The Benefits of Running PostgreSQL on Kubernetes

Today, Kubernetes is a popular option for running stateful workloads like Postgres. However the decision to deploy on K8s should be based on many factors.

Be careful with Pod specifications, specifically regarding primary and standby instances. And be sure to use a storage solution that lets you move data and Kubernetes objects like controllers and Persistent Volume Claims (PVC) between environments without time-consuming WAL replays.


PostgreSQL is a robust database for demanding applications, and it can scale to handle a high load. However, sharing PostgreSQL manually and managing replication across multiple servers can be challenging.

Kubernetes makes it easier to deploy and operate PostgreSQL scalably. It can automatically reschedule your data pods to new hosts based on performance and availability metrics. This improves write scalability by eliminating the need for application-level sharding and complex replication architectures.

Additionally, Kubernetes can create a Persistent Volume Claim (PVC) that persists in PostgreSQL data beyond the lifecycle of the Pod. This enables a database to survive if the Pod fails or is OOMkilled.

Additionally, Kubernetes can create CPU pinning for your PostgreSQL instances, which optimizes cache usage and lower-level workloads. This is particularly helpful for workloads that require IO-intensive operations, such as large queries and aggregations.


A key benefit of running PostgreSQL on Kubernetes the right way is that it provides consistent deployment and management regardless of the underlying infrastructure. This enables teams to take advantage of the flexibility and scalability of a containerized application while maintaining the security benefits of an immutable file system.

However, building a highly available database cluster is complicated. It requires a thorough understanding of Pod specifications and paying particular attention to primary and standby Pods. Many tasks must be performed regularly, including repacking, vacuuming, running many operations, upgrades, minor version upgrades, control restarts, and benchmarking.

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To help simplify this process, VMware Tanzu SQL with Postgres Operator automates all the steps needed to deploy and manage a highly available, self-healing database on Kubernetes. This includes defining the number of replicas, creating and managing Deployment resources, exposing a Postgres service with Kubernetes Pods, and creating and managing Persistent Volume Claims where PGDATA is stored. These tasks are critical to managing PostgreSQL and ensuring high availability and are executed consistently across different environments.

High availability

PostgreSQL has some potent tools for high availability. The pg pool is a tremendous load-balancing tool that analyzes connection requests to determine where the query should be sent in the cluster. This ensures that read-only questions are sent to read replicas while write operations go to the master. This reduces the load on the primary server and increases the overall availability of the database.

Kubernetes also supports many other tools to improve the reliability of your database. Kubernetes enables you to deploy multiple copies of your PostgreSQL deployment to have one available in case of a failover. Kubernetes can also automatically reschedule failed Postgres pods, significantly reducing the recovery time.

When running a multi-master PostgreSQL deployment with a master and a standby, it is essential to have a proper failover plan in place. One of the most popular methods is an open-source project that aims to automate the failover and failback processes. Another option is with a virtual IP configured to send connections across zones. This enables the instance in availability zone A to take requests while the secondary model in availability zone B takes over as master in case of a failure.


PostgreSQL is a robust database that offers scalability for both performance and capacity. It can handle a large number of connections and supports multiple replication configurations. It also provides a variety of performance enhancements, such as connection pooling and incoming query caching.

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With these features, it is ideal for high-volume workloads. However, setting up and maintaining a PostgreSQL database takes more work. It requires several side tools to function correctly, such as backup software, monitoring tools, and high-availability software. This is known as the “Stack Problem.”

Kubernetes is a container orchestration platform that can help to solve the Stack Problem and increase performance. It can be used to deploy and manage PostgreSQL at scale.

Kubernetes is designed to manage stateful applications, like a database.  It provides the parts needed to run stateful applications, such as persistent storage, unique network identifiers, automated rolling updates, and ordered deployments. This enables stateful applications to scale easily and quickly restore in the event of failure.


Postgres uses logs to record changes and to recover from a failure. These logs require fast read and write speeds to ensure they can be replayed quickly to recover from a failure. This is especially important if a Postgres pod gets rescheduled, which could cause data loss.

The Kubernetes platform offers several built-in features that improve performance, including multi-tenant architecture, a consistent deployment model, and a dynamically provisioning storage system. In addition, deploying Postgres as a stateful set with a headless Kubernetes service offers better support for changing database schemas and improved security by isolating the database from application changes.

However, implementing this infrastructure is complex and requires careful planning. A well-thought-out strategy is needed to plan for backups, load balancing across databases, metrics, monitoring, and more. A fully managed service is ideal for customers who want to run a stateful PostgreSQL cluster in Kubernetes that will automatically handle many of these tasks. This approach provides a safe and automated way to meet enterprise high availability requirements, backed by an open-source distributed consensus-based solution from the Cloud Native Foundation.

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