#Dredd Api Testing Framework

Supercharge Your API Testing with Cryload Ever wondered how your web services hold up under heavy traffic? Meet Cryload, a blazing fast, lightweight HTTP benchmarking tool built with the Crystal programming language. It is designed to take the guesswork out of performance testing by allowing you to simulate massive concurrent connections with ease. Whether you need to run specific request counts or stress test your API over a set duration, this tool provides clear, actionable metrics to identify bottlenecks. With handy features like custom headers and JSON output for your automated workflows, it is a perfect addition to any developer toolkit. Start stress testing today. Open source: sdogruyol/cryload Spotted on Hacker News. Follow for more open source project drops. #opensource #github #programming #coding #buildinpublic #cryload #hackernews

Tired of Postman? Meet the game-changer for API testing! 🚀 Simple, fast, and efficient—your new favorite tool is here! #codewitharjit #API #Developers #TechRevolution #CodingLife

50K Firecrawl credits — Grab them ASAP! #developers #ai #buildinpublic #saas #automation

Your API is slow. Redis processes 1M+ requests per second. Here’s the engineering breakdown most devs miss 👇 RAM is just the surface. The real magic is in: → O(1) data structure lookups → Single-threaded execution (no race conditions) → Pipelining for batch processing → Optimized C running close to hardware This is why Twitter, GitHub, and Instagram trust Redis for real-time systems. If you’re building APIs, caching layers, or session stores — you need to understand this. 💾 Save this for your next system design interview 📤 Share with a backend dev who needs this 💬 Comment “Redis” if you’ve used it in production Follow @akashcodeofficial for backend concepts explained the way they should be — clearly. #backenddevelopment #systemdesign #redis #devops #softwareengineering

Follow @cloud_x_berry for more info #APIResponseTime #SystemDesign #BackendPerformance #WebArchitecture #NetworkingProtocols API performance comparison, gRPC protocol, GraphQL API, REST API, WebSocket protocol, binary protocol, single endpoint API, HTTP caching, persistent connections, full HTTP request, latency optimization, network overhead, API throughput, real time communication, client server architecture, performance benchmarking, scalable APIs, backend optimization, protocol efficiency, user experience speed

💯 A Most asked questions❓for Backend Developers. How to optimise your API? Interview ready answer. 1- Asynchronous programming 2- Database query optimization 3-implement caching 4-dependency injection That's it to crack this question. #api #backenddeveloper #java #softwaredeveloper #coding #fast #database #interview #interviewprep

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]

This is where APIs become real. You search a movie… API fetches data… and your app turns it into something you can see. That’s how apps like Netflix actually work. If you understand this flow, you’re already ahead of most beginners. Save this — you’ll use it in your projects. Next: API Authentication (how APIs stay secure) 🔐 #api #webdevelopment #javascript #coding #instagood

HOW TO create a CRUD API in under 2 minutes. A super quick BEGINNER FRIENDLY coding tutorial on how to build out a Create, Read, Update, Delete API. We go over all four endpoints and then test it out at the end of the reel. Let me know if you have any questions and I hope this was helpful. I am attaching to this Reel a more basic version of this if you want to know just how to create an API. This is using FastAPI and Pydantic for the item class. Why Pydantic? We could do it without using it, but it does convert automatically. For instance, our input for our post is a string 999. By using pydantic, it does the string to float conversion for us automatically, which removes some validation check code for us. #coding #codingtutorial #computerscience #softwaredeveloper #softwareengineer

Comment “blog” & I’ll share the blog link & notes with you in your DM 🤝🏻 (Make sure to follow else automation won’t work) Problem: Persistence in Redis (your e-commerce website stores data in redis & suddenly, server restarts, but your data is not lost) Save for your future interviews 📩 #dsa #systemdesign #tech #coding #codinglife [dsa, system design, redis, tech, persistence]

Check the answer... 👇 Interviewer : How would you design a backend API that can reliably process 1M requests per second? 🚀 System Design: Handling 1 Million Requests per Second 🧭 Requirement Clarification • Traffic pattern (read vs write) • Latency SLA (p95 / p99) • Payload size & request type • Regional vs global traffic 🏗 High-Level Architecture • Client → CDN → Load Balancer → Stateless API Services • Cache + Message Queue + Scaled Database 🧩 Stateless Service Design • No server-side session storage • JWT / token-based authentication • Enables horizontal scaling 🌐 Traffic Distribution • L7 Load Balancers (Nginx / ALB) • Health checks & smart routing • Multi-region routing if required 📈 Horizontal Scaling • Containerized services (Docker) • Auto-scaling using CPU, RPS, latency • Kubernetes or cloud auto-scaling ⚡ Caching Strategy • CDN for static & cacheable responses • Redis / Memcached for hot data • Cache-aside or write-through patterns 🔄 Asynchronous Processing • Offload non-critical tasks ◦ Emails ◦ Notifications ◦ Audit logs • Message queues (Kafka / SQS / RabbitMQ) 🗄 Database Scaling • Read replicas for heavy reads • Sharding for write scalability • Proper indexing & query tuning • Separate transactional & analytics workloads 🔌 Network & Connection Optimization • HTTP/2 or gRPC • Connection pooling • Keep-alive enabled • Efficient serialization (JSON / Protobuf) 🛡 System Protection • Rate limiting per user / IP • Circuit breakers • Backpressure handling • Graceful degradation 📊 Observability & Reliability • Metrics: RPS, latency, error rate • Centralized logs • Distributed tracing • Load & stress testing 🎯 Interview Closing Line “I handle 1M RPS by scaling horizontally, reducing load with caching and async processing, protecting the system with rate limiting, and continuously monitoring performance.” #SystemDesignInterview #SystemDesign #TechSimplified #ScalableSystems #APIDesign BackendEngineering

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