Let’s be clear before we go any further: both MongoDB and PostgreSQL are impressive database management systems.
This post isn’t about picking one or either apart — our aim is to help you get a firm grasp of each database’s character and understand which use cases both databases serve best.
PostgreSQL or MongoDB: A quick Summary
Want a super-fast, super-convenient overview of our MongoDB vs PostgreSQL comparison? Read on …
MongoDB is perhaps the most well-known document database. This is built on a distributed architecture, and is recognized as a go-to cloud-based platform designed for managing data and delivering it to applications. It handles three types of workloads at scale: operational, analytical, and transactional. MongoDB is likely to be your top choice if you’re focused on developer productivity, time to market, agile methodologies, and lending DevOps support.
How about PostgreSQL? This is a reliable, enterprise-grade, open-source SQL database with more than three decades of history behind it. All you could ever look for in a relational database is here for you. PostgreSQL is also based on a scale-up architecture.
This is a terrific option if your concerns include exploring the limits of SQL, serving up a huge number of queries from many tables, and compatibility.
What Exactly Do You Need a Database For?
As you can see from the above MongoDB vs PostgreSQL comparison, both databases have lots to recommend them. They’re brilliantly made and dependable.
It all comes down to the type of database you’re looking for based on your unique requirements — a document database or a relational database.
If you need a distributed database designed for analytical and transactional applications working with ever-changing data, try MongoDB.
But if a SQL database is a better fit for your requirements, PostgreSQL should work for you.
Of course, it may take some time to understand which database is ideal for you, especially if you’ve never encountered either option before. We’ve written this article to offer greater insight into each database’s characteristics so you can make an informed choice and end up with the perfect solution.
At the start of development projects, it’s common for project leaders to have a clear understanding of the use case — but not of the specific features their users need in an application. That’s why they essentially gamble on what will work best.
Below, we’ll do our best to provide all the information you need to make this a safer bet.
PostgreSQL vs MongoDB: Summarizing Our Advice
Want another quick overview of our advice on whether to choose PostgreSQL or MongoDB?
Here’s a basic overview of our recommendations:
- If you’re starting a development project and want to understand your needs and data model through an agile development process, MongoDB should work wonders. Developers have the flexibility to reshape data independently as required. You can also manage data of any structure — not just tabular ones you define ahead of time.
- If you’re aiming to support an application that will need to scale (with regards to traffic volume, size of data, or both), and it has to be distributed throughout various regions for data locality, go for MongoDB. The scale-out architecture is capable of meeting your needs automatically.
- If you’re on the lookout for a multi-cloud database functioning like all public clouds, with capacity to store consumer data in numerous regions, and offering support for cutting-edge mobile-dev and serverless paradigms, MongoDB Atlas might just amaze you.
- If you run a SQL shop and feel adding a fresh paradigm will incur more expense than it’s worth, PostgreSQL is likely to satisfy your needs.
- If you’re a creatively-minded SQL developer with a hunger to push SQL further by leveraging advanced methods for the indexing, storage, and searching of various types of structured data, PostgreSQL will help you more than another RDBMS option. It’s also terrific for fine-turning the database to your heart’s content and making user-designed functions in a range of languages.
That’s our quick summary — now let’s take a deeper look at each database in turn before we reach our detailed comparison.
MongoDB: The Document Database That Has Come So Far
MongoDB’s document data model is designed to naturally map to objects in application code.
This means it’s easy for developers to pick up, learn, and put to good use. Documents empower you with the flexibility to represent hierarchy-based relationships to store arrays and others (even those that are significantly more complex) in a simple way.
Data can be stored in fields, arrays, or nested sub documents in JSON documents. As a result, related information may be stored together in a more organized way, ideal for quick query access via MongoDB’s expressive query language.
Data is stored as documents within BSON (Binary JSON) in MongoDB. In this binary representation, fields may differ from one document to the next — structures don’t need to be declared to the system, as documents are self describing.
When you want to introduce a new field to a document, you can do so without disrupting those other documents within the collection. There’s no need to update an ORM or a central system catalog, and you don’t have to take the system offline. You may also use schema validation to put data governance controls into effect for all collections.
MongoDB’s immense flexibility is fantastic for gathering information from a variety of sources or for accommodating document variations across a period of time. This is particularly valuable with the ongoing deployment of new application functionality.
Explaining the language of PostgreSQL and MongoDB
You’ll find that some of the concepts and terms featured within MongoDB’s document model match (or nearly match) those in PostgreSQL’s tabular model:
|Embedded documents, $lookup & $graphLookup, $unionWith
|On-demand Materialized Views
Document model improvements
With MongoDB, you can store data in virtually all structures. And every field may be indexed and searched easily (even when they’re nested deep within sub documents and arrays).
Also, MongoDB introduces elements to the document model and query engine, allowing them to handle time and geospatial data tagging. This increases the query types and analytics you can undertake on a database.
BSON boasts data types that are unavailable in JSON data, such as int, datetime, decimal128, and more. It provides type-strict handling for a variety of numeric types, rather than a universal “number” type.
With schema validation, you can apply data quality controls and governance to schemas.
ACID transactions for changing large numbers of documents
Thanks to ACID transactions, relational databases allow for simpler application writing. The defining and implementation of ACID transactions is highly complex, and we simply don’t have the space to detail it all here.
So much of the conversation in the world of computer science covers isolation levels in database transactions. PostgreSQL defaults to the read committed isolation level, enabling users to adjust it to the serializable isolation level.
One crucial factor to bear in mind: transactions allow a lot of database changes to be performed in a group or rolled back in a group.
Within a relational database, data would be modeled across distinct parent-child tables in a tabular schema. As a result, a transaction would be necessary to update every record at once.
Essentially, it’s simpler for document databases to implement transactions as they keep data clustered in a document, and no multi-document transaction is required as document reading is an atomic process. One field or more might be written in just one operation, including updates to numerous sub documents and array elements.
MongoDB guarantees total isolation when documents are updated: the update operation will be rolled back in the event of an error, reverting the change. This ensures that clients’ document view is consistent.
On top of this, MongoDB provides support for database transactions across numerous documents: pieces of related changes may be committed or rolled back as a group. Due to MongoDB’s multi-document transactions, this database is one of the few that combines the ACID guarantees of conventional relational databases with the flexibility, power, and speed the document model is known for.
From a programmer’s point of view, MongoDB transactions resemble those that developers will be familiar with from PostgreSQL. MongoDB transactions are multi-statement, featuring syntax that’s similar (for example, “starttransaction” and “committransaction”), and with snapshot isolation.
This makes it easier for a user who has previous transaction experience to contribute to any application.
MongoDB query language vs SQL
PostgreSQL uses a relational database model that depends on storing data within tables and utilizing SQL (Structured Query Language) for database access.
For this to be effective in PostgreSQL and any other SQL database, the database schema has to be built and data relationships must be established before data is added to the database. Information that’s related can be stored in separate tables and associated by using Foreign Keys and JOINs.
The majority of changes in schema require a migration procedure capable of taking the database offline or reducing the performance of an application while it’s not running.
SQL’s strength is its effective, well-known query language and extensive selection of tools. The difficulty of utilizing a relational database is the requirement to establish its structure ahead of time. It can be hard to adjust structure once data has been loaded: it needs several teams in development, Ops, and DBA to coordinate changes carefully.
With MongoDB (a document database), data structure doesn’t need to be planned in the database in advance, and it’s far easier to adjust. Developers can choose what’s essential in the application and make database alterations as required.
By default, MongoDB doesn’t use SQL — it provides users with a unique query language instead (MQL). This can be used to work with documents in MongoDB and take out data, and it delivers much of the flexibility and power that SQL does.
For instance, MQL enables users to reference data from numerous tables, transform it, aggregate it, and filter results for greater precision — like SQL. And unlike SQL, MQL functions in a way that’s idiomatic for every programming language.
You can accelerate MongoDB’s query performance if you make indexes on fields in documents and sub documents. This database enables all document fields to be indexed and queried simply, as well as those that are deep within sub documents and arrays.
Below, we’ll explore how SQL and MongoDB approach querying data, with a few helpful examples.
Boosting agility and collaborative capabilities
Thanks to the document model’s emergent properties, development and collaboration are both simpler and quicker.
MongoDB makes data a lot like code, from an individual developer point of view. A developer could define a BSON or JSON document’s structure, undertake some development work on it, see how they get on with it, introduce new fields whenever they like, and rework data as required. That’s one of the chief advantages of the document model.
This flexibility is a huge benefit for avoiding bottlenecks and delays resulting from asking a DBA to restructure data definition language statements, before recreating and reloading a relational database (or asking the developer to do all this work).
With a document database, developers or teams have the power to own documents (or parts of them) and change them as required. There’s no need for intermediation or complicated communication between teams.
The importance of scalability, security, and strength
As MongoDB was designed to scale out, use cases needing extremely fast queries and vast amounts of data (or both) may be handled by building ever larger clusters comprising small machines.
MongoDB relies on a distributed architecture allowing users to scale out across numerous instances. It’s capable of powering massive applications regardless of it being measured by data sizes or users. This scale-out approach depends on the use of a growing number of smaller, generally more cost-effective machines. It could incorporate hundreds of machines overall.
The scaling technique in PostgreSQL hinges on whether data is being written or read:
- In the case of writing, the scaling approach is based on a scale-up architecture: one primary machine runs PostgreSQL and should be made as powerful as it can be to scale.
- With reading, you can scale-out PostgreSQL if you create replicas — though each one has to have a complete copy of the database.
What makes MongoDB scalable is the concept of partitioning (sharing) data across instances within the cluster intelligently. This database doesn’t split documents into pieces — they’re independent units, which makes distributing them throughout various servers simpler, while the data is locally preserved.
Data can be distributed throughout regions easily in the MongoDB Atlas cloud service, which is fully managed. You can tag specific documents so they’re constantly stored in certain countries or global regions. This can help to:
- Reduce latency by keeping data stored close to its target audience
- Ensure compliance with laws determining where data should be stored legally
Every MongoDB shard is run as a replica set — a synchronized cluster consisting of three or more servers that keep replicating data between them. This provides redundancy and protection against any downtime that might occur in the event of a scheduled break for maintenance or a system failure.
Replicas may be installed across datacenters too. This provides some resilience against regional outages. MongoDB Atlas makes building and configuring these clusters simpler and quicker.
MongoDB offers a modern selection of cybersecurity controls and integrations for both its cloud and on-site versions. This features strong security paradigms such as client-side, field-level encryption — this enables users to encrypt data before sending it to the database via the network.
In PostgreSQL, you’ll find a comprehensive portfolio of security features, with a number of encryption types to choose from. This database is available in the cloud on every major cloud provider. However, developer and operational tooling differs from one cloud vendor to another, even though it’s all the same database.
As a result, migrations between multiple clouds are more complicated. MongoDB Atlas performs in the same way across the three biggest cloud providers, ensuring easier migration and multi-cloud deployment.
Mature platforms delivering better value
Growing databases are supported by an ecosystem made up of many services, partners, integrations, and other relevant products. The database is at the core of the MongoDB ecosystem, though there are numerous layers bringing users extra value and problem-solving capabilities.
MongoDB has enjoyed widespread adoption as it has become the biggest modern database — it’s considered the go-to database by many developers. Due to the dedicated MongoDB community and engineering, it’s become a comprehensive platform that serves developers’ needs to an exceptional degree.
You can run PostgreSQL as a version that you install and manage yourself, or you can opt for a database as a service option on the major cloud providers. Each implementation performs how the provider behind it intends it to. If you want PostgreSQL support, you need to utilize a cloud version or try third parties providing specialist services.
MongoDB is available in several forms:
- MongoDB Atlas: A database as a service designed to run on the biggest cloud platforms, such as Google Cloud Platform, Microsoft Azure, and AWS
- MongoDB Community Edition: A free, open database you can install on Windows, MacOS, or Linux
- MongoDB Enterprise: This is based on the above version but includes extra features accessible via the MongoDB Enterprise Advanced subscription. If you opt for this, you’ll receive more support, as well as enterprise features like on-disk encryption, LDAP and Kerberos support, and more. MongoDB Enterprise is suitable for Windows, MacOS, and Linux.
On top of this, MongoDB offers support for various programming languages. Idiomatic drivers are available for more than a dozen languages, but the MongoDB community has contributed plenty of others. You can take advantage of real-time aggregation, ad-hoc queries, and rich indexing to give powerful programmatic ways to access and examine data of all structure types.
MongoDB benefits from a committed community of developers spanning hobbyists, massive enterprises, government agencies, and emerging startups. Not to forget the numerous systems integrators and consultants delivering an extensive range of services.
MongoDB Atlas has been expanded via MongoDB Realm to make development of apps easier, through Lucene-powered Atlas Search. It has features supporting data lakes that have been built on cloud object storage.
MongoDB and PostgreSQL’s developer communities are typically ready to assist when needed.
MongoDB’s ideal purpose
Today, MongoDB provides the industry’s leading resiliency, security, performance, and scalability. But what is its ideal purpose?
MongoDB is especially capable of handling data structures that have been created by modern apps and APIs. It’s perfectly positioned to offer support for the agile, ever-changing development cycle seen in organizations today.
So, the biggest question to ask is what your data will become. Data can be represented by documents easily if it aligns with objects in application code. MongoDB is a fantastic fit throughout development and production — particularly if you need to scale.
But MongoDB might be a poor fit if you have a large number of incumbent apps based on regional data models and teams that have experience with SQL only.
While document databases are able to do JOINs, they’re performed in a different way from multi-page SQL statements that are often needed and generated automatically by BI tools. Still, MongoDB has an ODBC connector enabling SQL access primarily from BI tools.
PostgreSQL: The SQL Database Of Today
As with Linux, PostgreSQL is a great example of an open-source project that has been managed well. It’s one of the most widely adopted relational databases, and it emerged from the POSTGRES project that began in 1986 at the University of Berkeley. But it has certainly evolved to suit changing needs.
An object relational database
As you may know, PostgreSQL refers to itself as an open-source object-relational database system. This SQL database has approaches for boosting concurrency, handling indexing, introducing optimizations, and enhancing performance — such as table partitioning, advanced indexing, among additional functions.
The object portion of this database relates to the varied extensions allowing it to incorporate alternative types of data, including JSON data objects, XML, and key/value stores.
Primary support for SQL
PostgreSQL’s design principles place a heavy focus on SQL and relational tables, and allow considerable extensibility. This database provides a wealth of ways to enhance its efficiency, though it utilizes a scale-up strategy at its core.
As with MySQL and alternative open-source relational databases, PostgreSQL’s efficiency has been proven in the mix of demanding use cases spanning multiple areas of industry.
Let’s explore some ways in which PostgreSQL excels before we consider the primary issue for this comparison process — when is a tabular, relational model and SQL the ideal option for your application?
PostgreSQL employs an engineering-centric approach to almost everything. The company has stated that it works to conform with the latest SQL standard when that doesn’t contradict conventional features or may contribute to ill-founded architectural choices.
They have also highlighted that, at present, there are no relational databases that fully conform to that standard.
Some SQL engines are capable of handling specific simple queries to a high standard, and there are stronger SQL engines with query optimizers that can handle complex queries but finish with the right results every time. PostgreSQL is one of the latter.
This strength is due to the database’s stable progress over the years. One of the most impressive details about PostgreSQL is that it offers support for all transaction isolation levels specified in the SQL standard, along with serializable.
This standard of engineering is beyond that of many commercial databases — they typically don’t bother with it as it can be incredibly difficult to achieve with decent performance.
Security, performance, and consistency you can rely on
As PostgreSQL depends on a scale-up strategy for scaling writes or data volumes, it has to take full advantage of the computing resources made available to it. PostgreSQL achieves this via multiple indexing and concurrency strategies.
The database offers a range of impressive index types to match any query workload most efficiently. Its indexing strategies include multicolumn, B-tree, parial, and expressions. But advanced techniques are available too, such as SP-Gist, GiST, GIN, KNN Gist, and BRIN, spanning indexes and bloom filters.
As well as its mature query planner and optimizer, PostgreSQL provides such performance optimizations as table partitioning, read query parallelization, and JIT (just in time) expression compilations.
PostgreSQL complies with a wealth of security standards and includes various features for backup, reliability, and disaster recovery (typically via third-party tooling).
With PostgreSQL, extensibility is supported in a range of ways, such as stored functions and procedures, access from procedural languages (PL/PGSQL, Python, Perl, etc.), SQL/JSON path expressions, as well as foreign data wrappers. These use a standard SQL interface to link to other databases or streams.
Numerous extensions offer extra functionality, such as PostGIS — a geospatial analysis module.
Understanding standardization and leadership
It’s fair to assume that the majority of development tools and systems have been tested with PostgreSQL to ensure they’re compatible, considering it’s such a widely-used database.
Certain other databases have emulated PostgreSQL’s approach to linking APIs from languages to its databases. This simplifies moving a program running PostgreSQL to another SQL database (and the reverse).
PostgreSQL’s ideal purpose
Remember: the question we’re asking isn’t “should I choose PostgreSQL or MongoDB overall”? It’s actually “WHEN should I use a document database RATHER THAN a relational database?”. That’s because each of these databases is the finest version of its specific format available.
SQL’s advantages include a huge tool ecosystem, programming languages designed to use SQL databases, and integrations. You’ll probably be able to find assistance to make your general SQL project work properly, and for your specific PostgreSQL project too. Various deployment options for PostgreSQL are also available.
The Big Decision: When To Use MongoDB Or PostgreSQL?
If you choose to give up on SQL, that means leaving behind that expansive tech ecosystem that utilizes SQL already. That’s a simpler step to take if you’re working on a new application or intend to modernize one that already exists.
Plenty of BI and data management tools depend on SQL and create complex SQL statements to gather the right assortment of data from the database. PostgreSQL performs brilliantly in situations like these, as it’s a strong, enterprise-grade implementation that most developers understand.
Furthermore, if you’re working with a tabular data model that’s unlikely to change on a regular basis and has no need to scale-out, SQL and relational databases can be a terrific option.
However, while there are real benefits with SQL, there is a cost.
One disadvantage of PostgreSQL when compared to MongoDB is its reliance on relational data models that are unfriendly to data structures that developers use in code. They have to be defined in advance, which can delay progress as requirements fluctuate.
MongoDB, though, supports a fast, iterative development cycle so effectively due to the way in which document databases transform data into code under developer control. This speedy performance is disrupted by the nature of tighter tabular data models that are used in relational databases.
They typically need to be reshaped by database administrators via an intermediated process, slowing the overall flow of development. As a result, such issues can hamper innovation.
PostgreSQL users have to be prepared for the difficulties of scalability when an application is launched. PostgreSQL utilizes a scale-up strategy, so at one time or another in high-performance use cases, it’s possible to hit a wall.
You might be required to divert resources to find new solutions for scaling through caching or denormalizing data, or by employing alternative strategies.
These techniques tend to be unnecessary in MongoDB: scalability is in-built via native sharding, allowing for a horizontal scale-out approach. You can add further instances and continue scaling out after sharding a cluster effectively.
MongoDB Atlas has a globally aware, multi-cloud platform ready for you, which you completely manage yourself.
While PostgreSQL supports replication, features that are more advanced (e.g. automatic failover) require support by third-party products that have been developed independent of PostgreSQL. This approach tends to be more complex and works more slowly than MangoDB’s in-built ability to heal itself.