A load generator library written in Go.
import (
gohammer "github.com/ferdingler/go-hammer"
)
func main() {
config := gohammer.RunConfig{
TPS: 10,
Duration: 60,
}
hammer := gohammer.HTTPHammer{}
hammer.Endpoint = "https://www.google.com"
hammer.Method = "GET"
gohammer.Run(config, hammer)
}This library will have a number of built-in generic hammers that users can leverage to get started very quickly, like the HTTPHammer. But the real power and extensibility of the library comes by writting custom hammers that implement the Hammer interface and have custom logic that runs on every request.
type Hammer interface {
Hit() HammerResponse
}This library can also evolve to provide useful tools to the hammers to make their implementation easier, for example, a random data generator or a user credentials provider.
A load generator has the ability to generate concurrent requests against a defined target, and one of the nicest features in Go is actually Concurrency –– It is easy to program and also very resource efficient. Goroutines and Channels are the main characters involved in it. Goroutines are basically functions that run concurrently with other functions; Not to be confused with threads, goroutines are actually multiplexed to a limited number of OS threads and is one of the reasons why concurrency in Go is efficient. Channels are message pipes where Goroutines can communicate to each other in a race-condition-safe manner.
Goroutine per request
There are as many goroutines as TPS specified spawned every second. I'm calling them Hammers and they are short-lived because they die as soon as their request is over. There can be hammers of different types, for example, a hammer of type HTTP knows how to trigger HTTP requests, but a hammer of type IoT knows how to generate MQTT requests. This allows for extensibility and to decouple the actual testing logic from the load generation orchestration. The following diagram illustrates the goroutines involved, which are represented by circles:
My initial concern with this approach was the high number of goroutines created every second, but this seems to be less of a concern as goroutines are very efficient and lightweight. However I'm still worried about a situation where the endpoint under test is slow to respond, causing the Hammers to take longer to complete and the running active count of Hammers will start to pile up.
TODO: Find out if the aggregator goroutine will become a bottleneck? When the Hammers write to the responses channel they block until the aggregator reads; This is the nature of how channels work because they are also a synchronization mechanism, however, this may cause the aggregator to become a bottleneck as it will block the Hammers until it catches up draining the channel.
Long living goroutines
Create as many goroutines as TPS specified where each goroutine is in charge of generating a request (i.e. HTTP) every second. The challenge is that the goroutine waits for the request to resolve and if it takes longer than a second, the system as whole will not meet the desired TPS.
Functional testing is done by spinning up a local web server and then pointing the load generator against it. The local web server verifies that the expected number of requests generated are actually received.
go test -v
go test -cpuprofile cpu.prof -memprofile mem.prof -bench .
go tool pprof cpu.prof
The max TPS seems to be constrained by the host OS max open files limit. The package net/http opens a socket for every HTTP request in-flight. In my macbook this seems pretty low as it breaks at ~225 TPS. Command to find this limit:
ulimit -n
I have tested on an EC2 instance t2.micro with Amazon Linux 2 and was able to go to 500 TPS. Still need to test within a docker container running on Fargate.
Distribute requests evenly within the timeframe of a second?
The current implementation triggers all requests in the beginning of a second, obviously each consecutive request is triggered slightly (a few microseconds?) after the previous one, but this means that all the remaining time before the second is over is empty and quiet with no requests. I suspect that distributing all of these requests evenly within the timeframe of a second would mimic real-world traffic more realistically and will create less pressure on the system under test.