🧠 Introduction:

If your SQL queries “feel tired,” take forever to run, or make your server sound like it's preparing for takeoff…
don’t worry.

Almost everyone writes slow SQL at the beginning.

The truth is:

SQL performance problems rarely come from complicated bugs.
They come from simple mistakes repeated many times.

In this tutorial, we'll walk through the Top 10 SQL mistakes that beginners (and sometimes professionals) make — explained gently, clearly, and with easy examples.

Let’s make your queries faster and your database happier.

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✅ Mistake #1 — Using SELECT * Everywhere

This is the classic rookie mistake.

Beginners love writing:

SELECT * FROM users;

But this tells the database:
“Give me everything!”

Even if you only need two columns.

Why this is slow:

• More data must be read

• More data must be transferred

• Indexes can't be used efficiently

Fix:

SELECT name, email FROM users;

Small request → fast result.

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✅ Mistake #2 — No Index on WHERE Columns

Consider this query:

SELECT * FROM orders WHERE user_id = 12;

If user_id is not indexed, MySQL will scan the entire table.

Fix:

CREATE INDEX idx_user_id ON orders(user_id);

Indexes are your best friends.
They are like a book’s table of contents — fast, organized, and efficient.

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✅ Mistake #3 — Joining Tables Without Indexes

Joins can turn into disasters if you don’t index the join columns.

Example of a slow join:

SELECT *
FROM orders
JOIN users ON orders.user_id = users.id;

If orders.user_id and users.id lack indexes → boom → slow performance.

Fix:

CREATE INDEX idx_orders_user_id ON orders(user_id);
CREATE INDEX idx_users_id ON users(id);

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✅ Mistake #4 — Doing Too Much Work in One Query

Sometimes beginners write one massive SQL query with:

• nested subqueries

• multiple joins

• unnecessary calculations

• extra filters

Will it work? Yes.
Will it be slow? Also yes.

Fix:

Break big queries into smaller steps.

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✅ Mistake #5 — Returning Thousands of Rows at Once

Never do this:

SELECT * FROM logs;

That is how laptops overheat.

Fix:

Use LIMIT:

SELECT * FROM logs ORDER BY id DESC LIMIT 100;

Show only what’s necessary.

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✅ Mistake #6 — Using Wrong Data Types

Using VARCHAR(255) for everything is popular among beginners.

But it wastes memory and slows queries.

Example:

• Use INT for numeric IDs

• Use TINYINT for boolean

• Use DATETIME for timestamps

Optimized data types = faster rows, faster indexes.

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✅ Mistake #7 — Not Using EXPLAIN to Debug

Beginners often guess why a query is slow.

Databases don’t like guesswork.

Use:

EXPLAIN SELECT * FROM orders WHERE user_id = 10;

If you see:

type: ALL
rows: 500000

This means your table is being fully scanned → needs an index.

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✅ Mistake #8 — Storing Too Much Data in One Table

Huge tables (millions of rows) make everything slow, even with indexes.

Fix:

• Archive older records

• Purge unused logs

• Create partitioned tables

A lighter table is always faster.

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✅ Mistake #9 — Using Functions in WHERE Conditions

This makes indexes useless:

SELECT * FROM users WHERE LOWER(email) = LOWER('test@example.com');

Or:

WHERE DATE(created_at) = '2025-01-01'

These disable index usage.

Fix:

Transform your value, not the column:

WHERE email = 'test@example.com'

Or:

WHERE created_at >= '2025-01-01'
AND   created_at < '2025-01-02'

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✅ Mistake #10 — No Caching for Repeated Queries

If your homepage always loads the same data, don't hit the database every time.

Use:

• Redis

• Memcached

• Application-level cache

Caching = instant performance boost.

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🎯 Final Thoughts

Fixing slow SQL doesn’t require being a database expert.

You just need to avoid the common pitfalls:

• Don’t ask for too much

• Use indexes wisely

• Break large work into smaller parts

• Maintain your tables

• Use the tools SQL already provides (EXPLAIN)

With these corrections, your queries can go from 10 seconds → 0.1 seconds.