Mastering Database for Data on Kubernetes – A Step-By-Step Tutorial for Beginners
Kubernetes provides a great way to manage containerized applications. It also provides solutions to manage stateful workloads like databases.
It is common for pods to be rescheduled or shut down in a cluster. Your database should resist these failures with concepts such as failover elections, data sharding, and replication.
Deployment
Kubernetes was initially developed to manage containerized applications that don’t require data persistence but can be used with stateful workloads. It uses storage classes and persistent volumes to store and retrieve data, which is a reliable way to handle data in the cloud. However, the fact that Kubernetes frequently restarts and reschedules pods can create challenges for databases.
This requires an orchestration layer that will keep track of all the masters and replicas for your database and ensure that it elects a new master when one crashes or fails. The orchestration layer must also ensure the replicas are updated to the latest data version.
Some databases are better suited for this than others, depending on how critical the database is and how quickly stale data can be tolerated. For example, a healthcare solution showing patient history records may only be feasible if the database’s primary read/write copy is stale for a short time.
The best option for running the best database for data on Kubernetes cluster is often via an operator pattern. This approach encapsulates the database into a set of services that Kubernetes understands, such as pods, services, and secrets. This minimizes the complexity of deploying, monitoring, and managing the database while maximizing integration and automation.
Administration
Kubernetes is designed to manage the lifecycle of containers and their associated resources. It uses a persistent volume API and container storage interface (CSI) to allow for loose coupling between compute and storage in the form of a pod-based storage solution.
This can cause issues when storing data in a database, especially when the application is stateful and relies on multiple copies of the same data at any time. To address this, Kubernetes offers a variety of ways to provide persistence for pods, with varying levels of reliability and complexity.
Choosing the right database for your applications depends on several factors. It would be best if you considered the application’s language. You also need to consider capacity, performance, and scalability requirements. Finally, it would be best if you considered deployment topology.
As you consider your options, ensure your selected database has features that help you address its challenges. For example, it should support a combination of synchronous and asynchronous replication so that the system can continue to function if the primary copy of the data is unavailable for some reason. It should also have built-in concepts, such as failover elections or sharding, that can help you optimize how it works.
Monitoring
Kubernetes was initially designed to manage containers for stateless applications, but today, it also supports the management of database workloads. While many efficiencies of running a database can be beneficial, some challenges must be considered before deploying a database on the platform.
First, it’s essential to consider the characteristics of the database application and its data requirements. The temporary nature of pods can be challenging for a database, particularly one that requires high data availability. Kubernetes offers solutions to handle this, including persistent volumes and storage classes, which can provide a safe and abstract way to store and manage data. However, these solutions can be complicated to deploy and manage.
Additionally, databases must be able to deal with the frequent interruptions in a Kubernetes environment. This can include rescheduling pods, nodes going down, or dividing network segments. Ideally, a database engine can handle this volatility through concepts like sharding and failover elections.
For those wishing to utilize Kubernetes for a database, it’s best to choose an open-source solution with support for the platform and the ability to scale and handle performance tuning. Otherwise, administrators must be prepared for a more manual approach requiring more time and effort.
Scaling
Kubernetes is a modern container orchestration system that has revolutionized software development by making microservices scalable and reliable. Its cloud-native approach makes it a natural platform for databases to run on. It provides the flexibility to store and manage database data and connect it to frontends, customers, and analytics.
However, running a database requires catering to a certain level of volatility. It is common to see nodes shut down or rescheduled and networks fragmenting. The database needs built-in features to cope with this volatility, such as replication, failover elections, and sharding. Fortunately, the open-source community has created operators for many significant databases to make it easier to run them.
Persistent data for stateful applications is handled through persistent volumes and storage classes, which provide a safe and abstract way to manage data. Some companies outsource their databases to DBaaS solutions, eliminating the need for them to worry about spinning up, scaling, and managing database servers themselves. However, this comes at the cost of less control and the risk of relying on third-party services.
Other organizations use purpose-built, cloud-native databases, which can help make it easier to run stateful applications. These solutions often offer a high degree of reliability through eventual consistency, which ensures that all accesses to a particular piece of data will return the same value.
