Optimize Your SQL Queries with Database Indexes

If SQL queries are slowing you down, database indexes may be the answer. Indexes act as lookup shortcuts, accelerating SELECT queries and boosting performance. This article explains index types, how to create them, and tips for choosing the right one.

Index types and uses

Different indexes serve different purposes. Here’s a quick breakdown of the most useful types:

B-Tree Index: Speeds up exact match queries.

R-Tree Index: Used for geographic or spatial data.

Covering Index: Avoids reading full table data by covering query columns.

Partial Index: Covers specific parts of columns to save storage.

Fulltext Index: Enables full-text searches in text-heavy columns.

Each index type has specific use cases, so choose based on your query needs.

When and how to add an index

To improve SQL query speed, add an index when data grows beyond 100K rows or SELECT queries become sluggish. Use:

CREATE INDEX idx_name ON table_name (column_name);

Or, to modify an existing table:

ALTER TABLE table_name ADD INDEX idx_name (column_name);

For large datasets, expect slower indexing times due to disk writes and table copying.

FAQ

When should I use an index?

Use an index when you notice slower query performance or have large datasets (100K+ rows).

Which index type should I choose?

B-Tree indexes are most common. Use spatial, covering, or fulltext indexes for specific query needs.

Do indexes slow down my database?

Yes, for INSERT, UPDATE, and DELETE operations. SELECT queries are faster, though.

How can I check if my query uses an index?

Run EXPLAIN on your query to see if the index is being used.

Conclusion

Indexes are a key part of SQL optimization. By using B-Tree, Fulltext, and other index types, you can improve SELECT query speed and handle larger datasets. For an in-depth guide with examples, check out this article 10x Query Performance with a Database Index.