Apache Cassandra is a massive, distributed database that was originally used for the inbox search of Facebook. In July 2008, Facebook open-sourced the technology, making it available to the public. It has since been part of the Apache Incubator and is now an official top-level project of the Apache Software Foundation. To learn more about Apache Cassandra, read our introductory guide. We will also explore how this database is built and how it can benefit your business.
The project was started in 2007 by Facebook, which used the database to power its inbox search feature. In July 2008, Facebook announced that it was open-sourcing Cassandra, and in March 2009, the project was accepted by the Apache incubator. The goal was to create a system that would be highly consistent, fault-tolerant, and scalable. The project’s architecture combines the capabilities of Amazon’s Dynamo distribution model and Google’s BigTable storage engine. Cassandra’s design enables horizontal scalability across a cluster of nodes, and its log-structured storage engine is ideal for the most data-rich applications.
Cassandra’s performance is outstanding, with hundreds of thousands of writes per second. Because it focuses on speed and reliability, Cassandra allows organisations to manage vast amounts of data easily and quickly. Additionally, Cassandra is highly scalable, with the ability to expand by adding another rack to scale performance. As a result, organisations can use commodity-priced servers to increase their Cassandra clusters’ performance.
Although the complexity of Cassandra may put you off, it’s worth a try if you’re looking for a robust, highly-available database that scales enormously. Aside from providing top-tier performance, Cassandra also has a strong consistency guarantee. The tunable consistency setting in Apache Cassandra allows users to specify how much consistency they want their data to have. For a strong consistency system, write consistency is set to 3. Using read-only Cassandra with write-only access will allow a query to be performed a few times, without storing duplicate data.
When creating Cassandra clusters, remember to choose the correct compaction strategy. Changing this setting will lead to performance issues in production. Compaction is a process of rearranging data on disk and removing old data to maintain performance. DataStax offers great documentation about compaction and may introduce improved strategies in the future. So, choose the right compaction strategy based on the needs of your application. Then, you’ll be on your way to creating a highly-performance Cassandra database cluster.
In a Cassandra cluster, nodes are abstracted further to make sure that data is more evenly distributed across the database. A three-node cluster, for example, can be divided into 12 vNodes, or a single physical node may contain a number of vNodes, each with a different range of partitions and replicas of each other’s data. Once the vNodes have been written, the data is stored on disk.
Apache Cassandra is a non-relational database that is designed for distributing data across a network of commodity servers. It has tremendous scalability, allows for a high number of users, and offers high availability. It is also a type of NoSQL database, which means that it does not store tables in a table. Those using it in production can expect it to scale to a lot of servers.
