?? Mastering SQL Problem-Solving Skills! ??

In a recent deep-dive into SQL optimization, I encountered a classic scenario: retrieving parent-child relationships from a dataset from Ankit Bansal 's Youtube Video:

Data Analyst SQL Interview Question | Normal vs Mentos vs Mentos PRO Life

After exploring multiple approaches including mine, I realized that understanding patterns and query optimization techniques are key to becoming an effective problem solver. ??

When faced with SQL challenges, always aim to:

1. Recognize the pattern (e.g., parent-child, ranking, conditional data).
2. Start with the end goal in mind, reverse-engineering the query for simplicity.
3. Use window functions (ROW_NUMBER(), RANK()) and conditional aggregation (MAX(), MIN()) for efficient solutions.

Why I am writing this article again is to help you how you can write queries like a pro as mentioned in Ankit Bansal 's sir video , as he calls it a "Mentos Pro Life".

Disclamer: I am not going to share the solution of the video as you can directly watch from the link shared above.

Let's start with brief on how to achieve the problem solving skills, so that you can write queries like a pro.

To enhance your problem-solving skills and instinctively reach the most optimized solution, like approach 3 in the SQL example, here’s how you can improve and some strategies to consider:

1. Pattern Recognition:

Train yourself to recognize patterns in SQL problems. The third solution works because it uses conditional logic combined with aggregation functions for optimization. Look for these types of repetitive problem structures:

• Parent-child relationships (like family trees)
• Ranking and categorization problems (like finding highest/lowest)
• Filling in missing data (like the couples problem)

Example: For any problem involving hierarchical relationships (e.g., employees and managers, or parents and children), think of window functions, ROW_NUMBER(), or conditional aggregation with MAX() or MIN().

2. Efficient Query Design:

The third approach uses simpler logic, minimizing joins and extra operations. Develop the habit of reducing complexity by:

• Avoiding nested CTEs or overly complex joins when simple CASE statements suffice.
• Using window functions to reduce the need for additional groupings or subqueries.

Example: When working with queries that require ranking or selecting based on conditions (like choosing the highest and lowest salaries), always think of ROW_NUMBER() or RANK() instead of more convoluted subqueries.

3. Analyze from the End Goal:

Before writing a query, define the exact output structure you need. Reverse engineer the query from there to focus only on what’s necessary. This will help you arrive at optimized solutions faster.

Example: If you need the names of parents and children in a specific format, ask yourself how you can get the relationships in the most straightforward way, avoiding excessive data manipulation.

4. Use CASE and Window Functions:

Whenever you encounter problems that involve comparing rows or conditions within the same column, leverage CASE with aggregation functions (MAX, MIN) or window functions. These can often collapse multiple operations into a single, efficient query.

Example: If the task involves filtering based on conditions (like finding a father and mother from a list of parents), use CASE combined with MAX() to reduce the number of joins or operations.

5. Practice Similar Scenarios:

By practicing more real-world scenarios like:

• Top-N Queries: Who are the top employees by performance?
• Conditional Aggregation: How to assign ranks or fill in missing data based on the value of other columns?

By repeatedly solving such problems and recognizing the efficient SQL patterns, you’ll develop a mental framework to quickly arrive at the best solution.

The beauty of SQL lies in transforming complex logic into simple and clean queries.

?? Have you faced similar challenges? How do you approach SQL problems to drive optimization?

Let’s keep learning and growing in the world of data! ??