MSDN Documentation

Cosmos DB Indexing: A Deep Dive

Understanding and optimizing indexing is crucial for achieving high performance and predictable costs in Azure Cosmos DB. This article explores the fundamental concepts, strategies, and best practices for Cosmos DB indexing.

Introduction to Cosmos DB Indexing

Azure Cosmos DB is a globally distributed, multi-model database service that offers an advanced indexing strategy. By default, Cosmos DB automatically indexes all data written to the database and makes it available for query without requiring explicit schema or index management. This automatic indexing provides a seamless developer experience, but understanding its mechanics is key to optimizing performance.

How Indexing Works in Cosmos DB

Cosmos DB uses an inverted index, similar to traditional search engines. For every item inserted, updated, or deleted, the index is updated automatically. The index maps property paths to the values of those properties within your documents. This allows for efficient querying of data based on various criteria.

The indexing policy defines how data is indexed. It includes:

  • Indexing Mode: Controls whether data is indexed automatically or only when explicitly requested.
  • Automatic: Indexes all data.
  • None: No automatic indexing. You can still use query-time indexing if needed.
  • Consistent: Indexes all data, but with a slight latency; suitable for read-heavy workloads.
  • Composite Indexes: Allows you to define indexes on multiple properties, which can significantly improve the performance of queries that filter or sort on those properties in combination.
  • Spatial Indexes: Used for geospatial queries, supporting data types like Point, Polygon, and LineString.

Indexing Policy Configuration

The indexing policy is a JSON document that defines the indexing behavior for a container. You can customize it to optimize for your specific workload. 


{
    "indexingMode": "consistent",
    "automatic": true,
    "includedPaths": [
        {
            "path": "/*"
        }
    ],
    "excludedPaths": [
        {
            "path": "/"_etag""
        }
    ],
    "compositeIndexes": [
        [
            {
                "path": "/category",
                "order": "ascending"
            },
            {
                "path": "/price",
                "order": "descending"
            }
        ]
    ],
    "spatialIndexes": [
        {
            "path": "/location",
            "type": "Point"
        }
    ]
}

Indexing Strategies for Optimal Performance

Choosing the right indexing strategy can dramatically impact query latency and cost. Consider the following:

  • Index only what you need: By default, Cosmos DB indexes everything. If you have properties that are never queried, consider excluding them from the index using excludedPaths to reduce indexing overhead and storage costs.
  • Leverage composite indexes: For queries that involve filtering or sorting on multiple properties, defining composite indexes can provide substantial performance gains. Ensure the order of properties in the composite index matches the order in your queries.
  • Use range indexes for sorting: Cosmos DB automatically creates range indexes on all properties, which are efficient for sorting operations.
  • Optimize for point reads: If you frequently query for specific documents using their ID, ensure your partition key and ID are efficiently accessible.
  • Understand indexing costs: Indexing consumes Request Units (RUs). A more comprehensive index policy will generally lead to higher RU consumption for writes.
Cosmos DB Indexing Flow Diagram

Diagram illustrating the flow of data through the Cosmos DB indexing process.

Common Pitfalls and Best Practices

  • Over-indexing: Indexing every single path can lead to unnecessary storage costs and write RU consumption.
  • Ignoring `excludedPaths`: If certain paths are never queried, explicitly exclude them.
  • Incorrect composite index order: The order of properties in a composite index matters for performance.
  • Not considering geospatial needs: If you perform location-based queries, ensure spatial indexes are configured correctly.
  • Not monitoring index usage: Use monitoring tools to identify which indexes are being used and which might be candidates for removal.

Conclusion

Cosmos DB's automatic indexing simplifies data management, but a proactive approach to understanding and configuring your indexing policy is essential for unlocking the full performance potential of your database. By strategically including or excluding paths and leveraging composite and spatial indexes, you can ensure efficient data retrieval and cost-effective operation.