SQL query performance can suffer as datasets grow. The solution? Database indexes. By organizing data for fast lookups, indexes improve query speed significantly. In this guide, we’ll cover key index types, how to use them, and when to add them for the best results.
Types of database indexes
Here’s a breakdown of the most useful index types and their functions:
B-Tree Index: Best for standard SELECT queries.
R-Tree Index: Used for spatial (geographic) data.
Covering Index: Contains all columns needed for a query, avoiding full table reads.
Partial Index: Covers specific parts of a column to save space.
Fulltext Index: For full-text search in text-heavy columns.
These index types have unique uses, so pick the one that fits your needs.
When and how to add an index
The ideal time to create an index is when SELECT queries slow down or the row count surpasses 100K. To create an index, use:
CREATE INDEX idx_name ON table_name (column_name);
For existing tables, modify them with:
ALTER TABLE table_name ADD INDEX idx_name (column_name);
Be aware that indexing large tables takes time, as the table must be copied and updated.
FAQ
When should I create an index?
Create an index when SELECT queries slow down or your dataset grows beyond 100K rows.
What type of index should I use?
B-Tree indexes are most common. Use fulltext, spatial, or covering indexes for specific cases.
Will indexes slow down database performance?
Yes, for INSERT, UPDATE, and DELETE queries. SELECT queries run faster.
How do I know if my query uses an index?
Use the EXPLAIN command to check if an index is used in your query.
Conclusion
Database indexes are crucial for faster SQL queries. B-Tree, Fulltext, and other index types speed up SELECT queries and reduce query times. For an in-depth explanation and practical examples, check out this article 10x Query Performance with a Database Index.
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