Postgres vs MongoDB: a Complete Comparison in 2023

Therefore, in a relational database, the data would be modeled across independent parent-child tables in a tabular schema. PostgreSQL has been around 24 years, and it is a solid robust enterprise grade ready relational database with a vibrant development community. If you already have a data model that is not going to change much, then PostgreSQL would be the best option.

• PostgreSQL is a relational database that stores data in tables and rows with relationships between them.
• This means that updating all the records at once would require a transaction.
• This article aims to assist you in choosing the right type of database.
• Because MongoDB stores related data in a single document, it doesn’t need to perform joins to retrieve related data, as is often necessary for a relational database.
• Both can be excellent or horrible, depending on the data model, query patterns, and specific use cases.

Also, if you have a flat, tabular data model that isn’t going to change very often and doesn’t need to scale-out, relational databases and SQL can be a powerful choice. But if you have many incumbent applications based on relational data models and teams seasoned just in SQL, a document database like MongoDB may not be a good fit. Instead, to work with documents in MongoDB and extract data, MongoDB provides its own query language (MQL) that offers most of the same power and flexibility as SQL. For example, like SQL, MQL allows you to reference data from multiple tables, transform and aggregate that data, and filter for the specific results you need. Unlike SQL, MQL works in a way that is idiomatic for each programming language.

Some key differences between MongoDB and Postgres are tabulated below:

This is due to MongoDB’s NoSQL document-oriented data model, allowing faster data retrieval and manipulation. MongoDB provides driver support for most database languages like Python, Java, R, Scala, C, C++, C#, Nodejs, and more. Constraints are rules used to limit the values that can be inserted into a column or set of columns in a table. For example, a primary key constraint ensures that each row in a table has a unique identifier, while a foreign key constraint ensures that a value in one table references a valid value in another table.

These use a standard SQL interface to link to other databases or streams. 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. They have also highlighted that, at present, there are no relational databases that fully conform to that standard.

MongoDB vs. PostgreSQL: A Comprehensive Comparison

MongoDB uses primary node replication, which duplicates data into replica sets. A singular primary node receives the writes, and secondary nodes then replicate this data. MongoDB automatically triggers a failover that elects a new primary node if a primary node becomes unavailable. MongoDB uses MongoDB Query Language (MQL) which allows you to interact with the document-oriented structure of MongoDB.

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. MongoDB relies on a distributed architecture allowing users to scale out across numerous instances. postgresql vs mongodb scalability 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. 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.

PostgreSQL Vs MongoDB: Important feature comparison

This article provides you with a comprehensive analysis of both databases and highlights the major differences between them to help you make the MongoDB vs PostgreSQL decision easy. It also provides you a brief overview of both databases along with their features. Finally, it highlights a few challenges you might face when you use these databases.

There is lots of community and aid to help you interact with MongoDB using one of your preferred programming languages. PostgreSQL also offers partitioning, which splits large tables into smaller, more manageable parts. On the other hand, while PostgreSQL is easy to install and is adaptable to almost all platforms, its efficiency may differ from platform to platform.

The Big Decision: When To Use MongoDB Or PostgreSQL?

This database provides a wealth of ways to enhance its efficiency, though it utilizes a scale-up strategy at its core. 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. 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. Growing databases are supported by an ecosystem made up of many services, partners, integrations, and other relevant products.

There is a need for queries that their response time is in a reasonable time and a scalable architecture such as cluster or cloud environment. Hadoop fits well in that case as it can handle large scale data and support big data computations and analytics through MapReduce and some declarative query interfaces such as Hive [17], Pig [18] and Scope [19]. The main challenges in spatial partitioning are the spatial data skew problem which can result in bad response time through load imbalance and boundary objects problem which can lead to incorrect query results.

Scalability

Several modern day problems need to deal with large amounts of spatio-temporal data. As such, in order to meet the application requirements, more and more systems are adapting to the specificities of those data. The most prominent case is perhaps the data storage systems, that have developed a large number of functionalities to efficiently support spatio-temporal data operations. This work is motivated by the question of which of those data storage systems is better suited to address the needs of industrial applications. In particular, the work conducted, set to identify the most efficient data store system in terms of response times, comparing two of the most representative of the two categories (NoSQL and relational), i.e.

Moreover, it doesn’t have revising tools or reporting instruments that could show the current condition of the database. You may have to check the database continuously if something doesn’t go as planned to avoid noticing a failure when it’s too late. Mongo RealmDB is available free of charge to all Atlas users for evaluation and light usage, enabling developers to build and release mobile applications. Unlike MongoDB, PostgreSQL depends on a scale-up strategy (vertical scaling) for data volumes and scaling writes. It’s performed by adding more hardware resources like disks, CPUs, and memory to an existing database node.

PostgreSQL use cases

If you want all the data from both tables into a single table, you can use a Full Join. PostgreSQL, also known as Postgres is a free, open-source RDBMS that emphasizes extensibility and SQL Compliance. It was developed at the University of California, Berkeley, and was first released on 8th July 1996. Instead of storing data like documents, PostgreSQL stores it as Structured objects.