Best Practices for Writing Efficient SQL Queries
💡 Concept Name
Efficient SQL Query Writing – A set of coding practices aimed at optimizing SQL queries for performance, readability, and scalability.
📘 Quick Intro
Writing efficient SQL involves more than just getting the right results. It’s about reducing processing time, memory usage, and I/O load to ensure scalable and fast performance, especially with large datasets.
🧠 Analogy / Short Story
Imagine searching for a contact in your phone. If you use the search bar and type the exact name, you’ll find it quickly. But if you scroll manually through thousands of contacts, it’s inefficient. SQL works the same — smart filtering and indexing can save a lot of time.
🔧 Technical Explanation
- 🔍 Use SELECT only necessary columns instead of
SELECT *. - 🚀 Apply indexes on columns used in WHERE, JOIN, and ORDER BY clauses.
- ⚠️ Avoid functions on indexed columns in WHERE clauses (e.g.,
WHERE YEAR(Date) = 2024). - 📊 Use
EXISTSinstead ofINfor large subqueries when checking for existence. - ⏳ Minimize joins across large tables unless needed; prefer filtering early.
- 🧠 Use
WITH (NOLOCK)or equivalent cautiously to reduce locking (when safe). - 📦 Store intermediate logic in CTEs or temporary tables for complex queries.
- 🛑 Be careful with
DISTINCT,GROUP BY, andORDER BY— all are expensive operations.
💻 Real Code Example
-- Inefficient
SELECT * FROM Orders WHERE YEAR(OrderDate) = 2023;
-- Optimized
SELECT OrderID, CustomerID, OrderDate
FROM Orders
WHERE OrderDate BETWEEN '2023-01-01' AND '2023-12-31';
-- Better join with early filtering
WITH FilteredCustomers AS (
SELECT CustomerID FROM Customers WHERE Country = 'USA'
)
SELECT o.OrderID, o.OrderDate
FROM Orders o
JOIN FilteredCustomers fc ON o.CustomerID = fc.CustomerID;
❓ Interview Q&A
Q1: Why should we avoid SELECT * in production queries?
A: It fetches unnecessary data, increases memory usage, and can lead to performance degradation.
Q2: How do indexes improve query performance?
A: Indexes allow the database to quickly locate rows instead of scanning the entire table.
Q3: Why is using functions in WHERE clause problematic?
A: It disables index usage and causes full scans.
Q4: When should EXISTS be used over IN?
A: EXISTS is more efficient with large subqueries as it returns early upon finding a match.
Q5: Why is filtering early in joins recommended?
A: It reduces the number of rows being joined, lowering CPU and memory usage.
Q6: Are CTEs better than subqueries?
A: CTEs improve readability and can optimize repeated logic; performance is similar.
Q7: What’s a risk of using WITH (NOLOCK)?
A: It may lead to reading uncommitted (dirty) data; use it only when consistency isn't critical.
Q8: What tools help identify slow SQL queries?
A: EXPLAIN PLAN, Query Analyzer, SQL Profiler, and Execution Plans.
Q9: Is ORDER BY expensive?
A: Yes, especially without indexes; sorting large datasets takes time.
Q10: What’s the advantage of limiting results with TOP or LIMIT?
A: It reduces the number of rows fetched, improving performance during development or pagination.
📝 MCQs
Q1. Which clause should you avoid using on indexed columns?
- Functions like YEAR()
- DISTINCT
- COUNT(*)
- LIKE
Q2. Why is SELECT * discouraged?
- Faster
- More readable
- Fetches unnecessary columns
- Good for joins
Q3. What can help reduce rows before a JOIN?
- Nested joins
- DISTINCT
- Early filtering
- ORDER BY
Q4. What clause checks for existence efficiently?
- IN
- EXISTS
- GROUP BY
- HAVING
Q5. What happens when a WHERE clause uses a function on column?
- Faster execution
- Index is ignored
- No effect
- Better grouping
Q6. Which helps analyze slow queries?
- INSERT
- DELETE
- Execution Plan
- TOP
Q7. What SQL clause divides logic for clarity and reuse?
- JOIN
- GROUP BY
- CTE
- UNION
Q8. Which option may read dirty data?
- INNER JOIN
- TOP
- WITH (NOLOCK)
- LIMIT
Q9. How can you fetch only required columns?
- Use SELECT *
- Use joins
- Use explicit column names
- Use aliases
Q10. Why avoid ORDER BY without index?
- No rows returned
- Better performance
- It causes full sort scan
- Locks table
💡 Bonus Insight
Efficient SQL isn’t just about performance — it reduces server load, improves UX, and scales better under traffic. Test with real data sizes and leverage database stats and indexes to ensure optimal design.
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