Caching vs Database in Backend Systems

🚀 Backend Learning | Caching Patterns for High-Performance Systems While working on backend systems, I recently explored different caching strategies used to improve performance and scalability. 🔹 The Problem: • Frequent database hits increasing latency • High load under traffic • Need for faster response times 🔹 What I Learned: • Cache Aside (Lazy Loading): Load data into cache on demand • Write Through: Write to cache and DB simultaneously • Write Back (Write Behind): Write to cache first, DB updated later 🔹 Key Insights: • Cache Aside → Simple & widely used • Write Through → Strong consistency • Write Back → High performance but complex 🔹 Outcome: • Reduced database load • Faster API responses • Better system performance Caching is not just about storing data — it’s about choosing the right strategy. 🚀 #Java #SpringBoot #Redis #SystemDesign #BackendDevelopment #Caching #LearningInPublic

MongoDB Atlas offers a powerful document model, enabling you to store data as JSON-like objects that closely resemble your application code. Read more 👉 https://lttr.ai/Ap3jo #Java #NoSQL #MongoDB

Hi. My work is primarily around big data processing using spark and the azure ecosystem. I recently got interested in API design and high scale systems that cater to multiple users at once. In order to understand the space better I designed and implemented a high concurrency ticket booking system. The backend relies on python, FastAPI and PostgreSQL. A redis cache is implemented to offload the ticket booking system from the database. Redis being single threaded and in-memory ensures no double booking happens for the same event and handles race conditions gracefully while delivering sub 15ms response times. The user doesn't overload the database as all the requests first hit the redis cache. The ticket counts for the events are periodically reconciled between the cache and the persistent database. The system was load tested using the locust framework. It handles 500 requests per second. This was my first time working on an API development. It was really fun seeing it work. Feel free to have a look at the repo - https://lnkd.in/g_77-hQf

These fields showcase the flexibility of MongoDB’s schema design, allowing us to group related information and co-locate it efficiently. Read more 👉 https://lttr.ai/ApxSZ #Java #MongoDB

🚨 Your Spring Boot App Crashed… Not Because of Traffic — But Memory Everything was working fine. Until one API call: 📥 Fetch 5 million records from MongoDB 💥 Boom — OutOfMemoryError Most developers unknowingly do this: 👉 repository.findAll() 👉 Load everything into a List 👉 Process it in memory Sounds harmless… until your heap says goodbye 👋 🧠 Enter: MongoDB Cursor (Your Memory Saver) Instead of loading everything at once, MongoDB gives you a cursor — a stream-like way to read data in chunks. Think of it like: 🚰 Not a bucket (load all data) 💧 But a tap (consume gradually) ⚙️ How it helps in Spring Boot Using cursors, you: ✅ Fetch data in batches ✅ Process records one-by-one or in chunks ✅ Avoid loading entire dataset into heap ✅ Prevent OutOfMemoryError ✅ Improve performance for large datasets ⚠️ Important Gotchas ❗ Always close the stream (use try-with-resources) ❗ Keep cursor timeout in mind for long operations ❗ Avoid heavy processing inside stream without batching ❗ Use pagination if random access is needed 🔥 When should you use cursors? ✔️ Large datasets (lakhs/millions of records) ✔️ Batch processing / ETL jobs ✔️ Data migration scripts ✔️ Analytics pipelines 💡 Rule of Thumb If your query result can’t comfortably fit in memory, 👉 Don’t collect — Stream it. Most production outages aren’t caused by complexity… They’re caused by loading too much, too fast, into memory. Mongo cursors fix exactly that. #SpringBoot #MongoDB #BackendEngineering #Java #Performance #Scalability #Microservices

Race Conditions in Backend Systems:- A simple order service where users can place orders and inventory gets updated. Problem I faced :- Everything worked fine in testing. But in production, something weird started happening: Same product got sold more times than available Inventory went negative Duplicate updates started appearing No errors. No exceptions. Just wrong data. How I fixed it:- The issue was a race condition. Multiple requests were updating the same data at the same time. Here’s what helped: Added database-level locking for critical updates Used optimistic locking with version fields Introduced idempotency checks for repeated requests For high contention cases, used Redis distributed locks After that, updates became consistent again. What I learned: Concurrency issues don’t break loudly. They silently corrupt your data. And by the time you notice, it’s already too late. Question? Have you ever faced a bug where everything looked fine in logs… but the data was completely wrong? #Java #SpringBoot #Programming #SoftwareDevelopment #Cloud #AI #Coding #Learning #Tech #Technology #WebDevelopment #Microservices #API #Database #SpringFramework #Hibernate #MySQL #BackendDevelopment #CareerGrowth #ProfessionalDevelopment #RDBMS #PostgreSQL #backend

Distributed Systems are easy. Until they aren't. My biggest realization after 3 years of working with Java backends is that you don’t fight the algorithm; you fight the network. Everyone talks about building "highly available" and "perfectly consistent" applications, but we must face reality. The CAP Theorem dictates how we choose our infrastructure when a "Network Partition" (a failure in the network between nodes) occurs. The truth is that Partition Tolerance (P) is NOT OPTIONAL in a modern distributed system. Networks will fail. Prioritize Consistency (C): Choose accuracy. The system will go offline to reads/writes rather than risk returning inaccurate data. (Result: A CP System like HBase or MongoDB). Uptime King is temporarily dethroned. When that happens, you are forced to make The Crucial Trade-off: Prioritize Consistency (C): Choose accuracy. The system will go offline to reads/writes rather than risk returning inaccurate data. (Result: A CP System like HBase or MongoDB). Uptime King is temporarily dethroned. prioritize availability (A): Choose responsiveness. The system will always respond, even if the data it returns is slightly stale (it hasn’t replicated across the partition yet). This is the philosophy behind databases like Cassandra. (Result: An AP System like Cassandra or DynamoDB). Accuracy King is temporarily dethroned. Understanding that you must choose between Strong Consistency or High Availability the moment P occurs changed how I approach database selection. There is no perfect "everything-database"; there is only the best trade-off for your specific business logic. Are you building an AP system (Uptime King) or a CP system (Data King)? Tell me why in the comments. 👇 #SystemDesign #DistributedSystems #CAPTheorem #DatabaseArchitecture #SoftwareEngineering #Java #Cassandra #BigData #NoSQL

Thundering Herd Problem (When Everything Breaks at Once):- A caching layer to reduce database load for frequently accessed data. --- Problem I faced: Everything worked well… until cache expired. Suddenly: Huge spike in database queries CPU usage shot up API latency increased System became unstable All at the same moment. --- How I fixed it:- This was the Thundering Herd Problem. When cache expired, multiple requests tried to fetch fresh data simultaneously. Fixes applied: Added cache locking (single-flight) so only one request refreshes data Introduced randomized cache expiry (TTL jitter) to avoid simultaneous expiration Used stale-while-revalidate approach for smoother refresh Now: Only one request hits DB Others wait or get cached response System stays stable. --- What I learned:-- Caching reduces load… but poorly managed caching can create bigger spikes than no cache at all. --- Question? Have you ever seen your system fail not because of traffic… but because many requests did the same thing at the same time? #Java #SpringBoot #Programming #SoftwareDevelopment #Cloud #AI #Coding #Learning #Tech #Technology #WebDevelopment #Microservices #API #Database #SpringFramework #Hibernate #MySQL #BackendDevelopment #CareerGrowth #ProfessionalDevelopment #RDBMS #PostgreSQL #backend

Requirement: FIAS Data Sync Middleware Apply for this new project https://lnkd.in/dxYbZEpZ I need a compact middleware script—your choice of Python or Node.js—that pulls JSON payloads from a REST endpoint hosted on AWS and forwards them, in near real-time, to a local device that only understands the FIAS protocol over raw TCP/IP sockets. The flow is straightforward: authenticate to the AWS API, poll or subscribe for new records, validate and, when necessary, transform the JSON, store or stage it in the local PostgreSQL instance, then push each record down the wire using FIAS commands so the on-prem device stays perfectly in sync with the cloud source. Robust logging, reconnection logic, and graceful error handling are essential because the local connection can be unreliable. Configuration items such as AWS credentials, polling interval, socket host/port, and retry limits should live in a separate file or environment variables for quick tweaking without code edits. Deliverables • Clean, well-commented source code (Python or Node.js) • Sample config file with placeholders for secrets • Setup instructions and one-command launch script (systemd service file is a plus) • Short README that documents the data flow, FIAS message structure you implemented, and how to extend the field mapping • Proof-of-concept run that shows data fetched from AWS, written into PostgreSQL, and echoed by the local device via FIAS If you have prior experience with socket programming, FIAS, or AWS SDKs, let me know—otherwise I’m happy to share sample payloads and the device’s FIAS spec so you can start right away. right away. Skills Required Python NoSQL Couch & Mongo Amazon Web Services Node.js PostgreSQL JSON API Development REST API Mobisium → mkt@mobisium.com pratham.parab@mobisium.com Let’s build something impactful together at MOBISIUM #Hiring #BackendDevelopment #AWS #APIDevelopment #Python #NodeJS #PostgreSQL #SocketProgramming #SystemIntegration #Mobisium

Continuing my learning journey in Full Stack Development, I’ve been diving deeper into MongoDB—a powerful NoSQL database that plays a key role in the MERN stack. MongoDB stands out for its flexibility and scalability, allowing developers to store data in JSON-like documents. This makes it easier to handle dynamic data structures and build modern, high-performance applications. Some key takeaways from my learning: • Schema-less design for flexibility • High scalability and performance • Seamless integration with Node.js and Express • Efficient handling of large volumes of data Working with MongoDB has helped me better understand backend development and data management in real-world applications. Excited to continue exploring more in the world of databases and backend technologies! #MongoDB #Database #MERNStack #BackendDevelopment #WebDevelopment #LearningJourney