Cassandra is a flexible, distributed database that self-heals and scales across data centers. It’s a key component of many enterprises’ cloud-native applications, including those used by prominent companies.
Running Cassandra on Kubernetes is a challenge. But there are viable solutions, from open source to fully-featured SaaS products.
When deploying Cassandra in Kubernetes, the primary goal is to ensure that data doesn’t disappear or get wiped every time a pod gets rescheduled. There are multiple solutions for this problem – open source tools from Apache Cassandra and commercial vendors specializing in Site Reliability Engineering (SRE).
A common approach is to deploy a Cassandra cluster as a StatefulSet, which are workload API objects designed to manage stateful applications in Kubernetes. StatefulSets provides stable, unique network identifiers, persistent storage, ordered and smooth deployment and scaling, automated rolling updates, and more.
When a Cassandra StatefulSet is deployed, it’s crucial to monitor the requests that Cassandra receives at any given time. Knowing how much read and write activity your cluster receives can help you optimize performance based on your needs, such as compaction strategies and disk utilization. Monitoring operating system metrics, such as CPU usage and memory, is also a good idea. These metrics can tell you how well your cluster performs and help identify issues like a lack of free memory on a node or I/O bottlenecks.
Running Cassandra on Kubernetes enables you to deploy a scalable distributed database environment that keeps data and application operations closer. Cassandra already has fault tolerance and node placement features that work with the ephemeral nature of containers. However, marrying these two systems can be complex because they have different ideas about a cluster.
In Cassandra, a cluster is a set of nodes in a data center connected through a reliable network. A node can be a physical host, a machine instance in the cloud, or a containerized Cassandra pod that runs on Kubernetes.
A cluster’s data is replicated across all nodes in a data center so that you can continue to access the data in case one or more of the nodes fails. This is a core feature of Cassandra that provides a high level of reliability and availability.
The scalability of Cassandra is enhanced further because you can run it in multiple data centers. In addition to deploying your applications in different locations, you can spread the load across your data center. This helps reduce latency, which is vital for applications that rely on real-time performance.
Cassandra and Kubernetes are a logical dream team, sharing common concerns about distributed computing. Both platforms are designed to operate on large clusters of nodes for superior scalability and self-healing capabilities. This allows developers to scale applications based on demand without interruptions and provides additional resiliency for large-scale cloud applications.
The two systems also work well together, with Cassandra utilizing distributed data replication across a cluster for superior performance and reliability. This is why Spotify primarily utilizes Cassandra to replicate data between its U.S. and European data centers. This function ensures that customers can listen to music in both regions and eliminates any risk of disruption due to hardware or software failures.
In addition to the benefits of Cassandra’s distributed data replication, Cassandra offers a highly available and redundant storage system for data in the form of persistent disks. The combination of these features makes Cassandra a popular choice for mission-critical apps.
As such, knowing how to deploy Cassandra on a Kubernetes platform is important. While several tools exist to help simplify Day 0 deployment and performance tuning, there are still challenges around automation and managing edge case failures that cannot be solved by tools alone. As a result, site reliability engineering (SRE) expertise remains essential for running distributed workloads on Kubernetes.
Kubernetes provides a platform for deploying and managing applications. But for stateful workloads like a database, it doesn’t automatically understand how databases function. This can lead to a mismatched architecture, limited developer productivity, and expensive cloud computing costs.
Cassandra is a distributed database designed with multiclustering. This means multiple servers can be physically located in different data centers, and a single application can access data from each. A network interconnect can connect the data centers, making the cluster appear as a single unit to external users.
For these reasons, running Cassandra on Kubernetes is challenging. The ephemerality of Kubernetes means that containers are constantly being replaced, moved, and rescheduled. This can result in lost data if the underlying database doesn’t maintain a persistent state. Running Cassandra on Kubernetes requires combining automation and site reliability engineering (SRE) expertise to ensure it is configured correctly. It must also scale up to 1000 nodes, with backups and monitoring.
Fortunately, there are a variety of tools available to simplify this process. For instance, a device allows seamless migration to Kubernetes while maintaining the same database configuration.