Debunking MongoDB Myths: Security, Scale, and Performance

MongoDB has come a long way since its founding in 2007. Many people first encountered MongoDB during its early years. They formed opinions about the database based on impressions from 2012 to 2014. However, much has changed since then.

Over the past eleven years, MongoDB has made significant strides. Foremost being the launch of MongoDB Atlas in 2016. It has placed a substantial focus on improving the four critical areas that matter most to businesses and developers alike: security, durability, availability, and performance.

• Security: Protecting sensitive data from unauthorized access and ensuring regulatory compliance.

• Durability: Ensuring data remains intact and reliable, even during system failures or unexpected disruptions.

• Availability: Minimizing downtime and maintaining system operation, no matter what happens.

• Performance: Delivering fast, consistent application response times and scaling efficiently to meet growing demand.

These advancements have earned the trust of some of the world’s largest enterprises, including Toyota, Cisco, Wells Fargo, Bosch, and Verizon.

Yet despite this progress, outdated myths regarding MongoDB persist—particularly in these four foundational areas. In this blog, we will tackle those misconceptions head on and set the record straight about MongoDB’s security, durability, availability, and performance. Let’s dive in.

Myth 1: “MongoDB is not as secure as a relational database”

One of the most persistent myths about MongoDB is that it is not secure—certainly not as secure as traditional relational databases. This misconception likely stems from a series of ransomware attacks in the mid-2010s. Hackers exploited unsecured databases that lacked proper authentication and were left exposed on default TCP ports. While these incidents highlighted poor configuration practices, they have unfairly cast a shadow over MongoDB’s contemporary security capabilities.

MongoDB provides robust, intelligent security features designed to protect sensitive data at every stage of its lifecycle. MongoDB encrypts data both in transit and at rest, just like other leading NoSQL and relational databases. However, what sets MongoDB apart is its ability to keep data encrypted while in use. With Queryable Encryption, an industry-first innovation unique to MongoDB, sensitive data can remain encrypted even while it is queried. This eliminates the need to decrypt the data and reduces exposure to threats.

MongoDB also supports flexible authentication and authorization that seamlessly integrates with many identity management systems. Features like role-based access control and fine-grained permissions ensure users only have access to what they are authorized for. Concurrently, intuitive configuration makes these controls easy to implement.

Beyond encryption and access control, MongoDB includes powerful auditing tools to monitor database activity and advanced network security features, such as IP allow-listing and private networking. Together, these capabilities provide comprehensive protection against unauthorized access and help organizations meet strict compliance requirements.

Best of all, these advanced security features are included by default in both MongoDB Atlas and MongoDB Enterprise Advanced at zero cost. MongoDB’s approach simplifies security management while minimizing expenditure. This allows teams to focus on building applications with confidence that their data is protected.

Myth 2: “MongoDB’s multi-cloud capabilities do not set it apart from other databases”

At first glance, the claim that MongoDB is multi-cloud may not sound special. After all, plenty of databases are available through more than one cloud provider - however, this should not be confused with them all being multi-cloud. True multi-cloud supports ‘cross-cloud’ deployments, i.e. the ability to deploy individual nodes of a single cluster across multiple cloud providers. This distinction is often obfuscated by those vendors unable to run their clusters in such a configuration. Support for multi-cloud clusters in Atlas became generally available in October of 2020.

MongoDB Atlas enables deployment not only on Amazon Web Services (AWS), Microsoft Azure, or Google Cloud but also across all three clouds simultaneously with a single cluster. It is possible to set up and configure cross-cloud deployments solely from the Atlas management console. No further configuration is required via the individual cloud providers. This is more than just a convenience; it is a transformative capability that eliminates the boundaries between cloud providers. With MongoDB Atlas, it is as if AWS, Azure, and Google Cloud operate as one unified cloud environment.

Why does this matter?

For starters, deploying a single database cluster across multiple clouds removes the operational complexity of managing data replication and migration between providers. Seamless data mobility can be achieved. The hardest part of any application to move—the data—now becomes the easiest.

Multicloud also enables the creation of application architectures that exploit the best services from multiple cloud providers simultaneously. In addition, cross-cloud deployments deliver unmatched resiliency. With cross-cloud failover, in the event of an outage, data can be automatically switched to another cloud provider in the same geographic region. Thus ensuring uninterrupted service.

Finally, MongoDB Atlas provides the flexibility to meet regional and cloud provider preferences with ease. Atlas spans 115+ supported regions across all three major cloud providers. This makes it easy to meet customer demands or comply with local regulations using a single database.

Myth 3: “I get that MongoDB is built for horizontal scaling, but it is so painful to scale”

Horizontal scaling, also known as scale-out, is a core strength of MongoDB. It allows workloads to be distributed by adding more nodes as data and applications expand. However, some beliefs have perpetuated that scaling MongoDB is difficult and complex. The reality? MongoDB makes scaling not just possible, but seamless—whether scaling out horizontally or scaling up vertically.

With MongoDB Atlas, vertical scaling—or scale-up—is simple. By enabling auto-scaling, MongoDB Atlas dynamically adjusts cluster resources to meet workload demands. Adding more RAM, CPU, or storage capacity can be performed automatically and on-demand. This ensures optimal performance without continual manual intervention or oversight.

If you need to move beyond vertical scaling, MongoDB offers three flexible ways to scale horizontally:

• Hashed sharding: Data is distributed randomly across nodes using a hashed shard key. This ensures an even distribution of data and workloads to prevent bottlenecks.

• Ranged sharding: Data is distributed based on ranges of a specific field. This enables fine-grained control over how data is divided. This approach is especially useful for preventing hotspots in workloads.

• Zone sharding: Data is distributed geographically. This enables compliance with data residency requirements and reduces latency by keeping data closer to users.

What happens if the initial sharding strategy does not go as planned? MongoDB addresses this challenge with the ability to refine shard keys and reshard a collection with zero downtime. This ensures data distribution strategies can adapt as needs evolve, all without disrupting applications or users.

Myth 4: “Since MongoDB is built for flexibility, it must not be very performant”

One common misconception about MongoDB is that its flexibility and versatility must come at the expense of performance. After all, can such an agile database—one built for developers to model data however they want—really deliver the speed and efficiency of a performance-first solution? MongoDB is designed to provide both; unmatched flexibility and exceptional performance—all while keeping costs low.

MongoDB’s performance stems from its intelligent architecture and powerful features. Ad hoc queries, indexing, and real-time aggregations make it easy to access and analyze data quickly. How fast are queries? Primary key or indexed queries typically execute in milliseconds. Even complex queries that are not indexed remain efficient. Performance typically is dependent on factors like collection size and machine specifications.

What about workloads like search and analytics? Some developers might assume these would compete for resources and degrade performance on operational tasks. However, MongoDB solves this with workload isolation. This feature ensures that operational and nonoperational workloads are separated. This enables each to run at peak performance without requiring costly and time-consuming extract, transform, and load (ETL) processes.

Network latency? For globally distributed applications, MongoDB’s hedged reads enable the nearest replica nodes to be read from rather than waiting for a response from distant nodes. This reduces latency and ensures applications remain highly responsive.

MongoDB’s real-world performance is backed by incredible use cases:

• Amadeus processes 630 million bookings per year.

• Idealo supports 200,000 queries and 60,000 updates per second.

• Temenos achieves 150,080 transactions per second.

This was before the release of MongoDB 8.0, the most performant version of the database yet. MongoDB 8.0 has delivered:

• 36% faster reads

• 32% faster reads and updates

• 56% faster bulk inserts

• A stunning 200% improvement for time series queries

MongoDB Today

MongoDB has evolved far beyond the myths perpetuated during its early years. MongoDB 8.0 delivers robust capabilities across security, durability, availability, and performance. It encrypts sensitive data throughout its lifecycle and enables seamless cross-cloud deployments. It simplifies horizontal and vertical scaling and powers some of the world’s most demanding applications. These capabilities solidify MongoDB’s position as the database of choice for modern applications.