Stock calculation in Postgres - sql

Question

I have a table p1 with transactions in Postgres like this:
| id | product_id | transaction_date | quantity |
|----|------------|------------------|----------|
| 1 | 1 | 2015-01-01 | 1 |
| 2 | 1 | 2015-01-02 | 2 |
| 3 | 1 | 2015-01-03 | 3 |
and p2 table with products like this:
| id | product | stock |
|----|--------------|-------|
| 1 | Product A | 15 |
stock in p2' has been be reduced for every new record in p1.
How to reconstruct previous states to get this result?
| product | first_stock | quantity | last_stock |
|-----------|-------------|----------|------------|
| Product A | 21 | 1 | 20 |
| Product A | 20 | 2 | 18 |
| Product A | 18 | 3 | 15 |
I have tried using lead() to get the quantity after the current row.
SELECT p2.product, p1.quantity, lead(p1.quantity) OVER(ORDER BY p1.id DESC)
FROM p1 INNER JOIN p2 ON p1.product_id = p2.id;
But how to calculate leading rows from the current stock?

Answer 1

You don't just need lead() you need the running sum over all rows in between to reconstruct previous states from transaction data:
SELECT p2.product
, p2.stock + px.sum_quantity AS first_stock
, px.quantity
, p2.stock + px.sum_quantity - quantity AS last_stock
FROM p2
JOIN (
SELECT product_id, quantity, transaction_date
, sum(quantity) OVER (PARTITION BY product_id
ORDER BY transaction_date DESC) AS sum_quantity
FROM p1
) px ON px.product_id = p2.id
ORDER BY px.transaction_date;
Assuming the course of events actually indicated by transaction_date.
Use the aggregate function sum() as window-aggregate function to get the running sum. Use a subquery, since we use the running sum of quantities (sum_quantity) multiple times.
For last_stock subtract quantity of the current row (after adding it redundantly).
Nitpick
Theoretically, it would be cheaper to use a custom frame definition for the window frame to only sum quantities up to preceding row, so we don't add and subtract the quantity of the current row redundantly. But that's more complex and hardly faster in reality:
SELECT p2.id, p2.product, px.transaction_date -- plus id and date for context
, p2.stock + COALESCE(px.pre_sum_q + px.quantity, 0) AS first_stock
, px.quantity
, p2.stock + COALESCE(px.pre_sum_q, 0) AS last_stock
FROM p2
LEFT JOIN (
SELECT id, product_id, transaction_date
, quantity
, sum(quantity) OVER (PARTITION BY product_id
ORDER BY transaction_date DESC
ROWS BETWEEN UNBOUNDED PRECEDING AND 1 PRECEDING) AS pre_sum_q
FROM p1
) px ON px.product_id = p2.id
ORDER BY px.transaction_date, px.id;
Explanation for the frame definition in this related answer:
Grouping based on sequence of rows
While being at it, also demonstrating how to prevent missing rows and NUll values with LEFT JOIN and COALESCE for products that don't have any related rows in p1, and a stable sort order if there are multiple transactions for the same product on the same day.
Still assuming all columns to be defined NOT NULL, or you need to do some more for corner cases with NULL values.