Elasticsearch and Cassandra are two of the widely used databases today withElasticsearch showing a more recent resurgence due to its unique full text searchfeature, akin to that of a search engine, contrasting with the conventional querylanguage-based methods used to perform data searching and retrieval operations.

The demand for more powerful and better performing yet more feature rich andflexible databases has ever been growing. This project attempts to study how the twodatabases perform under a specific workload of 2,000,000 fixed sized logs and underan environment where the two can be compared while maintaining the results of theexperiment meaningful for the production environment which they are intended for.

A total of three benchmarks were carried, an Elasticsearch deployment using defaultconfiguration and two Cassandra deployments, a default configuration a long with amodified one which reflects a currently running configuration in production for thetask at hand.

The benchmarks showed very interesting performance differences in terms of CPU,memory and disk space usage. Elasticsearch showed the best performance overallusing significantly less memory and disk space as well as CPU to some degree.

However, the benchmarks were done in a very specific set of configurations and a veryspecific data set and workload. Those differences should be considered whencomparing the benchmark results.