Azure Cosmos DB Documentation

Optimizing Azure Cosmos DB Performance

This document provides a comprehensive guide to understanding and optimizing the performance of your Azure Cosmos DB solutions. Effective performance tuning can lead to lower latency, higher throughput, and reduced costs.

1. Understand Throughput (RU/s)

Azure Cosmos DB uses Request Units (RUs) as a measure of database throughput. Each operation (read, write, query) consumes a certain number of RUs. Understanding your workload's RU consumption is the first step to performance optimization.

• Provisioned Throughput: Manually set RU/s for containers or databases.
• Autoscale Throughput: Automatically scales RU/s based on demand, ideal for variable workloads.
• Monitoring RU Consumption: Use Azure Monitor to track consumed RU/s and identify bottlenecks.

2. Data Modeling Best Practices

A well-designed data model significantly impacts performance, especially for queries. Consider denormalization where appropriate, but balance it with update complexity.

• Partition Key Selection: Choose a partition key with high cardinality and even distribution of workload to avoid hot partitions.
• Denormalization: Embed related data within a single document to reduce the need for complex joins or multiple requests.
• Document Size: Keep documents within a reasonable size limit (e.g., 2MB) to avoid performance degradation.

3. Query Optimization

Inefficient queries can consume excessive RUs and lead to high latency. Optimize your queries by following these guidelines:

• Use Indexes Effectively: Cosmos DB automatically indexes all data. Understand the indexing policy and consider custom indexing if specific query patterns benefit.
• Filter Early: Apply filters as early as possible in your queries.
• Projection: Select only the fields you need using the SELECT clause to reduce payload size and processing.
• Avoid Cross-Partition Queries: Queries that span multiple partitions can be expensive. Design your partition key to minimize this.
• `TOP` and `ORDER BY` Clauses: Use these judiciously, as they can add overhead.

4. Indexing Strategies

The indexing policy determines how data is indexed and affects both write performance and query performance.

• Index All vs. Selective Indexing: Indexing all paths is the default but can impact write throughput. For specific scenarios, consider indexing only the paths you query frequently.
• Composite Indexes: Useful for queries with multiple ORDER BY or equality predicates on different fields.
• Spatial Indexes: For geospatial queries.

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

5. Client-Side Performance Tuning

Optimizations on the client application side can dramatically improve user experience and reduce server load.

• Connection Pooling: Reuse client instances to benefit from connection pooling.
• Batch Operations: Use bulk operations or batching to send multiple operations in a single request, reducing network latency and RU consumption.
• SDK Best Practices: Follow the recommended practices for the Cosmos DB SDK you are using (e.g., .NET, Java, Python).
• Request Options: Configure appropriate consistency levels and timeout values.

6. Scalability and Throughput Management

Properly scaling your Cosmos DB resources is crucial for maintaining performance under varying loads.

• Monitor Performance Metrics: Regularly review metrics like RU Consumption, Latency, and Throttled Requests in Azure Monitor.
• Capacity Planning: Forecast your expected throughput needs and provision resources accordingly.
• Autoscale vs. Manual Throughput: Choose the provisioning mode that best fits your workload's predictability.

7. Caching Strategies

Caching frequently accessed data can significantly reduce RU consumption and latency.

• Client-Side Caching: Implement caching within your application logic.
• Azure CDN: For read-heavy workloads, consider using Azure Content Delivery Network to cache static API responses.

8. Understanding Latency

Latency in Cosmos DB is influenced by several factors:

• Network Latency: Distance between your application and the Cosmos DB endpoint. Use the appropriate region for your application.
• RU Throttling: If you exceed your provisioned RU/s, requests will be throttled, increasing latency.
• Query Complexity: Complex queries, especially cross-partition ones, take longer to execute.
• Document Size: Larger documents take longer to serialize/deserialize and transfer.