MongoDB is frequently viewed as a go-to database for proof-of-concept (POC) applications. The flexibility of MongoDB’s document model enables teams to rapidly prototype and iterate. This allows for adaptation of the data model as requirements evolve during the early stages of application development. It is common for applications to continuously evolve during initial development. However, moving an application to production requires developers to add validation logic and fully define the data structures.
A frequent assumption is that because MongoDB data models can be flexible, they can not be structured. However, while MongoDB does not require a defined schema, it does support them. MongoDB allows users to precisely calibrate rules and enforcement levels for every component of data. This enables a level of granular control that traditional databases, with their all-or-nothing approach to schema enforcement, struggle to match.
Data model flexibility is not a binary choice between “schemaless” or “strictly enforced.” More accurately, it exists on a spectrum in MongoDB. Users can incrementally define schemas in parallel with the overall “hardening” of the application.
MongoDB’s approach to data modeling makes it an ideal platform for business-critical applications. It is designed to support the entire application lifecycle; from nascent concepts and initial prototypes, to global rollouts of production environments. Enterprise-grade features like ACID transactions and industry-leading scalability ensure MongoDB can meet the demands of any modern application.
Learning from the past
So why do misconceptions persist regarding MongoDB? These perceptions originated over a decade ago. Teams working with MongoDB back in 2014 or earlier faced challenges when deploying it in production. Applications could slow down under heavy loads, data consistency was not guaranteed when writing to multiple documents, and teams lacked tools to monitor and manage deployments effectively.
As a result, MongoDB gained a perception of being unsuitable for specific use cases or critical workloads. This perception has persisted despite a decade of subsequent development and innovation. Therefore, this is now an inaccurate assessment of today’s preeminent document database. MongoDB has evolved into a mature platform that directly addresses these historical pain points. Today’s MongoDB delivers robust tooling, guaranteed consistency, and comprehensive data validation capabilities.
Myth: MongoDB is a niche database
What are the top use cases for MongoDB? This question is difficult to answer because MongoDB is a general-purpose database that can support any use case.
The document model is the primary driver of MongoDB’s versatility. Documents are similar to JSON objects with data being represented as key-value pairs. Values can be simple types like strings or numbers. However, values can also be arrays or nested objects which allows documents to easily represent complex hierarchical structures. The document model’s flexibility allows data to be stored exactly as the application consumes it. This enables highly efficient writing and optimizes data for retrieval without needing to set up standard or materialized views, although both are supported.
While MongoDB is no longer a niche database, it does have advanced capabilities to support niche requirements. The aggregation pipeline provides a powerful framework for data analytics and transformation. Time-series collections store and query temporal data efficiently to support IoT and financial applications. Geospatial indexes and queries enable location-based applications to perform complex proximity calculations. MongoDB Atlas includes native support for vector search. This enabled Cisco to experiment with generative AI use cases and streamline their applications to production.
MongoDB handles the diverse data requirements that power modern applications. The document model provides the foundation for general use. Concurrently, advanced features ensure teams do not need to integrate additional tools as application requirements evolve. The result is a single platform that can grow from prototype to production, handling general requirements and specialized workloads with equal proficiency.
Myth: MongoDB is not suitable for enterprise-grade workloads
A common perception is that MongoDB works well for small applications but falls short at enterprise scale. Ironically, many organizations first consider MongoDB while struggling to scale their relational databases. These organizations have discovered MongoDB’s architecture is specifically designed to support scale-out distributed deployments.
While MongoDB matches relational databases in vertical scaling capabilities, the document model enables a more natural and intuitive approach for horizontal scaling. Related data is stored together in a single document. Therefore, MongoDB can easily distribute complete data units across shards. This contrasts with relational databases. Relational data is split across multiple tables. This makes it difficult to place all related data on the same shard. Horizontal scaling with MongoDB sets an organization up for better performance. Most MongoDB queries need to access only a single shard. Equivalent queries in a relational database often require costly cross-server communication. Telefonica Tech has leveraged horizontal scaling to nearly double their capacity with a 40% hardware reduction.
MongoDB Atlas further automates and simplifies these scaling capabilities through a fully managed service built to meet demanding enterprise requirements. Atlas provides a 99.995% uptime guarantee and availability across AWS, Google Cloud, and Azure in over 100 regions worldwide. This frees teams to focus on rapid development and innovation rather than infrastructure maintenance by offloading the operational complexity of deploying and running databases at scale.
Powering the enterprise applications of today and tomorrow
Over 50,000 customers and 70% of the Fortune 100 rely on MongoDB to power their enterprise applications. Independent industry reports from Gartner and Forester continue to recognize MongoDB as a leader in the database space. Do not let outdated myths prevent your organization from the competitive advantages of MongoDB’s enterprise capabilities.
To learn more about MongoDB, head over to MongoDB University and take our free Intro to MongoDB course.
Read more about customers building on MongoDB or start using MongoDB Atlas for free today.
Check out the full video to learn about the other 6 myths that we’re debunking in this series.
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