Why You Shouldn't Ignore Rate Limiting in Production APIs

𝗠𝗼𝘀𝘁 𝗛𝗧𝗧𝗣 𝗰𝗹𝗶𝗲𝗻𝘁𝘀 𝗶𝗻 𝗳𝗶𝗻𝘁𝗲𝗰𝗵 𝗹𝗼𝗼𝗸 𝘀𝗶𝗺𝗽𝗹𝗲… 𝘂𝗻𝘁𝗶𝗹 𝘆𝗼𝘂 𝗿𝘂𝗻 𝘁𝗵𝗲𝗺 𝗶𝗻 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 😅 At some point we had several external integrations (payment providers, AML, internal services), and on paper it was just “call REST API and handle response”. In reality, a naive HTTP client becomes a problem very quickly. Without proper connection pooling you start exhausting resources, badly tuned timeouts lead to blocked threads and latency spikes, and aggressive retries can easily amplify failures instead of handling them. We ended up treating HTTP clients as 𝗳𝘂𝗹𝗹-𝗯𝗹𝗼𝘄𝗻 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗰𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀. Each integration had its own setup: connection pool limits, timeouts, retry + backoff strategy, custom error handlers, and request/response interceptors for logging and tracing. On top of that — SSL configuration, consistent headers, and observability (latency, payload size, error rates). The interesting part is that most issues didn’t come from business logic, but from how we communicated with external systems. A poorly configured client can overload providers, block threads, hide real errors behind retries, or just slowly degrade performance. Once we started treating HTTP clients properly, a lot of “random instability” just disappeared 🙂 Curious how others approach this — centralized clients or per-integration tuning? 🤔 #java #springboot #backend #fintech #microservices #architecture #performance

𝗕𝗿𝗶𝗱𝗴𝗶𝗻𝗴 𝗹𝗲𝗴𝗮𝗰𝘆 𝗽𝗮𝘆𝗺𝗲𝗻𝘁 𝘁𝗲𝗿𝗺𝗶𝗻𝗮𝗹𝘀 𝘄𝗶𝘁𝗵 𝗺𝗼𝗱𝗲𝗿𝗻 𝗺𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝗶𝘀 𝘄𝗵𝗲𝗿𝗲 𝘁𝗵𝗶𝗻𝗴𝘀 𝗴𝗲𝘁… 𝗶𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 😅 We had a setup where payment terminals were sending raw messages over TCP, while our processing system was built as a typical HTTP-based microservice. Sounds simple — just “translate TCP to HTTP”, right? In reality, it’s a bit more complicated. We built a Go adapter that sits in between: it accepts TCP connections from terminals, translates incoming messages into HTTP requests, sends them to the processing service, and then writes the response back to the same TCP connection. The tricky part wasn’t the translation itself — it was everything around it. Each TCP connection is long-lived and stateful, while HTTP is short-lived and stateless. So you need to: – keep track of active connections – map responses back to the correct client – handle disconnects and retries – avoid blocking when one client becomes slow We ended up managing connections in-memory with per-client channels and bounded queues, so slow clients don’t bring the whole system down. If a client can’t keep up — we fail fast instead of blocking everything. Another interesting part was pushing asynchronous results back to terminals. After processing a transaction via HTTP, we exposed a separate endpoint that triggers a message back into the TCP layer, which then gets routed to the correct connection. So now your system is not just request-response anymore — it’s 𝗯𝗶𝗱𝗶𝗿𝗲𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗮𝗰𝗿𝗼𝘀𝘀 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗽𝗿𝗼𝘁𝗼𝗰𝗼𝗹𝘀 🙂 At that point you realize the main problem is not TCP or HTTP. It’s coordinating state and flow between systems that follow completely different communication models. Curious if anyone had to bridge legacy protocols with modern services — what was the hardest part for you? 🤔 #golang #java #backend #fintech #payments #systemdesign #microservices

💳 Pessimistic vs Optimistic Locking in Payment Systems — Which One Do You Choose? When building a high-scale payment application, handling concurrency correctly is not optional — it’s critical. Here’s how I think about it 👇 🔒 Pessimistic Locking Lock the data before making changes Guarantees consistency But… can lead to slower performance & deadlocks ⚡ Optimistic Locking Allow concurrent updates Validate before commit (using versioning) But… requires retry logic on conflicts 🚀 Real-world takeaway: 👉 For payments / wallet balance / inventory deduction→ Prefer Pessimistic Locking (safety first) 👉 For high-scale APIs / microservices / read-heavy systems→ Prefer Optimistic Locking (performance & scalability) 💡 What I’ve learned:In modern distributed systems, it's rarely about choosing one.The best systems use a hybrid approach based on use-case. ❓ What would you choose for a payment system?Safe & consistent OR fast & scalable? Let’s discuss 👇 #SystemDesign #BackendEngineering #Payments #Concurrency #Java #Microservices #TechArchitecture

Why You Should Always Validate Inputs at the API Layer 🔍 Your backend logic might be solid… but unvalidated inputs can break your system in unexpected ways. 💥 What goes wrong: • 🚨 Invalid data reaches the database • 🚨 Unexpected exceptions in business logic • 🚨 Security vulnerabilities (injection attacks) ⸻ 📌 Common mistake: Relying only on database constraints and skipping validation in APIs built with Spring Boot ⸻ ✅ What production systems do: • Validate requests at the API boundary • Use annotations like @Valid, @NotNull, @Size • Return clear, structured validation errors • Combine validation with proper exception handling ⸻ 💡 Why this matters: In fintech & banking systems: Data integrity is critical — bad input = bad business decisions. ⸻ Validate early… so your system doesn’t fail later. ⸻ #java #springboot #backenddeveloper #microservices #api #validation #securecoding #softwareengineering #systemdesign #distributedsystems #fintech #bankingtech #cloudnative #singaporejobs #techcareers

𝗘𝘅𝗽𝗹𝗼𝗿𝗶𝗻𝗴 𝗡𝗲𝘄 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗜𝗱𝗲𝗮𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗕𝗮𝗻𝗸𝗶𝗻𝗴 𝗗𝗼𝗺𝗮𝗶𝗻 (𝗕𝗙𝗦𝗜) As a Backend Engineer working with Java & Spring Boot, I’ve been diving deeper into the BFSI space and brainstorming some practical, real-world project ideas that go beyond basic CRUD systems. Here are a few concepts I’m excited about building: 🔹 Smart Loan Recommendation System A microservice-based solution that analyzes user financial behavior and suggests personalized loan options using rule engines or ML integrations. 🔹 Real-Time Fraud Detection System Using event-driven architecture (Kafka), transactions can be monitored in real time to detect suspicious patterns and trigger alerts instantly. 🔹 Digital Wallet with Multi-Currency Support A secure wallet system with currency conversion, transaction tracking, and integration with external payment gateways. 🔹 Credit Score Simulation API An API that evaluates user financial data and simulates a credit score helpful for fintech startups and internal banking tools. 🔹 Secure Document Signing Platform (e-Sign) Inspired by real-world use cases allow users to sign banking documents digitally with audit trails and S3 storage integration. 🔹 Transaction Analytics Dashboard A backend-powered analytics engine that provides insights like spending patterns, monthly reports, and anomaly detection. 💡 These ideas focus on scalability, security, and real-world applicability key aspects in modern banking systems. I’m planning to pick one of these and build it using: ✔️ Spring Boot (Microservices) ✔️ Kafka (Event Streaming) ✔️ Redis (Caching) ✔️ PostgreSQL ✔️ Docker & Kubernetes Would love to hear your thoughts 👇 Which one would you build first? #Java #SpringBoot #BackendDevelopment #BFSI #Microservices #Fintech #SoftwareEngineer

Rate Limiting vs Throttling — A Must-Know for Every Backend Developer! In modern APIs (especially FinTech & SaaS), controlling traffic is critical. Two commonly confused concepts are: Rate Limiting Limits how many requests a client can make. Example: 100 requests/min → exceeding returns HTTP 429. Throttling Controls how fast requests are processed. Example: Only 10 requests/sec are processed, others are delayed. Key Differences: Rate Limiting = Block excess requests Throttling = Slow down traffic What happens when limit exceeds? HTTP 429 (Too Many Requests) is returned Retry-After header tells when to retry In Distributed Systems: We use Redis to maintain a shared counter across multiple servers using atomic operations like INCR + EXPIRE. 🔥 Popular Algorithms: ✔ Token Bucket (allows bursts) ✔ Sliding Window (strict control) Real-world example: Payment APIs limit transactions per minute to prevent fraud and duplicate payments. Mastering this concept is a game-changer for system design interviews! #SystemDesign #WebAPI #DotNet #BackendDevelopment #SoftwareEngineering

Excited to share something I recently built — a production-style Banking Backend System! 🏦 The project ((https://lnkd.in/dM-WshQ4)) is built on Node.js + Express with a clean RESTful architecture — designed and developed entirely by me, from architecture to implementation. Here's what went into it: 🔐 User Authentication — JWT-based auth with secure token handling. Stateless sessions that keep the architecture clean and scalable from day one. 🏦 Account Management API — Full account lifecycle operations structured around RESTful principles, with a consistent response contract across every endpoint. 💸 Transaction Processing — Debit and credit operations handled with precision. Every transaction is validated, processed, and recorded — no money ever moves without a trace. 📒 Double-Entry Ledger System — The core of any real banking system. Every transaction creates two ledger entries — debit and credit — ensuring complete data consistency and auditability, exactly how production fintech systems work. 🧱 Industry-Standard Practices followed throughout: — MVC-inspired structure for clear separation of concerns — Environment-based configuration using dotenv — Centralized error handling middleware — Input validation at the API layer — Mongoose-style schema design with proper data modelling Building this end-to-end — from authentication to ledger consistency — was a deep exercise in thinking about financial systems seriously. Not just writing code that works, but code that is correct, traceable, and production-ready. Always learning. Always building. 🚀 #NodeJS #BackendDevelopment #Fintech #JavaScript #OpenSource #SoftwareEngineering #OpenToWork

⚡What makes a trading system different from a typical application? Is it scale? Not always. Is it the number of users? Not necessarily. Many consumer apps handle far more users. One major difference is often: 🚀 Latency + Reliability In trading systems, market movements can happen in milliseconds, and in some high-frequency environments, even faster. These systems must process market updates, user actions, and trade messages quickly while remaining stable and accurate. Why is this challenging❓ ⏱ Even small delays can matter. 📉 Downtime during market hours can directly impact users and business outcomes. How performance is improved: 🌐 Network Layer • Cross-connects instead of public internet • Colocation near exchanges/liquidity venues • Widely adopted protocols like FIX for easy integration or binary/native protocols for even lower latency ⚙️ Backend Layer • High-performance languages such as Java / C++ • Optimized threading and memory usage • Internal messaging systems such as Kafka, RabbitMQ, or other event pipelines, depending on the use case 🛡️ System Reliability • Monitoring and alerting • Redundancy/failover • Strong incident response That’s what makes trading systems interesting to me: they combine software engineering, infrastructure, and real-time decision making. 💬 What else would you add? #TradingSystems #FinTech #LowLatency #SRE #FIXProtocol #HFT #Engineering #Crossconnect

Retries are normal in distributed systems. In financial systems, they can break your ledger. Exactly-once processing is not a feature. It is a combination of patterns working together to make retries safe.   Mayank walks through how this is implemented in real systems.   #DistributedSystems #Fintech #TeamInfominez

𝗘𝘅𝗰𝗶𝘁𝗲𝗱 𝘁𝗼 𝘀𝗵𝗮𝗿𝗲 𝗼𝗻𝗲 𝗼𝗳 𝗺𝘆 𝗿𝗲𝗰𝗲𝗻𝘁 𝗳𝗶𝗻𝘁𝗲𝗰𝗵 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀. I built a JavaScript & PowerhShell based automation solution to streamline cash and trade file processing across multiple OMS and vendor platforms. The biggest challenge was handling different file structures, mapping rules, and validation requirements for each trading system. To solve this, I designed a dynamic transformation workflow that:  • Parses multiple financial file formats.  • Validates incoming trade and cash data.  • Applies configurable mapping rules.  • Converts outputs into OMS-ready standardized files.  • Supports secure transfer workflows. 𝗜𝗺𝗽𝗮𝗰𝘁: This significantly reduced manual configuration effort, minimized mapping errors, accelerated vendor onboarding, and improved reliability in real-time financial workflows. What I’m most proud of is building a scalable vendor-agnostic integration layer that improves interoperability between trading systems and financial institutions. Projects like this continue to strengthen my passion for backend engineering, fintech automation, and enterprise integrations. 𝗚𝗶𝘁𝗛𝘂𝗯: 𝗵𝘁𝘁𝗽𝘀://𝗴𝗶𝘁𝗵𝘂𝗯.𝗰𝗼𝗺/𝗨𝘀𝗺𝗮𝗻𝗦𝗵𝗮𝗿𝗲𝗖𝗼𝗱𝗲 #Fintech #JavaScript #BackendEngineering #Automation #SystemIntegration