Query Indexing Mistakes That Silently Kill Performance

Indexes are supposed to make queries fast. But the wrong indexing strategy can do the opposite - slowing everything down while looking totally innocent. Here are the silent killers you should watch for.

1. Indexing “Everything Just in Case”

New developers love creating indexes for every column. Feels safe, right? Not really. Each index adds write overhead. Inserts, updates, deletes - all get slower because the database has to maintain extra structures.

Rule: Only index columns that are actively used for lookups, joins, filtering, or sorting.

2. Missing Composite Indexes

A single-column index might look fine, but if your query filters by two or three fields, the database can’t magically combine them.

sql📋
SELECT * FROM orders
WHERE customer_id = 42 AND status = 'paid';

If you only indexed customer_id, you still get a slow scan. Fix: Add a composite index (customer_id, status) matching your query pattern.

3. Wrong Column Order in Composite Indexes

Composite indexes are left-to-right. If the leading column isn’t filtered in your query, that index becomes useless.

Example index:

sql📋
INDEX (created_at, status)

Query:

sql📋
SELECT * FROM orders WHERE status = 'shipped';

Here, status alone can’t use this index efficiently. Fix: Put the most selective (or most frequently filtered) column first.

4. Forgetting to Index Foreign Keys

Foreign keys get hit constantly during joins and cascading operations. Without indexes, you get nasty full-table scans in joins.

Example:

sql📋
SELECT * FROM comments WHERE post_id = 10;

If post_id isn’t indexed, you’ll feel the pain.

5. Indexing Low-Cardinality Columns

Columns like gender, is_active, or boolean flags look important. But indexing them rarely helps - values repeat too much.

An index where 90% of rows have the same value won’t filter anything efficiently.

Rule: Skip indexing columns with very low cardinality unless they’re part of a composite index.

6. Ignoring Covering Indexes

Sometimes the index contains all the columns your query needs. That means the database never touches the table - huge win.

If you do this:

sql📋
SELECT id, email FROM users WHERE email = '[email protected]';

An index on (email, id) becomes a covering index. Many teams miss this small trick.

7. Not Watching Query Plans

Developers often “think” their index is used. The database disagrees.

Run:

sql📋
EXPLAIN ANALYZE ...

If it says Seq Scan, you’re leaving performance on the table.

8. Overlooking Index Bloat

Dead tuples, outdated stats, oversized indexes - all slow down reads. PostgreSQL especially can accumulate bloat.

Fix:

• Run VACUUM / ANALYZE regularly
• Drop unused indexes
• Rebuild heavily bloated ones

9. Forgetting Partial Indexes

If you have a table where only a subset matters, partial indexes are gold.

Example:

sql📋
CREATE INDEX idx_active_users ON users (email)
WHERE deleted_at IS NULL;

Faster, smaller, smarter.

10. Using Functions Without Functional Indexes

If you filter with a function, your normal index becomes useless.

sql📋
WHERE LOWER(email) = '[email protected]'

Solution:

sql📋
CREATE INDEX idx_lower_email ON users (LOWER(email));

Final Thoughts

Query performance issues often hide in plain sight. A bad index is worse than no index - silent, expensive, and hard to spot.

Audit your indexes regularly. Check your slow query logs. Read your query plans.

Your database (and your users) will thank you.

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