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 shaves off 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.