It's strange that they seem to think this is an amazing amount of scaling and then go on to reveal that it's actually with 20 servers. And it's not like their solution is novel.
"After we had properly configured our cluster and the auto-scaling settings, we were able to lower it even more to only 4x EC2 c4.Large instances and the Elastic Auto-Scaling set to spawn a new instance if CPU goes above 90% for 5 minutes straight."
"I was confused by the numbers at first, so: that's 17k requests per second, spread out over 4 dual core Xeon (Haswell) machines, which works out to just over 4000 requests/s per machine. It's still a respectable number, but it's much closer to what one would expect given the task."
"Don't get me wrong, the most interesting part is definitely the implementation and as a Go noob I found it very useful - it's just a bit misleading for the headline to sum your request rate across all parallelized machines."
If you read to the end:
"After we had properly configured our cluster and the auto-scaling settings, we were able to lower it even more to only 4x EC2 c4.Large instances and the Elastic Auto-Scaling set to spawn a new instance if CPU goes above 90% for 5 minutes straight."
From last time's discussion on this (https://news.ycombinator.com/item?id=9845820):
This comment is kind of insightful:
https://news.ycombinator.com/item?id=9846606
"I was confused by the numbers at first, so: that's 17k requests per second, spread out over 4 dual core Xeon (Haswell) machines, which works out to just over 4000 requests/s per machine. It's still a respectable number, but it's much closer to what one would expect given the task."
"Don't get me wrong, the most interesting part is definitely the implementation and as a Go noob I found it very useful - it's just a bit misleading for the headline to sum your request rate across all parallelized machines."