MongoDB vs PostgreSQL 2023 Comparison

Early-stage startups who worked with us have raised over $140M in funding. Our expert developers deliver supportable and maintainable code for companies of all sizes. DevTeam.Space dedicated tech account managers and AI-powered agile process provide you with all the tools, notifications, and performance tracking to ensure ongoing success. However, if you want to store and process a range of data in different formats and in real or near-real time, document databases like MongoDB will suit your business requirements better. The syntax supported by both databases is quite different from each other.

PostgreSQL supports a range of data types, including dates, text, integers, and Booleans. MongoDB supports a rapid, iterative cycle of development so well because of the way that a document database turns data into code under the control of developers. Having a different syntax and structure of data than relational database management systems (RDBMSs), it stores data in the form of documents. 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.

Features of mongodb

But often at the beginning of a development project, the project leaders have a good grasp of the use case, but don’t really have clarity about the specific application features their business and users will need. The rest of this article aims to provide information that helps make a safe bet. MongoDB is a dynamic database system continually evolving to deliver optimized performance, robust security, and limitless scalability.

  • MongoDB has currency control mechanisms that use document-level atomicity and optimistic locking.
  • MongoDB does not support Foreign Keys whereas PostgreSQL does support them.
  • That’s easier to do if you are working on a new application, or plan on modernizing an existing one.
  • 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.
  • For point data, it uses a Z-order curve while for spatial data it uses XZ space filling curve.
  • In the sections below, we take a closer look at specific areas, including data types, performance, scalability, consistency, availability, and security.
  • While NoSQL databases work on storing data in key-value pairs as one record, relational databases store data on different tables.

Only in Q1 the response time presents smaller fluctuations between the DBMSs. PostgreSQL shines when data integrity, complex querying, and strong SQL capabilities are paramount. It is an excellent choice for applications involving financial transactions, data warehousing, and complex reporting.

Main Features:

The conclusion is pretty surprising as there isn’t really anything that MongoDB can do and PostgreSQL can’t. PostgreSQL also outperforms or at worst, matches MongoDB for performance. PostgreSQL follows the ACID properties of atomicity, consistency, isolation, and durability. ACID principles enable PostgreSQL for storing data and running critical transactions safely.

mongodb vs postgresql

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. This means it’s easy for developers to pick restaurant mobile app 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.

MongoDB and PostgreSQL Comparison Table

PostgreSQL supports various data types, including JSON, and offers advanced indexing techniques, making it versatile for different data requirements. MongoDB, a NoSQL database, shines in its adaptability, accommodating evolving data models and offering remarkable scalability. In contrast, PostgreSQL, a robust relational DBMS, excels in data integrity, SQL capabilities, and security. It is favored for applications demanding structured data, complex queries, and stringent data consistency, making it a top pick for industries like finance and healthcare. On the other hand, MySQL excels in use cases that require robust transactional support and strong data integrity.

mongodb vs postgresql

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. 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.

Query Language

MongoDB is the most popular NoSQL database today and with good reason. This e-book is a general overview of MongoDB, providing a basic understanding of the database. For example, here is how you define Connecticut by drawing a square around it on a map. This statement uses the GeoJSON geographical query features of MongoDB to do that. Structured Query Language is designed for performing CRUD (Create, Read, Update, Delete) operations on a database.

mongodb vs postgresql

For reads, it is possible to scale-out PostgreSQL by creating replicas, but each replica must contain a full copy of the database. If you are looking for a distributed database for modern transactional and analytical applications that are working with rapidly changing, multi-structured data, then MongoDB is the way to go. If you require a modern database to process data from various sources and in various formats, then go for MongoDB. If SQL database structure suits your application needs, PostgreSQL is a better choice. The distributed architecture of MongoDB means that different components spread across multiple platforms work together in collaboration. If there is a need to scale, it can be easily done through horizontal scaling of more platforms.

MongoDB vs PostgreSQL: Which Should You Choose?

The reason for this behavior is that the data stored in MongoDB are in GeoJson format and each record consist of many extra characters and a unique auto created id called ObjectId. Thus, each record is significant bigger in size than it was in its original CSV format. On the other hand, in PostgreSQL the data ingested in database as CSV, with the addition of the_geom column that contains the POINT geometries of each latitude and longitude. It is worth mentioning, that the selection of different vessels (ship_id) and time intervals follows a normal distribution, which was applied in the dataset at an early stage before the execution of queries. The results show that PostgreSQL outperforms MongoDB in almost all queries.

To get support for PostgreSQL, you have to use a cloud version or go to third parties offering specialized services. 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 problems. For instance, you want to build a simple visitor management application where you have to update the information of visitors present inside the building at any given point of time. An event driven architecture would be much suited, where we could send an event as soon as any visitor walks into the building and is checked in by the receptionist. This flexibility is hugely useful when consolidating information from diverse sources or accommodating variations in documents over time, especially as new application functionality is continuously deployed.

Free e-book: The Beginner’s Guide to MongoDB

Despite the different data models that MongoDB and PostgreSQL expose, many organizations face the challenge of picking either technology. 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. As you may know, PostgreSQL refers to itself as an open-source object-relational database system.

Bonus section: Optimizing your upgraded MongoDB

Developers can decide what’s needed in the application and change it in the database accordingly. PostgreSQL is a rock solid, open source, enterprise-grade SQL database that has been expanding its capabilities for 30 years. Everything you would ever want from a relational database is present in PostgreSQL. Take advantage of the opportunity to harness the full potential of MongoDB. By following these best practices, maintaining a proactive approach to database management, and seeking expert guidance when needed, you can ensure the success of your MongoDB deployment.

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Relationships between multiple tables of your database add more value to analysis and storage capabilities. Indexes are a type of data structure that can store a very small amount of data in an easily readable form. They are only one component of a join and make your data simple to understand and, thereby help you to resolve any queries with ease. 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.


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