Comparing MongoDB vs PostgreSQL

MongoDB vs PostgreSQL: A Comparison in Brief

MongoDB and PostgreSQL both offer capabilities for handling a myriad of use cases, ranging from simple data retrieval to complex transactions. However, MongoDB provides more flexibility than PostgreSQL when it comes to data modeling, horizontal scalability, and overall developer productivity.

The primary differences between PostgreSQL and MongoDB fall under how they store and query data. As the name suggests, PostgreSQL is based on Structured Query Language (SQL) and stores data in tables containing columns and rows. MongoDB stores data in JSON-like documents and is queried using the MongoDB Query API with a variety of programming languages.

So what’s MongoDB’s elevator pitch?

MongoDB is a flexible, general-purpose database built for everything from simple applications all the way to mission-critical use cases that require robust security, durability, availability, and performance at global scale. Its scale-out architecture seamlessly handles sharding across data centers and cloud regions, ensuring low-latency access worldwide. MongoDB’s document data model allows users to work with and manage dynamic, multi-structured data on the fly, while still enabling schema constraints when necessary.

PostgreSQL faces challenges with horizontal scaling and efficiently handling diverse data types, and it can lead to performance bottlenecks as a result of its architecture.

For businesses seeking a future-proof database that can handle any type of data at high volumes to keep up with evolving requirements and reach users around the world, MongoDB is an ideal choice. Its scalability, flexibility, and global reach are indispensable in today's competitive landscape.

What are the benefits of documents over relational tables?

MongoDB's document data model maps naturally to objects in application code, making it simple for developers to learn and use because it aligns to how they think and code. Documents provide the ability to represent hierarchical relationships and easily store complex data structures, including in fields, arrays, or even as nested subdocuments. Each field—even those deeply nested in subdocuments and arrays—can be indexed and efficiently queried because related information is stored together for fast access.

MongoDB stores data as documents in a binary representation called BSON (Binary JSON). BSON includes data types not present in JSON data (e.g., datetime, int, long, date, floating point, decimal128, and byte array) offering type-strict handling for multiple numeric types instead of a universal "number" type.

Documents support polymorphism, meaning fields can vary from document to document in the same database. Developers do not need to declare the document structure beforehand, and new fields can be added with a simple write command. This eliminates the need for maintaining a central system catalog or updating an object-relational mapper (ORM), and is done without any downtime or system impact.

As this may be sub-optimal for some use cases, MongoDB also supports schema controls through its schema validation features, allowing developers to enact and enforce data governance controls over each collection in the database. This flexibility is useful when consolidating information from diverse sources or accommodating variations in documents over time, especially as new application functionality is continuously deployed.

MongoDB’s document model and query engine also handle semantic search, vector search, geospatial, graph, and time series data. Moreover, MongoDB offers sharding, columnar compression, densification, deletes, and gap-filling for time series collections.

PostgreSQL and MongoDB terminology and concepts

Many of the terms and concepts used in MongoDB's document model are the same or similar to PostgreSQL's tabular model:

ACID transactions in each system

One of the most powerful features of relational databases is ACID transactions. The details of how ACID transactions are defined and implemented fill many computer science text books, largely concerning isolation levels in database transactions. In this case, both MongoDB and PostgreSQL support fully ACID-compliant transactions.

The important thing to remember is that transactions allow many changes to a database to be made in a group or rolled back in a group under an all-or-nothing guarantee. This is also known as atomicity.

In a relational database, the data in question would be modeled across separate parent-child tables in a tabular schema. This means that updating all the records at once would require a transaction.

In a sense, document databases have an easier time implementing transactions because they cluster data in a document, and writing and reading with a single document is an atomic operation. Therefore, transactions are only needed for multi-document updates that must be atomic. MongoDB guarantees complete isolation as a document is updated. Any errors will trigger the update operation to roll back, reverting the change and ensuring that clients receive a consistent view of the document.

MongoDB also supports database transactions across many documents, so chunks of related changes can be committed or rolled back as a group. With its multi-document transactions capability, MongoDB is one of the few databases to combine the ACID guarantees of traditional relational databases with the speed, flexibility, and power of the document model.

MongoDB also supports distributed transactions, which enable atomic multi-document operations even when the data lives across multiple shards and replicas. For a developer, transactions in MongoDB behave just like transactions within PostgreSQL. Transactions in MongoDB are also multi-statement, with similar syntax (e.g., starttransaction and committransaction) and snapshot isolation. This makes them easy for anyone with prior transactional or distributed systems experience to add to any application.

Data modeling and query languages

As a relational database, PostgreSQL uses Structured Query Language (SQL) for database access.

A prerequisite for working with SQL databases is to first establish the database schema and entity relationships. Related information may be stored in separate tables but associated through the use of foreign keys and join operations. After a schema is determined, data is loaded, and the database is brought online, further changes to schema can require downtime or other migration procedures that can degrade application performance.

One of the challenges of using a relational database is the need to define its structure in advance. Changing structure after loading data is often very difficult, requiring multiple teams across development, DBA, and DevOps to tightly coordinate changes.

In comparison, in MongoDB, the application’s schema does not need to be pre-determined and can change over time with little friction. This gives developers more control over the data model, allowing it to be driven by the needs of the application.

Much of MongoDB’s flexibility comes from the fact that it does not use SQL. Instead, to work with documents in MongoDB and extract data, the MongoDB Query API offers comparable power and flexibility as SQL. For example, like SQL, our Query API enables developers to reference data from multiple tables, transform and aggregate that data, and filter for specific results. Unlike SQL, the Query API works in a way that is idiomatic for each programming language.

Query performance in MongoDB can be accelerated by creating indexes on fields in documents and subdocuments. MongoDB allows any field of a document, including those deeply nested in arrays and subdocuments, to be indexed and efficiently queried.

Query language map

Both PostgreSQL and the MongoDB Query API give developers a range of capabilities for storing, updating, removing, searching, and retrieving information from the database. Below are a few examples of SQL statements and how they map to terms in MongoDB.

The following charts compare the SQL and MongoDB approaches to querying data and shows a few examples of SQL statements and how they map to MongoDB:

CREATE TABLE users (
user_id VARCHAR(20) NOT NULL,
age INTEGER NOT NULL,
status VARCHAR(10));

INSERT INTO users(user_id, age, status)
VALUES ('bcd001', 45,"A");

db.users.insertOne({
  user_id: "bcd001",
  age: 45,
  status: "A" 
})
// see note below table.

SELECT *
FROM users;

db.users.find()

UPDATE users
SET status = 'C'
WHERE age > 25;

db.users.updateOne(
    { age: { $gt: 25 } },
    { $set: { status: "C" } },
    { multi: true }
)
// see note below table.

START TRANSACTION;
INSERT INTO orders 
(order_id, product, quantity)
VALUES ('1a2b3c', 'T-shirt', '7');
UPDATE stock
SET quantity=quantity-7
WHERE product='T-shirt';
COMMIT;

session.startTransaction();
db.orders.insert ({
  order_id: '1a2b3c',
  product: 'T-shirt',
  quantity: 7
})
db.stock.updateOne (
  { product: { $eq: 'T-shirt', } },
  { $inc: { quantity: -7 } }
})
session.commitTransaction();

Note: db.collection.insertMany() can be used for more than one document.

Check out these resources for even more comparisons:

• SQL to MongoDB Mapping chart
• SQL to Aggregation Mapping chart

Agility and collaboration

MongoDB allows developers to think of their data similarly to how they structure their code. A developer can define the structure of a document, develop against that, and add new fields whenever necessary. If a new field is needed, they can simply add a function to capture it in their code and write it to their documents. There are no additional steps involved. This flexibility avoids the delays and bottlenecks associated with having to ask a DBA to restructure data definition language statements, then recreate and reload a relational database.

In a document database, a developer or team can own documents or portions of documents and evolve them as needed, without intermediation or complex dependency chains between different teams.

It is important to mention MongoDB’s optional schema governance capabilities once again here, which give developers the option to control who has the ability to add new fields to a document. While schema flexibility is generally a benefit, we understand the importance of governance when it comes to regulated industries and some specific application use cases.

Scalability and resilience

MongoDB is built to be both scalable and portable. The experience of using it is the same regardless of where it is deployed—from a developer’s laptop to a multi-shard cluster spanning multiple cloud regions or even across cloud providers.

MongoDB is based on a distributed architecture that allows users to scale out across many instances and is proven to power large, globally distributed applications. The scale-out, or horizontal scaling, strategy differs from PostgreSQL’s model of scaling up. MongoDB is designed to distribute data across multiple machines as data volume grows. This allows it to orchestrate reads and writes of its data across smaller commodity hardware. In turn, this results in lower cost and fewer performance bottlenecks, as each machine can scale independently based on the needs of the application.

With PostgreSQL, the approach to scaling is dependent on if the application workload is writing or reading data. For writes, it is based on scale-up architecture, in which a single primary machine running PostgreSQL must be made as powerful as possible in order to handle the application’s requirements. For reads, it is possible to scale out PostgreSQL by creating replicas, but each replica must contain a full copy of the database.

The architecture that makes MongoDB scalable is based on the idea of intelligently partitioning (sharding) data across instances in the cluster. MongoDB does not break documents apart; documents are independent units, which makes it easier to distribute them across multiple servers while preserving data locality.

Each MongoDB shard runs as a replica set—a synchronized cluster of three or more individual servers that continuously replicate data between them, offering redundancy and protection against downtime in the face of a system failure or planned maintenance. Replicas can also be installed across data centers, offering resiliency against regional outages. Creating and configuring multi-region clusters quickly on one or across different cloud providers is made even easier as a fully managed service with MongoDB Atlas.

Given PostgreSQL’s popularity, there are many database-as-a-service (DBaaS) options from various providers that aim to alleviate its shortcomings with scalability. These include Amazon Aurora, Google Cloud SQL, and others. While these offer solutions for aspects of PostgreSQL’s scalability constraints, they can't do so without imposing tradeoffs in other areas. Developers must weigh the value of the scalability enhancements against how each system differs from the features and behavior of the core PostgreSQL engine.

In contrast, MongoDB Atlas provides its scalability features without imposing any constraints on the underlying MongoDB database engine. MongoDB Atlas runs in the same way across all three major cloud providers, simplifying migration and multi-cloud deployment while providing the advantage of data and application portability.

Platform ecosystem and deployment options

As any fundamental technology like a database grows, it is supported by a platform ecosystem of services, integrations, partners, and related products. At the center of the MongoDB platform ecosystem is the database, but it has many layers that provide additional value and solve common problems.

MongoDB benefits from widespread adoption and is consistently the most popular non-relational database in the annual Stackoverflow developer survey. Thanks to the efforts of MongoDB engineering and the community, we have built a complete platform to serve the needs of developers across all industries and specialties.

PostgreSQL can be run as an installed, self-managed version, or as a database-as-a-service on all of the leading cloud providers. However, each of those implementations behave differently than core PostgreSQL. Support for PostgreSQL depends on its deployment model, with cloud providers offering support for their services, and multiple third parties offering support for self-managed deployments.

MongoDB is available in the following forms:

• MongoDB Atlas is a database-as-a-service offering that runs on all of the major cloud platforms (AWS, Microsoft Azure, and Google Cloud).
• MongoDB Community edition is an open and free database that can be installed on Linux, Windows, or Mac OS.
• MongoDB Enterprise Advanced is based on MongoDB Community edition with additional features that are available through a subscription. Enterprise Advanced includes comprehensive support for self-managed MongoDB deployments. It also adds enterprise features such as LDAP and Kerberos support, on-disk encryption, auditing, and operational tooling. MongoDB Enterprise Advanced can be installed on Linux, Windows, or Mac OS.

As is also true with PostgreSQL, MongoDB has a strong developer community that represents everyone from hobbyists to the most innovative startups to the largest enterprises and government agencies.

Where is your data model going?

The nature of your data and your target use cases are also vitally important. Those with a large ecosystem of SQL skills and tools and numerous existing applications may choose to continue using a relational data model.

MongoDB has succeeded because it opens the door to new levels of developer productivity, while rigid relational tables often introduce roadblocks. If you have data that needs to be delivered at scale, would benefit from developer control of the schema, powers a legacy application needing modernization, or meets a need not fully understood at the outset, it is important to consider a document database like MongoDB.

Furthermore, MongoDB Atlas is not just the only globally distributed, multi-cloud database but also a complete platform, which makes spinning up a database cluster and back-end-as-a-service easy and fast.