I recently completed a training session for one of our customer on best practices for achieving HA clusters with Patroni and Pgpool in PostgreSQL. During the sessions, different points were discussed, but I’d like to highlight a few that should be carefully considered when designing HA clusters.

When working with customers, a common question we get is: “Which standby type is best for our HA needs?” Before answering, we ensure they fully understand the concepts behind each standby type and provide the necessary guidance A standby server is essentially a copy of your primary database that can take over if the primary fails. There are different types of standby setups, each with its own use cases, pros, and cons. In this blog, we will discuss the three types: Cold Standby, Warm Standby, and Hot Standby.

When you are running mission-critical applications, like online banking, healthcare systems, or global e-commerce platforms, every second of downtime can cost millions and damage your business reputation. That’s why many customers aim for four-nines (99.99%) or five-nines (99.999%) availability for their applications n this post, we will walk through what those nines really mean and, more importantly, which PostgreSQL cluster setup will get you there.

In today’s digital landscape, downtime isn’t just inconvenient, it’s costly. No matter what business you are running, an e-commerce site, a SaaS platform, or critical internal systems, your PostgreSQL database must be resilient, recoverable, and continuously available. So in short: High Availability (HA) is not a feature you enable; it’s a system you design.

Data is a key part of any mission-critical application. Losing it can lead to serious issues, such as financial loss or harm to a business’s reputation. A common way to protect against data loss is by taking regular backups, either manually or automatically. However, as data grows, backups can become large and take longer to complete.

When running a PostgreSQL database in a High Availability (HA) cluster, it’s easy to assume that having multiple nodes means your data is safe. But HA is not a replacement for backups. If someone accidentally deletes important data or runs a wrong update query, that change will quickly spread to all nodes in the cluster. Without proper safeguards, that data is gone everywhere. In these cases, only a backup can help you restore what was lost. The case mentioned above isn’t the only reason backups are important. In fact, many industries have strict compliance requirements that make regular backups mandatory. This makes backups essential not just for recovering lost data, but also for meeting regulatory standards. Barman is a popular tool in the PostgreSQL ecosystem for managing backups, especially in High Availability (HA) environments. It's known for being easy to set up and for offering multiple types and modes of backups. However, this flexibility can also be a bit overwhelming at first. That’s why I’m writing this blog to break down each backup option in a simple and clear way, so you can choose the one that best fits your business needs.

When designing a highly available PostgreSQL cluster, two popular tools often come into the conversation: Pgpool-II and Patroni. Both are widely used in production environments, offer solid performance, and aim to improve resilience and reduce downtime; however, they take different approaches to achieving this goal. We often get questions during webinars/talks and customer calls about which tool is better suited for production deployments. So, we decided to put together this blog to help you understand the differences and guide you in choosing the right solution based on your specific use case. Before we dive into comparing these two great tools for achieving high availability, let's first take a quick look at some of the key components involved in building a highly available and resilient setup.

High Availability (HA) refers to a system design approach that ensures a service remains accessible even in the event of hardware or software failures. In PostgreSQL, HA is typically implemented through replication, failover mechanisms, and clustering solutions to minimize downtime and ensure data consistency. Hence, HA is very important for your mission-critical applications.  In this blog post, we will try to explore a critical failure condition known as a split-brain scenario that can occur in PostgreSQL HA clusters. We will first see what split-brain means, and then how it can impact PostgreSQL clusters, and finally discuss how to prevent it through architectural choices and tools available in the PostgreSQL ecosystem