39 million requests X 2.39 seconds per request = 93.21 million seconds of compute time performed each month. That works out at 2.95 years of computation for $370.
Although he mentioned that about half the traffic comes from cached results. So maybe it is more like 1.5 years of actual new computing each month. Compare the $370 against the AWS cost of running a VM for 1.5 years to do the same workload and I think your saving a lot of money.
It is 2.95 years of computation on less than 1 CPU core.
http://docs.aws.amazon.com/lambda/latest/dg/lambda-introduct...
> AWS Lambda allocates CPU power proportional to the memory by using the same ratio as a general purpose Amazon EC2 instance type, such as an M3 type. For example, if you allocate 256 MB memory, your Lambda function will receive twice the CPU share than if you allocated only 128 MB.
https://aws.amazon.com/ec2/instance-types/
> m3.large 2 7.5 1 x 32
> Each vCPU is a hyperthread of an Intel Xeon core except for T2 and m3.medium.
1 Core per 7.5GB RAM. Or ~1/28th of 1 core.
(2.95*365.25)/28 = ~39 Core Days.
That is really only impressive when you are comparing AWS prices to AWS prices which is a trap people seem to frequently fall into imo.
$150 (at most) dedicated server for instance.
No. I've pushed 1000 r/s (approx) on a price tracking tool at work for keeping track of competitors on one of those boxes. Their largest spike (even if you assume its 10x their average) would be at most 15% of that.
A) I'm not sure "one location" is a city, country, or province.
B) Yes, it was global as we had offices from China to the EU.
C) Given the OP is handling 15 r/s, 150 r/s is not going to be pushing 500 mbps unless something is very poorly designed.
D) Even at spikes of 40x, it would still only be 600 r/s which is still quite doable with a commodity server.
The only serious difference is it was very large batches of requests (20 * SKUs) so people were fine with waiting a few minutes.
As a concrete example, say you have an ecommerce app. Read requests take 80ms while write takes 200ms. Any good article/book I can read to get more insight regarding this?
With even moderate caching (say, 1m), you can saturate your VM's bandwidth before running out of CPU or memory.
Ultimately, you will always have a bottleneck. All you can do is identify the bottleneck (via load testing) and address it. If it's network (and you're in Amazon), you have to scale vertically until they remove that limit, or horizontally if you're already on unrestricted instances. If it's the CPU, you'll need to be more aggressive at caching completed computations, or work in a language with less overhead. If it's memory, start evaluating the tradeoffs between CPU and memory. If it's your DB, start investigating why it's slow (it's probably a poor index or configuration; DBs on decent hardware can handle millions of requests per second before degrading).
I don't have any book recommendations; I just recommend creating something and find its limits with a simple load test tool. Then figure out your bottleneck and fix that. Rinse and repeat, and learn.
> As a concrete example, say you have an ecommerce app. Read requests take 80ms while write takes 200ms.
An ecommerce site can be nearly 100% cached except for cart/session/search data. This gives it a massive advantage over everything else (since you can load the non-cached data via js and its relatively tiny).
I think the last time I benchmarked one of our front end servers it was ~500 r/s on 2 cores for our search page.
You really aren't going to know until you try it and benchmark it yourself tho.
It's unlikely that you'd get cheaper raw compute from any cloud vendor when using Lambda-like products, compared to their IaaS offerings. The savings are in lower ops costs, easier auto-scaling, integration with tons of other AWS products, no minimum cost for services with a small amount of requests, etc.
> In the last month, 52% (20.3MM out of 39MM) of our API requests were served from the cache, meaning less than half (18.7MM requests) required invoking our Lambda function. That $14 saves us around $240 a month in Lambda costs.