Common Mistakes to Avoid in SQL Queries
💡 Concept Name
SQL Query Mistakes refer to frequent errors that degrade performance, lead to incorrect results, or make code harder to maintain.
📘 Quick Intro
Common SQL pitfalls include using SELECT *, missing JOIN conditions, misusing NULL checks, and ignoring indexing. Even small mistakes can drastically slow queries or produce wrong results, especially with large datasets.
🧠 Analogy / Short Story
Writing bad SQL is like cooking without checking the recipe—you might get something edible, but it's likely inefficient, messy, or flat-out wrong. Precise ingredients (columns), careful steps (joins), and attention to detail (filter logic) all matter for a successful "data dish."
🔧 Technical Explanation
- 🚫 Using
SELECT *can slow queries and return unnecessary data. - 🧩 Missing
JOINconditions cause Cartesian products and inflated results. - 🔍 Misusing
WHERE col = NULLinstead ofIS NULLresults in no matches. - 📦 Using
GROUP BYwithout aggregation causes logical confusion. - 📉 Ignoring indexes or filtering on unindexed columns leads to performance hits.
- 🧨 Forgetting to limit rows with
LIMIT/TOPin test queries can overload dev databases.
🎯 Purpose & Use Case
- ✅ Improve query performance in large production databases.
- ✅ Ensure data accuracy by using correct logic and filters.
- ✅ Reduce server load and avoid accidental full table scans.
- ✅ Maintain readable and maintainable SQL code.
💻 Real Code Example
-- ❌ BAD: SELECT *
SELECT * FROM Orders;
-- ✅ GOOD: Select only needed columns
SELECT OrderID, CustomerID, OrderDate FROM Orders;
-- ❌ BAD: NULL comparison
SELECT * FROM Users WHERE Age = NULL;
-- ✅ GOOD: Use IS NULL
SELECT * FROM Users WHERE Age IS NULL;
-- ❌ BAD: Missing JOIN condition
SELECT * FROM Products, Categories;
-- ✅ GOOD: Proper JOIN
SELECT p.ProductName, c.CategoryName
FROM Products p
JOIN Categories c ON p.CategoryID = c.CategoryID;
❓ Interview Q&A
Q1: Why is using SELECT * discouraged?
A: It fetches unnecessary columns, increasing data transfer and reducing readability.
Q2: What happens when a JOIN lacks an ON condition?
A: It performs a Cartesian product, multiplying row counts unexpectedly.
Q3: What’s wrong with WHERE col = NULL?
A: NULL can’t be compared using =. Use IS NULL instead.
Q4: Why should indexes be considered when filtering?
A: Without indexes, filters cause full scans and poor performance.
Q5: When is GROUP BY a mistake?
A: When used without aggregation, it may return confusing or unintended results.
Q6: How do too many subqueries hurt performance?
A: Deeply nested subqueries can increase complexity and reduce optimizer efficiency.
Q7: What’s a safer alternative to SELECT * in dynamic queries?
A: Explicitly specify only required columns.
Q8: How does NULL affect joins?
A: NULLs can cause joins to miss matches unless handled carefully.
Q9: Should ORDER BY always be used with LIMIT?
A: Yes, to ensure consistent and predictable results.
Q10: What’s a common mistake in DELETE statements?
A: Forgetting the WHERE clause and deleting all rows unintentionally.
📝 MCQs
Q1. Which is a common SQL performance mistake?
- Using INNER JOIN
- Using SELECT *
- Using aliases
- Using parameters
Q2. What does missing a JOIN condition lead to?
- NULL result
- Syntax error
- Cartesian product
- No output
Q3. Which clause checks for NULL properly?
- = NULL
- IS NULL
- NULL = TRUE
- ISNOT NULL
Q4. GROUP BY should be used with...
- LIMIT
- WHERE
- SELECT *
- Aggregate functions
Q5. What can full table scans indicate?
- Proper joins
- Filtered rows
- Missing indexes
- ORDER BY usage
Q6. Which clause limits returned rows?
- OFFSET
- GROUP BY
- LIMIT
- HAVING
Q7. Which JOIN type requires a condition?
- CROSS JOIN
- INNER JOIN
- NATURAL JOIN
- SELF JOIN
Q8. What's the risk of DELETE without WHERE?
- Syntax error
- One row deleted
- No effect
- All rows deleted
Q9. What does SELECT DISTINCT do?
- Sorts data
- Removes duplicate rows
- Renames column
- Groups rows
Q10. Which tool shows query performance?
- CHECK PLAN
- EXPLAIN PLAN
- VIEW DESIGN
- DATA TRACE
💡 Bonus Insight
Use EXPLAIN to analyze performance. Avoid using functions on indexed columns in WHERE clauses (e.g., WHERE YEAR(date)). For large datasets, filter early, join wisely, and avoid row-by-row subqueries. Consistent formatting improves readability and debugging.
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