Tips on Performance Optimization of Cosmos DB
In this blog post, the reader will learn more about best practices and tips for optimizing performance in Azure Cosmos DB.
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Azure Cosmos DB is a highly scalable and globally distributed NoSQL database service offered by Microsoft. To ensure optimal performance and scalability of applications running on Cosmos DB, it's crucial to employ effective performance optimization techniques. In this blog post, we will explore best practices and tips for optimizing performance in Azure Cosmos DB.
As with the other databases, indexing is the first go-to option to improve query performance. The same is the case with Cosmos DB as well. Below are a few points which you can leverage to optimize the indexing strategy for Cosmos DB.
- By default, Cosmos DB indexes all properties in the document. Turn off the default policy and select the properties which you want to index, as this will greatly improve the write speeds.
- When you are using the ORDER BY clause on two or more properties, use the composite index on those properties to make the query fast.
- Regularly review and update indexing policies based on query patterns and evolving application needs. Remove unnecessary indexes and add new indexes if required.
Thoughtful data modeling is crucial for optimal performance in Cosmos DB. Consider the following points when creating a schema for cosmos documents.
- Denormalize data to minimize costly joins and improve query performance. When you have an upper bound on the length of a nested item, let's say X property can never exceed a length of 100 items, then you can store this property inside the document itself.
- Choose a partitioning key that evenly distributes data among partitions. For example, if you choose the partition key as Year and your users access only the frequent data, then only the current year partition will be accessed, while other partitions will be inactive. Choose the partition key carefully so the load is evenly distributed throughout all partitions.
- Choose Consistency level wisely. Cosmos provides various options ranging from strong to eventual consistency models. If your application does not require strong consistency levels, you can lower it, and it will greatly improve the write speeds.
Whether or not you follow other optimization techniques, you should definitely optimize your queries. To analyze and view query metrics, you need to enable logs from Cosmos DB. Once you enable the logs look for RU consumption and try to reduce RU consumption for each query. Here are some tips which help you write queries that consume less RUs.
- When retrieving a single item, do point reads. Points reads are basically querying an item with its partition key and id value.
- When querying from multiple partitions, specify all the partitions in the query itself.
- While writing an item, RU cost depends upon the number of properties to be indexed. Try to remove unnecessary properties from indexing to save RUs.
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