Adaptive C++ HTTP Server (from scratch)

"Instead of just writing APIs on top of servers like Nginx or Node.js, this project goes one level deeper — we built the HTTP server itself, from scratch, in C++."

It is lightweight, low-level, and designed to handle high concurrency with adaptive self-protection features.

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Key Features

• Custom HTTP Server in C++

  • Built using raw sockets and Windows IOCP (I/O Completion Ports)
  • Handles thousands of concurrent connections efficiently

• Fully Async I/O (WSASend + WSARecv)

  • Non-blocking sends via overlapped I/O — worker threads never block on send()
  • Partial send handling — automatically resumes if socket buffer is full
  • Keep-alive support — connections reset cleanly for next request

• Sharded Rate Limiter (Lock Striping)

  • 32 independent shards — only 1/32 of workers contend on the same mutex
  • Per-IP adaptive state with exponential moving average (EMA) thresholds
  • Runtime enable/disable toggle — zero overhead when disabled

• Optimized Request Parsing

  • std::string_view throughout — eliminates ~80% of string allocations
  • Single-pass header scanning — no repeated find() scans
  • std::from_chars for numeric parsing — no temporary string creation

• Static File Preloading

  • All static files loaded into RAM at startup
  • Zero disk I/O during request handling — served from memory cache
  • Cache is read-only at runtime — no locks needed

• IP-Based Limiter

  • Tracks IP addresses with their own adaptive state
  • Fair admission for multiple clients from same IP
  • Periodic eviction of stale IP entries

• Overload Admission Control

  • On accept(), if the server is overloaded:
    • Gracefully responds with HTTP 429: Too Many Requests
    • Closes the connection politely
  • Prevents the server from being overwhelmed

• Separation of Concerns

  • Request parsing, connection handling, and rate limiting are modular
  • Easy to extend for future improvements

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Architecture Overview

1. Connection Lifecycle

   • Client connects → checked by IPLimiter + admission control
   • If accepted → request handled asynchronously with IOCP worker threads

2. Adaptive Control Layers

   • Per-request adaptive limiter (per client)
   • IP-based limiter (global across clients from same IP)
   • Admission control at accept() (reject excess load early)

3. Concurrency Handling

   • Uses Windows IOCP threads to scale with CPU cores
   • Fully async sends and receives — no blocking operations

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Flow

flowchart LR
    Client --> AdmissionControl --> IOCPThreadPool --> HTTPParser --> Router --> AsyncWSASend --> Response

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Why is this Unique?

• Most developers never touch raw server internals — they just deploy APIs on existing frameworks.
• Here, the entire HTTP server core was implemented manually in C++, a low-level systems language.
• The design introduces novel adaptive mechanisms uncommon in scratch-built servers:
  • 🔹 Adaptive per-client rate limiter
  • 🔹 IP-aware fairness to prevent abuse
  • 🔹 Overload admission control with graceful rejection

This isn't just another "toy server" — it demonstrates how production-grade concepts (admission control, fairness, overload handling) can be built at the raw systems level while still offering an Express.js-like developer experience.

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Optimizations Implemented

The server has been significantly optimized from the baseline:

1. Async WSASend (Non-blocking I/O)

• Before: Blocking send() calls stalled worker threads
• After: Fully overlapped WSASend() via IOCP — threads never block
• Impact: 2–3× throughput improvement under load

2. Sharded Rate Limiter (Lock Striping)

• Before: Single global mutex — all workers serialized
• After: 32 independent shards — contention reduced by 32×
• Impact: Scales linearly with core count

3. String-View Request Parsing

• Before: 10–20+ heap allocations per request (split(), substr())
• After: std::string_view + single-pass scanning — 3–5 allocations minimum
• Impact: ~80% fewer allocations, better cache utilization

4. Static File Preloading

• Before: Disk read on every static file request
• After: All files loaded into RAM at startup — cache-first, zero-lock reads
• Impact: Eliminates disk I/O latency entirely

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Future Improvements

This project takes a different route: we built the web server itself — from scratch, in pure C++.

• Producer-consumer scheduling with smart request pipelines
• Zero-copy file serving (TransmitFile on Windows)
• Self-tuning worker thread pool
• Advanced congestion control (dynamic backlog admission)
• Smart request scheduling, prioritizing fast GET requests over POST requests

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Tech Stack

• Language: C++20
• Concurrency Model: IOCP (Windows I/O Completion Ports)
• Networking: Winsock2 async I/O (WSASend, WSARecv)
• Build System: CMake

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Getting Started

Note: It will work in windows only, and it will need C++20 version with windows API. You don't need to do anything, just get mingw latest compiler from winlibs.com.

git clone https://github.com/SrabanMondal/adaptive-cpp-http-server.git
cd adaptive-cpp-http-server
mkdir build && cd build
cmake ..
cmake --build .

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Run the server

cd ..
/build/adaptive-cpp-http-server.exe

Server starts on default port 3000. But you can change it in main.cpp. Try with:

curl http://localhost:3000/

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Developer-Friendly API (Express.js Style)

This server isn't just sockets and bytes — it comes with a clean, high-level API inspired by popular web frameworks like Express.js.

Example: define routes in a few lines:

Router router;
Server server(3000, router);

// Simple HTML route
router.addRoute("/", HttpMethod::GET, [](const HttpRequest& req) {
    return HtmlResponse("<h1>Hello from C++ HTTP server!</h1>");
});

// Serve binary data
router.addRoute("/favicon.ico", HttpMethod::GET, [&](const HttpRequest& req) {
    std::vector<char> data = ft.readBinaryFile("favicon.ico");
    return BinaryResponse(data, "image/x-icon", 200);
});

// Return JSON
router.addRoute("/getData", HttpMethod::GET, [](const HttpRequest& req) {
    std::string json = R"({"name":"C++ server","version":0.1})";
    return JsonResponse(json);
});

// Handle POST with JSON body
router.addRoute("/putData", HttpMethod::POST, [](const HttpRequest& req) {
    if (req.contentType != "application/json")
        return JsonResponse(R"({"message":"Need Json body"})", 400);

    auto bodyOpt = req.parseJsonBody();
    if (!bodyOpt) return JsonResponse(R"({"message":"Bad JSON"})", 400);

    json body = *bodyOpt;
    std::string name = body.value("name", "");
    return JsonResponse(R"({"message":"Name received )" + name + "\"}");
});

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Why this matters

• Plug-and-play routes → Add GET, POST, PUT, DELETE handlers with just lambdas.
• Multiple response types → JSON, HTML, text, binary.
• Extendable → CORS, middleware, authentication, DB integration can be added on top easily.
• Scalable abstraction → Beginner-friendly for API devs, yet built on low-level IOCP for performance.

Benchmarks

The server was benchmarked with wrk to measure raw throughput.

Environment

• Server: Windows 11, Intel i5-12400, 16 GB RAM
• Client: Ubuntu WSL2 (wrk)
• Network: Private IP over WSL2 virtual adapter
• Build: C++20 (MinGW), Windows IOCP, fully async WSASend

Test Target: GET / (static HTML (2.6Kb), served from memory cache)

Results

Throughput Benchmark (Without latency tracking)

Connections	Threads	Avg Latency	Stdev	Max Latency	Req/Sec	Transfer/sec
200	8	30.84ms	162.89ms	1.56s	52,712	137.84 MB/s
400	8	9.16ms	9.72ms	143.09ms	52,838	138.17 MB/s
800	8	16.26ms	11.67ms	341.06ms	50,408	131.82 MB/s

Command

wrk -t8 -c400 -d30s http://127.0.0.1:3000/

Measured without --latency for maximum throughput accuracy

Latency Distribution (wrk --latency)

Connections	P50	P75	P90	P99	Avg Latency
200	3.93ms	7.18ms	13.23ms	30.90ms	5.88ms
400	7.21ms	11.73ms	18.70ms	35.10ms	8.92ms
800	15.23ms	19.91ms	28.43ms	46.05ms	16.07ms

Command

wrk -t8 -c400 -d30s http://127.0.0.1:3000/ --latency

Measured with --latency; throughput is slightly lower due to measurement overhead

Latency Distribution (wrk Output)

Below are raw wrk --latency outputs for each concurrency level, showing full latency distribution and tail behavior (P99).

200 Connections

400 Connections

800 Connections

Observations:

• Latency remains tightly distributed at moderate concurrency (200–400 connections)
• Tail latency (P99) increases under higher load but remains under 50ms at 800 connections
• Throughput scales with concurrency, with predictable latency trade-offs
• Reported throughput here is slightly lower than the main results, as enabling --latency introduces measurement overhead

Summary

• Peak throughput: ~52,800 requests/sec at 400 connections
• Median latency: 9.16ms at 400 connections
• Total transfer: ~138 MB/sec sustained

Contributing

Contributions, issues, and feature requests are welcome! Feel free to fork the repo and submit a PR.

License

GNU License © 2025 Sraban Mondal

This project bridges the gap between low-level systems programming and high-level web development.