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N031-M3 Tier 3 · Intermediate · medium ecommerce · Brightlane

Return each qualifying customer's ID and their count of high-value orders

Part of Chained CTEs in SQL

The problem

Brightlane's finance team wants to identify customers who have placed high-value orders, along with how many such orders each customer has on record.

Write a query to return each qualifying customer's ID and their count of high-value orders.

Assumptions:

  • A high-value order has total_amount greater than $500.
  • A customer's high-value-order count is the number of high-value orders linked to that customer_id.
  • Only customers with at least one high-value order should appear.

Output:

  • One row per qualifying customer, with columns customer_id and big_order_count.
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
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Solution query
WITH
  big_orders AS (
    SELECT
      customer_id,
      total_amount
    FROM
      orders
    WHERE
      total_amount > 500
  ),
  customer_counts AS (
    SELECT
      customer_id,
      COUNT(*) AS big_order_count
    FROM
      big_orders
    GROUP BY
      customer_id
  )
SELECT
  customer_id,
  big_order_count
FROM
  customer_counts

The shape

Restrict first, then aggregate. The first CTE keeps only the high-value orders, and the second counts those qualifying rows per customer. Because the count runs over the already-filtered set, every counted row is by definition a big order and no row needs to be discounted at aggregation time.

Clause by clause

The first CTE drops every order below the threshold:

WITH big_orders AS (
  SELECT customer_id, total_amount
  FROM orders
  WHERE total_amount > 500
)

WHERE total_amount > 500 filters the orders before any grouping happens. The SELECT list carries customer_id forward because the next layer needs it as the grouping key, and total_amount could be dropped here but is harmless to keep.

The second CTE groups the qualifying orders by customer:

customer_counts AS (
  SELECT customer_id, COUNT(*) AS big_order_count
  FROM big_orders
  GROUP BY customer_id
)

FROM big_orders reads only the orders that survived the filter. GROUP BY customer_id produces one row per customer, and COUNT(*) counts the high-value orders in each group. Customers with zero big orders never appear in big_orders and therefore never appear in this layer either, which is the "at least one" requirement satisfied implicitly.

  • SELECT customer_id, big_order_count FROM customer_counts returns the final per-customer counts.

Why restrict in the first CTE instead of restricting after the aggregation

Moving the threshold up front is what makes "at least one high-value order" automatic. A customer with no qualifying orders contributes no rows to big_orders and therefore no row to customer_counts. If the filter were skipped and the count were computed first across all orders, the threshold would have to come back as a separate condition on the count, and the count itself would be "all orders" rather than "high-value orders." The order of operations changes what is being counted, not just how the answer is filtered. Restricting first is what makes the count a count of high-value orders.

You practiced layering two WITH stages — pre-restrict the source records in the first stage, then compute a per-customer count over only the qualifying records in the second.

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