Indexed locations refer to data storage positions explicitly cataloged within an auxiliary data structure (an index) for rapid retrieval. This index acts like a roadmap, enabling the system to find specific data items quickly without scanning every possible location, typically using keys like IDs or timestamps. Non-indexed locations, conversely, are data storage positions not referenced by such an index. To find data in a non-indexed location, the system must perform a sequential scan, examining each item one by one until the target is found, which is inherently slower for large datasets.

For example, in a database table, a column defined as a "PRIMARY KEY" creates an indexed location, allowing near-instantaneous lookups of a customer record by their unique ID. In contrast, searching for that same customer by their non-indexed "Comments" field would require scanning every row. Similarly, cloud storage buckets often offer indexed search for files with predefined metadata tags (like 'invoice' or '2024'), while finding files without assigned tags involves browsing folders manually - effectively a non-indexed search process.

The primary advantage of indexed locations is significantly faster query performance, especially critical for large datasets in databases or search engines. However, indexes require additional storage space and incur maintenance overhead during data writes (inserts, updates, deletes). Non-indexed locations save storage and have lower write overhead but result in slow searches. The trade-off between search speed and write performance/storage cost dictates their use; indexing is applied strategically to frequently searched fields, while less critical data often remains non-indexed.