Codestin Search App

Customer Order Frequency - Problem

Database Easy

You're managing an e-commerce platform and need to identify your most valuable customers - those who consistently spend big every month! 💰

Given three tables:

Customers: Contains customer information (ID, name, country)
Product: Contains product details (ID, description, price)
Orders: Contains order transactions (order ID, customer ID, product ID, date, quantity)

Your task is to find customers who spent at least $100 in BOTH June AND July 2020. These are your premium customers who deserve special attention!

Think of it like finding customers who hit a spending milestone in consecutive months - they're likely to be your most loyal buyers.

Return the customer_id and customer_name of these high-value customers in any order.

Input & Output

example_1.sql — Basic Case

$ Input: Customers: [(1,'Winston','USA'),(2,'Jonathan','Peru'),(3,'Moustafa','Egypt')] Product: [(10,'LC Phone',300),(20,'LC T-Shirt',10),(30,'LC Book',45),(40,'LC Keychain',2)] Orders: [(1,1,10,'2020-06-10',1),(2,1,20,'2020-07-01',1),(3,1,30,'2020-07-08',2),(4,2,10,'2020-06-15',2),(5,2,40,'2020-07-01',10),(6,3,20,'2020-06-24',2),(7,3,30,'2020-06-25',2),(9,3,30,'2020-05-08',3)]

› Output: [(1,'Winston')]

💡 Note: Winston spent $300 in June (1 LC Phone) and $110 in July (1 LC T-Shirt + 2 LC Books = $10 + $90). Both months >= $100, so he qualifies.

example_2.sql — Multiple Qualified Customers

$ Input: Customers: [(1,'Alice','USA'),(2,'Bob','Canada'),(3,'Carol','UK')] Product: [(1,'Laptop',1000),(2,'Mouse',25),(3,'Keyboard',50)] Orders: [(1,1,1,'2020-06-05',1),(2,1,2,'2020-07-10',4),(3,2,1,'2020-06-20',1),(4,2,3,'2020-07-15',2),(5,3,2,'2020-06-30',5)]

› Output: [(1,'Alice'),(2,'Bob')]

💡 Note: Alice: $1000 (June) + $100 (July) both >= $100. Bob: $1000 (June) + $100 (July) both >= $100. Carol only has June orders.

example_3.sql — Edge Case - No Qualified Customers

$ Input: Customers: [(1,'David','France'),(2,'Eve','Spain')] Product: [(1,'Pen',5),(2,'Notebook',15)] Orders: [(1,1,1,'2020-06-01',10),(2,1,2,'2020-07-01',3),(3,2,1,'2020-06-15',5),(4,2,2,'2020-08-01',10)]

› Output: []

💡 Note: David spent $50 (June) + $45 (July), both under $100. Eve has no July 2020 orders. No customers qualify.

Visualization

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Understanding the Visualization

Data Join

Combine Orders with Product prices and Customer info

Monthly Grouping

Group transactions by customer and calculate monthly totals

Threshold Filter

Keep only customers with $100+ spending in BOTH target months

Key Takeaway

🎯 Key Insight: Use conditional aggregation (CASE statements) to calculate spending for multiple months in a single query, then filter with HAVING clause for efficiency.

Time & Space Complexity

Time Complexity

⏱️

O(n)

Single pass through Orders table with efficient grouping

✓ Linear Growth

Space Complexity

O(k)

Space for grouping results where k is number of unique customers

✓ Linear Space

Constraints

1 ≤ customers.length ≤ 100
1 ≤ product.length ≤ 100
1 ≤ orders.length ≤ 1000
Product prices are between $1 and $1000
Order quantities are between 1 and 100
All dates are in format 'YYYY-MM-DD'

Asked in

a Amazon 45 G Google 38 f Meta 32 ⊞ Microsoft 28

The optimal approach uses conditional aggregation with CASE statements to calculate June and July spending in a single query. Group orders by customer and use HAVING clause to filter customers who spent $100+ in both months. This avoids multiple table scans and is much more efficient than nested subqueries.

Common Approaches

Approach	Time	Space	Notes
✓ Nested Subqueries (Brute Force)	O(n²)	O(n)	Separate subqueries for June and July spending with intersection
Conditional Aggregation (Optimal)	O(n)	O(k)	Single query with conditional SUM for both months simultaneously

Nested Subqueries (Brute Force) — Algorithm Steps

Create subquery to find customers with $100+ spending in June 2020
Create another subquery for customers with $100+ spending in July 2020
Join the results or use WHERE EXISTS to find intersection
Select customer details for matching customers

Visualization

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Step-by-Step Walkthrough

June Analysis

First subquery finds customers with $100+ spending in June 2020

July Analysis

Second subquery finds customers with $100+ spending in July 2020

Intersection

Find customers who appear in both result sets

Code -

solution.c — C

-- SQL Solution
SELECT DISTINCT c.customer_id, c.name as customer_name
FROM Customers c
WHERE c.customer_id IN (
    SELECT o.customer_id 
    FROM Orders o
    JOIN Product p ON o.product_id = p.product_id
    WHERE YEAR(o.order_date) = 2020 AND MONTH(o.order_date) = 6
    GROUP BY o.customer_id
    HAVING SUM(o.quantity * p.price) >= 100
)
AND c.customer_id IN (
    SELECT o.customer_id 
    FROM Orders o
    JOIN Product p ON o.product_id = p.product_id
    WHERE YEAR(o.order_date) = 2020 AND MONTH(o.order_date) = 7
    GROUP BY o.customer_id
    HAVING SUM(o.quantity * p.price) >= 100
);

Time & Space Complexity

Time Complexity

⏱️

O(n²)

Multiple scans of the Orders table for each month check

⚠ Quadratic Growth

Space Complexity

O(n)

Temporary storage for intermediate results from subqueries

⚡ Linearithmic Space

Constraints

1 ≤ customers.length ≤ 100
1 ≤ product.length ≤ 100
1 ≤ orders.length ≤ 1000
Product prices are between $1 and $1000
Order quantities are between 1 and 100
All dates are in format 'YYYY-MM-DD'

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Smart Actions

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Input & Output

Visualization

Time & Space Complexity

Related Problems

Constraints

Common Approaches

Nested Subqueries (Brute Force) — Algorithm Steps

Visualization

Code -

Time & Space Complexity

Constraints

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