, so the first sale gets covered; And for customer 2, it would be 2024-01-02, since the only payment cannot cover its first sale.

So far I use a window function to compute the cumulative sales and total payments. Then use this generated table to compute the latest uncovered sale, and other statistics.

with cumsum_entry as (
    select 
        id, date, customer_id, type, amount,
        sum(amount) filter (where type='PAYMENT') over win_customer
          - sum(amount) filter (where type='SALE') over win_cumsum as cover
    from entry
    window win_customer as (partition by customer_id),
        win_cumsum as (partition by customer_id order by date rows between unbounded proceeding and current row)
)
select 
    customer_id,
    sum(amount) filter (where date >= '2024-01-01') as monthly_total,
    min(date) filter (where cover < 0) as latest_uncovered
from cumsum_entry
where date <= '2024-01-31'
group by customer_id

In fact I store the generated table “cumsum_entry” as a view in PostgreSQL, and use Django’s orm to do the rest of the work, but the core logical Is the same as above.

Apparently I am not satisfied with the responding speed(2 seconds for ~10^3 customers / ~10^5 entries). How can I further optimize responding time?