Choose when to use a read-heavy versus write-heavy index strategy

Choosing the right indexing strategy is crucial when designing and implementing native applications using Microsoft Azure Cosmos DB. Two common strategies to consider are read-heavy indexing and write-heavy indexing. Each strategy has its benefits and trade-offs, and understanding when to use each can greatly optimize your application’s performance. In this article, we will explore both strategies and discuss the scenarios where they are most effective.

Read-Heavy Indexing Strategy

The read-heavy indexing strategy focuses on optimizing queries and read operations to ensure fast and efficient data retrieval. This strategy is particularly useful in applications where the majority of operations involve querying and reading data rather than modifying or writing data.

There are a few key considerations to keep in mind when using the read-heavy indexing strategy:

1. Use appropriate partition keys: Partition keys play a vital role in distributing data across multiple physical partitions. Choosing a good partition key is crucial for evenly distributing read operations across these partitions, as well as reducing cross-partition queries. It is advisable to select a partition key that is frequently used in your queries, allowing for efficient data retrieval.
2. Index necessary properties: Create indexes on the properties that are frequently used in queries. By doing so, you can avoid full scans of the entire data set and speed up data retrieval. However, keep in mind that creating too many indexes can negatively impact write performance and increase storage costs.
3. Leverage indexing policies: Cosmos DB allows you to define custom indexing policies based on your application’s specific needs. By carefully configuring indexing policies, you can optimize query performance, reduce index storage size, and control the indexing behavior for different collections or containers within your database.

Here’s an example of how you can configure indexing policies using the Cosmos DB SDK:

DocumentCollection collection = new DocumentCollection();
collection.Id = "your-collection-id";
collection.IndexingPolicy.Automatic = true; // Enables automatic indexing
collection.IndexingPolicy.IndexingMode = IndexingMode.Consistent;
collection.IndexingPolicy.IncludedPaths.Add(new IncludedPath
{
Path = "/*" // Indexes all properties
});

Write-Heavy Indexing Strategy

The write-heavy indexing strategy focuses on optimizing write operations by reducing the indexing overhead. This strategy is ideal for applications that heavily rely on write operations, such as logging systems or real-time data streams, where data is constantly being added or updated.

When implementing a write-heavy indexing strategy:

1. Disable automatic indexing: By turning off the automatic indexing feature, you can reduce the overhead of indexing during write operations. However, keep in mind that disabling automatic indexing means you will need to manually manage index updates whenever there are changes in your data model or indexing requirements.
2. Limit the use of indexes: In a write-heavy scenario, it may be beneficial to limit the number of indexes to minimize the indexing workload. Analyze your application’s query patterns and focus on indexing the properties that are crucial for read operations.
3. Use bulk execution for write operations: Cosmos DB provides the Bulk API, which allows you to perform large-scale write operations efficiently. By grouping multiple write operations into a single request, you can reduce the overhead associated with indexing on each individual operation. This significantly improves write performance.

Here’s an example of how you can use bulk execution:

List documents = new List();
// Add your documents to the list

await client
.CreateDocumentAsync(collectionUri, documents, new RequestOptions { EnableBulkExecution = true });


Conclusion

Choosing the appropriate indexing strategy is crucial for optimizing the performance of your native applications built with Azure Cosmos DB. By understanding the characteristics of read-heavy and write-heavy indexing strategies, you can select the most appropriate approach based on your application’s requirements. Remember to consider factors such as query patterns, data distribution, and the frequency of read and write operations when making your decision.