Top 20 Azure Cosmos DB Advanced Optimization Techniques

1. Strategic Partitioning Key Selection

Choosing the right partition key is paramount. It should be a property that is frequently used in your queries and has a high cardinality to distribute data and request units (RUs) evenly across partitions. Avoid hot partitions caused by uneven data distribution or highly frequent queries targeting a single partition key value.

// Example partition key: customerId for order data
const query = "SELECT * FROM c WHERE c.customerId = 'user123'";

Ensuring even data and RU distribution.

2. Optimized Indexing Policies

Customize your indexing policy to include only the paths you frequently query. Excluding unused paths can reduce storage costs and improve write performance. Understand composite indexes for efficient sorting and filtering on multiple properties, and spatial indexes for geospatial queries.

{
    "indexingMode": "consistent",
    "includedPaths": [
        {
            "path": "/customerId/?",
            "indexes": [
                {
                    "kind": "Range",
                    "dataType": "String"
                }
            ]
        },
        {
            "path": "/orderDate/?",
            "indexes": [
                {
                    "kind": "Range",
                    "dataType": "Number"
                }
            ]
        },
        {
            "path": "/totalAmount/?",
            "indexes": [
                {
                    "kind": "Range",
                    "dataType": "Number"
                }
            ]
        },
        {
            "path": "/location/*",
            "indexes": [
                {
                    "kind": "Spatial"
                }
            ]
        }
    ],
    "excludedPaths": [
        {
            "path": "/description/*"
        }
    ]
}
        

Tailoring indexing for query needs.

3. Writing Efficient Queries

Structure your queries to be partition-aware by including the partition key in the WHERE clause. Avoid cross-partition queries when possible, as they can be more expensive in terms of RUs. Use the IN operator with caution on non-partition keys. Leverage functions efficiently and understand their RU cost.

-- Partition-aware query (efficient)
SELECT * FROM c WHERE c.customerId = 'user123' AND c.orderDate >= '2024-01-01';

-- Cross-partition query (less efficient)
SELECT * FROM c WHERE c.productName = 'widget';

Crafting queries for optimal performance and cost.

4. Utilizing Projection to Reduce RU Consumption

Only select the properties you need in your queries using the SELECT clause. Reducing the size of the returned documents directly lowers the RU cost of the operation.

SELECT c.id, c.orderDate, c.totalAmount FROM c WHERE c.customerId = 'user123';

Minimizing the data transferred and RU cost.

5. Understanding and Managing RU/s Provisioning

Provision the appropriate amount of Request Units per second (RU/s) for your workload. Monitor your RU consumption and scale up or down as needed. Consider using autoscale to automatically adjust RUs based on traffic patterns. Understand the RU cost of different operations and optimize accordingly.

Optimizing throughput and cost with RU management.

6. Leveraging Change Feed for Incremental Processing

Use the Change Feed feature to efficiently process new or modified data. This avoids expensive full scans of your collections for tasks like real-time analytics or triggering downstream processes.

Efficiently processing new and updated data.

7. Optimistic Concurrency Control

Implement optimistic concurrency control using the _etag system property to prevent “lost updates” when multiple clients are updating the same document concurrently. This ensures data integrity without the performance overhead of pessimistic locking.

// Example update with optimistic concurrency control
const updateItem = async (client, databaseId, containerId, itemId, item, etag) => {
    await client
        .database(databaseId)
        .container(containerId)
        .item(itemId, undefined)
        .replace(item, { ifMatch: etag });
};

Ensuring data integrity with minimal performance impact.

8. Efficient Bulk Operations

Use the bulk execution capabilities of the Cosmos DB SDK to perform multiple operations (create, upsert, read, replace, delete) in a single request. This reduces network round trips and improves throughput for high-volume operations.

const itemsToCreate = [/* ... */];
const container = client.database(databaseId).container(containerId);
const bulkOperations = itemsToCreate.map(item => ({
    operationType: "Create",
    resourceBody: item
}));
const bulkResponse = await container.items.bulk(bulkOperations);

Optimizing throughput for multiple operations.

9. Connection Policy Configuration

Configure the connection policy in your Cosmos DB SDK to optimize network latency and throughput. Adjust settings like maxRequestsPerEndpoint, idleHttpAgentTimeoutMs, and preferredLocations based on your application’s access patterns and network topology.

const CosmosClient = require("@azure/cosmos").CosmosClient;
const client = new CosmosClient({
    endpoint: endpoint,
    key: key,
    connectionPolicy: {
        maxRequestsPerEndpoint: 16,
        preferredLocations: [ "East US 2", "West US 2" ]
    }
});

Fine-tuning network connection settings.

10. Handling Transient Errors with Retries

Implement robust retry policies in your application to handle transient errors (e.g., throttling). The Cosmos DB SDK provides built-in retry mechanisms, but you might need to customize them based on your application’s resilience requirements.

// Example retry logic (simplified)
async function executeWithRetries(operation, maxRetries = 3, delayMs = 1000) {
    for (let i = 0; i  setTimeout(resolve, delayMs * (i + 1)));
            } else {
                throw error;
            }
        }
    }
}

Improving application resilience and handling throttling.

11. Efficient Pagination for Large Result Sets

Implement proper pagination using continuation tokens when querying large result sets. Avoid fetching all documents at once, which can consume significant RUs and memory. Use the continuationToken from the previous response to retrieve the next page of results.

let continuationToken;
do {
    const { resources, headers } = await container.items
        .query("SELECT * FROM c WHERE c.customerId = 'user123'", { continuationToken, maxItemCount: 100 })
        .fetchAll();
    console.log(resources);
    continuationToken = headers["x-ms-continuation"];
} while (continuationToken);

Handling large datasets efficiently.

12. Optimizing Writes to Frequently Updated Properties

If you have properties that are updated very frequently, consider isolating them into a separate document (if your data model allows) to reduce the RU cost of updates on the main document, especially if other parts of the document are large and rarely changed.

Reducing RU cost for frequent updates.

13. Utilizing Stored Procedures, Triggers, and User-Defined Functions (UDFs)

For server-side logic and transactional operations, leverage stored procedures, triggers, and UDFs. These can execute closer to the data, reducing network latency and potentially RU consumption for complex operations.

// Example stored procedure
function updateOrderStatus(orderId, newStatus) {
    const collection = getContext().getCollection();
    const querySpec = {
        query: "SELECT * FROM c WHERE c.id = @orderId",
        parameters: [{ name: "@orderId", value: orderId }]
    };
    collection.queryDocuments(querySpec, (err, results) => {
        if (err || !results || results.length === 0) {
            getContext().getResponse().setBody('Error finding order');
        } else {
            const order = results[0];
            order.status = newStatus;
            collection.replaceDocument(order._self, order, (err, updatedOrder) => {
                getContext().getResponse().setBody(updatedOrder);
            });
        }
    });
}

Executing logic closer to the data.

14. Monitoring and Alerting on Key Metrics

Set up comprehensive monitoring and alerting for key Cosmos DB metrics like RU consumption, latency, error rates, and storage utilization. Azure Monitor provides insights and allows you to create alerts for proactive issue detection.

Proactive performance management.

15. Choosing the Right Consistency Level

Select the appropriate consistency level (Strong, Bounded Staleness, Session, Consistent Prefix, Eventual) based on your application’s data consistency requirements and latency tolerance. Strong consistency offers the highest consistency but with potential higher latency and RU cost compared to weaker consistency levels.

Balancing consistency, latency, and cost.

16. Optimizing Large Document Reads and Writes

For very large documents, consider strategies like breaking them down into smaller related documents or using the Azure Blob Storage for storing large binary data and referencing it in your Cosmos DB documents.

Handling large data efficiently.

17. Utilizing the Cosmos DB Emulator for Local Development

Use the Azure Cosmos DB Emulator for local development and testing. This allows you to develop and optimize your application without incurring costs or impacting production environments.

Cost-effective local development and testing.

18. Understanding SDK Performance Considerations

Be aware of performance considerations specific to the Cosmos DB SDK you are using (e.g., Node.js, .NET, Java). Follow best practices recommended by Microsoft for your chosen SDK.

SDK-specific optimization strategies.

19. Regularly Reviewing and Tuning Your Configuration

Periodically review your Cosmos DB configuration, including indexing policies, consistency level, and RU provisioning, as your application’s data and access patterns evolve. Tune these settings to maintain optimal performance and cost-efficiency.

Continuous optimization and adaptation.

20. Leveraging Azure Cache for Cosmos DB (Preview)

Consider using Azure Cache for Cosmos DB (currently in preview) to significantly reduce read latency and RU consumption for frequently accessed data. This fully managed, in-memory cache integrates seamlessly with your Cosmos DB accounts.

In-memory caching for ultra-low latency reads.