Mastering 11 key HTTP status codes for REST APIs

You are building a Rest API. You've just implemented an Exception handler to catch exceptions thrown by your controllers and services. Your team has a shared library with marker Exception classes. You're using one, which you throw to indicate that a client request contains references to records that do not exist in the database. As you handle this exception in your handler which http status code would you return to the client? a. 404 Not Found b. 204 No Content c. 304 Not Modified d. 400 Bad Request e. 406 Not Acceptable

🚀 Top 5 common ways to improve API performance every backend developer should know! 📄 𝟏. 𝐏𝐚𝐠𝐢𝐧𝐚𝐭𝐢𝐨𝐧 - When your API returns large datasets (e.g., list of users, products, or orders), sending everything at once is slow and memory-heavy. Pagination splits results into smaller chunks (pages). Example: • 𝙶𝙴𝚃 /𝚞𝚜𝚎𝚛𝚜?𝚙𝚊𝚐𝚎=𝟸&𝚕𝚒𝚖𝚒𝚝=𝟸𝟶 🛢️ 𝟐. 𝐂𝐚𝐜𝐡𝐢𝐧𝐠 - Frequently accessed data can be stored in a cache to speed up retrieval. Clients check the cache before querying the database, with data storage solutions like Redis offering faster access due to in-memory storage. 🗜️ 𝟑. 𝐏𝐚𝐲𝐥𝐨𝐚𝐝 𝐂𝐨𝐦𝐩𝐫𝐞𝐬𝐬𝐢𝐨𝐧 - Before sending a response, the server can compress the data using algorithms like GZIP, Brotli, etc. The client decompresses it automatically. Benefits: • Greatly reduces data transfer size (especially for JSON). • Improves speed over slow networks. • No change needed on client side — browsers or HTTP clients handle it. 🔗 𝟒. 𝐂𝐨𝐧𝐧𝐞𝐜𝐭𝐢𝐨𝐧 𝐏𝐨𝐨𝐥 - Each API request often needs to connect to a database or another service. Creating a new connection each time is expensive — it takes time and system resources. - Connection pooling reuses existing open connections instead of creating new ones for every request. Benefits: • Reduces connection overhead. • Reduces latency and improves throughput. • Prevents “too many connections” errors under load. ⚡ 𝟓. 𝐀𝐬𝐲𝐧𝐜𝐡𝐫𝐨𝐧𝐨𝐮𝐬 𝐋𝐨𝐠𝐠𝐢𝐧𝐠 - Normally, when your API handles a request, it might write logs (e.g., to a file or database). If this logging happens synchronously, the API waits for the log operation to finish before sending the response — which slows down performance. - Asynchronous logging makes the logging run in the background, so the request can finish faster. - This approach involves sending logs to a lock-free buffer and returning immediately, rather than dealing with the disk on every call. Logs are periodically flushed to the disk, significantly reducing I/O overhead. 👉 Over to you: 𝑾𝒉𝒂𝒕 𝒐𝒕𝒉𝒆𝒓 𝒘𝒂𝒚𝒔 𝒅𝒐 𝒚𝒐𝒖 𝒖𝒔𝒆 𝒕𝒐 𝒊𝒎𝒑𝒓𝒐𝒗𝒆 𝑨𝑷𝑰 𝒑𝒆𝒓𝒇𝒐𝒓𝒎𝒂𝒏𝒄𝒆? (Image Credit: - ByteByteGo)

New Feature Drop: GraphQL Mock Server For months, the need for a "GraphQL Mock Server" was the single most requested feature from our clients. The core issue: generic mocks can't keep pace with complex GraphQL schema and scenarios, leading to fragile client tests and slowing development. Our team took that feedback and engineered a solution for true parallel development - GraphQL Mock Servers, now one of the core features of our platform. What's inside: • Intelligent, Reliable Data: No more static JSON. AI-powered mocking analyzes your schema to generate realistic, contextual data that strictly honors Non-Null and List constraints. • Instant Contract-First Development: Simply upload your SDL or use live introspection to immediately provision a hosted, working endpoint. • Full Testing Coverage: Test every scenario, from complex data fetching (Queries), state changes (Mutations), real-time streams (Subscriptions), and efficient network loading (Batch Queries). • Resilience Testing, on Demand: Use the powerful Rules Engine to proactively inject chaos. Force specific GraphQL errors, or HTTP status codes (403/500), or inject latencies to ensure that your client is robust. If you or your teams are currently blocked by an unfinished GraphQL backend, this feature is specifically engineered to solve that bottleneck.

16 API Terms You Must Know → Resource: The fundamental concept in REST, representing data or service. → Request: A call made to a server to access a resource. → Response: The data sent back from the server to the client. → Response Code: Indicates the status of a HTTP request, like 404 not found. → Payload: Data sent within a request or response. → Pagination: The process of dividing response data into discrete pages. → Method: The HTTP actions such as GET, POST, PUT, DELETE. → Query Parameters: Data appended to the URL to refine searches. → Authentication: The verification of a user's identity. → Rate Limiting: Restricting the number of requests a user can make. → API Integration: Connecting various services using APIs. → API Gateway: A service that provides a single entry point for APIs. → API Lifecycle: The phases of API development and retirement. → CRUD: An acronym for create, read, update, delete. → Cache: Temporary storage to speed up data retrieval. → Client: The device or program that requests data from a server. What API term surprised you the most? #backenddevelopment #softwaredevelopment #api

Why Your API is Slow (And It's Probably Not the Database) Performance bottlenecks in APIs rarely stem from database limitations. More often, they result from architectural decisions that accumulate over time. This breakdown covers the 7 most common culprits: → N+1 query problems (101 queries where 1 would suffice) → Synchronous blocking calls to external services → Missing response caching layers → Inefficient serialization overhead → Poor connection pool management → Chatty API design patterns → Missing database indexes on critical columns The fix isn't always more infrastructure. It's smarter implementation. Before scaling horizontally or blaming your database, profile your actual bottlenecks. Use APM tools. Measure response times. Identify where latency compounds. Performance optimization is about knowing where to look, not guessing what might help. What's the most unexpected performance bottleneck you've discovered in production? #SoftwareDevelopment #APIDesign #BackendEngineering #PerformanceOptimization #WebDevelopment #TechLeadership #SoftwareEngineering #DeveloperTools

"Just make it faster." Every developer has heard this. Few clients understand what it actually means. Performance isn't one problem - it's a dozen different problems disguised as one symptom. Slow page load could be: → Unoptimised database queries pulling 10,000 rows when you need 10 → Missing indexes on tables with millions of records → External API calls blocking the main thread → Images loaded at 4MB when 400KB would do → Cache invalidation happening too frequently (or not at all) → Background jobs competing for resources → Memory leaks in long-running processes The real issue? Most businesses don't know which one they have. They just know their team is complaining and customers are leaving. This is why performance audits exist. You can't optimise what you haven't measured. I recently worked on a system where the client was convinced they needed to rebuild everything. Three days of profiling revealed two database queries responsible for 80% of the slowdown. Fixed those, added strategic caching, and response times dropped from 8 seconds to under 500ms. Total cost: fraction of a rebuild. Before you throw resources at performance problems: Measure where the time is actually spent Profile production workloads, not local development Fix the biggest bottleneck first (not the easiest one) Test the impact before moving to the next issue Performance isn't about making everything fast. It's about making the right things fast enough. #SoftwarePerformance #WebDevelopment #TechDebt #Laravel #BackendDevelopment #EngineeringExcellence

🚀 Built a clean and minimal REST API to sharpen my backend fundamentals! Kept it lightweight with a simple in-memory array as the data store — perfect for getting hands-on with real CRUD workflows before bringing in a full database. Tech I leveraged: ✨ Express.js for routing ✨ body-parser for handling request bodies ✨ nodemon for auto-reload ✨ Postman for API testing Endpoints implemented: ✔ GET – fetch users ✔ POST – add users ✔ PUT – update users ✔ DELETE – remove users This mini-build helped me double-down on: 👉 How routes actually work under the hood 👉 The difference between req.body, req.query, and req.params 👉 Real PUT/DELETE behavior during testing 👉 Why middleware order matters Keeping it simple, scalable, and future-ready. Database integration coming soon. 💪🔥

The Hidden Pain of Backward Compatibility. Sometimes we get excited to rewrite old features — and for good reason: - Legacy code is often difficult to maintain. - It can be complex to extend or adapt for new features. - Old database structures might contain redundant tables or columns. - A clean refactor feels like a fresh start — simpler, faster, and more readable. You refactor the code beautifully. You simplify tables, remove unused columns, and make the system leaner. Everything looks perfect — until just before deployment you realize… A new column needs to be added, and existing data (millions of rows!) must be migrated to fit the new structure. That’s when the real complexity begins — timing releases, data migrations, and maintaining backward compatibility (BC). After deployment, maybe a small condition that seemed “unnecessary” turns out to be critical — breaking parts of the system. As developers, we must remember: - Always estimate backward compatibility first. - Always read and understand legacy code deeply before refactoring. To those maintaining BC day and night — you have my full respect. It’s a hidden pain, but it keeps systems stable and businesses running smoothly.

Understanding JSON Schema Tired of unpredictable data causing havoc in your APIs and integrations? 😫 Imagine a world where every piece of JSON data flowing through your systems is perfectly structured and validated. That's the power of **JSON Schema**. It's not just a nice-to-have; it's absolutely crucial for validating the structure and types of your JSON documents. JSON Schema ensures unparalleled data consistency and reliability across all your different systems. This is an absolute game-changer for designing robust APIs, streamlining data exchange, and significantly reducing debugging time. Think of it as a blueprint or a contract for your data. It defines what your JSON *should* look like, preventing unexpected types, missing fields, or incorrect formats from ever making it into your applications. This means fewer errors, stronger integrations, and more predictable software behavior. How are you currently ensuring data integrity in your projects? Have you leveraged JSON Schema, or are you exploring other solutions? Share your experiences below! 👇 #JSONSchema #APIDesign #DataValidation #SoftwareDevelopment #TechTips #DataConsistency #WebDevelopment

Alerting is the backbone of observability. It tells you when to act, not just what is wrong. This guide walks through the full alerting workflow in OpenObserve, including how to define trigger logic, format payloads, and route notifications for both real-time and scheduled alerts. Perfect for teams who want actionable alerts instead of noise. https://lnkd.in/gNSJgWy2 #Observability #Alerting #Monitoring #DevOps #SRE #OpenObserve