Cassandra’s role in Non-linear scaling

A distributed NoSQL database built for non-linear scaling is called Cassandra. This means that even when the cluster’s nodes increase in number, it can manage a massive volume of data while retaining good performance and availability.

Traditional databases are built for vertical scalability, where you can improve a single node’s capability by adding more resources (such CPU or RAM). The scalability and cost-effectiveness of this strategy are constrained, though, as it is more and more difficult and expensive to add new resources to a single node.

In contrast, Cassandra is built for horizontal scalability, which entails expanding the cluster’s number of nodes to improve its capacity. This method is more economical and scalable since it enables you to add additional nodes as needed to meet growing data and traffic volumes.

Cassandra achieves non-linear scaling through a number of key features:

• Decentralized architecture: Cassandra implements a de-Centralised architecture with no single point of failure and equal treatment for each node in the cluster. As a result, adding new nodes to the cluster is simple and doesn’t require a lot of configuration or maintenance work.
• Peer-to-peer communication: The peer-to-peer communication paradigm used by Cassandra allows each node to directly communicate with any other node. This eliminates the need for a central coordinator and makes it simple to distribute and copy data throughout the cluster.
• Partitioning and replication: Data distribution and retrieval are made more effective by Cassandra’s partitioning and replication of data among cluster nodes. Additionally, it replicates data across numerous nodes for high availability and fault tolerance.
• Consistent hashing:  To distribute data equally among the cluster’s nodes, Cassandra employs a reliable hashing technique. This prevents hotspots that can affect performance and guarantees that data is dispersed properly.
• Linearizable consistency: Linearizable consistency is a feature of Cassandra that allows all nodes in the cluster to view the same data simultaneously. As the cluster’s nodes expand, this guarantees that the data is accurate and current.

Overall, cassandra is a good choice for applications that need to manage massive volumes of data while retaining great performance and availability because of its non-linear scaling characteristics. It can be used to support a variety of use cases, including IoT devices, real-time analytics, and online and mobile apps.

A distributed NoSQL database management system called Apache Cassandra was made to manage massive volumes of structured and semi-structured data across a number of commodity servers. Initiated by Facebook, it was subsequently open-sourced in 2008. Cassandra is renowned for its great scalability, fault tolerance, and high availability. It is based on the same ideas as Amazon’s Dynamo and Google’s Bigtable.

The architecture of Cassandra is built on a decentralized approach, in which every node in the cluster is identical and has a copy of the data on it. Data is divided across the nodes using a distributed hash table, and replication is employed to guarantee high availability and durability. Moreover, Cassandra features configurable consistency, which enables customers to strike a compromise between data availability and consistency.

Cassandra works really well if we want to write and store a large amount of data in a distributed system and don’t care much about ACID with good performance.