When To Use MongoDB Instead Of PostgreSQL For Web Projects
MongoDB is a very famous open-source NoSQL database for managing and delivering data to applications. On the other hand, PostgreSQL is a robust, open-source, enterprise-grade SQL database expanding its capabilities for years now. MongoDB handles transactional, operational, and analytical workloads at scale.
On the other end, if you have concerns about compatibility, serving up thousands of queries from hundreds of tables, taking advantage of existing SQL skills, and pushing SQL to the limit, PostgreSQL is the way to go.
PostgreSQL is better at accessing data by non-key attributes, sorting, and complex queries. On the other end, MongoDB is faster at inserting and for queries that use nested references instead of joins. In this article, we will try to find out which web based applications it is more appropriate to use MongoDB instead of PostgreSQL.
Database Structure of MongoDB
Traditional databases like PostgreSQL utilize the relational database structure whilst the modern ones like the MongoDB deploys the non-relational schema. This is the foremost distinguishing characteristic when choosing between the two.
In relational databases like PostgreSQL, data is stored in tables with rows and columns which form relations between tables that consist of specific entities. It uses SQL to store, access, retrieve and manipulate data from the database. PostgreSQL may be preferred for handling data streams which are structured. However, with the advent of Big Data and complex streams, maintaining such relations becomes immensely complicated and increases the complexity in time.
This is exactly where Non-Relational databases like MongoDB are more efficient because the data on the object is not limited to the same table and allows for Big data streams to be directly dumped into the system without the hassle of defining the ever growing need of complex relationships and keys. Data is often stored inside documents in JSON (JavaScript Object Notation) or BSON (Binary JavaScript Object Notation) instead of tables.
As the name implies, JavaScript also tends to have a larger appeal in the Software Engineering niche, and therefore, creates much more accessible and simpler groundings for implementation. MongoDB is the preferred choice when it comes to storing unstructured data, better performance. In addition it is open source and therefore much easier to customize and seek support online.
When Should You Choose MongoDB Instead of PostgreSQL?
Now that we know the difference and key offerings of MongoDB and PostgreSQL. Let us have a look at which database will be better for your web project.
If you wish to try out yourself, you may consider checking out our following articles on how to installing MongoDB Server on Azure and setting up PostgreSQL Server on AWS to experience the usage of both discussed in this section.
Need Fast Development
If you want your web project up and running quickly you should use MongoDB. It allows you to develop the website faster as compared to PostgreSQL. MongoDB allows developers to control the structure of data and they can follow modern agile methodologies like quick sprints and frequent code pushes to build the website as soon as possible.
Whereas if you use PostgreSQL the developers need to ask the administrator in order to make changes in the structure of data. Then you have to unload and reload the data that can slow the development process.
Moreover, MongoDB also provides features like ad-hoc queries for optimized real-time analytics. The value of these queries depends on a variable, and the result can be different whenever it is executed and hence cannot be determined before the execution of the query. You might have thousands to millions of variables that might need to be considered.
While designing a schema of a database, we cannot take every query in our consideration that is going to be performed by the end-user. Therefore, MongoDB has optimized how these ad-hoc queries are handled to give you real-time analytics and feedback.
Have Flexible Schema
When your schema is dynamic, and you need to store a large volume of data. Usually, applications that evolve with time and do not have fixed schema or structure use MongoDB. It is better for storing unstructured, semi-structured, or often structured data in MongoDB.
Contrarily, when you know that your schema is not likely to change much, and all your data can be identified into relationships. When you need to control the state of database and database validation, PostgreSQL provides you the reliability of ACID (Atomicity, Consistency, Isolation, and Durability) for your relational database. You should use PostgreSQL
It achieves this by giving you the features such as appropriate indexing for better query execution. MongoDB gives the option to create on-demand indices to accommodate real-time, every changing-query pattern and requirement.
Many other databases cannot provide appropriate indexing of the documents, resulting in query execution and load balancing problems. If the documents are not properly indexed, the database is forced to scan all the documents that affect speed and performance.
Handle Large Volumes of Traffic and Data
MongoDB implements the load balancing by using the scale-out architecture as opposed to the scale-up strategy used by PostgreSQL which is much more expensive. This affordable architecture allows you to make changes in the structure of the database and handle large volumes of data.
MongoDB is a cloud-based database platform for large-scale databases. This platform can handle multiple concurrent read and write requests for the same data with best-in-class concurrency control and locking protocols that ensure data consistency. There is no need to add an external load balancer each, and every user has a consistent view and quality experience with the data they need to access.
Require Horizontal Scalability and Zero Downtime
If you want to achieve horizontal scalability and zero downtime, MongoDB allows you to do that by storing the documents on multiple servers. Contrastingly, PostgreSQL requires additional engineering, upgrading the server or machine by adding more processing power, and storage of the machine by using the scale-up approach which is very slow and costly.
It achieves this by replication sets and sharding.
Replication for Better Data Availability and Stability
If you keep your data in a single database, your data is vulnerable. You might have heard that the websites and data centers of tech giants like Google, Microsoft, Amazon, Apple, etc., are each deployed on almost a million servers. The reason behind it is that making copies and replicas of the database saves your data from potential risks of a server crash, service interruptions, hardware failure, etc. Otherwise, it would be impossible to access or recover your data.
In MongoDB, copies of the same data (or shards of the same data) are kept on multiple servers, and in case of a critical failure, another server is elected to access or retrieve that data. This replication set helps in disaster recovery and data backup.
Sharding
MongoDB’s sharding helps the database distribute and better execute what might otherwise be problematic and cumbersome queries. Sharding is the process of splitting larger datasets across multiple distributed collections or “shards” in a database. The collection of distributed server shards forms a single database much better suited to handle the needs of a popular application.
Final Thoughts
The choice between relational and non-relational databases depends on the requirements of your project. If you are looking for a distributed database for modern transactional and analytical applications working with rapidly changing, multi-structured data, you should choose MongoDB. If you have structured data, then Postgres is a better option. This article’s goal was to help explain the nature and characteristics of each of these databases so that you can make an informed decision.
