DZone
Thanks for visiting DZone today,
Edit Profile
  • Manage Email Subscriptions
  • How to Post to DZone
  • Article Submission Guidelines
Sign Out View Profile
  • Post an Article
  • Manage My Drafts
Over 2 million developers have joined DZone.
Log In / Join
Please enter at least three characters to search
Refcards Trend Reports
Events Video Library
Refcards
Trend Reports

Events

View Events Video Library

Zones

Culture and Methodologies Agile Career Development Methodologies Team Management
Data Engineering AI/ML Big Data Data Databases IoT
Software Design and Architecture Cloud Architecture Containers Integration Microservices Performance Security
Coding Frameworks Java JavaScript Languages Tools
Testing, Deployment, and Maintenance Deployment DevOps and CI/CD Maintenance Monitoring and Observability Testing, Tools, and Frameworks
Culture and Methodologies
Agile Career Development Methodologies Team Management
Data Engineering
AI/ML Big Data Data Databases IoT
Software Design and Architecture
Cloud Architecture Containers Integration Microservices Performance Security
Coding
Frameworks Java JavaScript Languages Tools
Testing, Deployment, and Maintenance
Deployment DevOps and CI/CD Maintenance Monitoring and Observability Testing, Tools, and Frameworks

Last call! Secure your stack and shape the future! Help dev teams across the globe navigate their software supply chain security challenges.

Modernize your data layer. Learn how to design cloud-native database architectures to meet the evolving demands of AI and GenAI workloads.

Releasing software shouldn't be stressful or risky. Learn how to leverage progressive delivery techniques to ensure safer deployments.

Avoid machine learning mistakes and boost model performance! Discover key ML patterns, anti-patterns, data strategies, and more.

Related

  • Cosmos DB Disaster Recovery: Multi-Region Write Pitfalls and How to Evade Them
  • Evolution of Cloud Services for MCP/A2A Protocols in AI Agents
  • SQL Server Index Optimization Strategies: Best Practices with Ola Hallengren’s Scripts
  • Jakarta NoSQL 1.0: A Way To Bring Java and NoSQL Together

Trending

  • Scalable, Resilient Data Orchestration: The Power of Intelligent Systems
  • Accelerating AI Inference With TensorRT
  • Unlocking AI Coding Assistants Part 1: Real-World Use Cases
  • Rethinking Recruitment: A Journey Through Hiring Practices
  1. DZone
  2. Data Engineering
  3. Databases
  4. Tips on Performance Optimization of Cosmos DB

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.

By 
Harshvardhan Singh user avatar
Harshvardhan Singh
·
Jul. 06, 23 · Opinion
Likes (2)
Comment
Save
Tweet
Share
6.5K Views

Join the DZone community and get the full member experience.

Join For Free

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. 

Indexing Strategy

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.

Data Modeling

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. 

Query Optimization

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.
Cosmos DB Data modeling optimization NoSQL

Opinions expressed by DZone contributors are their own.

Related

  • Cosmos DB Disaster Recovery: Multi-Region Write Pitfalls and How to Evade Them
  • Evolution of Cloud Services for MCP/A2A Protocols in AI Agents
  • SQL Server Index Optimization Strategies: Best Practices with Ola Hallengren’s Scripts
  • Jakarta NoSQL 1.0: A Way To Bring Java and NoSQL Together

Partner Resources

×

Comments
Oops! Something Went Wrong

The likes didn't load as expected. Please refresh the page and try again.

ABOUT US

  • About DZone
  • Support and feedback
  • Community research
  • Sitemap

ADVERTISE

  • Advertise with DZone

CONTRIBUTE ON DZONE

  • Article Submission Guidelines
  • Become a Contributor
  • Core Program
  • Visit the Writers' Zone

LEGAL

  • Terms of Service
  • Privacy Policy

CONTACT US

  • 3343 Perimeter Hill Drive
  • Suite 100
  • Nashville, TN 37211
  • support@dzone.com

Let's be friends:

Likes
There are no likes...yet! 👀
Be the first to like this post!
It looks like you're not logged in.
Sign in to see who liked this post!