If you've ever tried to benchmark a high-performance backend, you've probably written a quick Go or Python script that spins up 10,000 concurrent threads to bombard your server. And if you've done that, you've probably noticed something frustrating: Your server doesn't crash. Your testing script does.
Between ephemeral port exhaustion, TCP Accept Queue limits, and Mutex lock contention, standard single-machine load testing tools often become the bottleneck before the target server even breaks a sweat.
I wanted to understand exactly how enterprise tools break past these physical OS limitations. So, instead of just booting up JMeter, I spent last week building my own distributed, stateful HTTP load-testing orchestration engine from scratch in Go.
I call it Chaos Swarm. Here is a deep dive into the architecture, the bottlenecks I hit, and how I engineered my way around them.
(If you just want to see the code, [here is the Github repository]. Don't forget to star it if you find the architecture interesting!)
Challenge 1: The Ephemeral Port Bottleneck
A single Linux machine is bound by physics; specifically, its ephemeral port limit. When you make an outbound HTTP request, your OS assigns a temporary port. You only have about 65,000 of these, and if you are pushing massive Requests Per Second (RPS), they get stuck in TIME_WAIT states faster than the OS can recycle them. Your tester runs out of sockets and crashes with bind: address already in use.
The Solution: Distributed RPC Orchestration
To achieve true enterprise-scale RPS, I couldn't rely on a single machine. I architected Chaos Swarm using Go's net/rpc to operate as a distributed cluster.
The Master Node: The user defines the attack scenario via a JSON file. The Master parses this payload, calculates the exact RPS and concurrency distribution, and dispatches instructions over TCP to the worker fleet.
The Worker Nodes: These are headless network assassins that can be deployed across multiple physical servers. They receive the payload, execute the localized attack utilizing an aggressively tuned
http.Transportconnection pool, and stream the telemetry back to the Master.
Challenge 2: CPU Contention and Mutex Locks
Tracking latencies and HTTP status codes for 100,000+ concurrent requests introduces a massive memory synchronization problem. If thousands of Goroutines try to append their latency results to a shared array, you have to use a sync.Mutex to prevent race conditions.
This creates massive CPU contention. The Goroutines end up waiting in line just to record their metrics, which artificially slows down the testing engine and ruins the accuracy of the latency percentiles.
The Solution: Lock-Free Atomic Histograms
To track P50, P95, and P99 latencies without locking the CPU, I completely ditched standard slices and Mutexes. Instead, Chaos Swarm uses Lock-Free HDR Histograms.
By pre-allocating static memory arrays and utilizing Go's sync/atomic package, we can record data with zero blocking.
// Pre-allocate a fixed array where the index represents milliseconds
var latencyHistogram [10000]int32
// Inside the Goroutine: Lock-free, O(1) atomic increment
ms := reqDur.Milliseconds()
if ms >= 0 && ms < 10000 {
atomic.AddInt32(&latencyHistogram[ms], 1)
}
Multiple Goroutines can concurrently hit this array at the nanosecond level. When the attack finishes, the Master simply aggregates the arrays from the workers and calculates the exact percentiles. No locks, no CPU bottlenecks.
Challenge 3: Dumb Traffic vs. Stateful Journeys
Most basic load testers just spam GET /api/data. That doesn't reflect reality. Real users hit your frontend, submit a POST request to log in, receive a session cookie, and then use that cookie to interact with protected routes like a shopping cart or checkout page.
The Solution: Isolated CookieJars
To simulate complex user behavior, Chaos Swarm parses multi-step scenario.json files.
{
"name": "VIP Checkout Flow",
"steps": [
{
"method": "POST",
"url": "http://localhost:8080/api/login",
"payload": "{\"email\":\"bot@swarm.com\",\"password\":\"hacktheplanet\"}"
},
{
"method": "POST",
"url": "http://localhost:8080/api/checkout",
"payload": "{\"ticketId\":\"VIP-13\"}"
}
]
}
Instead of sharing one global HTTP client, every single Goroutine is instantiated with its own isolated http.CookieJar. This allows thousands of concurrent virtual users to independently maintain session state across complex authentication flows, hitting your database exactly how a real swarm of users would.
The Ethical Safeguard (Please Don't DDoS People)
When you build a distributed tool capable of orchestrating massive, stateful network traffic, you are essentially building a localized DDoS engine.
To ensure this tool is used strictly for authorized Quality Assurance and not for taking down random websites, I hardcoded a cryptographic handshake protocol into the Swarm engine.
Before the Master Node dispatches an attack to the workers, it generates a secure token and requires you to place a swarm-auth.txt file on the root directory of the target server. The engine mathematically verifies domain ownership before firing a single bullet. If you don't own the server, the Swarm refuses to attack.
The Results
The resulting telemetry gives you a granular breakdown of exactly which step in your API journey failed, the distribution of 2xx/4xx/5xx HTTP codes, and the true P99 latencies under extreme duress.
Building Chaos Swarm was a massive learning experience in memory management, OS network limitations, and distributed systems architecture. If you are a backend engineer interested in testing the limits of your own staging environments, feel free to clone the repo, spin up a few worker nodes, and see where your server breaks.
Link to the Chaos Swarm GitHub Repository
Have you ever hit hardware limits while load testing? Let me know your stack and how you bypassed it in the comments below!
