Choose an appropriate index type

When designing and implementing native applications using Microsoft Azure Cosmos DB, choosing an appropriate index type is key to achieving efficient and performant queries on your data. Cosmos DB supports various types of indexes that cater to different query patterns and use cases. In this article, we will explore these index types and understand how they can be used effectively.

1. Range Indexing:

Range indexes are the default index type in Cosmos DB and are suitable for most scenarios. They enable efficient querying of data based on a range of values. For example, if you have a collection of products with a price attribute, a range index on the price field allows you to quickly retrieve all products within a specific price range.

To create a range index, you can set the “IndexingPolicy” property of your collection to include the desired paths. Alternatively, you can use the Azure portal or Azure CLI to add range indexes to specific paths within your documents.

{
"indexingMode": "consistent",
"includedPaths": [
{
"path": "/price/?",
"indexes": [
{
"kind": "Range",
"dataType": "Number",
"precision": -1
}
]
}
]
}

2. Hash Indexing:

Hash indexes are useful when you need to perform equality-based queries on a specific field. They provide efficient lookups for exact matches. For example, if you have a collection of user profiles and want to query users by their email address, a hash index on the email field will optimize these queries.

Similar to range indexes, you can configure hash indexes at the collection level or for specific paths within your documents. Here’s an example of creating a hash index using the Azure portal:

{
"indexingMode": "consistent",
"includedPaths": [
{
"path": "/email/?",
"indexes": [
{
"kind": "Hash",
"dataType": "String",
"precision": 1
}
]
}
]
}

3. Spatial Indexing:

Spatial indexes are designed for geospatial data and enable efficient queries based on geographic proximity and geometric shape. Cosmos DB supports spatial indexes for geometries like points, lines, and polygons. By creating a spatial index, you can easily find locations within a specific distance or perform spatial operations like intersection and containment.

To enable spatial indexing, you need to specify the spatial type when creating the index:

{
"kind": "Spatial",
"dataType": "Point"
}

In addition to these index types, Cosmos DB also provides composite indexes that combine multiple fields and spatial composite indexes for spatial data with multiple attributes.

It’s important to note that each additional index has storage and performance implications. Indexes can increase the amount of storage needed for your data and may impact write performance due to index maintenance. Therefore, it’s recommended to carefully consider the query patterns of your application and choose only the necessary indexes to strike a balance between performance and cost.

In summary, when designing and implementing native applications using Azure Cosmos DB, assessing the query patterns and choosing an appropriate index type is crucial. Whether it’s range indexing, hash indexing, or spatial indexing, Cosmos DB offers a variety of index types to optimize your data access. By leveraging the right index type, you can ensure efficient and responsive queries for your application’s needs.