There is nothing wrong to say that the relational database has been served as one of the strong groundwork for a very large number of applications. In the present scenario, PostgreSQL doesn’t need any introduction as it is widely accepted as one of the best relational databases.
It was developed in the year 1980 by UC Berkeley. Presently it is a very popular technology with a very large number of highly satisfied users. Because a lot of other technologies have come into existence, many people are comparing them with others. One of its prime competitors is PostgreSQL. This article on MongoDB vs PostgreSQL will help you to choose the best.
We would use the following pointers to compare the MongoDB vs PostgreSQL:
So let us get started with this MongoDB vs PostgreSQL article,
A very large number of experts say both are equal in several aspects. Actually, they both have their own pros and cons associated with them. There are several assumptions that some of the other technologies are better than PostgreSQL and one of its prime competitors is MongoDB.
In this post, we will put some spotlight on both these technologies to help you understand both. It is a well-known fact that the demand of users is changing at a very fast speed. Even after making a lot of efforts, businesses in the present scenario are able to cater to the needs of clients.
When it comes to databases, businesses always want to have something which can be trusted for the long run. In a true sense, both MongoDB and PostgreSQL are capable to cater to a lot of needs but there are several differences between them.
One of the prime issues in today’s world is businesses have to work with both structured as well as unstructured data and thus they want to implement something that is really helpful in this matter. This is one of the leading reasons why some non-relational databases such as MongoDB are gaining a lot of popularity. They are actually capable to cater all the needs of novel applications and thus ensure reliability in the businesses.
It is a well-known and in fact, the best Relational Database Management system known for its efficiency. The best part about it is its open-source nature. It is because of no other reason than this, many organizations adopt it. Actually, they can customize the same as per needs as it’s an open-source approach.
Currently, PostgreSQL is controlled by PostgreSQL Global Development. There are a very large number of contributors as well as organizations that are a part of it. This is one of the major factors that contribute to its success. The best thing about this database is it considers SQL for storing the data into the tables and for accessing the database.
Users are always free to pre-determine their database depending on the exact needs and can design policies to control the relations among the fields which are present in the table. There might be other information too but that can be stored in other tables. It is equipped with one of the best features and i.e. eliminating the repeated content or data. This can simply be done with the help of normalization.
Just like PostgreSQL, it is also open-source in nature and can simply be customized up to a great extent to cater to your needs. A lot of features of MongoDB and PostgreSQL are similar but still, there are certain facts that make them different from one another. MongoDB was developed by MongoDB Inc.
It generally stores its data in structures that don’t have similar structures. Related data or information is generally stored together. This is because it offers quick access to the query language. Users are always free to build and maintain records and for this, there is no need to define the structure.
However, this is an issue with PostgreSQL in which you need to define it first. A few fields can simply be integrated when it comes to changing the structure of all your documents in MongoDB.
Another best thing is storing arrays and representing the hierarchical relations is not at all a big deal. Data can simply be confined to other data and can be separated when the need for the same is felt. When it comes to data quality, users need not worry about the same.
Actually, controls can be considered for this and there are certain dynamic schemes that bring a lot of agility. MongoDB is capable to offer validation of documents which makes it an ideal choice for a very large number of organizations all over the world.
Users are always free to check the structure of documents and can make use of filters to search, analyze, or while modifying the data in case the need for the same occurs. Developers always have a choice of adding as many features as they want without worrying about anything. On the other side, PostgreSQL also has several features but not as many as in the case of MongoDB.
There are certain concepts that are common in both these technologies. Both of them are equipped with secondary Indexes. PostgreSQL contains Row, column as well Table while MongoDB contains Documents, Fields as well as collections for the same task.
[Related Article: What Is MongoDB]
There are certain challenges even in data scaling for those who consider PostgreSQL. There is a strict upper limit actually for distributing the database after a single node. Of course, this affects functionality and makes MongoDB useful than PostgreSQL when both of them are compared. MongoDB is quite reliable and users are always free to distribute the database to a number of nodes.
When it comes to flexibility, both these technologies are equally powerful. MongoDB makes use of Binary JSON for storing data. It is actually a binary representation and the documents which bear a common structure are organized as collections.
It is not always necessary that fields remain the same for all the documents. Actually, they can vary largely depending upon the type of document. One of the best things is documents are self-describing.
It basically means there is no need for you to mention their structure. Obviously, there are a lot of documents in a collection and in case the need to add a novel filed is felt, it can be done without affecting any other document. Also, there is no need to take the system offline and updating the system catalog.
When it comes to PostgreSQL, it is necessary that you mention the structure of the table prior to inserting the data. Although data can be collected and integrated but sometimes making changes to it is an expensive process.
When it comes to JSON support, actually, PostgreSQL was one of the earlier and in fact, the oldest relational databases which introduce support for JSON. The primary aim was to enable the developers to store all their documents easily and reliably but a lot of issues were there some of which even exist today. It is because of the fact that the architecture of PostgreSQL is relational in nature.
Users cannot do anything beyond retrieving and inserting JSON as a string. Thus not all the applications can be processed easily. Many times it fails to find the difference between the long, integer and thus provides no support for all data types.
In addition to this, in a JSON array, there is no provision to update the fields if you are considering PostgreSQL. The entire document needs to be written to the database which creates a lot of issues. The absence of JSON drivers makes it very difficult to manage a lot of tasks.
When it comes to Data models, MongoDB is widely considered as best as compared to PostgreSQL. It is because there are several features that are advanced and it is possible to build the functionality around the documents.
The language query is simply the best and it has secondary indexes. When it comes to multi-structured data types and nodes, all the features of MongoDB can simply be used without worrying about anything.
MongoDB Model also contains Dynamic Schema while PostgreSQL doesn’t. There are Field updates in MongoDB but they are limited in PostgreSQL. In addition to this, MongoDB is quite easy for programmers as compared to PostgreSQL. There is no data locality in PostgreSQL but MongoDB has the same present in it. Also, no data locality is there in PostgreSQL.
On the other side, MongoDB is a powerful approach that is very friendly with JSON. It supports JSON and all its features. Users can perform a lot of tasks without worrying about anything. Also, it works perfectly fine and several JSON drivers are available for MongoDB which enhances functionality.
When MongoDB and PostgreSQL are compared in terms of their query language, both are almost equally powerful. Programmers can easily understand them both and the best thing is that full support for both technologies is available
Although both these technologies have their own pros and cons as already mentioned, a lot of users prefer MongoDB. This is mainly because it is capable to handle a lot of tasks simply. Organizations irrespective of their size can consider it for building reliable applications at a very fast speed.
Also, if viewed in terms of an average basis, MongoDB has a better uptime. As per several experts, MongoDB is capable to cut down a lot of complexities from the development.
|Open Source Development Start||2009||1995|
|Schemas||Dynamic||Static and Dynamic|
|Supports Hierarchical Document Data||Yes||Yes (since 2012)|
|Supports Key-Value Data||Yes||Yes (since 2006)|
|Supports Relational Data / Normalized Form Storage||No||Yes|
|Joining of Data and Foreign Keys||No||Yes|
|Powerful Query Language||No||Yes|
|Transaction Support and Multi-Version Concurrency Controls||No||Yes|
|Atomic Transactions||Within a Document||Across the Database|
|Common Web Data Format Support||JSON (Document), Key-Value, XML||JSON (Document), Key-Value, XML|
|The easiest path to Scaling||Horizontal Scale-Out||Vertical Scale Up|
|Easy Integration with Other Data Stores||No||Foreign Data Wrappers to Oracle, MySQL, MongoDB, CouchDB, Redis, Neo4j, Twitter, LDAP, File, Hadoop, and more…|
|Primary Uses||To process Big Data with high concurrency updates where data integrity and consistency are not required.||Transactional and Operational Applications that benefit from normalized form joins, data constraints, and transactional support.|
Prasanthi is an expert writer in MongoDB, and has written for various reputable online and print publications. At present, she is working for Mindmajix, and writes content not only on MongoDB, but also on Sharepoint, Uipath, and AWS.