SingleStore ships 2.0. Scales in-memory database across hundreds of nodes, thousands of cores.

When we started SingleStore a little over two years ago, our goal was to deliver the fastest OLTP database ever. Inspired by the scale and architectures we saw at Facebook, we hoped to help every enterprise leverage in-memory technologies similar to those that leading web companies use.

As we worked with our early customers, we saw that an in-memory solution could provide the greatest value by enabling users to analyze real-time and recent historical data together. Customers like Zynga and Morgan Stanley not only wanted to quickly commit transactions to the database, they also wanted instant answers to questions about how their real-time data compared to historical data. This inspired us to build something new – a solution that supports highly concurrent transactional and analytical workloads at Big Data scale.

That brings us to today. We’re proud to announce that SingleStore’s real-time analytics platform is available for download. This is the first generally available version of SingleStore that scales horizontally on commodity hardware. It provides the blazing fast performance for which SingleStore is known, and now does it at Big Data scale. Customers have deployed SingleStore across hundreds of nodes and dozens of terabytes of data, and we’ve tested at even greater volumes and velocities. (Check out our calculator to get an idea of the number of reads and writes you can perform depending on the size of your cluster.)

This is also the first version to include SingleStore Watch, a visual web-based interface for monitoring and managing your cluster. We expect this to be the beginning of our foray into real-time visualizations as many of our customers look to operationalize their analytics.

Deploying a database can be difficult, so we’ve made it as simple as possible. Download SingleStore for free on our site and take it for a spin. You’ll definitely be impressed by the performance, but you’ll also be impressed by what’s missing:

• Batched loading – Don’t wait until the middle of the night to refresh your reports.
• Complicated programming languages (and a limited talent pool) – Use SQL for real-time analytics.
• An expensive, proprietary box (and a plan to rip and replace it in a few years) – Scale incrementally on commodity hardware.
• A lengthy implementation cycle – Launch your first SingleStore instance in minutes in the cloud.

We’re proud of the progress our engineering team has made building out an enterprise-grade software solution. Stay tuned to this blog to learn more about the real-time analytics challenges we are helping customers conquer. More to come.