#Loadbalancing

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#Loadbalancing Reel by @iamrajpadval - This is a classic real-world system design question every backend developer should know. Let's break it down : 1️⃣ Idempotency Key Definition: Assi
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@iamrajpadval
This is a classic real-world system design question every backend developer should know. Let’s break it down : 1️⃣ Idempotency Key Definition: Assign a unique ID to each API request so the backend recognizes duplicates. Example: User clicks “Pay Now” twice → processed only once using the same payment_id. ⸻ 2️⃣ Disable Button / Debounce Definition: Stop multiple clicks from triggering duplicate API calls. Example: Disable or delay the “Submit” button immediately after the first click. ⸻ 3️⃣ Database Lock or Unique Constraint Definition: Prevent duplicate entries at the database level. Example: Reject new records if the same order_id already exists. ⸻ 4️⃣ Queue Deduplication Definition: Definition: Ensures that repeated messages in a queue (like Kafka or SQS) aren’t processed multiple times. Example: If two messages have the same ID, only one is processed. esifn #systemd (API reliability, real-world system design, Stripe architecture, payments, stripe, razorpay, backend systems, scalable systems, api development, idempotent)
#Loadbalancing Reel by @akanksha_buchke - 1️⃣ Idempotency Key Definition: Assign a unique ID to each API request so the backend recognizes duplicates. Example: User clicks "Pay Now" twice → pr
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@akanksha_buchke
1️⃣ Idempotency Key Definition: Assign a unique ID to each API request so the backend recognizes duplicates. Example: User clicks “Pay Now” twice → processed only once using the same payment_id. ⸻ 2️⃣ Disable Button / Debounce Definition: Stop multiple clicks from triggering duplicate API calls. Example: Disable or delay the “Submit” button immediately after the first click. ⸻ 3️⃣ Database Lock or Unique Constraint Definition: Prevent duplicate entries at the database level. Example: Reject new records if the same order_id already exists. ⸻ 4️⃣ Queue Deduplication Definition: Definition: Ensures that repeated messages in a queue (like Kafka or SQS) aren’t processed multiple times. Example: If two messages have the same ID, only one is processed. #systemdesign #backenddevelopment #softwareengineering #microservices #apidesign scalablesystems distributedsystems programming developers techinterview coding systemdesigninterview
#Loadbalancing Reel by @backend.with.ai - This is a classic distributed systems problem. Called: duplicate requests. - 1️⃣ Problem Network retry double click slow response Same reques
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@backend.with.ai
This is a classic distributed systems problem. Called: duplicate requests. — 1️⃣ Problem Network retry double click slow response Same request can hit your server multiple times. — 2️⃣ Idempotency key Client sends a unique key: idempotency_key = xyz123 Every payment request has this key. — 3️⃣ Store the key Before processing: Check if key exists. • If NOT → process payment • If YES → return old response — 4️⃣ Database constraint Use unique constraint on the key. Prevents duplicate entries even under race conditions. — 5️⃣ Safe retries Now even if request comes 5 times, only one payment is processed. — 6️⃣ Real systems Used in: • Stripe • PayPal • Banking systems — Distributed systems can’t avoid retries. But they must handle them safely. — That’s how you prevent double charges. — Save this for interviews ✅ Follow @backend.with.ai 🚀 #systemdesign #apidesign #scaling #softwaredeveloper #programming systemdesign apidesign scaling 1millionrps distributedsystems loadbalancing caching microservices softwaredeveloper programming coding devops tech backenddeveloper backenddevelopment api interviews database learninganddevelopment tech [API Design] [System Architecture] [API Scaling] [1 Million RPS] [Distributed Systems] [Load Balancing] [Database Sharding] [High Availability]
#Loadbalancing Reel by @ankitcode99 (verified account) - When a user completes a payment via Razorpay, your backend needs to know: Did the payment succeed or fail? You could keep asking Razorpay again and
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@ankitcode99
When a user completes a payment via Razorpay, your backend needs to know: Did the payment succeed or fail? You could keep asking Razorpay again and again… But that’s inefficient. Instead, Razorpay uses webhooks. Here’s how it works: 1️⃣ You give Razorpay a callback URL (your API endpoint) 2️⃣ When an event happens (payment success, failure, refund), Razorpay sends a POST request to your server 3️⃣ Your backend receives the data and updates your database 👉 Webhooks are just like normal API endpoints, but instead of being triggered by your frontend, they are triggered by another service. So instead of you asking repeatedly, Razorpay pushes the update to you automatically. 👉 Client → Payment → Razorpay → Your Backend Webhooks are widely used in: • payments • GitHub actions • Stripe events • CI/CD pipelines Save this for your next backend interview. follow @ankitcode99 for more such web dev nuggets ( webhooks explained, razorpay webhook integration, backend event driven architecture, api callback vs polling, system design interview ) #software #tech #engineering #viral #follow
#Loadbalancing Reel by @backend.with.ai - Here are concepts you must know 👇 1️⃣ Rate Limiting Prevents one user or bot from overwhelming your API. Common approaches: • Token Bucket • Lea
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@backend.with.ai
Here are concepts you must know 👇 1️⃣ Rate Limiting Prevents one user or bot from overwhelming your API. Common approaches: • Token Bucket • Leaky Bucket • Sliding Window Used by almost every large system. --- 2️⃣ Idempotency If a request is sent twice, the result should still be the same. Example: Payment APIs. Without idempotency, users could be charged twice. --- 3️⃣ Caching Strategy Databases are slow under heavy load. Systems cache frequently accessed data in Redis or Memcached. This reduces database pressure and improves latency. --- 4️⃣ Load Balancing Traffic is distributed across multiple servers. Algorithms like: • Round Robin • Least Connections This allows systems to scale horizontally. --- 5️⃣ Message Queues Heavy tasks should not block APIs. Systems push jobs to queues (Kafka, RabbitMQ, SQS) Workers process them asynchronously. This improves reliability and throughput. --- Backend engineering is about understanding how systems behave at scale. Not just writing code. Save this for interviews. Follow @backend.with.ai 🚀 #systemdesign #apidesign #scaling #softwaredeveloper #programming systemdesign apidesign scaling 1millionrps distributedsystems loadbalancing caching microservices softwaredeveloper programming coding devops tech backenddeveloper backenddevelopment api interviews database learninganddevelopment tech [API Design] [System Architecture] [API Scaling] [1 Million RPS] [Distributed Systems] [Load Balancing] [Database Sharding] [High Availability]
#Loadbalancing Reel by @ecogrowthpath - Concept you must know.👇 Comment "SD" to get document in dm. 🔹 Backend Fundamentals What happens when you hit an API endpoint? How does request life
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@ecogrowthpath
Concept you must know.👇 Comment "SD" to get document in dm. 🔹 Backend Fundamentals What happens when you hit an API endpoint? How does request lifecycle work in Spring Boot / Node? What is idempotency? Why is it critical in APIs? How do you design a rate limiter? What is pagination and how do you implement it efficiently? 🔹 Database (DB) SQL vs NoSQL — when to use what? What are indexes? How do they improve performance? What is normalization vs denormalization? How do you handle DB connections at scale? What causes deadlocks and how do you resolve them? 🔹 Caching Why do we need caching? Cache aside vs write through vs write back? What happens when cache expires suddenly? How do you prevent cache stampede? Redis vs Memcached — key differences? 🔹 System Design Basics How would you design a URL shortener? How do you scale a system from 1K → 1M users? What is load balancing? Types? What is eventual consistency? How do microservices communicate? 🔹 API & Security How do you secure APIs? JWT vs OAuth — when to use what? What is API Gateway and why is it needed? How do you prevent DDoS attacks? What is CORS and why does it matter? 🔹 Concurrency & Core Concepts What is multithreading vs async? How does Java handle concurrency? What are race conditions? What is thread pool and why use it? How do you design a system to handle high concurrency? 🔹 Real-world Scenarios (Must Crack) Cache expired → DB crashed. What happened? Sudden traffic spike — how will your system behave? One service is slow — impact on entire system? How do you make system fault-tolerant? How do you monitor production systems? 💡 Pro Tip: Interviewers don’t test syntax. They test how you think under scale, failure, and trade-offs. You’re not ready for a Backend interview if you can’t answer these 👇 Master these concepts = Crack any system design round 💯 #SystemDesign #BackendDeveloper #TechInterviews #SoftwareEngineering 🚀 follow&Ready to level up your career, SystemmDesign ,tech leadership, and financial mindset. Get guided through 1:1 coaching and mentoring sessions designed for real growth. 📩 Book your session from Bio https://topmate.io/ecogrowthpath/
#Loadbalancing Reel by @backend.with.ai - If you can't clearly explain these, you're not ready yet 👇 1️⃣ Bottleneck Identification Every system has a limit. Good engineers ask first: Wher
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@backend.with.ai
If you can’t clearly explain these, you’re not ready yet 👇 1️⃣ Bottleneck Identification Every system has a limit. Good engineers ask first: Where will it break? • Database • CPU • Network • Lock contention --- 2️⃣ Caching Strategy You cannot scale only with databases. Hot data must be cached in Redis / Memcached. Otherwise your DB becomes the bottleneck. --- 3️⃣ Horizontal Scaling One powerful server is not the solution. Systems scale by adding more servers behind load balancers. --- 4️⃣ Asynchronous Processing Heavy tasks should never block APIs. Use queues: Kafka, RabbitMQ, SQS. Workers process jobs in the background. --- 5️⃣ Failure Handling Servers crash. Networks fail. Systems must survive failures without losing data. --- System design interviews don’t test frameworks. They test how you think about scale. Save this for interviews. Follow @backend.with.ai 🚀 #systemdesign #apidesign #scaling #softwaredeveloper #programming systemdesign apidesign scaling 1millionrps distributedsystems loadbalancing caching microservices softwaredeveloper programming coding devops tech backenddeveloper backenddevelopment api interviews database learninganddevelopment tech [API Design] [System Architecture] [API Scaling] [1 Million RPS] [Distributed Systems] [Load Balancing] [Database Sharding] [High Availability]
#Loadbalancing Reel by @backend.with.ai - If every like updated the database directly, the database would crash. So large systems use a different design. --- 1️⃣ Likes go to cache first E
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@backend.with.ai
If every like updated the database directly, the database would crash. So large systems use a different design. --- 1️⃣ Likes go to cache first Each like hits a fast in-memory store like Redis. Redis can handle millions of operations per second. --- 2️⃣ Atomic counters Instead of updating rows, the system uses counters. Example: INCR post_like_count This prevents race conditions. --- 3️⃣ Event queues Every like event is pushed to a queue (Kafka / internal queue). Workers process them asynchronously. --- 4️⃣ Batched DB updates Instead of 1M DB writes, likes are aggregated and written in batches. Huge performance improvement. --- 5️⃣ Eventual consistency The number you see instantly comes from cache. The database sync happens later. --- Large scale systems don’t update databases instantly. They buffer, batch, and scale. That’s real system design ⚙️ #systemdesign #apidesign #scaling #softwaredeveloper #programming systemdesign apidesign scaling 1millionrps distributedsystems loadbalancing caching microservices softwaredeveloper programming coding devops tech backenddeveloper backenddevelopment api interviews database learninganddevelopment tech [API Design] [System Architecture] [API Scaling] [1 Million RPS] [Distributed Systems] [Load Balancing] [Database Sharding] [High Availability]
#Loadbalancing Reel by @codempower_academy - Handling 1 Million RPS isn't about code - it's about smart architecture. 1️⃣ Traffic Distribution (Load Balancers) ➡️ Spreads incoming requests acros
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@codempower_academy
Handling 1 Million RPS isn’t about code — it’s about smart architecture. 1️⃣ Traffic Distribution (Load Balancers) ➡️ Spreads incoming requests across many servers so nothing overloads. Example: 1M requests split across 200 servers = ~5K requests per server. ⸻ 2️⃣ Scale Out, Not Up (Horizontal Scaling) ➡️ Add more machines instead of making one server bigger. Example: Flash sale traffic? Instantly launch 50 new API instances. ⸻ 3️⃣ Fast Reads with Cache ➡️ Use Redis/Memcached to avoid hitting the database every time. Example: Cached user data = millions of DB calls saved daily. ⸻ 4️⃣ Edge Delivery with CDN ➡️ Static content loads from servers closest to the user. Example: Users in Delhi fetch images from a Delhi CDN node. ⸻ 5️⃣ Background Work with Queues ➡️ Heavy tasks run asynchronously so APIs respond instantly. Example: Payment succeeds now, email receipt sent in background. ⸻ 6️⃣ Split the Database (Sharding) ➡️ Divide data across multiple databases to handle scale. Example: Usernames A–M on one shard, N–Z on another. ⸻ 7️⃣ Rate Limiting ➡️ Prevent abuse and traffic spikes from taking the system down. Example: Limit clients to 100 requests/sec to block bots from killing the API. ⸻ 8️⃣ Lightweights Payloads ➡️ Smaller payloads = faster responses + less bandwidth. Example: Send only required fields instead of massive JSON blobs. Follow @codempower_academy for daily coding tips #systemdesign #softwaredevelopers #programming #tech #interview Beginner projects 🚀 | VS Code tricks 🔥 | Career guidance 👩‍💻 |vscode | vscodeextensions | codinglife | developerlife | learncoding | programming | webdeveloper | techstudents | codersofinstagram | codingfun | javascript | frontenddeveloper |codempoweracademy | vscode | codinglife | javascript | webdeveloper | learncoding| programming | developerlife | techstudents | codersofinstagram
#Loadbalancing Reel by @backend.with.ai - OFFSET looks simple, but databases still scan rows you don't see. Example: SELECT * FROM orders LIMIT 10 OFFSET 1000000 To return page 1,000,000,
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@backend.with.ai
OFFSET looks simple, but databases still scan rows you don’t see. Example: SELECT * FROM orders LIMIT 10 OFFSET 1000000 To return page 1,000,000, the database still scans the previous 1,000,000 rows. Then discards them. So every new page becomes slower and slower. --- Why this becomes dangerous at scale: 1️⃣ Full row scanning The database walks through rows just to skip them. --- 2️⃣ Wasted I/O Millions of rows are read but never returned. --- 3️⃣ Performance degrades as OFFSET increases. Page 10 is fast. Page 1,000,000 is painful. --- How large systems fix this 👇 Use **cursor pagination**. Example: SELECT * FROM orders WHERE id > last_seen_id LIMIT 10; Now the database jumps directly to the next rows. No scanning. No wasted work. --- That’s why large systems avoid OFFSET pagination. Save this for backend interviews. Follow @backend.with.ai 🚀 #systemdesign #apidesign #scaling #softwaredeveloper #programming systemdesign apidesign scaling 1millionrps distributedsystems loadbalancing caching microservices softwaredeveloper programming coding devops tech backenddeveloper backenddevelopment api interviews database learninganddevelopment tech [API Design] [System Architecture] [API Scaling] [1 Million RPS] [Distributed Systems] [Load Balancing] [Database Sharding] [High Availability]
#Loadbalancing Reel by @mission_compile - 1 Million RPS isn't about code, it's about architecture. Here are the 8 key layers to scale your API architecture. 1️⃣ Load Balancer 👉 Distributes
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@mission_compile
1 Million RPS isn't about code, it's about architecture. Here are the 8 key layers to scale your API architecture. 1️⃣ Load Balancer 👉 Distributes traffic across multiple servers so no single machine melts. Example: 1M req/s split across 200 servers = only 5k req/s each. ⸻ 2️⃣ Horizontal Scaling 👉 Add more servers when traffic spikes instead of upgrading one big server. Example: Black Friday? Spin up 50 more API nodes in seconds. ⸻ 3️⃣ Caching Layer 👉 Serve frequent reads from Redis/Memcached to avoid DB overload. Example: User profile cached → avoids 10M database hits/day. ⸻ 4️⃣ CDN for Static Content 👉 Images and static assets load from edge servers near the user. Example: A user in Delhi gets images from a Delhi CDN node. ⸻ 5️⃣ Async Processing (Queues) 👉 Push heavy tasks to Kafka/SQS so API responds instantly. Example: Payment API returns fast → receipt email sent in background. ⸻ 6️⃣ Database Sharding 👉 Split huge datasets across multiple DB shards to scale reads/writes. Example: Users A–M on shard 1, N–Z on shard 2. ⸻ 7️⃣ Rate Limiting 👉 Block or throttle abusive clients to protect server capacity. Example: “100 requests/sec limit” prevents bots from killing the API. ⸻ 8️⃣ Lightweight Payloads 👉 Reduce JSON response size to cut latency and bandwidth. Example: Return only “id, name, price” instead of 20 unnecessary fields. #systemdesign #apidesign #developer #backenddeveloper #distributedsystems [API Design] [System Architecture] [API Scaling] [1 Million RPS] [Distributed Systems] [Load Balancing] [Database Sharding] [High Availability]
#Loadbalancing Reel by @build_with_kamran - In modern web applications and backend systems, protecting your APIs from excessive traffic and abuse is critical. Without proper control, too many re
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@build_with_kamran
In modern web applications and backend systems, protecting your APIs from excessive traffic and abuse is critical. Without proper control, too many requests can lead to server overload, performance issues, and even downtime. This is where rate limiting becomes essential. NGINX is not just a web server or reverse proxy—it can also act as a powerful rate limiter to control incoming traffic. It helps restrict the number of requests a client can make within a specific time window, ensuring better performance, security, and stability for your application. Rate limiting with NGINX is widely used in APIs, authentication systems, microservices architecture, and high-traffic applications to prevent DDoS attacks, brute force attempts, and resource abuse. It plays a key role in building scalable and production-ready backend infrastructure. In this reel, I explain how NGINX works as a rate limiter and why it’s important—a must-know concept for developers working with backend systems, DevOps, networking, and system design. If you want to build secure, high-performance, and scalable web applications, understanding rate limiting is essential. Follow for more content on web development, backend engineering, NGINX, system design, DevOps, and scalable architecture. #webdevelopment #backenddevelopment #nginx #ratelimiting #systemdesign

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