What does “Cosmos DB automatically indexes the documents” mean? Explain.

When it is mentioned that “Cosmos DB automatically indexes the documents,” it means that Cosmos DB automatically creates indexes to optimize the querying and retrieval of data from the documents stored in a container.

Here’s what it entails:

  1. Default Indexing: By default, Cosmos DB automatically indexes all properties within a document. This includes both string and numeric properties. This indexing allows efficient filtering, sorting, and querying of data based on these properties.
  2. Range Indexing: For properties with string or numeric data types, Cosmos DB creates range indexes. These indexes enable efficient querying using equality operators, range comparisons, sorting, and filtering based on these properties.
  3. Spatial Indexing: If a document contains spatial data types, such as points, polygons, or lines, Cosmos DB automatically creates spatial indexes. These indexes enable spatial queries, allowing efficient geospatial operations like distance calculations, intersection checks, and nearest neighbor queries.
  4. Self-Contained Indexes: The indexes created by Cosmos DB are self-contained within the database and are stored alongside the data. This ensures that the indexes are always available and consistent with the stored documents.
  5. Customization Options: While Cosmos DB automatically indexes all properties, you have control over indexing behavior. You can customize indexing policies by configuring composite indexes, specifying included paths, or defining index options based on your application’s specific requirements and query patterns.

By automatically indexing documents, Cosmos DB reduces the need for manual index management and allows for efficient querying and retrieval of data. It simplifies the development process by providing out-of-the-box index creation, ensuring that commonly accessed properties are indexed for optimal performance. However, it’s important to carefully consider index design and configuration based on the data model and query requirements to achieve the best performance and cost efficiency.

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