PostgreSQL is more than just an object-relational DBMS — it’s the Swiss Army knife of databases: reliable, versatile, and always within reach. One of its main advantages is that it is distributed free of charge, like a toolbox that anyone can download. Unlike costly proprietary systems, PostgreSQL gives you full access to its functionality, empowering you to install, test, and experiment without limitations.
I first encountered PostgreSQL when I decided to develop an app for managing personal finances. The task was straightforward: create a database to store expense categories and income and set spending limits. While it was a small project, I realized how easy PostgreSQL is to work with. Everything, from table relationships to automatic data validation, was simple to configure.
PostgreSQL excels at meeting the demands of both large corporate projects and small startups. For example, a team I collaborated with used PostgreSQL as the backbone of an application analyzing users' fitness habits. We loaded it with miles of data — from personal records to geolocation of runs — and it performed flawlessly, thanks to extensions like PostGIS.
From Flexibility to Versatility
PostgreSQL's greatest strength lies in its unique combination of the traditional relational database approach and its ability to handle JSON data. Imagine PostgreSQL as a universal warehouse: on one side, you have neat rows of packed boxes (structured data), while on the other, a creative assortment of boxes in various sizes and shapes (JSON documents).
This versatility once helped my colleague to cope with an unexpected task one day. Setting up a database for collecting and analyzing user reviews was necessary. The reviews varied widely — some were just a few lines, while others were lengthy texts filled with emojis, links, and even attachments. PostgreSQL proved the perfect solution, offering structured fields for standard data like name, rating, and date while using JSON to handle everything else.
When Simplicity Meets Power
Where would we be without extensions? One of the most interesting tools is PostGIS, which transforms a database into a geographic reference system. I once worked on a small project to calculate optimal delivery routes. Honestly, I assumed such a task would require something complex and highly specialized, but PostgreSQL handled it effortlessly, thanks to PostGIS.
If you think about it, PostgreSQL is like a reliable friend who is always ready to lend a hand. It ensures the safety of your data and adapts to any task — whether it’s for a small hobby project or a significant business operation.
History of PostgreSQL: From Academic Experiment to Global Success
PostgreSQL originated as a research project initiated in 1986 at the University of California, Berkeley. Led by Professor Michael Stonebraker, the project — named POSTGRES — became the first DBMS to support complex data types and object-oriented relationships. The goal was to develop a database management system that was more flexible and scalable than the existing options.
PostgreSQL Milestones
- 1986: The POSTGRES project begins.
POSTGRES marked a revolutionary step in database development, particularly with the introduction of multi-version concurrency control (MVCC). This innovation enabled efficient transaction processing without the need for locks.
- 1994: Addition of SQL support.
SQL was integrated into the POSTGRES project in 1994 to ensure compatibility with existing systems. This decision boosted its popularity among developers and marked the start of the system's widespread adoption in the industry.
- 1996: Renaming to PostgreSQL.
In 1996, the project was renamed PostgreSQL to highlight its support for SQL. This year also marked the transition to open source, which garnered global attention from developers and significantly expanded its community.
- 2000s: Major Expansion of Functionality.
During these years, PostgreSQL introduced support for extensions like PostGIS (for geographic information systems) and began to actively compete with commercial DBMSs such as Oracle and Microsoft SQL Server.
- 2010s: Introduction of JSON.
Adding JSON and JSONB allowed PostgreSQL to compete with NoSQL systems like MongoDB. This extension proved pivotal for many hybrid applications requiring structured and unstructured data storage.
- 2020s: Cloud Integration and High Performance.
PostgreSQL has continued to evolve in recent years with significant performance improvements, support for parallel query processing, and seamless integration with cloud platforms such as AWS, Google Cloud, and Azure.
How PostgreSQL Works
When I first encountered PostgreSQL, it reminded me of a wise librarian in a vast library, where the books represented the data, and I was the student searching for specific information. This librarian was always ready to respond to my queries with precision and speed, even when I posed complex questions like, "How many people checked out philosophy books in the last five years and returned them a month later?"
Basics of PostgreSQL Architecture
PostgreSQL is built on a client-server architecture. Think of it like a postal service: a client sends a request — similar to a letter addressed to the server — and the server processes the request, retrieves the necessary data, and sends it back. The client could be anything from a mobile app for ordering pizza to a sophisticated analytics platform for managing supply chains. All interactions occur through SQL queries, which PostgreSQL processes remarkably efficiently as if SQL were its native language.
I remember once setting up PostgreSQL for a personal finance project. The server stored data on expenses, income, and planned purchases, while the app showed me exactly how much I could spend on coffee by the end of the month. My server worked like a Swiss watch: fast, accurate, and error-free.
Multiversion Concurrency Control (MVCC)
One feature that makes PostgreSQL exceptional in multi-tasking systems is its MVCC (multiversion concurrency control). Imagine a restaurant with several waiters: one takes the table's order, another serves the drinks, and a third delivers the dessert. This happens simultaneously, with each waiter performing their task without interfering with the others.
I experienced the power of MVCC while working on a project for a small online store. Users could simultaneously update their carts, add products, and proceed to checkout. Thanks to MVCC, no one encountered "blocked shelves" — everyone could see their progress in real-time without delays. That was when I realized how PostgreSQL maintains order amidst the chaos of concurrent queries.
Example: Imagine two employees simultaneously editing the price of an item in an online store. PostgreSQL handles this by working with two separate versions of the data—one for each user — and then merging the changes while maintaining data integrity.
Transaction and ACID Support
PostgreSQL uses the ACID (atomicity, consistency, isolation, durability) standard to manage transactions. This ensures that they either complete successfully or are rolled back if errors occur. This is particularly crucial for systems where data consistency and accuracy are paramount, such as banking applications.
ACID is more than just a technical acronym — it’s a philosophy that ensures databases operate predictably and reliably. These principles can be compared to the rules of conduct in an ideal team, where everyone knows their responsibilities and mistakes are not ignored.
Imagine ordering a lunch through a delivery service. If the courier forgets to bring one of the items, you’re entitled to return the entire order. The same principle applies to transactions: everything must be completed in full or not at all.
In one of my projects with an online store, there was a similar case: the system added multiple items to a shopping cart, but if a server error occurred, the database rolled back the process to its original state. Without atomicity, users could see incomplete purchases in the cart or, worse, face an overpayment.
Think of it like taking an exam: if you’ve done everything correctly, the system will verify it and assign you the appropriate grade. The same principle applies to databases — they always transition from one consistent state to another, adhering to their internal rules. I once set up a CRM for a small startup, and we had to ensure that the client tables remained consistent even during failures. Without this, our client would have risked losing critical data.
Imagine a café where the barista serves only one customer at a time, ensuring no confusion over orders. A similar principle applies in databases: while one transaction is processed, others cannot interfere.
In my experience, this principle proved invaluable when setting up an appointment booking system. Initially, two clients could accidentally book the same time slot. Transaction isolation resolved this issue, preventing such overlaps and ensuring smooth operation.
Imagine writing an important letter and saving each paragraph immediately after finishing it, ensuring that neither a power outage nor a computer crash can erase your work. Databases operate similarly: they save the changes made by a transaction so that no system failure can undo your work.
I once had to deal with a database where durability was misconfigured. As a result, user data would vanish after a server reboot, leading to a flood of complaints. Fixing this taught me to appreciate the importance of durability like never before truly.
ACID is more than just a technical standard — it’s a set of principles that transforms data chaos into stable order. While the terminology might sound boring, these principles address real-world scenarios, simplifying users' lives and giving developers greater confidence in their work.
Example: If a user places an order in an online store, the system will complete the entire operation—from debiting the card to updating the product status in the warehouse—within a single transaction. If a failure occurs at any point, the transaction will be rolled back, ensuring that no data is corrupted.
Indexing and Query Optimization
One of PostgreSQL's strengths is its support for various indexes, enabling significantly faster query execution. Indexes allow data to be located quickly, minimizing the number of database operations required. For instance, GIN (Generalized Inverted Index) is ideal for text data, while GiST (Generalized Search Tree) is commonly used for geospatial data.
Example: Suppose you manage an extensive catalog of products. Each product has multiple attributes, such as name, price, and category. By using an index to search these fields, users can quickly find the products they need, even when dealing with vast amounts of data.
Examples of Companies Using PostgreSQL
PostgreSQL is widely used across various industries, from small startups to some of the world’s largest corporations. Let’s explore a few examples to see how they address real business challenges.
Netflix
Netflix uses PostgreSQL to process user data and generate personalized recommendations. Every movie or TV series users watch updates their preferences in real-time. PostgreSQL's ability to handle massive volumes of data with minimal latency enables Netflix to deliver tailored recommendations to millions of users seamlessly.
How It Works:
When a user selects a new movie or show, PostgreSQL updates their preferences in the database. This allows the recommendation algorithm to analyze the updated information and suggest new films or shows that might interest the user.
Airbnb
Airbnb relies on PostgreSQL to store reservations, users, and property data. The platform manages vast amounts of information on accommodations, prices, and availability, and PostgreSQL ensures fast query processing even under heavy workloads.
How It Works:
When a user searches for lodging, PostgreSQL quickly processes queries based on availability and price. The system works with millions of records of apartments, houses, and hotels worldwide, which requires high performance and reliability.
Spotify
Spotify relies on PostgreSQL to manage song, album, and artist metadata. The system stores information about the songs users listen to and their preferences. PostgreSQL ensures high query processing speed, which is critical for a platform that handles millions of interactions daily.
How It Works:
When a user adds a song to their playlist, PostgreSQL updates the data and supplies the platform with the necessary information to generate personalized recommendations.
Benefits of PostgreSQL
PostgreSQL offers many benefits that make it one of the best tools for data management.
- Open Source
As I wrote above, PostgreSQL is a free and open-source solution. It is released under the PSQL license( PostgreSQL License), similar to BSD (Berkeley Software Distribution) or MIT (The MIT License). This licensing model allows companies to avoid additional costs and allows them to modify the database to meet specific business needs.
BSD and MIT licenses can be compared to universal keys to software treasure troves. They enable you to freely use, modify, and even commercialize creations built under their terms.
- The BSD (Berkeley Software Distribution) is like a key to an old treasure chest belonging to the University of California, Berkeley. This open-source license grants the freedom not only to use the software but also to modify it to fit your needs, much like discovering a cherished family recipe and adding your special touch.
- The MIT (The MIT License) is like a more modern-looking key. Introduced at the Massachusetts Institute of Technology (MIT) in 1988, it quickly became the gold standard for permissive licenses. The MIT License grants complete freedom—allowing you to copy, modify, merge, and even sell software. Its flexibility is so vast that you could build your startup under its terms.
- Scalability and Performance
PostgreSQL is like an aquarium that can expand into an oceanarium. It scales with your needs, whether by increasing the size of the tank (vertical scalability) or adding more aquariums that work together in unison (horizontal scalability).
In one of my projects, we configured database replication for a fast-growing startup. This gave the team confidence that their data would remain intact even under peak loads.
- Security
Working with PostgreSQL feels like being in a secure bank — entry is strictly controlled, and every action is recorded. Its encryption mechanisms, access controls, and audit logging make it an excellent choice for handling sensitive data.
In one of my projects, PostgreSQL was used fto store medical records Properly configured access rights helped prevent data leaks and ensure compliance with privacy regulations.
- Flexibility and Extensibility
PostgreSQL is like a modular building set, allowing you to add any component—even if it’s not included in the standard package. Need a custom data type or a unique index? No problem.
For example, I used the PostGIS extension to handle geographic data in one of my projects. It proved the perfect solution for a startup that calculates delivery routes.
- NoSQL Support
PostgreSQL is a hybrid database, like a car with two engines. Using JSON and JSONB, it combines relational and non-relational data while remaining as fast and reliable. It is an ideal tool for applications where data can be structured or partially organized.
Limitations of PostgreSQL and When It May Not Be the Right Fit
Despite all its advantages, PostgreSQL is not without its limitations.
- Resource Intensity
PostgreSQL requires significant computing resources to efficiently process large amounts of data, which can be challenging for small businesses with limited budgets.
- Steep Learning Curve
Due to its flexibility and extensive configuration options, PostgreSQL can be challenging for beginners. Effective use requires a solid understanding of SQL and the core principles of relational database management systems (RDBMS).
- Not Always the Best Fit for Certain Tasks
PostgreSQL may not be as efficient as specialized NoSQL solutions like MongoDB or Cassandra for purely non-relational workloads- such as managing large volumes of unstructured data.
PostgreSQL Security
Security is a key aspect of any database, especially in enterprise and mission-critical systems. PostgreSQL is a robust and secure DBMS offering a variety of mechanisms to protect data from unauthorized access, maintain integrity, and prevent data loss. Let's look at how PostgreSQL solves security problems and what tools it provides to protect information.
- Access Control and Authentication
PostgreSQL employs a multi-layered access control system to ensure only authorized users can interact with the database:
- Role-Based Access Control (RBAC): PostgreSQL uses a role-based system (users and groups) that allows you to fine-tune who can perform what actions in the database. Each user can be assigned specific access rights to certain tables or data.
- Authentication Methods: Passwords, Certificates, and Kerberos – PostgreSQL supports multiple authentication methods, including standard password authentication, certificate-based authentication (SSL), and Kerberos authentication, enabling seamless integration with enterprise security systems.
Example: If you have an analytics team that only needs access to specific data, you can create a dedicated role for them and grant permissions only to the necessary tables. All other tables will remain protected.
- Data Encryption
PostgreSQL offers several tools to safeguard data, both at rest and in transit:
- Connection-Level Encryption (SSL): PostgreSQL supports SSL (Secure Sockets Layer) to encrypt data transmitted over the network. This prevents data interception, which protects against man-in-the-middle attacks.
- Storage-Level Encryption – To ensure data protection at rest, PostgreSQL supports encryption through third-party extensions such as pgcrypto. This enables encryption of fields within tables or even entire databases, safeguarding sensitive information.
Example: If your system processes sensitive data — such as credit card numbers or medical records — you can implement storage-level encryption to ensure the highest level of data security.
- Auditing and Logging
PostgreSQL offers robust logging capabilities to track database access and data modifications. Through configuration settings, administrators can enable detailed logging of all queries, changes, and errors — an essential feature for security audits and incident tracking.
- pgAudit – This PostgreSQL extension enhances auditing by recording all critical operations, including SELECT, INSERT, UPDATE, DELETE, and more. It is especially valuable for organizations in highly regulated industries, such as finance and healthcare.
Example: If your database stores financial data, you can configure pgAudit to log all transactions. This enables you to track any changes and proactively prevent fraud.
- Protection Against Attacks
PostgreSQL protects against a variety of attacks, including SQL injection. It achieves this through the use of parameterized queries and secure data APIs:
- Parameterized queries – Unlike traditional string-based queries, which allow arbitrary data insertion, parameterized queries mitigate the risk of SQL injection by automatically escaping user input. This ensures that malicious code cannot be executed within a database query.
- Security through permission minimization – PostgreSQL enables administrators to restrict user permissions to only the data and operations necessary for their tasks.
Advanced PostgreSQL Features: Clustering, Scaling, and Integration
PostgreSQL has traditionally been considered a powerful relational DBMS demonstrating excellent single-node performance. However, horizontal scaling has historically challenged relational databases, including PostgreSQL. Nevertheless, modern tools and approaches allow us to efficiently solve High Availability (HA), High Load (HL), and clustering problems, making PostgreSQL a worthy competitor to commercial solutions.
Paid solutions based on PostgreSQL
One of PostgreSQL’s key advantages is its open-source nature, which has enabled the development of numerous commercial solutions based on its architecture. For example:
- EDB Postgres Advanced Server (EnterpriseDB) extends PostgreSQL with additional data management features, enhanced security and performance optimizations, and seamless migration tools for databases like Oracle.
- Citus from Microsoft is a horizontal scaling solution for PostgreSQL, turning it into a distributed DBMS for handling vast amounts of data.
- AWS Aurora PostgreSQL from Amazon is a cloud solution compatible with PostgreSQL that offers automatic scaling, data recovery, and high fault tolerance.
Clustering and Scaling
PostgreSQL offers several solutions for high availability:
- Patroni is a PostgreSQL cluster management tool that uses etcd, Consul, or ZooKeeper to store cluster state information. Patroni ensures automatic failover in case of failure, maintaining system stability and minimizing downtime.
- Pgpool-II and PgBouncer are utilities for load balancing and connection management. Pgpool-II additionally offers replication and request caching.
- Streaming Replication – a built-in PostgreSQL mechanism for data synchronization between the primary and replica databases. This lets you create separate read replicas for analytical query processing, offloading the main server.
Solutions such as Citus can be used for horizontal scaling. Citus enables data sharding, which divides data into segments (shards) and distributes them across multiple nodes.
External search indexes
Integrating PostgreSQL with external search indexes like Elasticsearch or OpenSearch becomes an essential tool when working with large volumes of data, especially textual data.
- Elasticsearch – a high-performance search engine optimized for fast full-text searches. PostgreSQL can integrate with Elasticsearch using extensions like logstash-output-jdbc or ZomboDB.
- OpenSearch – Amazon’s fork of Elasticsearch, designed for search indexing and analytics.
These external indexes are particularly useful for handling complex text and semi-structured data queries, where PostgreSQL’s built-in search capabilities may be less efficient.
Architectural Design
Building scalable PostgreSQL-based solutions requires careful planning and architectural design. Key strategies include:
- Creating separate read replicas (read-replicas) and specialized nodes for performing lookup queries.
- Load balancing across different data types: The primary database handles mission-critical transactions, while external search indexes or analytics platforms process secondary queries to optimize efficiency.
- Using extensions like Foreign Data Wrapper (FDW) to integrate PostgreSQL with other databases and data warehouses.
While PostgreSQL was not originally designed as a distributed database, modern tools, and methodologies enable it to perform effectively under high-load conditions and fault tolerance requirements. By leveraging commercial solutions like Citus for horizontal scaling and Patroni, along with integration with external search engines, developers can build scalable, high-performance systems that meet the demands of even the most complex projects.
Comparing PostgreSQL with Other DBMSs
I've already said plenty about the hero of our article. Compared to other solutions, it has been — and remains — the most versatile option. But let’s put that claim to the test. Instead of making unsubstantiated statements, let’s compare. Let's bring its counterparts into the spotlight and determine which solution is best suited for different scenarios. Each of these systems has its features, strengths, and weaknesses. Let’s dive into the comparison.
PostgreSQL vs MySQL
Let's start with the battle of the two Titans. Comparing PostgreSQL and MySQL is like choosing between two tools — each perfectly suited for its own set of tasks. PostgreSQL is a versatile fighter with rich functionality and support for complex operations. MySQL, on the other hand, is a fast and easy-to-use option, ideal for tasks where speed is essential.
Flexibility and Power vs. Speed and Simplicity
PostgreSQL is renowned for its ability to handle complex and unconventional data types. Imagine designing a geographic information system (GIS) that needs to track coordinates and their relationships. PostgreSQL becomes the ultimate tool for working with geographic data with the PostGIS extension. Additionally, native JSON support enables efficient handling of hybrid data structures, seamlessly combining relational tables with document-oriented approaches.
However, PostgreSQL's power comes at a cost. It's like driving an SUV — you get maximum off-road capability but at the expense of higher fuel consumption. Under heavy workloads or when handling large volumes of data, PostgreSQL may demand more processing power and memory.
MySQL, on the other hand, is like a compact city car. It is ideal for web applications where simplicity and speed are key priorities. Handling a high volume of read requests is its strong suit. If your project doesn’t require complex analytical operations, MySQL can do the job with minimal resource consumption.
When to Choose What
If you are developing an analytical system, working with geographic data, or building a platform for processing large datasets, PostgreSQL is the better choice. Think of it as hiring a versatile expert who can tackle any task, even those that are complex and require a non-standard approach. But if you create a small online store or a blog where simplicity and speed are the priority, MySQL will show itself from the best side. It is a reliable tool for web applications that do not require complex logic or extensive flexibility.
PostgreSQL vs. MongoDB: Salt and Sugar in the World of Data
Comparing PostgreSQL and MongoDB is like deciding whether salt or sugar is more important in the kitchen. Both are indispensable, but they serve completely different purposes. PostgreSQL is a relational database designed for structured data and complex relationships between elements. In contrast, MongoDB is a flexible NoSQL solution that excels at handling unstructured data in JSON document format.
Personally, I see PostgreSQL as a kind of accounting cabinet where every shelf has a designated place. If you want to add something new, you'll need clear instructions to ensure it fits correctly. MongoDB, on the other hand, feels more like a traveler's suitcase — you can pack items of various shapes and sizes, and as long as it closes, everything works.
Flexibility vs. Structure
MongoDB offers freedom. Imagine you are building a house and want to experiment with the layout. Today, you need a single-story space, but tomorrow, you will add an attic. Thanks to its flexibility, MongoDB easily adapts to such changes. Conversely, PostgreSQL is like a meticulous architect who requires a well-defined blueprint from the start. Everything must be precisely planned — walls, doors, windows — each element in its designated place.
MongoDB's flexibility is especially valuable in applications where data changes on the fly. When my colleague was developing a delivery tracking system for a small project, the data format kept evolving. First, they needed to add the courier's location, then delivery time, and later, customer feedback. MongoDB allowed him to implement these changes quickly without completely restructuring the database.
JSON vs. JSONB: Text or Binary Code
Imagine writing two letters to understand the difference between JSON (MongoDB uses) and JSONB (PostgreSQL uses). One is handwritten on paper, while the other is recorded as a voice memo.
The paper letter (JSON) is easy to read and edit manually, but for a machine to process it, it must first be scanned and converted into text. The voice memo (JSONB), on the other hand, is harder to modify on the fly, but a computer can "listen" to it instantly since it's already encoded in a machine-readable format.
When to Use What
If your task is to analyze large data sets, running complex queries with transactions and strict rules, PostgreSQL will be your best choice. Think of it as hiring a meticulous accountant who ensures that no detail is overlooked.
MongoDB, however, is better suited for projects where data formats frequently change, such as social networks or chat applications. It’s like working with a creative manager who effortlessly adapts to any changes.
Personal Example: How I Chose Between PostgreSQL and MongoDB
When developing a Telegram bot for polling and analyzing user responses, choosing a database became one of the key tasks. On one hand, the bot needed to store user data and their responses while maintaining data integrity. On the other hand, the structure of questions and possible answers constantly evolved as the client frequently introduced new surveys with unique formats and parameters.
I initially tried using MongoDB. Thanks to its flexibility, I could store surveys as JSON documents, with each survey represented as a separate document with a dynamic structure. For example, one survey might have three answer choices, while another could have five—MongoDB handled this variability seamlessly.
However, when it came to analyzing the collected data, I ran into difficulties. Queries became increasingly complex and time-consuming, especially when comparing user responses across different surveys. Then, I decided to use PostgreSQL for the analytical part. I exported data from MongoDB to PostgreSQL, where it was converted into a structured table format. This allowed me to easily create reports, count statistics, and visualize the results.
Ultimately, I found that MongoDB was ideal for storing dynamic survey data, while PostgreSQL was essential for analysis and reporting. This hybrid approach allowed me to create a reliable and easy-to-use bot.
PostgreSQL vs. Oracle: Pure Commerce
Our next competitor, Oracle Database, bursts through the door. The bad news for most developers? It’s a commercial product. Oracle is widely used by large corporations to manage massive volumes of data. Unlike PostgreSQL, which is free and open source, Oracle has high licensing and support costs. However, there’s a silver lining—Oracle offers a comprehensive suite of tools and integrations, making it a preferred choice for large-scale and highly complex projects.
PostgreSQL and Oracle offer similar capabilities in transaction processing, big data management, and security. However, Oracle provides additional features, such as built-in cloud scalability and advanced enterprise integration mechanisms. This makes Oracle an ideal choice for large corporations that require premium support and mission-critical data handling.
PostgreSQL, on the other hand, delivers comparable functionality for most tasks because it does not require expensive licenses and can be customized to meet any need. This makes PostgreSQL an excellent choice for organizations that don’t need complex enterprise integrations but still want a powerful database management system (or DBMS) with strong community support.
PostgreSQL vs. MS SQL: Microsoft’s Corporate Workhorse
Comparing PostgreSQL and MS SQL is like choosing between a Swiss Army knife and a multi-functional office tool. Both are good, but their strengths depend on the tasks you are solving. Let’s take a closer look at what sets them apart.
PostgreSQL is known for its flexibility and robust architecture, which seems to be designed for complex analytical queries. Imagine you need to analyze a massive dataset to identify correlations between weather patterns and umbrella sales. With extensions like Citus, PostgreSQL excels at breaking down large-scale computations into smaller tasks and processing them in parallel — like hiring a highly efficient team of analysts working together to tackle a problem from multiple angles. GitHub has detailed instructions on installing and customizing these tools so you can feel the difference tomorrow.
MS SQL is an enterprise workhorse that performs exceptionally well within the Microsoft ecosystem. If your project operates within a large company running Windows Server with deep automation processes, MS SQL will feel like a perfectly tailored glove. It is optimized for vertical scaling, meaning it enhances performance under the hood rather than relying on additional servers.
Compatibility is another key factor. PostgreSQL is like a cosmopolitan that runs seamlessly on Windows, macOS, and Linux and integrates easily with cloud providers. Historically, MS SQL was closely tied to Windows, but starting with version 2017, it took a major step forward — now supporting Linux and Docker containers. It’s like seeing a strict corporate professional suddenly dressed in jeans — unexpected but surprisingly convenient.
Personally, PostgreSQL seems to be a more universal solution. I once helped colleagues on a project that required working with heterogeneous data. Imagine a library with books from different genres and multiple languages. With support for JSON and geographic structures, PostgreSQL proved to be the perfect choice. Meanwhile, a friend of mine used MS SQL for a different project—building reports in a BI system. Thanks to its integration with Power BI, he was able to set up a convenient monitoring of key indicators of the company in just a day.
Ultimately, choosing between PostgreSQL and MS SQL comes down to the specific requirements of your project. If you need flexibility, advanced analytics, and cross-platform compatibility, PostgreSQL is the way to go. However, if performance within the Microsoft ecosystem is your priority, MS SQL will handle the job more effectively.
Conclusion of Comparing
PostgreSQL is an ideal choice for projects that require high flexibility, robust transaction support, and complex analytical queries. It surpasses MySQL in functionality and advanced data processing capabilities while also being better suited for handling loosely structured data than MongoDB. At the same time, Oracle may be a better option for large organizations that require specialized tools and support, although PostgreSQL is often a more cost-effective alternative.
General Conclusion
PostgreSQL is a flexible and powerful RDBMS that provides all the necessary tools for managing data at any scale. Its blend of relational and non-relational capabilities, high performance, and open-source nature make it an excellent choice for any company. PostgreSQL is well-suited for a wide range of use cases — from transaction processing to building analytical systems and working with geospatial data.
Whether you’re launching a startup or developing a large-scale enterprise application, PostgreSQL strikes the perfect balance between functionality and performance. If you need a solution that will grow with you, PostgreSQL is a solid foundation for your success.
Author’s Note: Personal Experience Using PostgreSQL
In my experience, I used PostgreSQL to develop a registration system for a web-based game. The primary task was to create a database for storing user information, including logins, encrypted passwords, and email addresses. To ensure data security, I implemented a password encryption mechanism before storing credentials in the database, aligning with modern user data protection standards.
Additionally, I configured the system to send verification codes via email to confirm user registrations. In this project, PostgreSQL was used alongside a server application, enabling local testing of the web app. This application created an isolated network with a bound IP address to simulate a web server, which was especially useful for debugging the game and testing functionality in a real-world environment.
In general, I had a choice of either using a website service that provides the ability to create a database, or using a local solution. I chose the latter because it was simply easier to work with. However, I knew that at some point, I would likely need to switch to a different approach, as I wouldn’t be able to keep the local database running 24/7. Some experienced developers might ask, “Why not use a server? You could just deploy it there!”—and they’d be absolutely right. You can either use a web-based solution from the start or work with PostgreSQL locally and later migrate it to a dedicated server running around the clock.
Let’s move forward. Why did I find PostgreSQL more convenient? In practice, it demonstrated great flexibility in managing access rights. s part of my project, I created separate roles for different access levels: some users could only read data, while others had permissions to add new records or manage tables. Fine-grained role configuration made it possible to separate responsibilities and ensure data security.
Despite its clear advantages, I encountered PostgreSQL’s steep learning curve. For beginners, the system can seem overwhelming due to its rich feature set and numerous configuration options. However, the time invested is well worth it—PostgreSQL offers reliability and scalability, making it an indispensable tool for projects of any scale and complexity.
