Why Your Database is Slow: A Developer’s Guide to SQL Indexing

In development, your database has 10 rows. Everything is instant. In production, your database has 10 million rows. Suddenly, a simple user lookup takes 5 seconds and crashes the server. The culprit is almost always a missing index.

The Phonebook Analogy

Imagine a phonebook that isn't alphabetized. If I ask you to find "John Smith," you have to start at page 1 and read every single name until you find him. In database terms, this is a Full Table Scan. It is O(n) complexity, which is a disaster for performance.

An Index is like alphabetizing the book. Now you can jump to "S", then "Sm", and find the name in logarithmic time O(log n). The difference between scanning 10 million rows and doing 3 binary search hops is milliseconds vs. minutes.

The "EXPLAIN" Command

Before you rewrite your backend code, open your SQL console and prepend the word EXPLAIN to your query. The database will tell you exactly how it plans to execute the search.

If you see type: ALL (MySQL) or Seq Scan (Postgres), you are in trouble. You want to see ref or Index Scan.

Common Anti-Patterns

• Selecting * (Everything): Don't fetch columns you don't need. It bloats memory usage and network transfer.
• Functions on Columns: Querying WHERE YEAR(date_column) = 2024 kills the index because the database has to run the function on every row. Instead, use a range: WHERE date_column BETWEEN '2024-01-01' AND '2024-12-31'.
• Wildcards at the Start: LIKE '%term' cannot use an index. LIKE 'term%' can.

Formatting Matters

Complex queries are hard to debug. A messy 50-line JOIN statement hides performance bugs. Use a SQL Formatter to structure your queries. Readable code is optimized code. Seeing the structure clearly helps you identify where subqueries can be turned into joins, or where indexes are missing.