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🚀 Tejas-DB: High-Performance Distributed Key-Value Store

C++ReactArchitectureThroughput

A highly concurrent, fault-tolerant, and masterless distributed Key-Value store built entirely from scratch in Modern C++, featuring a real-time visual dashboard built in React & Tailwind CSS.

Engineered to handle massive scalability, this system implements core distributed systems concepts inspired by Amazon Dynamo and Apache Cassandra.

✨ Core Architecture & Features

1. 🔄 Consistent Hashing & Data Distribution

  • Implemented a Ring Topology using the 64-bit FNV-1a Hash Algorithm to ensure uniform data distribution and eliminate hot spots.
  • Configured with 100 Virtual Nodes (vNodes) per physical server to achieve perfect load balancing and seamless scaling.

2. 🛡️ Fault Tolerance & Replication

  • Strict Quorum Logic (W=2, R=2): Ensures consistency across cluster nodes. Writes are acknowledged once replica quorums are achieved.
  • Write-Ahead Logging (WAL): Disk-backed crash recovery. Every node recovers its exact state from local logs instantly upon reboot.
  • Gossip Protocol: Background heartbeat threads monitor cluster health, instantly detecting node failures and dynamically updating the routing topology.

3. ⚡ High-Performance Concurrency

  • Architected a thread-safe custom Storage Engine utilizing std::shared_mutex (Read-Write Locks).
  • Achieved 33,685 Write Requests Per Second (RPS) with a highly concurrent load of 100 simultaneous threads, maintaining an average latency of just ~2.97ms (Benchmarked via Apache ab).

4. ⚖️ Elastic Scaling & Live Rebalancing

  • Dynamically supports adding or removing nodes.
  • Exposes an /admin/rebalance endpoint that intelligently calculates hash boundaries and migrates live data to new nodes without cluster downtime.

5. 🖥️ Interactive Visual Dashboard (Control Center)

  • A real-time React/Vite dashboard that visualizes the Consistent Hashing Ring.
  • Chaos Engineering Controls: Inject massive workloads or simulate node deaths with a single click to visually demonstrate data rebalancing and fault tolerance in real-time.

🛠️ Tech Stack

  • Backend Engine: Modern C++ (C++17)
  • Networking/HTTP:cpp-httplib
  • Frontend UI: React.js, Vite, Tailwind CSS
  • Performance Testing: Apache Bench (ab) & Custom Bash Scripts

🚀 Getting Started

Prerequisites

  • GCC/G++ (Supports C++17)
  • CMake 3.10+
  • Apache Bench (ab tool for load testing)
  • Node.js & npm (For the UI)

1. Build the Backend (Release Mode with -O3 Optimizations)

# Clone the repository
git clone https://github.com/Tejas-Raj01/distributed-system.git
cd distributed-system
# Create release build with maximum optimizations
cmake -B build -DCMAKE_BUILD_TYPE=Release -DCMAKE_CXX_FLAGS="-O3"
cmake --build build

2. Start the Backend Server

# Ensure data directory exists for WAL logging
mkdir -p data
# Start a server node on port 8080
./build/kv_server 8080

⚡ Performance Benchmarking & Load Testing

To run high-concurrency performance benchmarks matching our test setup (100 concurrent threads, 50,000 requests), follow these steps:

Step 1: Install Apache Benchmark (ab)

  • Ubuntu/Debian:sudo apt install apache2-utils
  • Fedora/RHEL:sudo dnf install httpd-tools
  • Arch Linux:sudo pacman -S apache

Step 2: Start Server in Background

./build/kv_server 8080 > data/server.log 2>&1&

Step 3: Write (POST) Benchmark Execution

  1. Create a post payload file: 
    echo"key=StressTestKey&value=MassiveDataLoad"> payload.txt
  2. Run Apache Bench for 50,000 POST requests with 100 concurrent connections: 
    ab -n 50000 -c 100 -p payload.txt -T "application/x-www-form-urlencoded""http://127.0.0.1:8080/put"

Step 4: Read (GET) Benchmark Execution

Run Apache Bench for 50,000 GET requests under 100 concurrent connections:

ab -n 50000 -c 100 "http://127.0.0.1:8080/get?key=StressTestKey"

📊 Benchmark Results & Detailed Analytics

Tested on Release build (-O3 optimized) with 50,000 requests under 100 concurrent threads:

Metric / ParameterWrite (POST /put)Read (GET /get)
Total Requests50,00050,000
Concurrency Level100 connections100 connections
Throughput (Requests/sec)33,684.94 req/sec1,809.14 req/sec
Mean Latency (Average)2.969 ms55.275 ms
Concurrent Request Latency0.030 ms0.553 ms
Success / Error Rate100% Success (0 Errors)100% Success (0 Errors)

Latency Percentile Breakdown

PercentileWrite Latency (ms)Read Latency (ms)
50% (Median)1 ms49 ms
66%2 ms50 ms
75%2 ms50 ms
80%2 ms51 ms
90%3 ms71 ms
95%4 ms72 ms
98%5 ms73 ms
99%5 ms73 ms

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var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '
GitHub - Tejas-Raj01/distributed-system · GitHub
Skip to content
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🚀 Tejas-DB: High-Performance Distributed Key-Value Store
C++ReactArchitectureThroughput
A highly concurrent, fault-tolerant, and masterless distributed Key-Value store built entirely from scratch in Modern C++, featuring a real-time visual dashboard built in React & Tailwind CSS.
Engineered to handle massive scalability, this system implements core distributed systems concepts inspired by Amazon Dynamo and Apache Cassandra.
✨ Core Architecture & Features
1. 🔄 Consistent Hashing & Data Distribution
  • Implemented a Ring Topology using the 64-bit FNV-1a Hash Algorithm to ensure uniform data distribution and eliminate hot spots.
  • Configured with 100 Virtual Nodes (vNodes) per physical server to achieve perfect load balancing and seamless scaling.
2. 🛡️ Fault Tolerance & Replication
  • Strict Quorum Logic (W=2, R=2): Ensures consistency across cluster nodes. Writes are acknowledged once replica quorums are achieved.
  • Write-Ahead Logging (WAL): Disk-backed crash recovery. Every node recovers its exact state from local logs instantly upon reboot.
  • Gossip Protocol: Background heartbeat threads monitor cluster health, instantly detecting node failures and dynamically updating the routing topology.
3. ⚡ High-Performance Concurrency
  • Architected a thread-safe custom Storage Engine utilizing std::shared_mutex (Read-Write Locks).
  • Achieved 33,685 Write Requests Per Second (RPS) with a highly concurrent load of 100 simultaneous threads, maintaining an average latency of just ~2.97ms (Benchmarked via Apache ab).
4. ⚖️ Elastic Scaling & Live Rebalancing
  • Dynamically supports adding or removing nodes.
  • Exposes an /admin/rebalance endpoint that intelligently calculates hash boundaries and migrates live data to new nodes without cluster downtime.
5. 🖥️ Interactive Visual Dashboard (Control Center)
  • A real-time React/Vite dashboard that visualizes the Consistent Hashing Ring.
  • Chaos Engineering Controls: Inject massive workloads or simulate node deaths with a single click to visually demonstrate data rebalancing and fault tolerance in real-time.
🛠️ Tech Stack
  • Backend Engine: Modern C++ (C++17)
  • Networking/HTTP:cpp-httplib
  • Frontend UI: React.js, Vite, Tailwind CSS
  • Performance Testing: Apache Bench (ab) & Custom Bash Scripts
🚀 Getting Started
Prerequisites
  • GCC/G++ (Supports C++17)
  • CMake 3.10+
  • Apache Bench (ab tool for load testing)
  • Node.js & npm (For the UI)
1. Build the Backend (Release Mode with -O3 Optimizations)
# Clone the repository
git clone https://github.com/Tejas-Raj01/distributed-system.git
cd distributed-system
# Create release build with maximum optimizations
cmake -B build -DCMAKE_BUILD_TYPE=Release -DCMAKE_CXX_FLAGS="-O3"
cmake --build build
2. Start the Backend Server
# Ensure data directory exists for WAL logging
mkdir -p data
# Start a server node on port 8080
./build/kv_server 8080
⚡ Performance Benchmarking & Load Testing
To run high-concurrency performance benchmarks matching our test setup (100 concurrent threads, 50,000 requests), follow these steps:
Step 1: Install Apache Benchmark (ab)
  • Ubuntu/Debian:sudo apt install apache2-utils
  • Fedora/RHEL:sudo dnf install httpd-tools
  • Arch Linux:sudo pacman -S apache
Step 2: Start Server in Background
./build/kv_server 8080 > data/server.log 2>&1&
Step 3: Write (POST) Benchmark Execution
  1. Create a post payload file:

    echo"key=StressTestKey&value=MassiveDataLoad"> payload.txt
  2. Run Apache Bench for 50,000 POST requests with 100 concurrent connections:

    ab -n 50000 -c 100 -p payload.txt -T "application/x-www-form-urlencoded""http://127.0.0.1:8080/put"
Step 4: Read (GET) Benchmark Execution
Run Apache Bench for 50,000 GET requests under 100 concurrent connections:
ab -n 50000 -c 100 "http://127.0.0.1:8080/get?key=StressTestKey"
📊 Benchmark Results & Detailed Analytics
Tested on Release build (-O3 optimized) with 50,000 requests under 100 concurrent threads:
Metric / ParameterWrite (POST /put)Read (GET /get)
Total Requests50,00050,000
Concurrency Level100 connections100 connections
Throughput (Requests/sec)33,684.94 req/sec1,809.14 req/sec
Mean Latency (Average)2.969 ms55.275 ms
Concurrent Request Latency0.030 ms0.553 ms
Success / Error Rate100% Success (0 Errors)100% Success (0 Errors)
Latency Percentile Breakdown
PercentileWrite Latency (ms)Read Latency (ms)
50% (Median)1 ms49 ms
66%2 ms50 ms
75%2 ms50 ms
80%2 ms51 ms
90%3 ms71 ms
95%4 ms72 ms
98%5 ms73 ms
99%5 ms73 ms
About
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try {
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var __re = new RegExp('^' + ".*" + '
GitHub - Tejas-Raj01/distributed-system · GitHub
Skip to content
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Repository files navigation
🚀 Tejas-DB: High-Performance Distributed Key-Value Store
C++ReactArchitectureThroughput
A highly concurrent, fault-tolerant, and masterless distributed Key-Value store built entirely from scratch in Modern C++, featuring a real-time visual dashboard built in React & Tailwind CSS.
Engineered to handle massive scalability, this system implements core distributed systems concepts inspired by Amazon Dynamo and Apache Cassandra.
✨ Core Architecture & Features
1. 🔄 Consistent Hashing & Data Distribution
  • Implemented a Ring Topology using the 64-bit FNV-1a Hash Algorithm to ensure uniform data distribution and eliminate hot spots.
  • Configured with 100 Virtual Nodes (vNodes) per physical server to achieve perfect load balancing and seamless scaling.
2. 🛡️ Fault Tolerance & Replication
  • Strict Quorum Logic (W=2, R=2): Ensures consistency across cluster nodes. Writes are acknowledged once replica quorums are achieved.
  • Write-Ahead Logging (WAL): Disk-backed crash recovery. Every node recovers its exact state from local logs instantly upon reboot.
  • Gossip Protocol: Background heartbeat threads monitor cluster health, instantly detecting node failures and dynamically updating the routing topology.
3. ⚡ High-Performance Concurrency
  • Architected a thread-safe custom Storage Engine utilizing std::shared_mutex (Read-Write Locks).
  • Achieved 33,685 Write Requests Per Second (RPS) with a highly concurrent load of 100 simultaneous threads, maintaining an average latency of just ~2.97ms (Benchmarked via Apache ab).
4. ⚖️ Elastic Scaling & Live Rebalancing
  • Dynamically supports adding or removing nodes.
  • Exposes an /admin/rebalance endpoint that intelligently calculates hash boundaries and migrates live data to new nodes without cluster downtime.
5. 🖥️ Interactive Visual Dashboard (Control Center)
  • A real-time React/Vite dashboard that visualizes the Consistent Hashing Ring.
  • Chaos Engineering Controls: Inject massive workloads or simulate node deaths with a single click to visually demonstrate data rebalancing and fault tolerance in real-time.
🛠️ Tech Stack
  • Backend Engine: Modern C++ (C++17)
  • Networking/HTTP:cpp-httplib
  • Frontend UI: React.js, Vite, Tailwind CSS
  • Performance Testing: Apache Bench (ab) & Custom Bash Scripts
🚀 Getting Started
Prerequisites
  • GCC/G++ (Supports C++17)
  • CMake 3.10+
  • Apache Bench (ab tool for load testing)
  • Node.js & npm (For the UI)
1. Build the Backend (Release Mode with -O3 Optimizations)
# Clone the repository
git clone https://github.com/Tejas-Raj01/distributed-system.git
cd distributed-system
# Create release build with maximum optimizations
cmake -B build -DCMAKE_BUILD_TYPE=Release -DCMAKE_CXX_FLAGS="-O3"
cmake --build build
2. Start the Backend Server
# Ensure data directory exists for WAL logging
mkdir -p data
# Start a server node on port 8080
./build/kv_server 8080
⚡ Performance Benchmarking & Load Testing
To run high-concurrency performance benchmarks matching our test setup (100 concurrent threads, 50,000 requests), follow these steps:
Step 1: Install Apache Benchmark (ab)
  • Ubuntu/Debian:sudo apt install apache2-utils
  • Fedora/RHEL:sudo dnf install httpd-tools
  • Arch Linux:sudo pacman -S apache
Step 2: Start Server in Background
./build/kv_server 8080 > data/server.log 2>&1&
Step 3: Write (POST) Benchmark Execution
  1. Create a post payload file:

    echo"key=StressTestKey&value=MassiveDataLoad"> payload.txt
  2. Run Apache Bench for 50,000 POST requests with 100 concurrent connections:

    ab -n 50000 -c 100 -p payload.txt -T "application/x-www-form-urlencoded""http://127.0.0.1:8080/put"
Step 4: Read (GET) Benchmark Execution
Run Apache Bench for 50,000 GET requests under 100 concurrent connections:
ab -n 50000 -c 100 "http://127.0.0.1:8080/get?key=StressTestKey"
📊 Benchmark Results & Detailed Analytics
Tested on Release build (-O3 optimized) with 50,000 requests under 100 concurrent threads:
Metric / ParameterWrite (POST /put)Read (GET /get)
Total Requests50,00050,000
Concurrency Level100 connections100 connections
Throughput (Requests/sec)33,684.94 req/sec1,809.14 req/sec
Mean Latency (Average)2.969 ms55.275 ms
Concurrent Request Latency0.030 ms0.553 ms
Success / Error Rate100% Success (0 Errors)100% Success (0 Errors)
Latency Percentile Breakdown
PercentileWrite Latency (ms)Read Latency (ms)
50% (Median)1 ms49 ms
66%2 ms50 ms
75%2 ms50 ms
80%2 ms51 ms
90%3 ms71 ms
95%4 ms72 ms
98%5 ms73 ms
99%5 ms73 ms
About
distributed-system-phi.vercel.app
Resources
Readme
Activity
Stars
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Watchers
0 watching
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0 forks
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, 'i');
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GitHub - Tejas-Raj01/distributed-system · GitHub
Skip to content
BranchesTags
Repository files navigation
🚀 Tejas-DB: High-Performance Distributed Key-Value Store
C++ReactArchitectureThroughput
A highly concurrent, fault-tolerant, and masterless distributed Key-Value store built entirely from scratch in Modern C++, featuring a real-time visual dashboard built in React & Tailwind CSS.
Engineered to handle massive scalability, this system implements core distributed systems concepts inspired by Amazon Dynamo and Apache Cassandra.
✨ Core Architecture & Features
1. 🔄 Consistent Hashing & Data Distribution
  • Implemented a Ring Topology using the 64-bit FNV-1a Hash Algorithm to ensure uniform data distribution and eliminate hot spots.
  • Configured with 100 Virtual Nodes (vNodes) per physical server to achieve perfect load balancing and seamless scaling.
2. 🛡️ Fault Tolerance & Replication
  • Strict Quorum Logic (W=2, R=2): Ensures consistency across cluster nodes. Writes are acknowledged once replica quorums are achieved.
  • Write-Ahead Logging (WAL): Disk-backed crash recovery. Every node recovers its exact state from local logs instantly upon reboot.
  • Gossip Protocol: Background heartbeat threads monitor cluster health, instantly detecting node failures and dynamically updating the routing topology.
3. ⚡ High-Performance Concurrency
  • Architected a thread-safe custom Storage Engine utilizing std::shared_mutex (Read-Write Locks).
  • Achieved 33,685 Write Requests Per Second (RPS) with a highly concurrent load of 100 simultaneous threads, maintaining an average latency of just ~2.97ms (Benchmarked via Apache ab).
4. ⚖️ Elastic Scaling & Live Rebalancing
  • Dynamically supports adding or removing nodes.
  • Exposes an /admin/rebalance endpoint that intelligently calculates hash boundaries and migrates live data to new nodes without cluster downtime.
5. 🖥️ Interactive Visual Dashboard (Control Center)
  • A real-time React/Vite dashboard that visualizes the Consistent Hashing Ring.
  • Chaos Engineering Controls: Inject massive workloads or simulate node deaths with a single click to visually demonstrate data rebalancing and fault tolerance in real-time.
🛠️ Tech Stack
  • Backend Engine: Modern C++ (C++17)
  • Networking/HTTP:cpp-httplib
  • Frontend UI: React.js, Vite, Tailwind CSS
  • Performance Testing: Apache Bench (ab) & Custom Bash Scripts
🚀 Getting Started
Prerequisites
  • GCC/G++ (Supports C++17)
  • CMake 3.10+
  • Apache Bench (ab tool for load testing)
  • Node.js & npm (For the UI)
1. Build the Backend (Release Mode with -O3 Optimizations)
# Clone the repository
git clone https://github.com/Tejas-Raj01/distributed-system.git
cd distributed-system
# Create release build with maximum optimizations
cmake -B build -DCMAKE_BUILD_TYPE=Release -DCMAKE_CXX_FLAGS="-O3"
cmake --build build
2. Start the Backend Server
# Ensure data directory exists for WAL logging
mkdir -p data
# Start a server node on port 8080
./build/kv_server 8080
⚡ Performance Benchmarking & Load Testing
To run high-concurrency performance benchmarks matching our test setup (100 concurrent threads, 50,000 requests), follow these steps:
Step 1: Install Apache Benchmark (ab)
  • Ubuntu/Debian:sudo apt install apache2-utils
  • Fedora/RHEL:sudo dnf install httpd-tools
  • Arch Linux:sudo pacman -S apache
Step 2: Start Server in Background
./build/kv_server 8080 > data/server.log 2>&1&
Step 3: Write (POST) Benchmark Execution
  1. Create a post payload file:

    echo"key=StressTestKey&value=MassiveDataLoad"> payload.txt
  2. Run Apache Bench for 50,000 POST requests with 100 concurrent connections:

    ab -n 50000 -c 100 -p payload.txt -T "application/x-www-form-urlencoded""http://127.0.0.1:8080/put"
Step 4: Read (GET) Benchmark Execution
Run Apache Bench for 50,000 GET requests under 100 concurrent connections:
ab -n 50000 -c 100 "http://127.0.0.1:8080/get?key=StressTestKey"
📊 Benchmark Results & Detailed Analytics
Tested on Release build (-O3 optimized) with 50,000 requests under 100 concurrent threads:
Metric / ParameterWrite (POST /put)Read (GET /get)
Total Requests50,00050,000
Concurrency Level100 connections100 connections
Throughput (Requests/sec)33,684.94 req/sec1,809.14 req/sec
Mean Latency (Average)2.969 ms55.275 ms
Concurrent Request Latency0.030 ms0.553 ms
Success / Error Rate100% Success (0 Errors)100% Success (0 Errors)
Latency Percentile Breakdown
PercentileWrite Latency (ms)Read Latency (ms)
50% (Median)1 ms49 ms
66%2 ms50 ms
75%2 ms50 ms
80%2 ms51 ms
90%3 ms71 ms
95%4 ms72 ms
98%5 ms73 ms
99%5 ms73 ms
About
distributed-system-phi.vercel.app
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
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})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
GitHub - Tejas-Raj01/distributed-system · GitHub
Skip to content
BranchesTags
Repository files navigation
🚀 Tejas-DB: High-Performance Distributed Key-Value Store
C++ReactArchitectureThroughput
A highly concurrent, fault-tolerant, and masterless distributed Key-Value store built entirely from scratch in Modern C++, featuring a real-time visual dashboard built in React & Tailwind CSS.
Engineered to handle massive scalability, this system implements core distributed systems concepts inspired by Amazon Dynamo and Apache Cassandra.
✨ Core Architecture & Features
1. 🔄 Consistent Hashing & Data Distribution
  • Implemented a Ring Topology using the 64-bit FNV-1a Hash Algorithm to ensure uniform data distribution and eliminate hot spots.
  • Configured with 100 Virtual Nodes (vNodes) per physical server to achieve perfect load balancing and seamless scaling.
2. 🛡️ Fault Tolerance & Replication
  • Strict Quorum Logic (W=2, R=2): Ensures consistency across cluster nodes. Writes are acknowledged once replica quorums are achieved.
  • Write-Ahead Logging (WAL): Disk-backed crash recovery. Every node recovers its exact state from local logs instantly upon reboot.
  • Gossip Protocol: Background heartbeat threads monitor cluster health, instantly detecting node failures and dynamically updating the routing topology.
3. ⚡ High-Performance Concurrency
  • Architected a thread-safe custom Storage Engine utilizing std::shared_mutex (Read-Write Locks).
  • Achieved 33,685 Write Requests Per Second (RPS) with a highly concurrent load of 100 simultaneous threads, maintaining an average latency of just ~2.97ms (Benchmarked via Apache ab).
4. ⚖️ Elastic Scaling & Live Rebalancing
  • Dynamically supports adding or removing nodes.
  • Exposes an /admin/rebalance endpoint that intelligently calculates hash boundaries and migrates live data to new nodes without cluster downtime.
5. 🖥️ Interactive Visual Dashboard (Control Center)
  • A real-time React/Vite dashboard that visualizes the Consistent Hashing Ring.
  • Chaos Engineering Controls: Inject massive workloads or simulate node deaths with a single click to visually demonstrate data rebalancing and fault tolerance in real-time.
🛠️ Tech Stack
  • Backend Engine: Modern C++ (C++17)
  • Networking/HTTP:cpp-httplib
  • Frontend UI: React.js, Vite, Tailwind CSS
  • Performance Testing: Apache Bench (ab) & Custom Bash Scripts
🚀 Getting Started
Prerequisites
  • GCC/G++ (Supports C++17)
  • CMake 3.10+
  • Apache Bench (ab tool for load testing)
  • Node.js & npm (For the UI)
1. Build the Backend (Release Mode with -O3 Optimizations)
# Clone the repository
git clone https://github.com/Tejas-Raj01/distributed-system.git
cd distributed-system
# Create release build with maximum optimizations
cmake -B build -DCMAKE_BUILD_TYPE=Release -DCMAKE_CXX_FLAGS="-O3"
cmake --build build
2. Start the Backend Server
# Ensure data directory exists for WAL logging
mkdir -p data
# Start a server node on port 8080
./build/kv_server 8080
⚡ Performance Benchmarking & Load Testing
To run high-concurrency performance benchmarks matching our test setup (100 concurrent threads, 50,000 requests), follow these steps:
Step 1: Install Apache Benchmark (ab)
  • Ubuntu/Debian:sudo apt install apache2-utils
  • Fedora/RHEL:sudo dnf install httpd-tools
  • Arch Linux:sudo pacman -S apache
Step 2: Start Server in Background
./build/kv_server 8080 > data/server.log 2>&1&
Step 3: Write (POST) Benchmark Execution
  1. Create a post payload file:

    echo"key=StressTestKey&value=MassiveDataLoad"> payload.txt
  2. Run Apache Bench for 50,000 POST requests with 100 concurrent connections:

    ab -n 50000 -c 100 -p payload.txt -T "application/x-www-form-urlencoded""http://127.0.0.1:8080/put"
Step 4: Read (GET) Benchmark Execution
Run Apache Bench for 50,000 GET requests under 100 concurrent connections:
ab -n 50000 -c 100 "http://127.0.0.1:8080/get?key=StressTestKey"
📊 Benchmark Results & Detailed Analytics
Tested on Release build (-O3 optimized) with 50,000 requests under 100 concurrent threads:
Metric / ParameterWrite (POST /put)Read (GET /get)
Total Requests50,00050,000
Concurrency Level100 connections100 connections
Throughput (Requests/sec)33,684.94 req/sec1,809.14 req/sec
Mean Latency (Average)2.969 ms55.275 ms
Concurrent Request Latency0.030 ms0.553 ms
Success / Error Rate100% Success (0 Errors)100% Success (0 Errors)
Latency Percentile Breakdown
PercentileWrite Latency (ms)Read Latency (ms)
50% (Median)1 ms49 ms
66%2 ms50 ms
75%2 ms50 ms
80%2 ms51 ms
90%3 ms71 ms
95%4 ms72 ms
98%5 ms73 ms
99%5 ms73 ms
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, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
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function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
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if (location.href !== lastUrl) {
lastUrl = location.href;
setTimeout(tryTheater, 500);
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}, 1000);
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observer.observe(document.body, { childList: true, subtree: true });
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} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - Tejas-Raj01/distributed-system · GitHub
Skip to content
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Repository files navigation
🚀 Tejas-DB: High-Performance Distributed Key-Value Store
C++ReactArchitectureThroughput
A highly concurrent, fault-tolerant, and masterless distributed Key-Value store built entirely from scratch in Modern C++, featuring a real-time visual dashboard built in React & Tailwind CSS.
Engineered to handle massive scalability, this system implements core distributed systems concepts inspired by Amazon Dynamo and Apache Cassandra.
✨ Core Architecture & Features
1. 🔄 Consistent Hashing & Data Distribution
  • Implemented a Ring Topology using the 64-bit FNV-1a Hash Algorithm to ensure uniform data distribution and eliminate hot spots.
  • Configured with 100 Virtual Nodes (vNodes) per physical server to achieve perfect load balancing and seamless scaling.
2. 🛡️ Fault Tolerance & Replication
  • Strict Quorum Logic (W=2, R=2): Ensures consistency across cluster nodes. Writes are acknowledged once replica quorums are achieved.
  • Write-Ahead Logging (WAL): Disk-backed crash recovery. Every node recovers its exact state from local logs instantly upon reboot.
  • Gossip Protocol: Background heartbeat threads monitor cluster health, instantly detecting node failures and dynamically updating the routing topology.
3. ⚡ High-Performance Concurrency
  • Architected a thread-safe custom Storage Engine utilizing std::shared_mutex (Read-Write Locks).
  • Achieved 33,685 Write Requests Per Second (RPS) with a highly concurrent load of 100 simultaneous threads, maintaining an average latency of just ~2.97ms (Benchmarked via Apache ab).
4. ⚖️ Elastic Scaling & Live Rebalancing
  • Dynamically supports adding or removing nodes.
  • Exposes an /admin/rebalance endpoint that intelligently calculates hash boundaries and migrates live data to new nodes without cluster downtime.
5. 🖥️ Interactive Visual Dashboard (Control Center)
  • A real-time React/Vite dashboard that visualizes the Consistent Hashing Ring.
  • Chaos Engineering Controls: Inject massive workloads or simulate node deaths with a single click to visually demonstrate data rebalancing and fault tolerance in real-time.
🛠️ Tech Stack
  • Backend Engine: Modern C++ (C++17)
  • Networking/HTTP:cpp-httplib
  • Frontend UI: React.js, Vite, Tailwind CSS
  • Performance Testing: Apache Bench (ab) & Custom Bash Scripts
🚀 Getting Started
Prerequisites
  • GCC/G++ (Supports C++17)
  • CMake 3.10+
  • Apache Bench (ab tool for load testing)
  • Node.js & npm (For the UI)
1. Build the Backend (Release Mode with -O3 Optimizations)
# Clone the repository
git clone https://github.com/Tejas-Raj01/distributed-system.git
cd distributed-system
# Create release build with maximum optimizations
cmake -B build -DCMAKE_BUILD_TYPE=Release -DCMAKE_CXX_FLAGS="-O3"
cmake --build build
2. Start the Backend Server
# Ensure data directory exists for WAL logging
mkdir -p data
# Start a server node on port 8080
./build/kv_server 8080
⚡ Performance Benchmarking & Load Testing
To run high-concurrency performance benchmarks matching our test setup (100 concurrent threads, 50,000 requests), follow these steps:
Step 1: Install Apache Benchmark (ab)
  • Ubuntu/Debian:sudo apt install apache2-utils
  • Fedora/RHEL:sudo dnf install httpd-tools
  • Arch Linux:sudo pacman -S apache
Step 2: Start Server in Background
./build/kv_server 8080 > data/server.log 2>&1&
Step 3: Write (POST) Benchmark Execution
  1. Create a post payload file:

    echo"key=StressTestKey&value=MassiveDataLoad"> payload.txt
  2. Run Apache Bench for 50,000 POST requests with 100 concurrent connections:

    ab -n 50000 -c 100 -p payload.txt -T "application/x-www-form-urlencoded""http://127.0.0.1:8080/put"
Step 4: Read (GET) Benchmark Execution
Run Apache Bench for 50,000 GET requests under 100 concurrent connections:
ab -n 50000 -c 100 "http://127.0.0.1:8080/get?key=StressTestKey"
📊 Benchmark Results & Detailed Analytics
Tested on Release build (-O3 optimized) with 50,000 requests under 100 concurrent threads:
Metric / ParameterWrite (POST /put)Read (GET /get)
Total Requests50,00050,000
Concurrency Level100 connections100 connections
Throughput (Requests/sec)33,684.94 req/sec1,809.14 req/sec
Mean Latency (Average)2.969 ms55.275 ms
Concurrent Request Latency0.030 ms0.553 ms
Success / Error Rate100% Success (0 Errors)100% Success (0 Errors)
Latency Percentile Breakdown
PercentileWrite Latency (ms)Read Latency (ms)
50% (Median)1 ms49 ms
66%2 ms50 ms
75%2 ms50 ms
80%2 ms51 ms
90%3 ms71 ms
95%4 ms72 ms
98%5 ms73 ms
99%5 ms73 ms
About
distributed-system-phi.vercel.app
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
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0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
// Remove or un-stick sticky/fixed headers that block content
(function() {
function unstick() {
document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) {
if (el.style.position === 'fixed' || el.style.position === 'sticky' || getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {
el.style.position = 'static';
el.style.top = 'auto';
el.style.zIndex = 'auto';
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unstick();
var observer = new MutationObserver(unstick);
observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });
})();
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - Tejas-Raj01/distributed-system · GitHub
Skip to content
BranchesTags
Repository files navigation
🚀 Tejas-DB: High-Performance Distributed Key-Value Store
C++ReactArchitectureThroughput
A highly concurrent, fault-tolerant, and masterless distributed Key-Value store built entirely from scratch in Modern C++, featuring a real-time visual dashboard built in React & Tailwind CSS.
Engineered to handle massive scalability, this system implements core distributed systems concepts inspired by Amazon Dynamo and Apache Cassandra.
✨ Core Architecture & Features
1. 🔄 Consistent Hashing & Data Distribution
  • Implemented a Ring Topology using the 64-bit FNV-1a Hash Algorithm to ensure uniform data distribution and eliminate hot spots.
  • Configured with 100 Virtual Nodes (vNodes) per physical server to achieve perfect load balancing and seamless scaling.
2. 🛡️ Fault Tolerance & Replication
  • Strict Quorum Logic (W=2, R=2): Ensures consistency across cluster nodes. Writes are acknowledged once replica quorums are achieved.
  • Write-Ahead Logging (WAL): Disk-backed crash recovery. Every node recovers its exact state from local logs instantly upon reboot.
  • Gossip Protocol: Background heartbeat threads monitor cluster health, instantly detecting node failures and dynamically updating the routing topology.
3. ⚡ High-Performance Concurrency
  • Architected a thread-safe custom Storage Engine utilizing std::shared_mutex (Read-Write Locks).
  • Achieved 33,685 Write Requests Per Second (RPS) with a highly concurrent load of 100 simultaneous threads, maintaining an average latency of just ~2.97ms (Benchmarked via Apache ab).
4. ⚖️ Elastic Scaling & Live Rebalancing
  • Dynamically supports adding or removing nodes.
  • Exposes an /admin/rebalance endpoint that intelligently calculates hash boundaries and migrates live data to new nodes without cluster downtime.
5. 🖥️ Interactive Visual Dashboard (Control Center)
  • A real-time React/Vite dashboard that visualizes the Consistent Hashing Ring.
  • Chaos Engineering Controls: Inject massive workloads or simulate node deaths with a single click to visually demonstrate data rebalancing and fault tolerance in real-time.
🛠️ Tech Stack
  • Backend Engine: Modern C++ (C++17)
  • Networking/HTTP:cpp-httplib
  • Frontend UI: React.js, Vite, Tailwind CSS
  • Performance Testing: Apache Bench (ab) & Custom Bash Scripts
🚀 Getting Started
Prerequisites
  • GCC/G++ (Supports C++17)
  • CMake 3.10+
  • Apache Bench (ab tool for load testing)
  • Node.js & npm (For the UI)
1. Build the Backend (Release Mode with -O3 Optimizations)
# Clone the repository
git clone https://github.com/Tejas-Raj01/distributed-system.git
cd distributed-system
# Create release build with maximum optimizations
cmake -B build -DCMAKE_BUILD_TYPE=Release -DCMAKE_CXX_FLAGS="-O3"
cmake --build build
2. Start the Backend Server
# Ensure data directory exists for WAL logging
mkdir -p data
# Start a server node on port 8080
./build/kv_server 8080
⚡ Performance Benchmarking & Load Testing
To run high-concurrency performance benchmarks matching our test setup (100 concurrent threads, 50,000 requests), follow these steps:
Step 1: Install Apache Benchmark (ab)
  • Ubuntu/Debian:sudo apt install apache2-utils
  • Fedora/RHEL:sudo dnf install httpd-tools
  • Arch Linux:sudo pacman -S apache
Step 2: Start Server in Background
./build/kv_server 8080 > data/server.log 2>&1&
Step 3: Write (POST) Benchmark Execution
  1. Create a post payload file:

    echo"key=StressTestKey&value=MassiveDataLoad"> payload.txt
  2. Run Apache Bench for 50,000 POST requests with 100 concurrent connections:

    ab -n 50000 -c 100 -p payload.txt -T "application/x-www-form-urlencoded""http://127.0.0.1:8080/put"
Step 4: Read (GET) Benchmark Execution
Run Apache Bench for 50,000 GET requests under 100 concurrent connections:
ab -n 50000 -c 100 "http://127.0.0.1:8080/get?key=StressTestKey"
📊 Benchmark Results & Detailed Analytics
Tested on Release build (-O3 optimized) with 50,000 requests under 100 concurrent threads:
Metric / ParameterWrite (POST /put)Read (GET /get)
Total Requests50,00050,000
Concurrency Level100 connections100 connections
Throughput (Requests/sec)33,684.94 req/sec1,809.14 req/sec
Mean Latency (Average)2.969 ms55.275 ms
Concurrent Request Latency0.030 ms0.553 ms
Success / Error Rate100% Success (0 Errors)100% Success (0 Errors)
Latency Percentile Breakdown
PercentileWrite Latency (ms)Read Latency (ms)
50% (Median)1 ms49 ms
66%2 ms50 ms
75%2 ms50 ms
80%2 ms51 ms
90%3 ms71 ms
95%4 ms72 ms
98%5 ms73 ms
99%5 ms73 ms
About
distributed-system-phi.vercel.app
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Universal Dark Mode - works on any site
(function() {
var enabled = true;
function applyDarkMode() {
if (!enabled) return;
// Create style element if it doesn't exist
var style = document.getElementById('universal-dark-mode-style');
if (!style) {
style = document.createElement('style');
style.id = 'universal-dark-mode-style';
document.head.appendChild(style);
}
// Dark mode CSS - inverts colors but preserves images/video
style.textContent = '
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filter: invert(1) hue-rotate(180deg) !important;
background: #1a1a2e !important;
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filter: invert(1) hue-rotate(180deg) !important;
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filter: none !important;
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} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();
GitHub - Tejas-Raj01/distributed-system · GitHub
Skip to content
BranchesTags
Repository files navigation
🚀 Tejas-DB: High-Performance Distributed Key-Value Store
C++ReactArchitectureThroughput
A highly concurrent, fault-tolerant, and masterless distributed Key-Value store built entirely from scratch in Modern C++, featuring a real-time visual dashboard built in React & Tailwind CSS.
Engineered to handle massive scalability, this system implements core distributed systems concepts inspired by Amazon Dynamo and Apache Cassandra.
✨ Core Architecture & Features
1. 🔄 Consistent Hashing & Data Distribution
  • Implemented a Ring Topology using the 64-bit FNV-1a Hash Algorithm to ensure uniform data distribution and eliminate hot spots.
  • Configured with 100 Virtual Nodes (vNodes) per physical server to achieve perfect load balancing and seamless scaling.
2. 🛡️ Fault Tolerance & Replication
  • Strict Quorum Logic (W=2, R=2): Ensures consistency across cluster nodes. Writes are acknowledged once replica quorums are achieved.
  • Write-Ahead Logging (WAL): Disk-backed crash recovery. Every node recovers its exact state from local logs instantly upon reboot.
  • Gossip Protocol: Background heartbeat threads monitor cluster health, instantly detecting node failures and dynamically updating the routing topology.
3. ⚡ High-Performance Concurrency
  • Architected a thread-safe custom Storage Engine utilizing std::shared_mutex (Read-Write Locks).
  • Achieved 33,685 Write Requests Per Second (RPS) with a highly concurrent load of 100 simultaneous threads, maintaining an average latency of just ~2.97ms (Benchmarked via Apache ab).
4. ⚖️ Elastic Scaling & Live Rebalancing
  • Dynamically supports adding or removing nodes.
  • Exposes an /admin/rebalance endpoint that intelligently calculates hash boundaries and migrates live data to new nodes without cluster downtime.
5. 🖥️ Interactive Visual Dashboard (Control Center)
  • A real-time React/Vite dashboard that visualizes the Consistent Hashing Ring.
  • Chaos Engineering Controls: Inject massive workloads or simulate node deaths with a single click to visually demonstrate data rebalancing and fault tolerance in real-time.
🛠️ Tech Stack
  • Backend Engine: Modern C++ (C++17)
  • Networking/HTTP:cpp-httplib
  • Frontend UI: React.js, Vite, Tailwind CSS
  • Performance Testing: Apache Bench (ab) & Custom Bash Scripts
🚀 Getting Started
Prerequisites
  • GCC/G++ (Supports C++17)
  • CMake 3.10+
  • Apache Bench (ab tool for load testing)
  • Node.js & npm (For the UI)
1. Build the Backend (Release Mode with -O3 Optimizations)
# Clone the repository
git clone https://github.com/Tejas-Raj01/distributed-system.git
cd distributed-system
# Create release build with maximum optimizations
cmake -B build -DCMAKE_BUILD_TYPE=Release -DCMAKE_CXX_FLAGS="-O3"
cmake --build build
2. Start the Backend Server
# Ensure data directory exists for WAL logging
mkdir -p data
# Start a server node on port 8080
./build/kv_server 8080
⚡ Performance Benchmarking & Load Testing
To run high-concurrency performance benchmarks matching our test setup (100 concurrent threads, 50,000 requests), follow these steps:
Step 1: Install Apache Benchmark (ab)
  • Ubuntu/Debian:sudo apt install apache2-utils
  • Fedora/RHEL:sudo dnf install httpd-tools
  • Arch Linux:sudo pacman -S apache
Step 2: Start Server in Background
./build/kv_server 8080 > data/server.log 2>&1&
Step 3: Write (POST) Benchmark Execution
  1. Create a post payload file:

    echo"key=StressTestKey&value=MassiveDataLoad"> payload.txt
  2. Run Apache Bench for 50,000 POST requests with 100 concurrent connections:

    ab -n 50000 -c 100 -p payload.txt -T "application/x-www-form-urlencoded""http://127.0.0.1:8080/put"
Step 4: Read (GET) Benchmark Execution
Run Apache Bench for 50,000 GET requests under 100 concurrent connections:
ab -n 50000 -c 100 "http://127.0.0.1:8080/get?key=StressTestKey"
📊 Benchmark Results & Detailed Analytics
Tested on Release build (-O3 optimized) with 50,000 requests under 100 concurrent threads:
Metric / ParameterWrite (POST /put)Read (GET /get)
Total Requests50,00050,000
Concurrency Level100 connections100 connections
Throughput (Requests/sec)33,684.94 req/sec1,809.14 req/sec
Mean Latency (Average)2.969 ms55.275 ms
Concurrent Request Latency0.030 ms0.553 ms
Success / Error Rate100% Success (0 Errors)100% Success (0 Errors)
Latency Percentile Breakdown
PercentileWrite Latency (ms)Read Latency (ms)
50% (Median)1 ms49 ms
66%2 ms50 ms
75%2 ms50 ms
80%2 ms51 ms
90%3 ms71 ms
95%4 ms72 ms
98%5 ms73 ms
99%5 ms73 ms
About
distributed-system-phi.vercel.app
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
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Packages
Contributors
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