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Use multiple CTEs to perform a comprehensive customer analysis: calculate total spending and invoice count, then combine them.
Write a query with two CTEs: one for total spending, one for invoice counts. Then join them in the final query.
| Column | Type |
|---|---|
| CustomerId | INTEGER (Primary Key) |
| FirstName | TEXT |
| LastName | TEXT |
| Company | TEXT |
| Address | TEXT |
| City | TEXT |
| State | TEXT |
| Country | TEXT |
| PostalCode | TEXT |
| Phone | TEXT |
| Fax | TEXT |
| TEXT | |
| SupportRepId | INTEGER (Foreign Key → Employee.EmployeeId) |
| Column | Type |
|---|---|
| InvoiceId | INTEGER (Primary Key) |
| CustomerId | INTEGER (Foreign Key → Customer.CustomerId) |
| InvoiceDate | TIMESTAMP |
| BillingAddress | TEXT |
| BillingCity | TEXT |
| BillingState | TEXT |
| BillingCountry | TEXT |
| BillingPostalCode | TEXT |
| Total | NUMERIC(10,2) |
WITH cte1 AS (...),
cte2 AS (...)
SELECT ... FROM cte1 JOIN cte2 ...;Your result should show top 10 customers with spending and invoice counts: | FirstName | LastName | TotalSpent | InvoiceCount | |-----------|------------|------------|--------------| | Helena | Holý | 49.62 | 7 | | Richard | Cunningham | 47.62 | 7 | | Luis | Rojas | 46.62 | 7 | | Ladislav | Kovács | 45.62 | 7 | | Hugh | O'Reilly | 45.62 | 7 | | ... | ... | ... | ... |
Double-counting metrics by using COUNT(*) after joining parent and child tables. When joining an invoice table with an invoice lines table, a single invoice multiplies across all its line items, causing COUNT(invoice_id) to return line counts instead of unique invoice counts.
Interviewers check whether you notice 1-to-many cardinality multiplication and use COUNT(DISTINCT col) or pre-aggregate child records before joining.
Construct the solution logically from first principles to avoid typical edge case pitfalls.
Identify the base table and apply preliminary WHERE filters.
FROM TableName WHERE is_active = true
Group by primary business keys and compute aggregate expressions.
SELECT category, COUNT(DISTINCT item_id) AS total_items, SUM(amount) AS revenue GROUP BY category
Order by specified metrics descending and apply limit clauses.
ORDER BY revenue DESC LIMIT 10;
WITH CustomerSpending AS (SELECT CustomerId, SUM(Total) AS TotalSpent FROM Invoice GROUP BY CustomerId), CustomerInvoiceCounts AS (SELECT CustomerId, COUNT(*) AS InvoiceCount FROM Invoice GROUP BY CustomerId) SELECT Customer.FirstName, Customer.LastName, CustomerSpending.TotalSpent, CustomerInvoiceCounts.InvoiceCount FROM Customer JOIN CustomerSpending ON Customer.CustomerId = CustomerSpending.CustomerId JOIN CustomerInvoiceCounts ON Customer.CustomerId = CustomerInvoiceCounts.CustomerId ORDER BY TotalSpent DESC, Customer.CustomerId LIMIT 10;
Real code patterns candidates submit that fail the grading suite.
SELECT a.Name, COUNT(t.TrackId) FROM Artist a JOIN Album al ON a.ArtistId = al.ArtistId JOIN Track t ON al.AlbumId = t.AlbumId GROUP BY a.Name;
Three recurring syntax and semantic traps relevant to this problem domain.
Joining a fact table with child lines multiplies fact table rows, distorting sums and counts.
SELECT c.id, SUM(i.total) FROM customer c JOIN invoice i ON c.id = i.customer_id JOIN invoice_line il ON i.id = il.invoice_id -- ❌ Inflated SUM
SELECT c.id, SUM(i.total) FROM customer c JOIN invoice i ON c.id = i.customer_id GROUP BY c.id; -- ✅ Avoids line multiplication
Using COUNT(*) when duplicate rows exist due to joins counts duplicate records.
SELECT artist_id, COUNT(album_id) ... -- ❌ Counts duplicate occurrences
SELECT artist_id, COUNT(DISTINCT album_id) ... -- ✅ Distinct unique entities
In SQL, dividing integers like 5 / 10 results in 0. Cast at least one operand to FLOAT or NUMERIC.
SELECT solved_count / total_count AS rate ... -- ❌ Returns 0
SELECT CAST(solved_count AS FLOAT) / total_count AS rate ... -- ✅ Returns 0.5
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