SQLMaxx
Sign in Start free
N038-E3 Tier 3 · Intermediate · easy ecommerce · Brightlane

Return the ID, name, category, and price of every product, plus the average price across the product's category on each row

Part of Window Functions Introduction (OVER, PARTITION BY) in SQL

The problem

Brightlane's pricing analyst wants to compare each product's price to the average price across its category.

Write a query to return the ID, name, category, and price of every product, plus the average price across the product's category on each row.

Assumptions:

  • The products table has one row per product with an id, a name, a category_id, and a price.
  • A category's average price is the average of price across every product in that category_id. The same value should appear on every row that shares a category_id.

Output:

  • One row per product, with columns id, name, category_id, price, and category_avg_price.
Schema · ecommerce 5 tables
categories
id integer
name text
parent_id? integer
products
id integer
name text
category_id integer
price numeric
stock_qty integer
attributes? jsonb
order_items
id integer
order_id integer
product_id integer
quantity integer
unit_price numeric
customers
id integer
name text
email text
city? text
country text
created_at timestamptz
is_active boolean
orders
id integer
customer_id integer
ordered_at timestamptz
status text
total_amount numeric
Check answerShift Ctrl ↵

Run previews · Check grades

Write a query, then run it to see results here.
Worked solution Try it yourself first
Solution query
SELECT
  id,
  name,
  category_id,
  price,
  AVG(price) OVER (
    PARTITION BY
      category_id
  ) AS category_avg_price
FROM
  products

The shape

AVG(price) OVER (PARTITION BY category_id) computes a separate average for each category_id and writes that category's average onto every row in that category. Rows from category 1 see category 1's average; rows from category 2 see category 2's average. The individual product rows stay intact.

Clause by clause

  • SELECT id, name, category_id, price returns each product's identifier, name, category, and individual price, one row per product.
  • The window column is:
AVG(price) OVER (PARTITION BY category_id) AS category_avg_price

PARTITION BY category_id splits the row set into one group per distinct category_id. AVG(price) runs inside each group independently, so the value attached to a given product is the average of price across only the rows that share its category_id. Every row in the same category sees the same category_avg_price; rows in different categories see different averages.

  • FROM products reads every product. The pricing analyst is comparing each product to its category, so every row stays in.

Why this and not GROUP BY category_id

GROUP BY category_id would collapse the catalog to one row per category, leaving the analyst with the category average but no individual product to compare it to. The job is to show each product's own price alongside its category's average, on the same row. PARTITION BY is the window-function equivalent of GROUP BY that does the grouping without the collapsing.

You practiced AVG(...) OVER (PARTITION BY ...) — compute a per-partition average and replicate the partition value onto every row that shares the partition key.

How you actually get good at SQL

Reading explains SQL. Writing it, over and over with instant feedback, is what makes you fluent.

That's the whole SQLMaxx loop: 600+ real problems, instant AI feedback, mastery you can actually see, and spaced review that won't let you forget.

A stack of SQL practice problem cards, the top card showing an employees table.
615 problems · 66 concepts

Real problems. Not toy examples.

615 hand-built problems spanning all 66 concepts, from basic SELECTs to window functions, built on real schemas and real business questions, the kind you'll actually get asked on the job. Enough reps to make SQL automatic.

A retro computer showing a SQL query marked correct with a green checkmark.
Instant AI feedback

Write a query. Know if it's right in one second.

No copying an answer and hoping it clicked. The AI grader checks your real query against real data, catches exactly what's wrong, and explains the fix in plain English, like a senior analyst reading over your shoulder on every problem.

A circular mastery progress dial filling from blue to green, the SQLMaxx diamond at its center.
Mastery tracking

Stop guessing whether you actually know it.

SQLMaxx tracks every concept and shows you what you've mastered and what's still shaky. Your skills fill in one concept at a time, so 'I think I get joins' becomes something you can prove.

A SQL query editor circled by a blue return arrow with a clock, scheduled to come back for review.
Spaced review

Learn it once. Keep it for good.

Most of what you learn this week fades by next week. So when a concept comes due for review, SQLMaxx hands you a fresh problem to solve from a blank editor, not a flashcard to re-read. A research-backed spaced-repetition algorithm (FSRS) times each return for right before you'd forget, so your SQL is still there months later, when the interview or the job actually needs it.

Practice, feedback, mastery, review. That's the loop that turns reading into real skill.

Start free

No account, no credit card. Start solving in under a minute.