PostgreSQL has a similar setup with a single master, and passive nodes can be configured for reading. Scaling is inherently built into MongoDB, but with PostgreSQL an extension is required to add that capability. There are numerous extensions to choose from to achieve scalability with PostgreSQL. difference between postgresql and mongodb You can have as many nodes as needed in a sharded cluster with MongoDB, and PostgreSQL has no limit on database size. In our Decision Maker’s Guide to Open Source Databases, we provide battlecards for the top open source databases available today — including insights from our database experts.

• Our future plan is to expand the comparison with more systems that support spatiotemporal functionality.
• A study published in May 2020 identified a bug that affects the claim that Mongo performs ACID transactions at an acceptable level.
• Our information is based on key factors like architecture, ACID compliance, extensibility, replication, security, and support to name a few.
• On the other hand, non-relational databases like MongoDB are better suited for applications that require flexibility, scalability, and fast changes.
• This makes MongoDB perfect for scenarios where the data constantly changes and there’s no defined data structure, such as social media platforms where users can upload their own content.

MongoDB is popular among developers due to its flexible schema and use of JSON-like documents. It aligns well with modern application development practices, making it an attractive choice for agile development teams. 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. 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 vs. MongoDB: Features and Benefits Comparison

PostgreSQL has a full range of security features including many types of encryption. While it is all the same database, operational and developer tooling varies by cloud vendor, which makes migrations between different clouds more complex. MongoDB Atlas runs in the same way across all three major cloud providers, simplifying migration and multi-cloud deployment. In a relational database, the data in question would be modeled across separate parent-child tables in a tabular schema. This means that updating all the records at once would require a transaction. As an astute reader should already be able to tell, the real question is not MongoDB vs. Postgres, but the best document database versus the best relational database.

Since it’s non-relational, MongoDB uses collections instead of tables. A foreign key is simply a set of attributes in a table that refers to the primary key of another table. BSON skips the keys that aren’t useful for the query, thus making it faster to retrieve data.

Geospatial Support:

While MongoDB doesn’t have the same level of community maturity, it does offer drivers for many programming languages. There is lots of community and aid to help you interact with MongoDB using one of your preferred programming languages. It has a strong open-source community with lots of PostgreSQL support libraries, tools, extensions, and general support available.

In a table, every row represents individual data points, and each column defines the type of information that you store there. PostgreSQL supports a range of data types, including dates, text, integers, and Booleans. To illustrate this difference, PostgreSQL uses Structured Query Language (SQL) to query relational databases composed of multiple tables with a defined relationship between them. SQL is a widely known language that cuts across many great tools and platforms, including Oracle and MySQL.

Flexible and Evolving Data Structures:

Lots of data management and BI tools rely on SQL and programatically generate complex SQL statements to get just the right collection of data from the database. PostgreSQL does very well in such contexts because it is a robust, enterprise-grade implementation that is understood by many developers. PostgreSQL can be run as an installed, self-managed version, or as a database-as-a-service on all of the leading cloud providers. Each of those implementations work the way the cloud provider that created them wants them to work. To get support for PostgreSQL, you have to use a cloud version or go to third parties offering specialized services.

The JSON data support in PostgreSQL is much more advanced compared to MySQL. There are several JSON-specific operators and functions, making data searches in JSON documents very efficient. The JSONB feature from PostgreSQL-9.4, which stores JSON in a binary format, started supporting Full-Text Indexing (known as GIN Indexing), and this really makes Full-Text searching on JSON documents much faster.

Use Cases for PostgreSQL

However, I would note that doing so really requires expert knowledge of PostgreSQL programming and is not for shops unwilling to dedicate to using advanced features. Since my preference is not something I see listed I will give it to you. PostgreSQL uses joins to combine data from multiple tables into a single table. As long as you have 2 tables, you can use joins to combine them in PostgreSQL. Similar to traditional SQL, there are 4 types of joins in PostgreSQL- Inner, Left, Right, and Full Join. If you want all the data from both tables into a single table, you can use a Full Join.

Without full SQL compliance, writing efficient and well-performing SQL queries can be a challenge. Tablespaces for spreading data across multiple disks are a challenge, since tablespaces are only supported in InnoDB and cannot accommodate table partitions. Simple queries to hit tables can be made to complete faster by creating B-TREE Indexes.

Data relationships

Hevo Data, a No-code Data Pipeline helps to load data from any data source such as Databases, SaaS applications, Cloud Storage, SDK,s, and Streaming Services and simplifies the ETL process. Hevo not only loads the data onto the desired Data Warehouse but also enriches the data and transforms it into an analysis-ready form without having to write a single line of code. It is a source-available cross-platform document-oriented database program that uses JSON (JavaScript Object Notation)-like documents and optional schemas to store your data.

With 8 years of experience in the field, she has a deep understanding of complex technical concepts and the ability to communicate them clearly and concisely to a wide range of audiences. At Verpex Hosting, she is responsible for writing blog posts, knowledgebase articles, and other resources that help customers understand and use the company’s products and services. When she is not writing, Yetunde is an avid reader of romance novels and enjoys fine dining. This article provides a detailed comparison of MongoDB and PostgreSQL, highlighting their differences, advantages, and application suitability. Understanding these distinctions is vital for developers and businesses, as it greatly impacts project success and efficiency. Most companies use databases to support their internal infrastructure, both Postgres and MongoDB permit this usage.

5 Cluster setup – AWS

This question may be a bit obvious, but understanding why we need databases helps when it comes to choosing a database structure for your stack. Databases are a basic foundation of software development, and they serve many purposes for building projects of all sizes and types. MongoDB has tried to solve this by introducing multi-dimensional data types where you can embed one document store inside another.