Navigating NoSQL Databases: MongoDB Atlas vs Cosmos DB (RUs vs vCore) - A Comparative Guide

Introduction to MongoDB Atlas vs Cosmos DB

In the realm of NoSQL databases, MongoDB and Azure Cosmos DB are two powerhouse solutions catering to diverse application needs. Here are brief overviews and recent developments for each:

MongoDB: The leading document-oriented NoSQL database, MongoDB has just unveiled MongoDB 8.0, a landmark release focusing on enhanced performance, security, and scalability. Key highlights include:

• Improved Query Performance: Enhanced query optimization and indexing capabilities.

• Client-Side Field Level Encryption (FLE) 2.0: Strengthened data protection with more seamless encryption.

• Time-Series Data Support: Native support for efficient time-series data storage and querying.

• Enhanced Aggregation Framework: More powerful data processing with improved aggregation capabilities.

Azure Cosmos DB: Microsoft's globally distributed, multi-model database service. Recent updates include the general availability of vCore provisioning for more flexible compute resource allocation, alongside enhancements to its change feed and analytics capabilities.

Key Differences, Strengths, and Pitfalls

MongoDB

• Strengths:

  • Maturity and wide adoption.

  • Robust query language and indexing capabilities, further enhanced in MongoDB 8.0.

  • Flexible deployment options (self-managed, MongoDB Atlas).

  • Native Time-Series Support

  • Embedded Vector Search capabilities

  • Queryable Encryption

• Pitfalls:

  • Global distribution and multi-region writes can be somewhat complex to setup and manage unless you're familiar with sharding.

  • May require additional tools for full-stack monitoring and security, though Client-Side FLE 2.0 enhances security posture.

Azure Cosmos DB (Provisioned RUs vs vCore)

Provisioned RUs (Request Units)

• Strengths:

  • Simplified pricing for predictable workloads.

  • Works for point queries.

  • Auto-scaling for throughput (RUs) with Azure Cosmos DB's autoscale feature.

  • Comprehensive security and monitoring within Azure.

• Pitfalls:

  • Can be costly for workloads with variable throughput.

  • Really poor performance for large ingestion or bulk reads.

  • No way to develop against it locally.

  • Over-provisioning to ensure peak performance can lead to wasted resources.

  • Has notable limitations when it comes to MongoDB API support, some of them are not well documented (my personal favorite is $sample stage in aggregation not returning uniformly random results)

vCore (Virtual Core) Model

• Strengths:

  • Better suited for compute-intensive, variable workloads.

  • More cost-effective for applications with high storage needs but lower throughput demands.

  • Enhanced flexibility with separate scaling for compute, storage, and I/O.

• Pitfalls:

  • Pricing complexity due to separate billing for compute, storage, and I/O.

  • Requires more precise resource planning to avoid under/over-provisioning.

  • Has broader functionality limitations as of now than RU (e.g. ChangeStreams are still not supported) although the gap is closing quickly.

5 Simple Guidelines for Choosing the Right Database Solution

1. Predictable Throughput, Simple Scalability Needs:

   • Choose MongoDB (especially with MongoDB 8.0's enhancements) if you prefer a self-managed or serverless MongoDB Atlas setup for its straightforward scalability and robust query capabilities.

   • Opt for Cosmos DB with Provisioned RUs if your workload fits well within predictable throughput boundaries and you're deeply integrated with the Azure ecosystem.

2. Variable/Compute-Intensive Workloads:

   • Consider Cosmos DB vCore for its flexibility in scaling compute resources independently, ideal for applications with fluctuating demands.

   • Evaluate MongoDB (with its enhanced performance in MongoDB 8.0) if your primary concerns are query complexity and data model flexibility, and you can manage scalability through sharding or third-party tools.

3. Multi-Region Writes and Conflict Resolution are Key:

   • Azure Cosmos DB (either RUs or vCore) is preferable due to its built-in, customizable conflict resolution mechanisms and seamless global distribution capabilities.

   • MongoDB may require more application-level logic for conflict resolution, but can still be suitable if your team is experienced with MongoDB's ecosystem.

4. Time-Series Data and Enhanced Security are Priorities:

   • MongoDB 8.0 is an attractive choice with its native time-series support and enhanced Client-Side Field Level Encryption (FLE) 2.0 and Queryable Encryption for strengthened data protection.

5. Ecosystem and Operational Preferences:

   • Azure Ecosystem: If deeply invested in Azure services, Cosmos DB (either provisioning model) integrates seamlessly.

   • Cross-Cloud or On-Premises Flexibility: MongoDB offers more deployment flexibility across various environments.

Choosing Between Azure Cosmos DB's API for MongoDB vs Native MongoDB Atlas

• Use Azure Cosmos DB's API for MongoDB if:

  • You're already invested in the Azure ecosystem.

  • Need global distribution with automatic conflict resolution.

  • Want to leverage Azure's security and monitoring tools.

  • Are comfortable with potential feature lag compared to native MongoDB.

• Stick with Native MongoDB if:

  • You prefer more control over database configuration.

  • Are deeply invested in the MongoDB ecosystem (e.g., MongoDB Atlas, MongoDB Compass).

  • Require specific MongoDB features not yet supported by Azure Cosmos DB's API for MongoDB, such as the latest enhancements in MongoDB 8.0.

Conclusion

Selecting between MongoDB, Cosmos DB with Provisioned RUs, or Cosmos DB vCore involves weighing your application's unique needs, scalability patterns, ecosystem preferences, and the latest feature enhancements, such as those introduced in MongoDB 8.0. By understanding the strengths and pitfalls of each, you can make an informed decision that optimizes both performance and cost for your NoSQL database solution.

Next Steps

• Evaluate your application's specific requirements against the criteria outlined above.

• Test and prototype with the chosen database solution to ensure optimal performance and cost alignment.

• Stay informed about the latest updates and features from both MongoDB and Azure Cosmos DB to future-proof your database strategy.

• We will be periodically revising our stance and sharing new updates as they come in. Stay tuned!

If the choice is not obvious for you, no need to worry. Now you have a way to switch or test out a different solution later on with 0 effort! Adiom's dsync supports online migrations from Cosmos DB to MongoDB Atlas, from MongoDB Atlas to Cosmos DB, and from Cosmos DB with provisioned RUs to Cosmos DB vCore.

At Adiom, we built dsync to help developers and DevOps perform live migrations and real-time replication easily. Dsync is a fast, reliable, easy-to-use and Open Source solution. Using dsync helps to accelerate and derisk projects, and allows teams to get their applications and services onto a new database in minutes instead of months.

Give dsync a try by downloading it from GitHub. It's distributed as a binary that you can just run anywhere, including your laptop, and it doesn't require specialized infrastructure or any complex setup.

Adiom offers help and commercial support on terms that suit your projects best. Get in touch with us here.