CrateDB is a distributed SQL database, purpose-built for querying huge volumes of machine data in real-time.
- It is open-source (Apache 2)
- It is natively distributed, with a shared-nothing architecture, automatic data rebalancing, and automatic table partitioning
- It performs aggregations, JOINs and sub-selects
- Its schemas are completely dynamic, being possible to add columns anytime without slowing performance or downtime
- Written in Java
Yes, CrateDB is actually an amazing choice for time series, especially if you have very high volumes: machine data is time-series data. CrateDB has excellent performance with real-time queries, together with group by’s or general roll-up queries over a huge data set without losing accuracy. The dynamic schemas of CrateDB are also great for time series.
CrateDB was built with the features that are most important for handling challenging machine data use-cases, as industrial IoT. And its architecture was designed for this matter: for example, instead of focusing on strong consistency, CrateDB focuses on data availability and partition tolerance. Efficiency is another key pillar of CrateDB, which focuses on maximizing data availability. At the same time, the SQL access makes it easy for developers.
Some years ago, our founders (in lovely Austria) were working on some of the biggest websites in Europe at that time. They soon got obsessed with data, being inspired to build a database that gave Elasticsearch the SQL access it lacked. The first iteration of CrateDB was soon born—winning the TechCrunch Disrupt Europe award.
CrateDB is built to make it easy to scale indefinitely. Sharding, replication, and data rebalancing are automatic—to grow the database capacity, just add more nodes to the cluster... Or choose the ease of CrateDB Cloud to increase capacity anytime.
Our own users can answer this one better than me:
CrateDB’s unmatched concurrency capabilities and simple scaling made it the best solution for us. We tried other solutions, including MongoDB, but it was difficult and expensive to scale for our needs
(Waseem Javid Nasiri, Senior Developer – Roomonitor)
Postgres couldn’t keep up with the data we have; Datastax Enterprise had ingest scaling issues with spatial data; Cassandra didn’t have spatial query operations. CrateDB was the only database we found that could smoothly process data for our users. We fell in love with it immediately
(Kartik Venkatesh, CTO – Spatially Health)
We didn’t want to use a sharded and clustered MySQL database, since maintaining it would have been labor-intensive and use up our engineering resources. I started looking at CrateDB and was impressed by the quality of the code. Switching from MySQL to CrateDB took only a couple of days
(Jeff Nappi, Director of Engineering – ClearVoice)
CrateDB’s ability to query enormous amounts of data expands the realm of what’s possible with Clickdrive. We tried a few different SQL and NoSQL databases, and CrateDB offered the best combination of high performance, scalability, and ease-of-use
(Mark Sutheran, Founder - Clickdrive.io)
See you around 🐐