Apache Cassandra and its benefits

What Is Apache Cassandra?

It offers a uniquely robust and expressive interface for modeling and querying data. What follows is an overview of several desirable database capabilities, with accompanying discussions of what Cassandra has to offer in each category. Apart from the following features, Apache Cassandra has a large user base including some top technology firms such as Apple, Netflix, and Instagram, who also open-source new features. It is being actively developed and has one of the largest open-source communities with close to 200 contributors and over 20,000 commits. It is an open-source NoSQL data storage system that leverages a distributed architecture to enable high availability, scalability, and reliability, managed by the Apache non-profit organization. NoSQL databases enable rapid, ad-hoc organization and analysis of extremely high-volume, disparate data types. That’s become more important in recent years, with the advent of Big Data and the need to rapidly scale databases in the cloud. Cassandra is among the NoSQL databases that have addressed the constraints of previous data management technologies, such as SQL databases.

One of the most important attributes of Cassandra is that its databases are distributed. That yields both technical and business advantages. Cassandra databases easily scale when an application is under high stress, and the distribution also prevents data loss from any given data center’s hardware failure. A distributed architecture also brings technical power; for example, a developer can tweak the throughput of read queries or write queries in isolation.

Key Benefits of Apache Cassandra

Let us look at some of the major aspects of Apache Cassandra that makes it highly beneficial.

Horizontal Scalability

Horizontal scalability refers to the ability to expand the storage and processing capacity of a database by adding more servers to a database cluster. A traditional single-master database's storage capacity is limited by the capacity of the server that hosts the master instance. If the data set outgrows this capacity and a more powerful server isn't available, the data set must be shared among multiple independent database instances that know nothing of each other. Your application bears responsibility for knowing to which instance a given piece of data belongs. Cassandra, on the other hand, is deployed as a cluster of instances that are all aware of each other. From the client application's standpoint, the cluster is a single entity; the application need not know, nor care, which machine a piece of data belongs to. Instead, data can be read or written to any instance in the cluster, referred to as a node; this node will forward the request to the instance where the data actually belongs. The result is that Cassandra deployments have an almost limitless capacity to store and process data. When additional capacity is required, more machines can simply be added to the cluster. When new machines join the cluster, Cassandra takes care ?of rebalancing the existing data so that each node in the expanded cluster has a roughly equal share. Also, the performance of a Cassandra cluster is directly proportional to the number of nodes within the cluster. As you keep on adding instances; the read and write throughput will keep increasing linearly.

Another exciting point about scaling in Cassandra is that there is no slow down, pause, or hitch in the system during the process. This means end-users would not feel the effect of whatever happened, ensuring smooth service to all individuals connected to the network.

High Availability

The simplest database deployments are run as a single instance on a single server. This sort of configuration is highly vulnerable to interruption: if the server is affected by a hardware failure or network connection outage, the application's ability to read and write data is completely lost until the server is restored. If the failure is catastrophic, the data on that server might be lost completely. Master-slave architectures improve this picture a bit. The master instance receives all write operations, and then these operations are replicated to follower instances. The application can read data from the master or any of the follower instances, so a single host becoming unavailable will not prevent the application from continuing to read the data. A failure of the master, however, will still prevent the application from performing any write operations, so while this configuration provides high read availability, it doesn't completely provide high availability. Cassandra, on the other hand, has no single point of failure for reading or writing data. Each piece of data is replicated to multiple nodes, but none of these nodes holds the authoritative master copy. All the nodes in a Cassandra cluster are peers without a master node. If a machine becomes unavailable, Cassandra will continue writing data to the other nodes that share data with that machine and will queue the operations and update the failed node when it rejoins the cluster. This means that in a typical configuration, multiple nodes must fail simultaneously for there to be any application-visible interruption in Cassandra's availability.

Write Optimization

Traditional relational and document databases are optimized for read performance. Writing data to a relational database will typically involve making in-place updates to complicated data structures on disk, in order to maintain a data structure that can be read efficiently and flexibly. Updating these data structures is a very expensive operation from a standpoint of disk I/O, which is often the limiting factor for database performance. Since writes are more expensive than reads, you'll typically avoid any unnecessary updates to a relational database, even at the expense of extra read operations. Cassandra, on the other hand, is highly optimized for write throughput and, in fact, never modifies data on disk; it only appends to existing files or creates new ones. This is much easier on disk I/O and means that Cassandra can provide astonishingly high write throughput. Since both writing data to Cassandra and storing data in Cassandra are inexpensive, denormalization carries little cost and is a good way to ensure that data can be efficiently read in various access scenarios.

Fault Tolerance

Cassandra is fault-tolerant primarily because of its data replicative ability. Data replication denotes the ability of the system to store the same information at multiple locations or nodes. This makes it highly available and tolerant of faults in the system. Failure of a single node or data center does not bring the system to a halt as data has been replicated and stored across other nodes in the cluster. Data replication leads to a high level of backup and recovery.

Peer-to-Peer Architecture

Cassandra is built on a peer-to-peer architectural model where all nodes are equal. This is unlike some database models with a “slave to master” relationship. That is where one unit directs the functioning of the other units, and the other unit only communicates with the central unit or master. In Cassandra, different units can communicate with each other as peers in a process called gossiping. This peer-to-peer communication eliminates a single point of failure and is a prominent defining feature of Cassandra.