Raj S @rjsada

Git - GitHub Cheatsheet

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This technique is called password hashing. Signup: Password ↓ Hash (bcrypt/Argon2) ↓ Store the hash Login: Password typed ↓ Hash it ↓ Compare hashes Important takeaway The servers do not store or compare plain-text passwords, but rather hashes of them. So even if the data breach happens, the hackers will get only hashes.

Sliding windows fix the fixed-window edge burst, but they make the state more interesting. - A sliding-window log stores timestamps for recent requests, often in a Redis sorted set. - On each request: - remove timestamps older than the window - add the new timestamp - count what remains - That gives a strict rolling limit. - If the limit is two requests per minute, no 60-second interval can contain more than two accepted requests. - The cost is memory. - The limiter keeps individual timestamps, including timestamps from rejected requests until they expire. - A sliding-window counter uses less state - current-window count plus a weighted contribution from the previous window. - It smooths the boundary and is much cheaper, but it is an estimate. - It assumes the previous window's requests were spread out evenly when they may not have been. - The trade is simple to state - exact rolling enforcement costs more memory - a cheaper counter accepts a small amount of approximation.

🚀 Python Basics Part 8 🐍🔥 ✅ Git, GitHub & Python Project Structure for Beginners Writing code is only one part of becoming a Python developer. Professional developers also know how to: ✅ Track code changes ✅ Collaborate with others ✅ Organize projects ✅ Share projects online In this part, you'll learn: ✅ Git Basics ✅ GitHub Basics ✅ Common Git Commands ✅ Python Project Structure ✅ Virtual Environments ✅ Best Practices 🧠 1. What is Git Git is a Version Control System. It helps you: ✔ Track changes in your code ✔ Restore previous versions ✔ Collaborate with other developers ✔ Manage projects efficiently Think of Git as a "save history" for your code. 🌐 2. What is GitHub GitHub is a cloud platform where you can store Git repositories. You can use GitHub to: ✔ Backup your projects ✔ Build your portfolio ✔ Collaborate with developers ✔ Showcase your work to recruiters 💻 3. Initialize a Git Repository Open your project folder and run: git init This creates a Git repository for your project. 📂 4. Check Project Status git status This command shows: ✔ Modified files ✔ New files ✔ Files ready to commit ➕ 5. Add Files Stage all project files: git add . Or stage a specific file: git add app.py 💾 6. Commit Changes Save your work with a message. git commit -m "Initial project setup" A commit is like taking a snapshot of your project. ☁ 7. Push Code to GitHub After connecting your repository: git push origin main This uploads your latest changes to GitHub. 📁 8. Recommended Python Project Structure Example: project/ ├── main.py ├── requirements.txt ├── README.md ├── .gitignore ├── data/ ├── images/ ├── notebooks/ ├── src/ └── tests/ Keeping projects organized makes them easier to maintain. 🌍 9. Virtual Environments A virtual environment creates an isolated workspace for your project. Create one: python -m venv Activate: Windows venv\Scripts\activate macOS / Linux source venv/bin/activate 📦 10. Install Project Dependencies pip install pandas Save installed packages: pip freeze > requirements.txt Install from requirements file: pip install -r requirements.txt 📖 11. Why is README.md Important A good README explains: ✔ Project overview ✔ Features ✔ Installation steps ✔ Usage instructions ✔ Technologies used Every portfolio project should include one. 🛠 Beginner Portfolio Projects ✅ Calculator App ✅ Expense Tracker ✅ Weather App ✅ Password Generator ✅ Student Management System ✅ To-Do List App ✅ File Organizer ✅ API-Based Data Viewer 🔥 Common Beginner Mistakes ❌ Not using Git from the beginning ❌ Uploading unnecessary files to GitHub ❌ Skipping virtual environments ❌ Poor project folder organization ❌ Missing README file 💡 Pro Tip Every project you build should include: ✔ Clean folder structure ✔ Meaningful commit messages ✔ README.md ✔ requirements.txt ✔ .gitignore These small habits make your projects look professional. Keep building projects, practice consistently, and you'll continue improving every day. 💬 Double Tap ❤️ For More

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3 days ago

🚀 Python Basics Part 7 🐍🔥 APIs, Web Scraping & Automation with Python Now that you've learned Python fundamentals, OOP, and essential libraries, it's time to use Python for real-world automation. This is where Python becomes incredibly powerful 💻 In this part, you'll

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Confused between CNN, RNN, LSTM, GRU, and Transformers? 🤔 This quick cheat sheet breaks down the most important deep learning architectures in one place. 🚀 #AI #DeepLearning #ML #Developers #ArtificialIntelligence

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Two types of message-driven communication you need to know: - Orchestration - Choreography Working with distributed systems is both fun and challenging at the same time. One of those challenges is designing effective communication between services. More centralization or less centralization? More coupling or less coupling? More control or less control? These are only a few questions you need to answer. Orchestration is a centralized approach to Microservices communication. One of the services takes on the role of the orchestrator and coordinates the communication between services. Choreography is a decentralized communication approach. Choreography uses event-driven communication - as opposed to orchestration, which uses commands. Which communication approach do you use and why?

A full-stack app needs a front end + auth, a database, an API, and a reliable way to deploy everything. In this tutorial, @cloud_girl1 teaches you how to build a full-stack CRUD app with React, AWS Lambda, DynamoDB, API Gateway, and Cognito auth. You'll also learn how to use AWS CDK to deploy the backend and CloudFront to ship the frontend over HTTPS. freecodecamp.org/news/full-stac…

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System Design From Scratch — Day 16 Rate Limiting 1. Why? Without limits, a single user or bot can flood your server with requests. This can slow down or even crash your application. 2. Simple Explanation Rate Limiting controls how many requests a user can make within a given time. Example: 100 requests / minute If the limit is exceeded, the server temporarily rejects additional requests. 3. Real-World Example When you log in repeatedly or spam an API on GitHub or OpenAI, you'll eventually receive a 429 Too Many Requests response. 4. Key Takeaways -Prevents abuse -Protects servers -Ensures fair usage -Improves reliability 5. Question Should one user be allowed to send 1 million requests while everyone else waits?

System Design Fundamentals - Caching and Performance - Day 36 Health Checks & Failover: What Happens When a Server Goes Down? You have 3 application servers behind a load balancer. ┌──► Server A Users ──► Load Balancer ──► Server B └──► Server C Everything works perfectly. Then... Server B crashes. If the load balancer continues sending traffic to Server B: Request │ ▼ Server B │ X 500 / Timeout Users start seeing failures. So how does the load balancer know that a server should stop receiving traffic? That's where Health Checks come in. What Is a Health Check? A load balancer periodically checks whether each backend instance is healthy. Load Balancer │ ├──► Server A → Healthy ├──► Server B → Healthy └──► Server C → Healthy If a server repeatedly fails its health check: Server B │ Health Check Failed │ Marked Unhealthy │ Removed From Rotation New requests are sent only to healthy servers. Liveness vs Readiness This distinction is extremely important. Liveness Asks: "Is the application process alive?" If not, the process may need to be restarted. App Running? │ Yes → Alive No → Restart Readiness Asks: "Can this instance serve traffic right now?" An application can be alive but not ready. For example: - Database connection unavailable - Critical dependency unavailable - Application still starting - Internal initialization incomplete In that case, the instance should remain running but temporarily stop receiving traffic. Application │ Alive? → YES │ Ready? → NO │ Don't Send Traffic What Should a Health Check Verify? A simple endpoint might return: GET /health 200 OK But a useful readiness check may verify critical dependencies. For example: Application ✓ Database ✓ Redis ✓ Critical Service ✓ However, be careful. If every dependency is included blindly, one minor downstream failure could mark every application instance unhealthy and cause a larger outage. Health checks should reflect whether the instance can meaningfully serve the traffic it receives. Active vs Passive Health Checks Active Health Check The load balancer periodically sends a request. Load Balancer │ ▼ GET /health │ ▼ Server If multiple checks fail, the server is marked unhealthy. Passive Health Check The load balancer observes real traffic. If a backend repeatedly returns: Timeouts Connection Errors 5xx Responses It may temporarily remove that backend from rotation. What Is Failover? Health checks detect failure. Failover determines what happens next. Imagine: Primary Server │ X Failed │ ▼ Backup Server Traffic automatically switches to a healthy alternative. That's failover. Server Failover Suppose you have: Load Balancer │ ├──► Server A ✓ ├──► Server B X └──► Server C ✓ Server B fails. The load balancer removes it. Traffic continues through A and C. Users │ Load Balancer │ ├──► Server A └──► Server C Users may never even know that Server B went down. Database Failover Failover isn't only for application servers. Imagine: Application │ ▼ Primary DB │ Replication │ Replica DB If the primary database fails, depending on the database architecture and configuration, a replica can be promoted to become the new primary. Primary DB X │ Failover │ Replica │ New Primary This process may be automatic or manual. Multi-Zone Failover Production systems often spread infrastructure across multiple availability zones. Load Balancer │ ┌────┴────┐ │ │ Zone A Zone B │ │ Servers Servers If one zone becomes unavailable, traffic can continue through another zone. This reduces the risk of a single infrastructure failure taking down the entire application. Failure Detection Isn't Instant Imagine health checks run every: 10 seconds And the system requires: 3 consecutive failures It could take roughly 30 seconds, plus timing effects, before the server is removed. If checks are too frequent: More monitoring traffic. If thresholds are too aggressive: Temporary network issues may cause healthy servers to be removed. If thresholds are too relaxed: Users may continue hitting failed instances. Health-check configuration is a trade-off. Recovery Matters Too Suppose Server B recovers. Should traffic immediately return? Not always. A recovering server may need time to: - Warm caches - Initialize connections - Load configuration - Become fully ready A safer flow is: Server Starts │ Liveness Passes │ Readiness Passes │ Health Checks Stable │ Return to Load Balancer │ Receive Traffic Real Production Flow Users │ ▼ Load Balancer │ Health Checks │ ├──► Server A ✓ ├──► Server B X └──► Server C ✓ │ ▼ Database Primary │ Replication │ Replica Server failure? Remove unhealthy server. Database failure? Fail over to a suitable replica when the architecture supports it. Zone failure? Route traffic to healthy capacity elsewhere. Key Takeaway High availability isn't about preventing every failure. Failures will happen. The goal is to: Detect failure quickly → Stop sending traffic to unhealthy components → Fail over safely → Recover without disrupting users. Health checks tell you: "Is this instance capable of serving traffic?" Failover answers: "Where should traffic go when it isn't?" Next Day - Circuit Breaker Pattern: How to Stop One Failing Service from Taking Down Your Entire System

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