When Should You Use Composite Indexes for Database Optimization?

When to Utilize Composite Indexes: A Comprehensive Guide

A composite index is a database index that encompasses multiple columns, offering efficient lookup performance when queries involve multiple columns. It's a powerful tool that can significantly enhance database performance, but its application should be meticulously considered to maximize its benefits.

When to Use a Composite Index

Composite indexes are ideal when:

• Join Optimization: Queries that join tables based on multiple columns can significantly benefit from a composite index that encompasses those columns.
• Range Queries: When queries involve range filtering on multiple columns, a composite index can speed up lookup operations by utilizing the B-tree data structure, which allows efficient range queries.
• Left-Most Subsets: Queries that filter or join on a subset of columns in the composite index can also leverage the existing index, speeding up the lookup process.

Performance Ramifications

Composite indexes offer significant performance advantages but also come with some trade-offs:

• Increased Index Size: Composite indexes are generally larger than single-column indexes as they store additional columns' data.
• Write Impact: Updates, inserts, or deletions on any column included in the composite index will trigger index updates, potentially impacting write performance.

Example Use Case

In your example, where you have a homes table with columns such as geolat, geolng, sqft, and year_built, a composite index on (geolat, geolng) will be beneficial if you frequently execute queries that filter or join based on both location coordinates.

• Query with Composite Index:

  SELECT * FROM homes
  WHERE geolat BETWEEN ... AND ...
  AND geolng BETWEEN ... AND ...

This query will leverage the composite index (geolat, geolng) to efficiently filter based on both location coordinates, resulting in faster execution.

• Query without Composite Index:

  SELECT * FROM homes
  WHERE geolat = ... AND geolng = ...

Without the composite index, the database must perform a full table scan to retrieve the matching records, leading to slower execution.

Additional Considerations

In your updated scenario, with the additional schema and query details, the composite index on (geolat, geolng) is still recommended. The EXPLAIN output indicates that the query is utilizing the existing single-column indexes on geolat and geolng but is not combining them for range filtering. A composite index on both columns would combine them and potentially further optimize the query performance.

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