Optimize index performance

When designing and implementing native applications using Microsoft Azure Cosmos DB, it is essential to optimize index performance for efficient data access. Azure Cosmos DB provides various features and techniques to enhance query performance and minimize response times. In this article, we will explore some best practices to optimize index performance in Azure Cosmos DB.

1. Understand indexing policies

Azure Cosmos DB offers two types of indexing policies: Automatic and Manual. Automatic indexing is the default option, where the system automatically indexes all properties based on their usage patterns. Manual indexing allows you to specify which properties need to be indexed. By carefully selecting the indexing policy, you can avoid unnecessary indexing and improve overall performance.

{
"indexingPolicy": {
"automatic": true, // or false for manual indexing
"includedPaths": [
{
"path": "/*"
}
],
"excludedPaths": [
{
"path": "/unindexedContent/*"
}
]
}
}

2. Choose the right indexing paths

Review your query patterns and identify the frequently accessed properties. By including these paths in the indexing policy, you enable efficient query execution. Exclude paths that are not commonly queried to reduce unnecessary indexing overhead. Carefully consider the trade-off between query performance and storage costs.

3. Composite indexes

Azure Cosmos DB supports composite indexes, which combine multiple properties into a single index. Composite indexes are beneficial when you frequently query on multiple properties together. By defining composite indexes, you can improve the performance of multi-property queries.

{
"indexingPolicy": {
"compositeIndexes": [
[
{ "path": "/property1", "order": "ascending" },
{ "path": "/property2", "order": "descending" }
],
[
{ "path": "/property3", "order": "ascending" }
]
]
}
}

4. Partitioning

Distribute your data across logical partitions to achieve horizontal scalability. Azure Cosmos DB automatically partitions data based on the partition key. Be mindful of selecting an appropriate partition key to evenly distribute data and parallelize query execution. A well-designed partitioning strategy can significantly enhance query performance.

5. Query performance tuning

Consider the following factors while designing queries to optimize their performance:

• Avoid cross-partition queries: Cross-partition queries incur higher latency as they access multiple partitions. Whenever possible, design queries to operate within a single partition.
• Leverage query metrics: Utilize Azure Cosmos DB’s query diagnostics and metrics to analyze query performance, identify bottlenecks, and optimize queries accordingly.
• Use pagination: Implement result pagination instead of retrieving large result sets in a single query. This approach improves response times and reduces resource consumption.

6. Monitor and optimize throughput

Continuously monitor the throughput (Request Units) of your Azure Cosmos DB container. Adjust the provisioned throughput based on your application’s needs. By optimizing the throughput, you can ensure consistent and predictable performance.

In conclusion, optimizing index performance is crucial when designing and implementing native applications using Azure Cosmos DB. By leveraging appropriate indexing policies, composite indexes, partitioning, tuning queries, and monitoring throughput, you can achieve highly efficient data access and improve the overall performance of your Azure Cosmos DB applications.