While containers were built with applications in mind, many organizations want to run their databases within them. But can Kubernetes support these stateful workloads’ high availability and performance requirements?
Fortunately, there are ways to optimize Kubernetes for database management. To do so, it’s essential to understand how Kubernetes handles stateful applications.
Kubernetes supports horizontal scalability by deploying multiple instances of your database in parallel. It also allows you to vertically scale up vertically or down your deployment for resources such as CPU or memory. In addition, it has features that allow you to deploy applications in a highly available manner. It can automatically roll out updates across a cluster, and you can configure the system to perform rolling back in case of failure.
However, scalability does come with some trade-offs. Since pods are mortal, databases based on them will have to contend with more frequent failures and restarts. Ensuring your database is designed to handle these failures well is essential. It’s great to use the best database for data on Kubernetes with concepts such as sharding and failover elections built into its DNA.
Another way to improve scalability is by using a load balancer. Kubernetes can provision a load balancer in the cloud provider’s infrastructure and set it up to route traffic to your service. However, this approach can be costly if you’re running a lot of resources.
Another option is to create an ingress resource and set up routing rules that direct traffic to your service based on the hostname or path of the request. This can be more complex to set up, but it offers greater flexibility in terms of scalability and availability.
While Kubernetes has been praised for its ability to manage stateless workloads, many organizations still doubt its capability to handle the database management process. One of the main concerns revolves around data persistence.
Another concern revolves around database migrations. Attempting to migrate database deployments from a physical server to a Kubernetes cluster can be difficult and time-consuming. This problem can be mitigated by choosing a database with built-in features that make it compatible with Kubernetes.
Other ways to improve the reliability of a database in Kubernetes include using a solution that enables you to leverage Volume Placement Strategies. This feature helps ensure that database pods and volumes aren’t positioned on the same worker node in your cluster. This will help avoid a situation where the failure of one database node could take your entire database cluster offline.
In addition, it’s essential to understand the replication modes enabled in your database and how they interact. For instance, asynchronous modes can lead to data loss if a pod dies before the replicated data has been written to persistent storage.
The deployment and management of database workloads are more complex than other applications, requiring special considerations for states, availability to other application layers, and redundancy. Kubernetes provides various automation benefits, making it an excellent option for running databases in the container platform.
One of the most important benefits is its auto-healing capabilities, which can automatically reschedule or re-provision a failing database pod, bringing the application back online. This capability is instrumental in the event of a disaster recovery scenario.
Another feature is its capacity scaling, which the orchestration system can automatically trigger to respond to demand spikes. This allows for near-perfect linear scale up and down without wasting resources, making it ideal for data-intensive applications.
Additionally, Kubernetes supports a variety of storage mechanisms, including persistent volume sets (PVS), persistent disk slices (PDS), and persistent caches. This can help organizations manage database workloads on their preferred cloud provider or across multiple providers for greater agility and efficiency.
Finally, Kubernetes is highly secure and protects data at the container level with built-in features such as network pairing and service mesh integration. This helps ensure that only the intended application can access and read sensitive information, especially when running mission-critical databases in production.
Kubernetes has a range of native tools for securing networking resources. This includes encryption-in-transit and encryption-at-rest. It also has access controls, monitoring, and firewalls for limiting network traffic. These features are all helpful in preventing the exposure of data stored inside a cluster.
However, you must consider the high volatility of a Kubernetes environment when deploying a database. It is common for pods to be rescheduled and for nodes to go down, and it’s important that your database can resist these disruptions. It should support failover elections, data sharding, and replication.
While securing a cluster requires attention to detail, it is easy. Start by using scanning tools to scan the cluster for vulnerabilities and misconfigurations. Next, create a trust model that aligns with your threat model and implement it using label taxonomies and governance. Finally, use security policies to control access to the Kubernetes API server with multi-factor authentication and secure HTTPS for data in transit.
Kubernetes is a powerful tool for managing applications in a containerized environment. It can help you scale your business and reduce the time to deploy new applications. It also improves productivity and allows you to run your applications in any environment.