Built a simple REST API with Express.js and learned CRUD workflows

In BACKEND DEVELOPMENT we talk about API, REST API, QUEUE, SYSTEM DESING, DATA BASES and many more things which is all about DATA or holds some kind of data or information.

𝙒𝙝𝙮 𝙈𝙤𝙨𝙩 “𝙁𝙖𝙨𝙩” 𝙎𝙮𝙨𝙩𝙚𝙢𝙨 𝘽𝙚𝙘𝙤𝙢𝙚 𝙎𝙡𝙤𝙬 𝙞𝙣 𝙋𝙧𝙤𝙙𝙪𝙘𝙩𝙞𝙤𝙣 Every developer has had that moment, the app runs perfectly in dev, but once it hits production, it starts crawling. Here’s what usually goes wrong 👇🏽 1️⃣ Your local setup hides latency. When everything runs on localhost, network latency is near zero. But in production, every API call, DB connection, or external service adds milliseconds and they add up. 2️⃣ Too many synchronous dependencies. Your backend waits for everything to finish in sequence, external APIs, file uploads, email services, etc. The fix? Offload heavy or slow tasks to background jobs. Use queues like RabbitMQ, Kafka, or BullMQ (for Node.js). 3️⃣ Chatty services. Microservices make sense until they start sending too many small requests to each other. Batch them, use caching, or merge endpoints to cut unnecessary round trips. 4️⃣ Logging in the wrong way. Developers love logs until they realize console.log can block event loops or flood I/O. Use async loggers or structured logging tools like Winston, Pino, or Serilog. 5️⃣ Database queries. That innocent .findAll() with no limit? It can destroy performance when the table hits millions of rows. Always paginate. Always index. Always measure query plans.

🚀 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)

🧱𝐁𝐮𝐢𝐥𝐭 𝐚 𝐅𝐮𝐥𝐥-𝐒𝐭𝐚𝐜𝐤 𝐓𝐫𝐚𝐯𝐞𝐥 & 𝐀𝐜𝐜𝐨𝐦𝐦𝐨𝐝𝐚𝐭𝐢𝐨𝐧 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦 — “𝐁𝐧𝐛𝐥𝐢𝐬𝐬” Recently completed a full-stack project built using MongoDB, Express, Node.js, and EJS — a travel and accommodation platform where users can upload and explore stays. I didn’t just follow tutorials. I first understood how each part works — routing, sessions, middleware, and data flow — and then implemented everything step by step. Here’s what I built and handled myself: 🧭 Search functionality with live suggestion dropdowns 🗂️ Filtering listings based on categories 🗺️ Map integration using Leaflet.js for location visualization 📸 Image upload & storage with Cloudinary + Multer 🔐 Authentication and session management using Passport.js 🌐 Deployment on Render Through this project, I got hands-on with: RESTful routing MVC structure Express middleware and error handling Working with environment variables Connecting backend logic to UI templates Handling real-world issues like validation, flash messages, and async errors You can check the deployed version here 👇 🔗 https://lnkd.in/gcDXXd8a And the codebase here 👇 💻 https://lnkd.in/gWyyzJ2h

API was slow. Everyone blamed .NET.” Turns out, .NET wasn’t the villain. The database was. A single missing index was turning a 300ms query into a 4-second bottleneck. One line fixed it all: CREATE INDEX IX_UserId ON Orders(UserId); Lesson learned? Before you optimize code — optimize what your code talks to. Backend issues often live one layer below where we’re looking.

If there's one thing I've learned about performance issues over 25 years, it is that the performance bottleneck is always somewhere else than I assume. 🤭👌 Very often go for bugs too.

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

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

🔧 Technical Implementation Update — Excel Mapping API using Apache POI Recently, I worked on an interesting backend task — creating an API to generate and map data into an editable Excel file, allowing users in a banking application to directly download all entered details from the UI. Here’s how I approached it 👇 1️⃣ Integrated Apache POI dependencies • Added poi-ooxml to handle Excel (.xlsx) operations efficiently. • Used XSSFWorkbook, XSSFSheet, and CellStyle for structured data representation and styling. 2️⃣ Implemented cell formatting and bordered styles • Applied custom CellStyle to differentiate mapped fields and headers. • Ensured consistent formatting for readability and compliance with financial data standards. 3️⃣ Mapped dynamic data into Excel • Retrieved mapped details from service layer responses. • Created rows and cells dynamically using POI APIs for each data record. 4️⃣ Generated Base64-encoded response • Converted the generated Excel workbook into a byte array stream. • Encoded it as a Base64 string and wrapped it in the API response with file type metadata. 5️⃣ Enabled seamless UI download • The frontend decodes the Base64 content and triggers direct file download — giving users an editable Excel file with all mapped details. 📈 Impact: This approach provides a reusable, secure, and efficient way to export structured data from backend to UI. Especially in the banking domain, where accuracy, traceability, and format consistency are crucial, this implementation simplifies customer data handling. #Java #SpringBoot #ApachePOI #BackendDevelopment #APIDesign #Microservices #ExcelExport #BankingTechnology #DeveloperExperience #CodingJourney