Some providers of enterprise WebSocket servers claim their real-time web technology scales to millions of users. However, AFAIK, only MigratoryData currently offers evidence of how this scaling is achieved. Some claims:
Most likely, all the products mentioned above come with some form of clustering. Thus, this feature can be used to scale horizontally and push data to millions of concurrent users by deploying multiple WebSocket server instances. In this case, scaling to millions of users is possible but expensive.
MigratoryData implements built-in high availability clustering, thus it scales horizontally. But more important, MigratoryData demonstrates (as published in their Performance Benchmarking Guide) that pushing real-time data while having 1 million concurrent users connected is possible from a single instance running on a 1U blade. Thus, MigratoryData is able to push data to millions of users and, in fact, it is currently used in production to push real-time data to millions of end-users every day.
Over the next couple of weeks, I’m going to work on the performance benchmarking of the just released MigratoryData WebSocket Server 4.0 (see the Architecture Guide for an overview of the new version 4.0).
Last time when I performed benchmark tests for MigratoryData server 3.5, I compared the results with those published by Caplin Systems for their WebSocket server Caplin Liberator. Caplin Liberator’s performance results were very good in terms of latency and throughput and I think (if I recall correctly) we’ve achieved comparable results, slightly better for some use-cases and slightly worse for other ones. Caplin Systems’ target at that time was not high vertical scalability, as suggested by Martin Tyler.
I’m interested if any WebSocket server provider that claims high vertical scalability can offer any published results so that I can compare them with the new results of MigratoryData 4.0. MigratoryData benchmark results for version 4.0 are going to be released soon.