MediumSQL challenge #108CASE WHENGROUP BYConditional Aggregation

Calculate Payment Success Rate per Method with CASE WHEN

For each payment method, show transactions, successful payments, success rate and revenue.

The challenge runs on the SQLabHub practice database (SQLite) and uses the payments table. You can solve it in the browser, with nothing to install.

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The task

Analyze payment methods: show transaction count, success rate (%), total revenue per method.

A correct answer returns 8 rows with 5 columns: method, total_transactions, successful, success_rate_pct, revenue.

Tables used in this challenge

payments 200 rows

id INTEGER (PK) · order_id INTEGER · user_id INTEGER · amount REAL · method TEXT · status TEXT · paid_at TEXT · transaction_id TEXT

First 3 rows of the payments table
idorder_iduser_idamountmethodstatuspaid_attransaction_id
146105921.62Gift Cardsuccess2022-05-18TXNHTN4GIK5L
2157371723.82Bank Transfersuccess2024-11-11TXNKJPIXJOFP
34536978.76Bank Transferrefunded2023-10-24TXNU2RW4MK1R

The first rows of each table are shown. See how all the tables relate on the database schema page.

Expected output

Expected output of the challenge
methodtotal_transactionssuccessfulsuccess_rate_pctrevenue
Cryptocurrency342470.628333.72
Debit Card28217524174.39
Bank Transfer382360.522173.03
Apple Pay271659.315593.63
PayPal24187514884.42
Gift Card221359.113872.44
Google Pay131076.912646.29
Credit Card141285.712582.68

The complete result: 8 rows. This is the output of the model solution on the practice database.

What you practise

  • Summarising many rows into one value with COUNT, SUM, AVG, MIN or MAX
  • Grouping rows with GROUP BY to get one result row per group
  • Turning conditions into values with CASE WHEN
  • Sorting the result with ORDER BY

Hint and approach

Try the challenge first. Open these only when you need them.

Show the hint

Use CASE WHEN inside SUM/COUNT for conditional aggregation

Show the step-by-step approach

How the model solution is built, in the order the database evaluates it:

  1. Read from the payments table.
  2. Group the rows by method, so the result has one row for each value.
  3. For each group, compute COUNT(*), a conditional count (SUM(CASE WHEN … THEN 1 ELSE 0 END)) of the rows where status equals 'success' and a conditional sum of amount over the rows where status equals 'success'.
  4. ROUND() trims the calculated figures to a fixed number of decimals.
  5. Sort the result by revenue descending.

Model solution

Checking your session…

What to watch for

Multiplying by 100.0 rather than 100 forces decimal division; with integers, SQLite would truncate every rate to a whole number. Putting the status test inside SUM(CASE ...) keeps failed payments in the denominator, which a WHERE filter would remove.

  • GROUP BY. Every selected column must either be in GROUP BY or sit inside an aggregate. SQLite accepts other columns and returns an arbitrary value for them; most databases raise an error.
  • CASE WHEN. CASE stops at the first WHEN that is true, so overlapping conditions must be ordered from most to least specific. Without an ELSE, unmatched rows get NULL.
  • ORDER BY. Without ORDER BY the row order is not guaranteed, even if it looks stable. Rows that tie on the sort columns can come back in any order.
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