Optimizing Azure Cosmos DB Query Performance

Understanding Request Units (RUs)

Azure Cosmos DB provisions throughput in terms of Request Units (RUs). Each database operation, including queries, consumes RUs. Understanding how your queries consume RUs is the first step to optimization.

• Read Operations: Consuming RUs based on document size and consistency level.
• Write Operations: Consuming RUs based on document size.
• Query Operations: Consuming RUs based on the complexity of the query, the number of documents scanned, and the number of partitions accessed.

A high RU consumption can lead to throttled requests (HTTP 429 errors) and increased costs. Optimizing queries directly impacts RU consumption.

Strategies for Efficient Querying

1. Leverage Partition Keys

Queries that include the partition key in their filter clauses are the most efficient, as they can target specific partitions, minimizing the scope of the scan. This is often referred to as a "query on the partition key."

SELECT * FROM c WHERE c.partitionKey = "someValue"

2. Indexing Strategies

Azure Cosmos DB's automatic indexing can be tuned. Ensure your indexing policy aligns with your common query patterns. Consider including only necessary paths and using range indexing for efficient range queries.

Example of an indexing policy snippet:

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

3. Use the `SELECT *` Clause Sparingly

Selecting only the fields you need reduces the amount of data transferred and processed, thereby lowering RU consumption. Use specific field projections.

SELECT c.name, c.email FROM c WHERE c.city = "New York"

4. Avoid Cross-Partition Queries When Possible

Queries that don't filter on the partition key may need to scan multiple partitions, significantly increasing RU cost and latency. If unavoidable, ensure you have sufficient throughput provisioned.

5. Optimize Joins and Subqueries

While Cosmos DB supports joins and subqueries, they can be computationally expensive. Rewrite them using denormalization or alternative data structures if performance is a concern.

6. Understand SQL Functions and Expressions

Certain SQL functions might be more resource-intensive than others. Profile your queries to identify bottlenecks. For example, using string functions on large text fields can be costly.

7. Use Appropriate Data Types

Ensure data types are consistent for filtering and comparisons. Mismatched types can lead to inefficient scans or unexpected results.

8. Batching and Bulk Operations

For multiple writes or reads of individual items, consider using the bulk operations API or batching your requests to reduce network overhead and improve throughput.

9. Utilize Read Feed and Change Feed

For scenarios where you need to process changes to your data, the Change Feed is an efficient mechanism that avoids expensive continuous queries.

Monitoring and Profiling

Regularly monitor your Cosmos DB account's performance metrics in the Azure portal. Pay close attention to:

• Request Units Consumed
• Throttled Requests (429s)
• Query Latency
• Document Scans

Use the Cosmos DB query metrics to analyze the cost of individual queries. This output provides details on the RU consumption and execution details of your query.

When executing a query in the Azure portal's Data Explorer, you can view the query metrics:

Query Metrics:
  // ... details about RU consumption, document counts, etc. ...

Advanced Techniques

• Vector Search: For AI-driven similarity searches, explore Cosmos DB's vector search capabilities.
• Materialized Views: For complex aggregations or transformations, consider implementing materialized views using serverless functions or other compute options.