What Happens Behind the Scenes When You Modify a Row in PostgreSQL?

Data is often called the new gold, and databases are where we store and manage this precious resource as it constantly changes and grows. At first glance, updating data might seem like a simple task—just modify a row. But behind the scenes, it’s more complex to ensure that data remains consistent and accessible. In today’s blog, I’ll answer some frequently asked questions from our customers and dive into why PostgreSQL relies on a process called VACUUM to efficiently manage data updates.Updating a row in PostgreSQL isn’t as straightforward as directly changing the existing data. Instead, PostgreSQL avoids in-place updates, meaning it doesn’t overwrite rows directly. But what does this actually mean? When an update occurs, PostgreSQL creates a new row, inserts the updated data there, and marks this new row as the latest version. The old row, meanwhile, is flagged as obsolete row.A similar process applies to deletes, where rows are marked as outdated rather than removed immediately. This raises an interesting question on why did PostgreSQL choose this more complex approach for handling updates and deletes? The answer lies in its design philosophy, which is rooted in Multi-Version Concurrency Control (MVCC). MVCC ensures data consistency and allows for high concurrency.What is Multi-Version Concurrency Control (MVCC)?Multi-Version Concurrency Control (MVCC) allows PostgreSQL to manage multiple transactions at once, enabling consistent data views for each transaction without interference.Imagine a library with a single book titled The Ultimate Guide to Databases. Without Multi-Version Concurrency Control (MVCC), if two people want to check out the book at the same time, one would have to wait for the other to finish reading it. This is similar to a traditional database where transactions can block each other, preventing simultaneous access.With MVCC, the process works differently. When the first person checks out the book, the library creates a copy just for them. The second person can also check out the book at the same time, but they receive their own copy. Both individuals can read and make notes in their respective copies without affecting the other’s experience. Once they’re done, they return the copies, and the library can clean them up for future use.In this analogy, the book represents a data record in a database, and the copies of the book are like different versions of that data. MVCC allows PostgreSQL to create and manage multiple versions of data, enabling multiple transactions to access and modify the data concurrently without interfering with each other. This ensures that each transaction gets a consistent view of the data while allowing for high performance and concurrency.However, just like the library ends up with multiple copies of the book that are no longer being read, PostgreSQL ends up with versions of the data that are no longer needed, called dead tuples. These dead tuples are like outdated copies of the book that no one is checking out anymore. Over time, as more transactions occur, these dead tuples accumulate, taking up space and potentially slowing down the system. This is where the process of vacuuming comes in—just like the library regularly clears out old, unused books to make room for new ones, PostgreSQL uses vacuuming to clean up dead tuples, reclaim storage, and maintain optimal performance.
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Autovacuum in PostgreSQL

Scenarios That Trigger Autovacuum in PostgreSQL

PostgreSQL is widely known for its Multi-Version Concurrency Control (MVCC) model, which allows multiple transactions to occur simultaneously without interfering with each other. However, one side effect of MVCC is the creation of dead tuples—old versions of data rows that are no longer needed but still occupy space. Dead tuples also lead to a phenomenon known as table bloat, which refers to the excessive unused space in a table caused by dead tuples that haven't been cleaned up, resulting in inefficient storage and reduced performanceTo address the issues of dead tuples and table bloat, autovacuum comes into play. It's an automatic process designed to clean up these dead tuples and maintain optimal database performance.In this blog, we will explore the main situations when autovacuum should run:
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Offline PostgreSQL Installation on RHEL 9: Solving the No Internet Challenge

PostgreSQL is one of the most loved databases, especially by developers, for its simplicity, easy configurations, and massive community support. It's an open-source powerhouse known for handling everything from small projects to large-scale applications. While major cloud providers, like AWS, Google, and Microsoft offer robust solutions for hosting databases on the cloud, not all businesses can or want to go this routeMany companies, choose to store their databases in secure, closed environments—machines without internet access or outside the cloud. This is often done to maintain tight control over sensitive data and to meet strict security requirements. However installing PostgreSQL in a restricted, offline environment can be a real challenge, as it limits access to typical installation tools. Recently, I worked on a client project with a similar setup—a secure, offline environment without internet access—where we needed to install and configure PostgreSQL from scratch. If you’re facing the challenge of setting up PostgreSQL in a closed environment, this blog will guide you through the process step-by-step.
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Implementing Bi-Directional Replication in PostgreSQL

In today's fast-paced digital world, ensuring that your data is always up-to-date and accessible is crucial. For businesses using PostgreSQL, replication is a key feature that helps achieve this. While many are familiar with streaming replication, bi-directional replication offers unique advantages that can enhance data availability and reliability. In this blog post, we'll explore what bi-directional replication is, how it differs from streaming replication, and provide a practical example to setup bi directional replication in PostgreSQL
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Transform PostgreSQL into a Columnar Database Using Citus

Columnar databases are transforming the way we handle large datasets by storing data in columns rather than rows. This approach enhances performance, especially for analytical queries, by allowing faster data retrieval and efficient storage. As businesses generate more data than ever, understanding the benefits of columnar databases becomes crucial. In this blog, we'll explore how these databases work, their advantages over traditional row-based systems, and why they are becoming a popular choice for data-driven organizations.
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