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Find duplicated returns · SQL scenario · D8LooP

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Find duplicated returns

Finance suspects some orders were refunded twice. A duplicated return means more than one row in shop_returns pointing at the same order. Return one row per affected order, ordered by order_id.

Result columns · in this order

`order_id`	The order that was refunded more than once.
`return_rows`	How many return rows point at it.
`refunded`	Total refunded across those rows.

How to approach it

Group by the key you think should be unique, then keep only the groups that are not.

Sample input

shop_returnsOne row per returned item. order_id is NULL when the return was never matched.

return_id	order_id	reason	refund_amount	returned_on
1	1001	damaged	60	2026-01-09
2	1004	wrong size	50	2026-02-05
3	1004	wrong size	50	2026-02-20
4	1007	changed mind	90	2026-03-01
5	null	unlinked	25	2026-02-11
6	1008	damaged	150	2026-02-16
7	1011	late delivery	130	2026-03-20
8	1002	damaged	80	2026-01-15
9	1002	damaged	80	2026-01-18
10	1008	damaged	150	2026-02-20

10 rows — all rows shown.

Expected output

The exact result your query should return for this input.

order_id	return_rows	refunded
1002	2	160
1004	2	100
1008	2	300

3 rows — all rows shown.

Constraints

1Only orders with more than one return row appear.
2refunded is the total refunded across those rows — the amount actually at risk.
3Return rows with a NULL order_id are not duplicates of anything and must be excluded.
4Do not list the individual return rows; one row per affected order.

Worked example

Order 1004 has two return rows of 50.00 each, so it reports return_rows 2 and refunded 100.00. Without the NULL filter, the unmatched return forms its own group. It has only one row so it fails the HAVING test here — but on data with two unmatched returns it would appear as a phantom duplicated order with no id, which is why the filter is written rather than left to luck.

What this tests

The duplicate-detection idiom — GROUP BY the key, then HAVING COUNT(*) > 1 — and remembering that NULLs group together into a bucket of their own.

SQL

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