autocannon VS algorithms

Autocannon GitHub Repository

To make things interesting, let’s send some requests to this server with autocannon:

Using autocannon, I did the following script to simulate 500 concurrent connections over 30 seconds:

We then used autocannon to measure the throughput (requests per second) of each runtime server-rendering our React app.

You could also use tools like autocannon to see how many requests/second you can achieve with various methods. : https://github.com/mcollina/autocannon

I used autocannon to test the function at 100 concurrent executions for 30 seconds. I also used Lambda Power Tuning to find the ideal memory configuration, which proved to be 512MB. All the results are available.

Moreover, if your blocking issue is appearing only on heavy load, you can easily test it using the very nice --autocannon CLI param (see it with clinic flame --help) where you can specificy autocannon options to generate some HTTP load on your web service.

I packaged them both with Docker and deployed them to an EC2 instance, each behind an Nginx reverse proxy I setup in docker-compose. I'm currently testing performance using autocannon from my laptop like this: `autocannon -a 5000 -w 10 URL` (5000 requests with 10 workers), and both apps complete in around 40 seconds. The EC2 instance is in Oregon and I'm testing from Toronto.

You can test it by yourself using console.time(). You can use autocannon to stress-test your http server to see what is really the best options.

When I test this with autocannon making three simultaneous requests (autocannon --connections 3 --amount 3 --timeout 10000 --no-progress http://localhost:5678/):

Rather than implementing mine, I'll be using the priority queue from this library. We'll replace our @timers array with a priority queue instead. Here's how our queue will work: