Getting a database to work with Kubernetes can be tricky. Developers expect their containerized applications to be temporary but still need an underlying data store that doesn’t get wiped.
Cassandra offers a solution to this problem with built-in mechanisms designed specifically for data replication. This allows developers to create a distributed architecture without relying on an operator.
Scalability
Cassandra is a distributed database that uses nodes spread across multiple locations to store data. This enables it to scale out, handling large volumes of data. In addition, it has built-in replication capabilities, making it possible to replicate data to multiple locations. This provides scalability without needing separate distributed data storage systems.
Cassandra also works well with Kubernetes, an open-source container orchestration system. This lets you deploy, run, and manage applications in a highly available, scalable fashion. Kubernetes can run on any infrastructure – whether on-premises VMs, in a public cloud, or on bare metal servers. It also supports multi-cloud and hybrid cloud.
Cassandra on Kubernetes automates the process of installing and managing Cassandra in a Kubernetes environment. It includes a Cassandra container compatible with any Kubernetes version and a pre-configured dashboard for monitoring the database. In addition, it integrates with other tools from the open-source community, such as Reaper for database repair automation and Medusa for backups.
Reliability
Cassandra is designed to be a highly reliable database, relying on multiple nodes in a cluster to replicate data across nodes in the same data center or even in different ones. This enables high availability and fault tolerance so that the data will be restored even in a node failure.
However, it is important to remember that Kubernetes is a temporary platform, meaning your Kubernetes pods will disappear, get rescheduled, or be killed periodically. This translates to the need for an underlying storage system that won’t go missing, which is where Cassandra comes in.
Cassandra can be deployed on bare-metal, virtual machines or containers and is proven at scale by enterprises such as Apple, Netflix, Spotify, and CapitalOne. It offers masterless replication, an easy-to-use CQL programming language, and a familiar set of SQL commands. And, with support for multi-cluster architectures and the ability to host in multiple public cloud environments, Cassandra is a trusted and powerful database choice for mission-critical applications.
Scalability
Cassandra is designed to scale and enables a highly available distributed database solution capable of handling massive amounts of data. Its inherent replication across a set of data centers, called a ring, ensures that data is always available, even if a single center goes down. This self-healing capability makes it vital for many global enterprises like Spotify and CapitalOne, who depend on it to operate their music personalization system.
Kubernetes simplifies deployment, scaling, and lifecycle management for distributed systems, making it the perfect platform to run Cassandra. Kubernetes’s ability to easily resize stateful sets, a clustering API object used to manage stateful applications, helps to automate the process of resizing a Cassandra database.
Additionally, several open-source technologies allow you to simplify the process of running Cassandra on Kubernetes. One of these is the Cassandra operator, which allows developers to write scripts for managing and deploying Cassandra. This approach also makes it easy to use consistent and reproducible environments in development, QA, and production. This is a critical feature when developing cloud-native applications.
Flexibility
Cassandra is a masterless database that requires nodes to be located in a ring (similar to a database schema in a relational database). Data stored is replicated across multiple nodes within the ring and can be accessed from any node. This flexibility makes it ideal for analytical architectures that combine transactional workloads with analytical workloads.
Cassandra’s inherent replication also enables it to survive failures in individual data centers. This means that Spotify’s music personalization system will always be able to reach users regardless of whether a single data center is unavailable.
Combined with Kubernetes, Cassandra offers the flexibility of linear scalability and proven fault tolerance on commodity hardware or cloud infrastructure, ensuring your applications can grow without fail and deliver the lowest latency.
Scalability
Kubernetes enables you to scale the number of application containers and automatically provide resources. But it’s harder to do the same for stateful data containers. That’s why it is critical to design the database and the cluster with a shared architecture. This makes it easier for read and write operations to take place close together, avoiding latency, and allows you to run the database at scale with high availability.
Traditional databases use careful replication to ensure that different copies of data are up to date. However, that sacrifices scalability and limits how fast data can be retrieved from the database. In contrast, Cassandra achieves scalability by relaxing consistency. In this way, a Cassandra deployment can have a very high availability without losing any data in the case of a node failure. This is made possible by the built-in intelligence in Cassandra nodes that track other nodes in the cluster, distribute data, and load across them.
