Asynchronous Layer-4 Load Balancer & Distributed In-Memory Key-Value Store

A high-performance, lightweight, distributed in-memory caching engine and custom Layer-4 traffic router built completely from scratch using raw Python sockets, asynchronous network programming interfaces, and native I/O multiplexing.

This project demonstrates low-level systems execution without relying on any external web frameworks (like Django, Flask, or FastAPI), highlighting core fundamentals in transport-layer protocols, event-driven concurrency models, and lock-free distributed data structures.

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🏗️ System Architecture

The architecture utilizes a decoupled control and data plane split configured as an out-of-band Layer-4 Redirect Router. Instead of proxying entire data payloads through the load balancer (which creates a massive CPU and memory bottleneck), the load balancer acts as a swift connection broker, offloading direct client sessions directly onto target sharded backend nodes.

       +--------------------------------------------+
       |             CLIENT SUBSYSTEM               |
       |  (stress_test.py / AsyncClient Driver)     |
       +--------------------------------------------+
         /                                        \
        / [Phase 2: Lease Request]                 \ [Phase 3: Direct Data Stream]
       /  Target: 127.0.0.1:8888                    \  Target: 127.0.0.1:12345
      v                                              v
+--------------------------+                 +---------------------------+
|      LOAD BALANCER       |                 |    SHARDED DATA NODE A    |
|      (Control Plane)     |                 |       (Data Plane)        |
|       Port: 8888         |                 |        Port: 12345        |
+--------------------------+                 +---------------------------+
      ^                                      | - Custom Atomic HashMap   |
      |                                      | - Volatile Memory Cache   |
      | [Phase 1: Registration]              +---------------------------+
      | Broadcast: "REGISTER:12345\n"                      ^
      |                                                    |
      +----------------------------------------------------+ [Phase 4: Out-of-Band Health Probe]
                                                             Periodic Socket Pings (Every 3s)

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🧩 Core Components

• load_balancer.py (The Control Plane): Orchestrates stateless traffic distribution across active cluster nodes using deterministic Round-Robin allocation rules. Runs an asynchronous event loop to process incoming token leases instantly and drop connections to keep overhead minimal.
• server.py (The Data Plane): A high-throughput storage worker node handling concurrent client allocations using non-blocking asynchronous stream readers and writers. Features a custom generic HashMap data structure designed from first principles.
• client.py (The Client Layer): An interactive utility featuring atomic wire protocol framing and robust network connection retry recovery handlers.
• stress_test.py (The Validation Driver): A highly concurrent, event-driven benchmarking engine that saturates the local network stack to evaluate the system's structural and throughput limits.

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⚡ Key Technical Highlights & Structural Design

1. Lock-Free HashMap with Single-Threaded Task Atomicity

Instead of using Python's built-in dictionary, this engine features a fully custom generic HashMap implementing Separate Chaining via nested list structures to handle hash collisions.

• Zero-Lock Overhead: By migrating the storage nodes to a single-threaded asynchronous event loop via asyncio, the system completely eliminates the need for heavy kernel-level synchronization primitives (like threading.RLock). Memory mutations are guaranteed to be naturally atomic because task context-switching only occurs at explicit await boundaries.
• Dynamic Amortized $O(1)$ Bounds: Tracks real-time density using a load factor calculation ($\text{size} / \text{capacity}$). Crossing a strict $0.75$ threshold triggers an automated dynamic array expansion—doubling memory bounds and executing a global bucket rehash to guarantee average-case $O(1)$ search profiles under dense parallel loads.

2. Low-Level Kernel Tuning & C50K Optimization

To break past standard operating system network limits and process tens of thousands of concurrent connections on a single machine, the system bypasses default configuration bottlenecks:

• I/O Multiplexing Overrides: Configured to explicitly leverage the WindowsProactorEventLoopPolicy utilizing native Windows I/O Completion Ports (IOCP), bypassing the rigid 64-socket limitation found in traditional select() loop wrappers.
• Buffer Expansion: Expanded the incoming socket listen backlog buffer size to 8192 to absorb massive initial connection bursts without dropping TCP handshakes.
• Protocol Compaction: Designed a rigid, single-line framed application wire protocol (OP:KEY:VALUE\n) allowing the server to stream and parse transaction arguments via single-pass readline() executions.

3. Dynamic Service Discovery & Fault Isolation

The cluster features an empty routing table at startup. Backend storage instances utilize an autonomous initialization hook to establish a temporary socket connection back to the load balancer, broadcasting a REGISTER:{PORT}\n payload.

Concurrently, an asynchronous background daemon task (_health_check_loop) wakes up inside the load balancer every 3 seconds to ping the registered nodes. If a server fails to respond within a 0.5-second timeout, the daemon instantly evicts the broken target port from the pool, preventing incoming client traffic from being routed to a degraded node.

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📊 Performance Validation Under Load

The system's structural integrity, connection threshold, and network capacity were validated under a high-concurrency automated driver engine. The stress testing script injected 50,000 parallel client loops executing atomic mutation blocks against the sharded storage cluster over a local loopback interface.

Launching C50K Validation Driver: Injecting 50000 concurrent tasks...

================ SYSTEM TEST RESULTS ================
Total Attempted Connections  : 50000
Successful Task Completions  : 50000
Failed Task Completions      : 0
Total Execution Runtime      : 40.91 seconds
Empirical System Throughput  : 2444.59 operations/sec
=====================================================
🎯 SUCCESS: The system successfully sustained 50,000 concurrent connections with 100% integrity!

Engineering Analysis:

• 100% Data Integrity: Achieved a perfect task completion rate with zero dropped packets, connection timeouts, or socket memory leaks under peak saturation.
• Throughput Metrics: Reached a native unthrottled throughput of 2,444.59 operations/second, proving the efficiency of the single-threaded asynchronous multiplexing architecture.
• Kernel Constraints: Sequential back-to-back testing runs normalize to 1,554.18 operations/second over 64.34 seconds. This minor delta is a documented result of Windows kernel socket throttling due to TIME_WAIT port accumulation (allocating 100,000 unique socket identities per run), proving that the performance ceiling is governed by OS network recycling configurations rather than application-layer execution delays.

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🚀 Quick Start & Automated Execution Playbook

Verify this distributed cluster locally using VS Code's integrated terminal layout or its built-in debugging profiles:

Option A: The Automated VS Code Way (Recommended)

This repository includes a pre-configured .vscode/launch.json file that automates the cluster's orchestration:

1. Open the repository in VS Code.
2. Press Ctrl + Shift + D to open the Run and Debug menu.
3. Select 🚀 Launch Distributed Cluster from the top dropdown menu.
4. Press F5. VS Code will instantly spin up three independent background terminal windows, initialize the load balancer, and boot both storage servers on ports 12345 and 12346.

Option B: The Manual Native Way

Open separate terminal panes to observe real-time routing and network registration handshakes:

1. Start the Control Plane (Load Balancer)

python load_balancer.py

2. Spin Up Sharded Storage Nodes

python server.py 12345

python server.py 12346

3. Run the Benchmarking Driver

python stress_test.py

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🛠️ Tech Stack & Core Primitives

• Core Language: Python 3 (featuring static Type Hinting generics via the typing module)
• Networking Architecture: Native OS Sockets (socket), TCP/IP Stream Multiplexing, Non-blocking Stream Readers/Writers
• Concurrency Models: Asynchronous I/O Multiplexing (asyncio), Windows I/O Completion Ports (IOCP), Task-Level Atomicity, Non-blocking Background Daemons
• Automation Profile: VS Code Workspace Compound Configurations (launch.json)