What Is MongoDB? An Expert Guide

Jeffrey Erickson | Senior Writer | October 30, 2024

In This Article

MongoDB was created in 2007 by a couple of developers who wanted to track humongous—hence the name—numbers of small transactions in the ad-serving business. The new database, which was initially dubbed 10gen, held data in a simple, document “bucket” of JSON-type files, and it was able to scale up very quickly. It didn’t need much of a data model or exacting transaction concurrency because it was simply counting ad impressions, and the stakes were low.

Turns out, however, MongoDB delivered the kind of database simplicity for which developers hungered. It was launched under the open source development model in 2009, moved to SSPL (Server Side Public License) in 2018, and has evolved to become the de facto standard data store for many open source development stacks, with a customer list that includes Expedia, Lyft, eBay, and many more. Let’s see what makes it tick.

What Is MongoDB?

MongoDB is a popular open source document database that’s widely used in modern web and mobile applications. It’s categorized as a NoSQL database, which means it takes a flexible, document-oriented approach to storing data rather than a traditional table-based relational method. A big part of MongoDB’s appeal is its simplicity and developer focus. For example, Mongo interactions are defined by the acronym CRUD, for create, read, update, delete.

MongoDB saves data in JSON documents that make it relatively easy to use stored data—whether it’s structured, unstructured, or semistructured—for different kinds of applications. MongoDB’s flexible data model allows developers to store unstructured data while offering indexing support for faster file access and replication for data protection and availability. That means developers can design and build sophisticated applications using MongoDB.

While MongoDB was developed to track impressions across thousands of ad-serving sites, it soon gained wide popularity as a flexible data store in open source web development. It’s continually evolved since its 2007 launch, accumulating a robust feature set that includes ad hoc queries, indexing, and real-time aggregation. A key benefit of MongoDB for developers is that, relative to most popular relational databases, it’s intuitive to use and quick to get started with. The type of JSON documents stored in MongoDB map to familiar data types found in popular programming languages, such as JavaScript or Python dictionaries. Mongo also provides a thorough menu of client libraries with driver support for most programming languages, including PHP, .Net, Java, Python, Node.js, and many others.

Like all tech tools, MongoDB is strong in some areas and weak in others. It was designed to track online advertising, which required fast simultaneous access but needed only loose transactional accuracy and little real-time analysis. Even today, MongoDB is formed around BASE principles, which stand for availability, scalability, and eventual consistency. As such, MongoDB is typically used in scenarios where high availability and scalability are primary design considerations. In contrast, for jobs such as financial operations or in mission-critical enterprise environments, developers generally opt for a relational database. These offer ACID transactions (atomicity, consistency, isolation, and durability) to help ensure the reliability and consistency of database operations. More recently, however, the tech industry is offering solutions that can give developers the best of both worlds via the development simplicity of JSON and the benefits of SQL.

MongoDB Environments

MongoDB comes in a range of configurations and service levels to fit the needs of developers working on small, midsize, and even large enterprise projects.

  • MongoDB Atlas is a database-as-a-service offering from MongoDB to deploy and manage databases across cloud providers. Atlas automates many administrative tasks, such as scaling and backups. MongoDB Atlas runs as a managed service in many regions of Google Cloud, AWS, and Microsoft Azure.
  • MongoDB Community is an open source version of the database tailored to suit small and midsize projects looking for a NoSQL solution. As it’s open source, it’s suited to modification and innovation, and it offers developers a robust community to find assistance. However, the Community version lacks official support and service-level agreements (SLAs), has fewer security options, and offers only limited management tools.
  • MongoDB Enterprise Advanced is the premium, commercially available version of MongoDB Community. It offers enhanced security options and an in-memory storage engine to support enterprise-grade use cases.

Key Takeaways

  • MongoDB is a popular NoSQL database used for storing structured, semistructured, and unstructured data.
  • Instead of using tables, as in a traditional relational database, MongoDB stores data in JSON documents organized into collections.
  • Because MongoDB does not require rigid schemas, it allows for a flexible data model that can evolve to match changes in application functionality.
  • MongoDB was originally engineered for fast storage and recall in the ad-serving business, with little regard for transaction consistency or rapid data analysis. Later developments, such as sharding features, extend MongoDB’s capabilities.
  • Because MongoDB offers different strengths than a traditional relational database, developers often seek ways to get the best of both approaches.

MongoDB Explained

MongoDB is a NoSQL database that uses a document-oriented data model, where each record is a document stored in a collection, instead of the rows and columns common to popular relational databases, such as MySQL.

MongoDB stores the JSON documents using a format called BSON, or binary JSON. The nonrelational nature of these documents mean they can store—and the database can process—structured application data as well as semistructured and unstructured data. Unlike relational databases, MongoDB doesn’t use rigid schemas. Instead, the documents are flexible and can contain arrays and nested documents, allowing for complex and hierarchical data storage.

When handling extremely large data sets, document databases, such as MongoDB, scale out or distribute data across multiple nodes or clusters using a technique called sharding. That model allows for fast storage and recall. This architecture makes sense given that MongoDB was created for ad serving, where potentially millions of ads might need to be called up across thousands of websites at any moment. There was no inherent need to analyze one ad against another, which allowed data to be physically distributed and separated.

Hierarchical document databases are very fast for read operations, but data analysis can be slow because systems must analyze data in all nested entities. Relational databases, by contrast, store their data in separate tables, and a single “object” may be referenced in many tables within the database, allowing for more efficient analytical operations at scale. Given these differing strengths, development teams will generally opt for the best data management system for their application’s current needs. Or they may choose a multimodal database that provides full SQL access to both relational and JSON document data as well as many other data types.

ACID vs. BASE

Which you choose depends on the needs of your application.

ACID (atomicity, consistency, isolation, durability) BASE (basically available, soft state, eventually consistent)

Atomicity: Ensures an entire transaction is treated as a single unit. Either all changes succeed, or none of them do. This prevents partial updates that could leave your data in an inconsistent state.

Consistency: Guarantees that the database transitions from one valid state to another after a transaction. Enforces business rules and data integrity.

Isolation: Ensures that concurrent transactions do not interfere with one another. Each transaction appears to be executed in isolation, even if multiple transactions happen simultaneously.

Durability: Once a transaction is committed, the changes are written to permanent storage and won’t be affected by system failures, such as crashes.

Basically available: Focuses on maximizing data availability. The system strives to remain operational even during partial failures, allowing most read and write operations to proceed.

Soft state: Data consistency is not immediately guaranteed after a write operation. There might be a slight lag before changes are reflected across all replicas, leading to temporary inconsistencies.

Eventually consistent: Over time, consistency is achieved via background processes that sync changes across replicas.

Pros:

High data integrity and strong consistency make ACID ideal for applications that demand accuracy, such as financial transactions.

Pros:

High availability and scalability make BASE ideal for applications requiring high uptime and responsiveness, especially in distributed systems. Relaxed consistency requirements allow for faster write speeds and better scalability.

Cons:

Performance overhead means maintaining ACID guarantees can lead to slower write speeds. Strict consistency requirements can become challenging to manage in highly scalable environments.

Cons:

Temporary inconsistencies can occur during data synchronization, making BASE less suitable for applications where strict data integrity and immediate consistency are critical.

How Does MongoDB Work?

MongoDB stores data in collections, which are analogous to tables in relational databases. Each collection holds multiple documents, which can vary in structure. There is no need to declare the structure of documents to the system, as documents are self-describing—meaning each document contains metadata describing each field within the document.

To improve performance, MongoDB supports indexing on any field in a document. Indexes support the efficient execution of queries and can include primary and secondary indices. MongoDB’s query language supports CRUD (create, read, update, delete) operations and allows for complex aggregation, text searching, and geospatial queries. To help improve response times, MongoDB provides an aggregation framework, which lets developers set up complex data processing on the server side. That means it’s able to do analytics on the cluster where the data resides. This can reduce the amount of data that’s transferred to and from clients.

MongoDB works to provide high availability and improve performance by supporting replica data sets. Replicas can be used for load balancing by distributing read and write operations across all instances. These replica sets also provide redundancy and increase data availability via multiple copies of data on different database servers. In case of hardware failure or maintenance, replica sets allow MongoDB to provide automatic failover and data redundancy.

For scalability, MongoDB supports horizontal scaling through sharding, which is a way to distribute data across multiple databases on multiple machines. A sharded cluster can consist of many replica sets. Sharding is configured by defining a shard key, which determines how the data is distributed across the shards. This technique can help manage large data sets and high-throughput operations by dividing the data set and load over multiple servers.


How Sharding Works

Each shard is an independent database instance that hosts subsets of a sharded database’s data.

How sharding works diagram
The diagram shows a unidirectional flow from a client application at the top to the database shards at the bottom.

MongoDB vs. RDBMS

Each type of database—relational, such as MySQL, Postgres, and Oracle Database, or document-oriented, such as CouchDB, DynamoDB, and MongoDB—has strengths and weaknesses, and the choice between them generally depends on the specific requirements and constraints of the application being developed.

A relational database management system (RDBMS) uses a Structured Query Language (SQL), whereas MongoDB's document-focused format uses document store APIs. Even so, MongoDB Query Language (MQL) uses a JavaScript-like language with operations such as creating, reading, updating, and deleting documents.

MongoDB has no concept of tables and rows and lacks schemas, so there’s less structure to define before the database can be used. With no central schema, however, each app that accesses the collections needs to understand the document. So the “schema” is in the application code and not defined in the database. If one app changes the schema, other apps may break. Compared with relational databases, where a schema is essentially a blueprint for the RDBMS and data organization and interrelation are explicitly defined, MongoDB lacks the inherent concept of relationships between data.

The flexibility of data stores is notable, as MongoDB uses different formats for data such as key-value stores, graphs, and documents, and data structures can change over time. This differs from an RDBMS, which uses strict definitions, hierarchies, and validation procedures based on these to help ensure data integrity.

While setting up a basic MongoDB instance is straightforward, configuring and maintaining a large-scale, distributed MongoDB cluster with sharding and replicas can be complex and requires a good understanding of its architecture and configuration options.


Key Differences

Relational MongoDB
Data model Uses tables with fixed rows and columns, and data is structured in a predefined schema. Uses collections of documents, which are JSON-like structures with dynamic schemas.
Schema flexibility Requires a predefined schema that must be set up before data can be added. Has a dynamic schema. New fields can be added to a document without affecting all other documents in the collection.
Query language Uses SQL, which is very powerful for complex queries, for defining and manipulating data. Uses a document-based query language that is considered more intuitive but less complete and versatile than SQL.
Scaling Traditionally scales vertically, thus adding more power to the existing machine, although mature features, such as sharding and Oracle Real Application Clusters offer support for horizontal scaling. Designed to scale horizontally across multiple machines using sharding, which distributes data across a cluster of machines.
Transactions Supports multi-row transactions and is ACID-compliant, making it suitable for applications where no data can be lost or corrupted. Supports multidocument transactions, but is known to be less robust than most traditional relational databases, especially across distributed data.
Performance Built to ensure accurate transactions, but performance can be lower for large data volumes. However, analytic performance is generally better. Built for high read performance across large volumes of data.

Why Use MongoDB?

MongoDB is suitable for a wide range of uses, from simple CRUD applications, such as a blogging or note-taking app, to complex platforms, such as Amazon Prime. MongoDB is often selected for content management systems (CMSes), gaming apps where data sync must be fast, and biometric healthcare data, among many other use cases. Its versatility has made it a cornerstone of popular open source development stacks, such as MEAN and MERN.

Choose it when you need:

  • Flexibility. MongoDB’s JSON document format provides a simple and intuitive way to represent hierarchical data structures that would otherwise require complicated joins via SQL queries.
  • Availability. MongoDB’s distributed database features offer high availability, even with large, oft-changing data sets.
  • Scalability. MongoDB is designed to collect, process, and analyze large, fast-changing, and diverse data sets.
  • Performance. Performance optimization by way of such methods as replication, sharding, and others makes MongoDB a viable choice for large applications in areas such as media and entertainment.
  • Compatibility. MongoDB’s JSON-type documents provide easy compatibility with familiar data types found in popular programming languages. In addition, MongoDB client libraries offer drivers for most programming languages, such as PHP, .Net, JavaScript, and many more.
  • Community support. MongoDB is a de facto standard data store in many open source development stacks, where community support is abundant.

MongoDB Features

MongoDB has become popular with developers in part due to its intuitive API, flexible data model, and features that include:

  • Ad hoc queries. MongoDB supports field, range, and regular-expression queries that can return entire documents, specific fields of documents, or random samples of results.
  • Indexing. MongoDB supports several different index types, including single field, compound (multiple fields), multikey (array), geospatial, text, and hashed.
  • Replication. MongoDB provides high availability with replica sets including two or more copies of the data. Writes are handled by the primary replica, while any replica can serve read requests. If the primary replica fails, a secondary replica is promoted to become the primary replica.
  • Scalability. Scaling in MongoDB databases is enhanced with sharding, as clusters store only a portion of the data in a collection. Sharding keys determine the distribution of that data.
  • Load balancing. MongoDB can scale vertically and horizontally, and thanks to sharded clusters, load balancing can be handled by the database’s basic structure. Replication can be used to reduce loads on primary servers.
  • File storage. Data is stored in documents that readily map to objects in most programming languages, providing easy access within applications.
  • Batch processing. Data processing can be accomplished in several ways. Sometimes it’s done in the documents themselves, other times with a bulk write method that reduces network operations.

MongoDB Advantages

MongoDB’s popularity with open source community is attributable to the many ways it makes application development and maintenance more intuitive and scalable. These advantages include:

  • Ease of use for developers. Developers often choose MongoDB because it’s easy to download or access on the cloud, which means that they can get started quickly—partly because it’s easier to work with documents rather than creating a data model and working with tables.
  • Efficiency. JSON provides a number of efficiencies, with small document files and human-readable content. MongoDB encodes documents in binary format (BSON), which is more compact and faster to parse compared to plain text.
  • Flexible schemas. MongoDB’s document data model allows schemas that are flexible and self-descriptive, allowing fields to vary from document to document.
  • Simple query language. The MongoDB Query Language (MQL) is designed to be easy to use for developers, providing the ability for complex queries and indexes to speed up commonly used queries.
  • Cloud native. MongoDB Atlas is a cloud native database, so it gets frequent updates and quickly adapts to new technology. Its use also makes it easier to migrate an application to the cloud.

MongoDB Disadvantages

While MongoDB offers many advantages, particularly for applications requiring flexibility and high performance amid large data volumes, it does come with many potential drawbacks.

  • Transaction support. MongoDB transactional support is not as mature or robust as that found in traditional relational databases. Complex transactions, especially those that span multiple operations, may not perform as well and can be challenging to implement in MongoDB.
  • Data consistency. MongoDB’s use of “eventual consistency” for replica sets can lead to situations where all users aren’t reading the same data at the same time. For applications that demand strong consistency, this can be a serious drawback.
  • Join operations. MongoDB doesn’t support joins the way SQL databases do. It does, however, offer options that perform a similar function, though they are generally less efficient and can lead to more complex queries and slower performance—especially when dealing with complex relationships between documents.
  • Memory use. MongoDB stores its most frequently used data and indexes in RAM, so its performance is highly dependent on having sufficient RAM. As a result, a MongoDB database can consume more memory resources and, potentially, more hardware than other databases.
  • Storage overhead. The self-containing document paradigm used by MongoDB can lead to larger storage requirements compared to the highly normalized tables in relational databases. Additionally, MongoDB’s dynamic schema can cause data redundancy and fragmentation that can increase storage use—and costs.
  • Indexing limitations. MongoDB supports many indexing options, but maintaining a large number of indexes can degrade write performance. It’s just not built for frequent writes, because each write operation might need to update multiple indexes—often pitting query performance against write performance.
  • Cost. In scenarios where high availability and horizontal scaling are required, the cost associated with running and maintaining a MongoDB cluster—especially in cloud environments—can be significant. The need for lots of RAM and storage can also drive up costs. That’s especially true in high-availability situations where replica databases require an equal number of resources.

MongoDB Compatibility

MongoDB is a NoSQL database that works well within that ecosystem, but it’s also built to interact with other types of database management systems through various data integration tools and connectors. This toolset includes an ETL (extract, transform, load) infrastructure for extracting and migrating data out of MongoDB and vice versa. This is useful for sending data to a relational database for reporting and complex data analytics. MongoDB applications can also communicate across different database platforms using REST APIs.

Running MongoDB Workloads in Oracle Autonomous Database

A good example of MongoDB compatibility is the Oracle Database API for MongoDB, which lets developers use MongoDB's open source tools and drivers connected to an Oracle Autonomous JSON Database. This gives them access to Oracle’s multimodel capabilities and helps them avoid moving data to a separate database for analytics, machine learning (ML), and spatial analysis. Think of Autonomous JSON Database as a multimodal alternative to MongoDB Atlas. Often, few or no changes are required for existing applications.

Migrate MongoDB Workloads to Oracle Autonomous JSON Database

Instead of accessing MongoDB functionality via APIs, developers can simply migrate their JSON-centric workloads to an Oracle Autonomous JSON Database on Oracle Cloud Infrastructure (OCI). This provides a cloud document database service for JSON-centric applications that features NoSQL-style document APIs (Simple Oracle Document Access, or SODA, and Oracle Database API for MongoDB), serverless scaling, high performance ACID transactions, comprehensive security, and low pay-per-use pricing. There is no downtime because migration from MongoDB to Oracle Autonomous JSON Database is achieved with Oracle Cloud Infrastructure (OCI) GoldenGate.

Get Started with Autonomous Database

MongoDB users now have a more versatile way to build JSON-centric applications. Oracle Autonomous Database gives developers the flexibility to react to business demands using a single data platform that can help meet all their needs—letting developers use SQL, JSON documents, graph, geospatial, text, and vectors in a single database to rapidly build new features.

In addition, a revolutionary new feature in Oracle Database, JSON Relational Duality, provides the benefits of both relational tables and JSON documents, without the tradeoffs of either model.

Autonomous Database offers integrated AI services and in-database machine learning (ML) to enhance apps with text and image analysis, speech recognition, or personalized recommendations. In addition, Autonomous Database Select AI automatically translates natural language into database queries and allows you to have a contextual conversation with the database, without any custom coding or manual operations via a complex interface. And because the database is fully autonomous, it enables development teams to stay focused on building applications by ensuring uptime and safeguarding data through automated security measures and continuous monitoring.

You can get started today for free, and even try a workshop to learn how to use SQL, JSON, and Oracle Graph in the same app.

With use cases that include ecommerce platforms, IoT applications, and more, MongoDB has proven its versatility across industries. Its ability to handle diverse data types and support complex queries positions it as an able component of modern technology stacks. As businesses seek to extract maximum value from their data, MongoDB will be instrumental in success.

Developers and their business colleagues alike are excited by the next generation of low- and no-code development tools. Learn more and check out nine more hot cloud trends.

MongoDB FAQs

What is the difference between SQL and MongoDB?

MongoDB saves unstructured data, which is unsuitable for a Structured Query Language (SQL).

Is MongoDB a back-end language?

No, but it can be used as part of a back-end web application.

Is MongoDB a language or framework?

It is a database management system using unstructured data stored in documents instead of tables.