Offset vs Cursor-Based Pagination Strategies for Large Datasets

“Your code works… but would it survive 100,000 users?” That’s the question most developers ignore. Until it’s too late. After getting comfortable with Arrays / Lists, one thing became very clear… 👉 In real-world systems, efficiency is NOT optional — it’s survival. Let’s break it down 👇 Imagine you’re building: a booking platform or an e-commerce system At the start: ✅ 100 users → everything feels fast But then growth hits: • 10,000 users • 100,000 users • Millions of requests per day Now your “working code” faces reality: ⚡ Either it performs smoothly or 🐌 It becomes the bottleneck that breaks everything This is where Time Complexity (Big-O) quietly decides your fate. • O(1) → Instant (best case for lookups, caching) • O(log n) → Scales beautifully (search, indexing) • O(n) → Works… until it doesn’t 💥 Real-world truth: That innocent “loop through all records” can turn into a production nightmare. 🧠 What we can do differently: • Eliminate unnecessary loops • Choose the right data structures • Think about scale before it breaks 💡 Final takeaway: Good code runs. Great code scales. And understanding Time Complexity is where that shift begins. What’s one performance mistake you’ve made (or learned from)? 👇 #DSA #SystemDesign #Scalability #Programming #Python #WebDevelopment

🚨 Two days. Two very different lessons — one in algorithms, one in real backend building. Day 38 & 39 of my Backend Developer Journey — and this felt like 👉 DSA + System Design coming together 🧠 LeetCode Breakthrough (Day 38) Worked on a Dynamic Programming heavy problem 💡 What clicked: → Think in terms of states & transitions → Use prefix sums for optimization → Track multiple states to maximize score ⚡ The Real Trick 👉 Not just DP… 👉 DP + Prefix Sum + State Optimization 🔍 Key Insight 👉 Break problem into smaller states 👉 Store intermediate results smartly 👉 Optimize transitions instead of brute forcing ⚡ From exponential → efficient DP solution Link:https://lnkd.in/gcCqeq2j 🧠 LeetCode Learning (Day 39) Solved another problem using 3D DP (Grid + Constraints) 💡 What clicked: → Add one more dimension (k constraint) → Track valid paths carefully → Handle impossible states properly 🔥 Key Insight 👉 DP is not about memorizing patterns 👉 It’s about modeling the problem correctly Link:https://lnkd.in/guZ8ryvS ☕ Spring Boot Progress (Major Project 🚀) 🏗️ Project Development — Lovable AI Clone These 2 days were HUGE for backend progress 👇 👉 Created all core entities 👉 Designed Project & User tables 👉 Established relationships 👉 Connected everything with PostgreSQL DB 🔌 Database Integration 👉 Configured DB using login credentials 👉 Tables successfully created 👉 Backend ↔ Database connection working ⚡ Big Realization 👉 Writing entities is just step 1 👉 Designing them correctly = real backend skill 🧠 The Shift 👉 DSA builds problem-solving 👉 Backend builds real-world systems 👉 Growth = combining both consistently 🔗 GitHub Repo (Lovable Clone): https://lnkd.in/gwHmAZaK 📈 Day 38 & 39 Progress: ✅ Learned advanced DP patterns ✅ Handled multi-dimensional DP ✅ Built complete entity layer for project ✅ Connected backend with PostgreSQL 💬 What was harder for you — Dynamic Programming or Database Design? 👇 #100DaysOfCode #BackendDevelopment #SpringBoot #Java #LeetCode #DynamicProgramming #SystemDesign #CodingJourney

💡 You Don’t Need to Learn Everything in Programming to Get Started Here’s something more people need to hear: You don’t have to master every concept, framework, or language to break into tech. In fact, a huge amount of real-world software — from companies like Google, Microsoft, IBM, to Indeed — relies heavily on just a few core data structures: 👉 Dictionaries (key-value pairs) 👉 Arrays / Lists 👉 Nested data structures 👉 JSON (basically structured dictionaries) That’s it. 🧠 Why This Matters Most backend systems, APIs, and databases are just: Receiving JSON Processing dictionaries/lists Sending JSON back If you understand how to work with nested data, you already understand a huge portion of backend engineering. 🐍 Python Example (Real-World Style) user = { "name": "John", "email": "jdoe@email.com", "roles": ["admin", "editor"], "profile": { "age": 30, "location": "USA" } } # Accessing data print(user["profile"]["location"]) # Modifying data user["roles"].append("viewer") # Converting to JSON import json json_data = json.dumps(user) ⚙️ The Reality You don’t need to: Memorize every algorithm Learn 10 languages at once Know every framework You do need to: Understand how data is structured Know how to read & manipulate it Be comfortable with JSON and nested objects Industry Truth Even at scale: APIs = JSON Microservices = JSON Cloud systems = JSON Whether it’s Python, JavaScript, or even systems at Google (yes, they use multiple languages including Python), the data layer looks very similar everywhere. 🚀 Final Thought If you're overwhelmed learning to code, simplify: 👉 Master dictionaries + lists + JSON 👉 Practice transforming data 👉 Build small API-style projects That foundation alone can take you much further than you think. 💬 Keep it simple. Learn what’s actually used. #Python #Programming #BackendDevelopment #TechCareers #LearnToCode #SoftwareEngineering

🚀 Starting DSA? Don’t skip this foundation. Before solving problems… Before jumping into LeetCode… 👉 You need to understand how data is stored. That’s where Data Structures come in. --- 🧠 Think of it like organizing your room: - Books on a shelf 📚 - Clothes in a wardrobe 👕 - Files in folders 📂 When everything is organized… 👉 You find things faster. 👉 You work more efficiently. Same in programming. --- 💡 What is a Data Structure? It’s a way to store and organize data so you can: - Access it quickly ⚡ - Modify it easily 🔧 - Build scalable apps 🚀 --- 🌍 Why this matters in real projects: - In React → managing lists & state - In APIs → structuring JSON responses - In databases → organizing records 👉 Better structure = better performance + clean code --- 🔥 Key Takeaway: Don’t just write code that works… 👉 Write code that handles data smartly. --- 💬 Question for you: Where have you used arrays/lists in your projects without realizing it's a data structure? --- #DSA #Python #SoftwareEngineering #Coding #LearnInPublic

🚀 #Day30/300 — Mastering DSA Challenge Continuing my 300-day journey to strengthen my Data Structures & Algorithms skills using Java. 📌 Daily Goals • Solve at least 1 problem every day • Focus on pattern recognition and optimized solutions • Share consistent learning updates along the journey 🧠 Today’s Problem: Find Median from Data Stream Solved this problem using the Two Heaps approach (Max Heap + Min Heap). Maintained one heap for the smaller half of elements and another for the larger half to efficiently calculate the median at any point. 💡 Key Takeaways: • Use Max Heap for the left half and Min Heap for the right half • Balance both heaps to maintain size difference ≤ 1 • Median can be retrieved in O(1) time • Time Complexity per insertion: O(log n) This problem strengthened my understanding of heap balancing, stream processing, and real-time median calculation. Staying consistent and improving every day in this 300-day DSA journey. 💪 #DSA #Java #ProblemSolving #BackendDeveloper #FullStackDeveloper #300DaysChallenge #LearningInPublic #Heap #PriorityQueue #Algorithms #SDE #React #ReactNative #JavaFullStack #SpringBoot

Excited to share something I've been heads-down building over the past 3 months — CoderX, a competitive programming platform that automates the two hardest parts of running one: creating problems and judging submissions. No manual test cases. No human judges. The platform runs itself. Here's how it works under the hood: The system is split into 6 microservices communicating over REST, Redis queues, and WebSockets. — An AI service (FastAPI + LangChain + LLaMA-3) generates full problem sets autonomously: title, description, test cases, and language stubs. Before saving, it runs semantic deduplication via AstraDB vector search to keep the problem set diverse. — A submission service picks up your code, records the attempt, and enqueues it via BullMQ for processing. — An evaluator service pulls the job, spins up an isolated Docker container (Python, Java, or C++), and runs your code against all test cases with hard resource limits — 100MB RAM, 2 second timeout. Results are written back to the database as Accepted, Wrong Answer, or TLE. — A WebSocket service streams the verdict to your browser the moment execution completes. No polling, no page refresh. — The frontend uses a protected triple-editor where boilerplate is locked and only your logic section is editable — keeping submissions clean and consistent across all languages. Building this taught me a lot about distributed system design, async job orchestration, and secure code execution. Still shipping features, but the core loop is solid end to end. Github Link : https://lnkd.in/gSvuwrjV Open to feedback from anyone who's built something similar. #SystemDesign #Microservices #BuildInPublic #CompetitiveProgramming #SoftwareEngineering

I’m starting to realize backend development is a lot more than just creating endpoints. This week, I went deeper into backend development with FastAPI and started connecting the pieces behind how APIs actually work. Beyond just building endpoints, I began understanding how APIs communicate through HTTP status codes and how data is managed behind the scenes. One thing that stood out to me: Understanding status codes makes debugging much easier. Instead of guessing what went wrong, you can quickly narrow down whether the issue is coming from the client or the server. I also started exploring the data side of backend systems, how databases store information and how SQL is used to perform operations like creating, reading, updating, and deleting data. Step by step, I’m starting to see how APIs, databases, and backend logic all connect to form real backend systems. Still early in the learning process, but it’s exciting to see the bigger picture becoming clearer. Screenshots: Image 1: FastAPI endpoint tested using Swagger UI, demonstrating query parameter filtering (book_rating) and the JSON response returned by the API. #BackendDevelopment #FastAPI #Python #SoftwareEngineering #LearningInPublic #APIs #WebDevelopment #RESTAPI #BackendEngineer #CodingJourney

🚨 I keep seeing this everywhere… “Just use async, your performance will improve.” No. If it were that simple, every app would be fast. ✍️ Let me say this clearly: Async ≠ Multithreading And if you use them in the wrong place… your performance will actually DROP. 🧵 What does Multithreading do? → Multiple threads (same process) → Shared memory → Constant context switching 👉 Best for: File I/O API calls Database queries BUT… In Python, the GIL quietly blocks true parallel CPU execution. 👉 Meaning: “10 threads ≠ 10x speed” ⚡ What does Async do? → Single thread → Event loop → Non-blocking execution 👉 While one task is waiting… another starts immediately. No waiting. No extra threads. Just efficient flow. 💥 The biggest mistake: Most developers think — “Async is faster, so use it everywhere” ❌ Wrong. 🧠 Real understanding: 👉 If your task involves WAITING (API, DB, network) → Async 🔥 👉 If you're stuck with blocking libraries → Multithreading 👍 👉 If it's CPU-heavy work → Neither. Use multiprocessing. ⚡ Simple analogy (you’ll remember this): Multithreading = 5 workers sharing one stove 🔥 Async = 1 smart worker who never stays idle ⏱️ 💀 Reality check: Most developers: → Use async without understanding → Use threads without need → Then say “Python is slow” 🔥 Final takeaway: Choosing the right concurrency model is the real skill. Writing code is the easy part. 💬 Be honest — Have you ever used async just because it’s “trending”? #Python #AsyncIO #Multithreading #BackendDevelopment #SystemDesign #Developers

I still remember the first time I heard about LRU Cache… It sounded simple — “remove least recently used item” 🤔 But when I actually tried to build it… That’s when the real learning started. 🧩 The Problem Design a system where: get(key) → return value if exists put(key, value) → insert/update If capacity is full → remove least recently used And the twist? ⚡ Everything must run in O(1) time. 💡 My Thought Process At first, I thought: 👉 “Let’s just use an array or list…” But then reality hit: Searching = O(n) ❌ Updating order = costly ❌ So I asked myself: 👉 What combination gives both fast access AND fast updates? 🚀 The Breakthrough I used: HashMap → for O(1) access Doubly Linked List → for O(1) insertion & deletion 🧠 How It Works Most recently used → stays near head Least recently used → near tail 🔹 On get(key) If not found → -1 If found: Remove node from its position Move it to head (recently used) 🔹 On put(key, value) If key exists: Update value Move to head If new key: If capacity full: Remove node before tail (LRU) Insert new node at head ⚡ Complexity 💡 Time Complexity (TC): get() → O(1) put() → O(1) 💡 Space Complexity (SC): HashMap + Linked List → O(capacity) 🌱 What This Problem Taught Me This wasn’t just about coding… It taught me: 👉 The real power comes from combining data structures 👉 Clean design > brute force 👉 Thinking in systems is what separates good from great engineers Coming from a UI/UX background, building something this low-level felt like a big win for me 🚀 And honestly… this is what makes the journey exciting. If you're also learning DSA or system design fundamentals, keep going — these concepts show up everywhere in real-world systems. Let’s grow together 🤝 #LRUCache #SystemDesign #DataStructures #Java #CodingJourney #SoftwareEngineering #TechLearning

🚀 #Day31/300 — Mastering DSA Challenge Continuing my 300-day journey to strengthen my Data Structures & Algorithms skills using Java. 📌 Daily Goals • Solve at least 1 problem every day • Focus on pattern recognition and optimized solutions • Share consistent learning updates along the journey 🧠 Today’s Problem: Smallest Range in K Lists Solved this problem using a Min Heap (Priority Queue) approach along with tracking the current maximum element. The idea is to always consider one element from each list and minimize the range between the minimum and maximum values. 💡 Key Takeaways: • Use Min Heap to track the smallest element among K lists • Keep updating the current maximum to calculate range • Helps in solving multi-pointer problems efficiently • Time Complexity: O(n log k) This problem strengthened my understanding of heap-based merging, range optimization, and handling multiple sorted lists. Staying consistent and improving every day in this 300-day DSA journey. 💪 #DSA #Java #ProblemSolving #BackendDeveloper #FullStackDeveloper #300DaysChallenge #LearningInPublic #Heap #PriorityQueue #Algorithms #SDE #React #ReactNative #JavaFullStack #SpringBoot