Indexes play a pivotal role in enhancing query performance, particularly in speeding up data retrieval operations. The following table compares an attribute, full-text, and spatial index:
| Index type | Description | Preferred field data types | Use case | Advantages | Disadvantages |
|---|---|---|---|---|---|
An attribute index is a data structure that speeds up data retrieval by quickly locating records based on attribute values. | Common data types for attribute fields include the following:
| Attribute indexes allow for fast record retrieval, avoiding full dataset scans, and are useful for optimizing queries and enhancing filtering in data analysis. For example, you can filter global earthquake data to show only those from the past five years with a magnitude of 6 or higher. | Attribute indexes significantly improve query performance and data retrieval speed, especially in large datasets. | Attribute indexes require additional storage space and can slow down data modifications because the indexes need to be updated whenever significant changes are made to the underlying data. | |
A full-text index is a specialized index that enables efficient searching within natural-language text data by indexing individual words and phrases. | Common text-based attribute field data types include the following:
| Full-text indexes allow for quick and efficient searches by breaking down text into searchable tokens. They are particularly useful for tasks such as analyzing crime reports for keywords (for example, burglary, assault) to help law enforcement identify trends and hot spots. | Full-text indexes improve both the speed and accuracy of searches and support complex queries, including phrase searches. | Full-text indexes require specific database configurations, may increase storage needs, and can require additional processing time to maintain as data changes. | |
In ArcGIS Pro, a spatial index is a grid-based system that spans the extent of features in a feature class, similar to a locator grid on a road map, and improves the performance of spatial queries, such as identifying, selecting, panning, and zooming in on features. | Common spatial data types include the following:
| Spatial indexes improve the performance of spatial queries. For example, if you have thousands of city park locations, a spatial index can speed up tasks such as finding parks in a specific area, selecting parks by drawing a box, or quickly panning and zooming to parks on the map. | Spatial indexes are automatically created and maintained, which improves query performance and saves users time by eliminating the need for manual index management. | Spatial indexes can consume additional storage space, which might be significant for large datasets. |
Learn more about attribute, full-text, and spatial indexes.