Chronix natively speaks time series. You can store nearly every kind of data type within a time series due to its flexible design. You decide what a time series looks like. Chronix is built to store time series highly compressed and for fast access times. In comparison to related time series databases, Chronix does not only take 5 to 171 times less space, but it also saves 83% of the access time, and up to 78% off the runtime on a mix of real world queries. For the measurements we used a commodity hardware laptop computer and Chronix using the Apache Solr scenario (single node). Chronix supports three different scenarios, pursuing different goals:
Chronix Storage: Use Chronix as a small storage library and plug it into your application. It stores the time series using Apache Lucene.
Chronix Server: Combine Chronix with Apache Solr for a typical client-server scenario. Apache Solr offers several useful features like scalability, fault tolerance, distributed indexing, or replication.
Chronix Spark: Whenever you need a parallel and distributed time series processing, integrate Chronix with Apache Spark. Leverage Apache Spark to process a time series in parallel.
The whole Chronix Stack is open source and free to use for everyone without any restrictions. The stack has Chronix at its core but several other open source projects like logstash, collectd, fluentd, Grafana and Zeppelin are tightly integrated.