Azure Cosmos DB Indexing: A Comprehensive Guide

This guide provides an in-depth look at indexing in Azure Cosmos DB, a critical component for optimizing query performance and managing throughput.

Introduction to Indexing
Understanding the Indexing Policy
Indexing Modes: Consistent vs. Lazy
Indexing Paths: Includes vs. Excludes
Composite Indexes
Spatial Indexes
Performance Considerations
Best Practices
Conclusion

Introduction to Indexing

Azure Cosmos DB is a globally distributed, multi-model database service that supports various APIs. To achieve low latency and high throughput for your applications, effective indexing is paramount. Cosmos DB automatically indexes data as it's ingested, providing a powerful query engine that eliminates the need for explicit index management in many scenarios.

Understanding the Indexing Policy

The indexing policy in Cosmos DB defines how your data is indexed. It's a JSON document that specifies which paths within your documents should be indexed, the indexing mode, and the data types to be indexed. You can define a default indexing policy or a custom one for specific containers.

Here's an example of a default indexing policy:

                
{
    "indexingMode": "consistent",
    "automatic": true,
    "includedPaths": [
        {
            "path": "/*"
        }
    ],
    "excludedPaths": [
        {
            "path": "/"_etag""
        }
    ]
}
                
            

Indexing Modes: Consistent vs. Lazy

Cosmos DB offers two primary indexing modes:

Consistent: The default mode. Indexes are updated synchronously with data operations. This ensures that queries always reflect the most recent data but incurs a slight performance overhead.
Lazy: Indexes are updated asynchronously. This can improve the performance of write operations, but queries might not immediately reflect recent changes. This mode is suitable for scenarios where eventual consistency is acceptable for read operations.

Consistent Mode Details

When using consistent indexing, every document write (create, update, delete) triggers an index update. This is the most common mode and provides the best query experience without needing to worry about stale data.

Lazy Mode Details

Lazy indexing is beneficial when you have a high volume of writes and can tolerate a brief period of eventual consistency for queries. The index is typically updated within a few seconds of the data operation.

Indexing Paths: Includes vs. Excludes

The includedPaths and excludedPaths arrays within the indexing policy control which parts of your documents are indexed.

Included Paths: Specifies the paths that should be indexed. Using /* indexes all paths within a document. You can also specify specific paths like /product/name to index only the 'name' field under the 'product' object.
Excluded Paths: Specifies paths that should not be indexed. This is useful for avoiding indexing large or rarely queried fields, which can reduce storage costs and improve write performance. The /_etag path is often excluded as it's typically not used in queries.

Example of excluding a specific path:

                
{
    "indexingMode": "consistent",
    "automatic": true,
    "includedPaths": [
        {
            "path": "/*"
        }
    ],
    "excludedPaths": [
        {
            "path": "/description"
        }
    ]
}
                
            

Composite Indexes

Composite indexes allow you to create indexes on multiple fields within a single document. These are essential for efficiently querying based on multiple criteria in a single SQL or NoSQL query.

Composite indexes are defined in the indexing policy as an array of paths. The order of paths in a composite index matters. For example, an index on /category and /price would be defined as:

                
{
    "path": "/category",
    "order": "ascending"
},
{
    "path": "/price",
    "order": "descending"
}
                
            

Spatial Indexes

For geospatial queries, Azure Cosmos DB supports spatial data types and indexing. By indexing a property that contains GeoJSON data, you can perform efficient spatial queries like finding points within a given polygon or calculating distances.

To enable spatial indexing, ensure your path definition includes the appropriate data type for your GeoJSON data.

Performance Considerations

Indexing significantly impacts query performance and Request Units (RUs) consumed by your operations. However, it also adds overhead to write operations and consumes storage.

Query Optimization: Well-defined indexes can drastically reduce query latency by allowing Cosmos DB to find relevant data without performing full scans.
Write Overhead: Each indexed field adds overhead to write operations. Over-indexing can slow down writes.
Storage Costs: Indexes consume storage space. Excluding unnecessary paths can help manage storage costs.
RU Consumption: Queries that leverage indexes consume fewer RUs than those that don't.

Best Practices

Index what you query: Only index fields that are frequently used in your query predicates (WHERE clauses) and ORDER BY clauses.
Avoid broad indexing (/*) if possible: While convenient, it can lead to over-indexing. If you know your schema, be more specific.
Exclude unnecessary fields: Use excludedPaths for large or rarely queried fields like /description or /large_binary_data.
Understand composite index ordering: The order of fields in a composite index is crucial for query efficiency.
Monitor RU consumption: Use the Azure portal or SDKs to monitor RU consumption for your queries and adjust indexing policies accordingly.
Consider a separate index for specific query patterns: Sometimes, defining specific included paths for common query patterns can be more efficient than a broad /*.

Conclusion

Effective indexing in Azure Cosmos DB is a balance between query performance, write latency, and storage costs. By understanding the indexing policy, modes, and path definitions, you can tune your Cosmos DB solution to meet the demands of your application. Regularly review your indexing strategy as your application evolves to ensure optimal performance and cost-efficiency.

Last updated: October 26, 2023