The Appliance

Our next version of analytic software for computer performance data will have a totally new architecture and design, to support large or small customer accounts, using same stock base software. From Perl to Lua, from a time series data store to a very powerful in cache data structure server, all these changes were made to support more devices, efficiently use the computing power and deliver value. So what we did:
  • we dropped Perl and FastCGI
  • we dropped RRDtool
  • we started to use Lua programming language
  • we moved to OpenResty for a fast and robust HTTP processing
  • we switched to Redis for in cache memory statistics
  • and we are still designing a new exploratory data module, for direct interaction with raw data
And the results are amazing. We have a more powerful architecture which lets us build nice ready data appliances for large data-center customers and small and medium sized businesses.

We are using the Data-Driven Document JS library, the D3, a fresh and powerful data visualization library, based on standard HTML, SVG and CSS. So there are lots of new things versus our old analytic release 0.70. And we expect to have the first demo somewhere in late October for certain key accounts.

During all our development process we been heavily reading and researching in cache memory data structure servers, web analytic, real-time data processing. Two reference books we are using heavily for our software development and data streaming:

"Redis In Action", by Josiah L. Carlson
a must have about Redis and data analytic software implemented in Python or Lua. The author is a master in Redis and he is frequent contributor to Redis community. Many thanks Josiah for your support and advices for our analytic software product.

Another good book about real-time web analytic software is "Real-Time Analytics", by Byron Ellis a nice description of data analytic software technologies, methods and techniques to develop and support such applications. It does include Redis database but the others too: MongoDB, Cassandra.


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