How Redis and Smart Diffing Improved Our System Performance

🧠 The Essence of Performance — Beyond Databases “Performance isn’t about one technology — it’s about where you put the bottleneck, and how you control it.” Over the past few days, I revisited a deceptively simple database problem — a “reservation system” that caps bookings at 3 slots. But what looked like a database question actually opened a deeper topic: the essence of performance and concurrency. Here are my core guiding principles when designing high-performance systems 👇 ⚙️ 1. Database is not your concurrency layer If your logic depends on SELECT … FOR UPDATE, you’ve already lost scalability. The DB should guarantee durability, not throughput. Concurrency must move outward — to memory, queues, and distributed coordination. ⚡ 2. Performance is about ownership of serialization You can’t avoid serialization — you just decide where to put it: • Inside the DB (slow, global) • In Redis (fast, but local) • Or at the Actor / partition level (isolated, distributed) The last one — actor-based single writers — is how modern high-concurrency systems scale without losing consistency. 🧩 3. Design for read-parallelism and write-isolation Let writes be serialized and predictable. Let reads fan out, cached, eventually consistent. Performance = control of contention, not elimination of it. 🧠 4. Observe first, optimize second Before tuning anything, trace where serialization naturally occurs — CPU queues, Redis locks, Kafka partitions, DB commits. Only after observing contention flow can you place the right boundaries. 🚀 5. State is asynchronous by nature Every distributed system is a delayed reflection of state. Once you accept that, you can design with confidence — with message queues, cache snapshots, and idempotency instead of fear of “race conditions.” I’m currently building a teaching-grade distributed reservation system on GitHub (FastAPI + Kafka + Redis + PostgreSQL). It demonstrates exactly these ideas — from Redis atomic ops to actor-based message processing. Stay tuned. I’ll share the repo soon. 🔥 💬 What’s your view on where serialization should live — DB, cache, or message layer? Would love to hear from engineers who’ve fought real-world concurrency dragons. #SystemDesign #DistributedSystems #PerformanceEngineering #FastAPI #Kafka #Redis #PostgreSQL #Architecture #Concurrency #Scalability

𝐑𝐞𝐝𝐢𝐬: 𝐌𝐨𝐫𝐞 𝐓𝐡𝐚𝐧 𝐉𝐮𝐬𝐭 𝐚 𝐂𝐚𝐜𝐡𝐞 🚀 Ever clicked “Add to Cart” and had the site instantly remember your item? Or watched a live leaderboard update in real time? Chances are—you’ve already experienced the power of Redis. 𝐁𝐮𝐭 𝐰𝐡𝐚𝐭 𝐞𝐱𝐚𝐜𝐭𝐥𝐲 𝐢𝐬 𝐑𝐞𝐝𝐢𝐬? 🤔 At its core, Redis (REmote DIctionary Server) is an open-source, in-memory data structure store. Here’s why that matters: ⚡ In-Memory Speed: Unlike traditional databases that store on disk, Redis keeps data in RAM. That means microsecond response times for reads/writes. 🗂 Data Structures Beyond Key-Value: Strings, lists, sets, sorted sets, hashes—you can model complex problems natively. 🛡 Persistence for Safety: Snapshots and append-only files (AOF) ensure durability while maintaining speed. 🧵 Single-Threaded Simplicity: No lock management, atomic operations by design. 🌍 Versatile Use Cases: - Caching: Speed up apps by serving frequent queries. - Session Storage: Keep carts, sessions, and user state. - Real-time Analytics: Live leaderboards, comments, and pub/sub systems. - Message Broker: Lightweight, efficient queue management. 🔑 In a nutshell: Think of Redis as your app’s super-fast notepad—it sits between your app and your slower, permanent DB (PostgreSQL/MySQL), taking the load off while delivering a blazing-fast experience. 💡 Have you used Redis in a project? What’s your favorite use case? Drop it in the comments! 👇 #Redis #InMemoryDatabase #Caching #Database #Tech #SoftwareEngineering #Developer #WebDevelopment #Performance #OpenSource

Redis needs a great ORM in rust. Sprinkle procedural derive macros with a little bit of Prisma-flare and transform into Redis Query Engine indexes with optional relational fetches with configurable depth. Maybe Redis Sets for many-to-many join table abstractions and a repository builder pattern. Node has something like this called Redis-OM. It's a good start, but I'd love to see a compile time variant that goes wayyyy further. I'd love it to support multi tenancy and branching dbs (like Neon) ootb and eventually support migration between versioned entity schemas. Redis/Valkey as a primary database is a fascinating topic for me. Once you get used to the speed of in-memory dbs for distributed, real-time systems, it's hard to go back to sql. Redis came out in 2009 and was always meant to be a primary database. Most of us get introduced to Redis as a cache only system but that's barely the half of what it can do at scale. Have a great weekend... The road to redis 8: https://lnkd.in/gqgX_d2Q

“The #cache made it faster… until it #broke everything.” Every backend engineer loves caching. Redis, Memcached, CDN — they’re our secret performance weapons. ⚡ But one small mistake — and suddenly, your cache becomes your biggest enemy. Let’s talk about when caching hurts instead of helps. 👇 --- 🧠 1️⃣ Stale Data Syndrome Your cache doesn’t update when the source data changes. Users keep seeing outdated info. 💡 Fix: Use proper TTL (Time-to-Live) and cache invalidation strategies like Write-through or Write-behind. --- ⚙️ 2️⃣ Cache Stampede 1000 requests hit your backend at once because a popular key expired. Your DB collapses. 😬 💡 Fix: Add lock mechanisms or staggered expirations to prevent thundering herd effects. --- 🧩 3️⃣ Over-Caching You cached everything — even data that changes every few seconds. Now, your cache is just as big as your database… but less reliable. 💡 Fix: Cache only expensive, read-heavy queries. Not every response deserves caching. --- 🔥 4️⃣ Inconsistent Layers Frontend, backend, and database all cache separately — and none are in sync. Users get unpredictable results. 💡 Fix: Centralize critical cache logic or maintain cache coherence between layers. --- 🧭 5️⃣ No Cache Monitoring You have no idea what’s actually being cached or missed. Without observability, you’re flying blind. 💡 Fix: Track cache hit/miss ratios and eviction counts. Use Prometheus or Grafana dashboards for visibility. --- Caching is like caffeine ☕ — A little gives you speed, too much gives you chaos. Use it wisely. Because the only thing worse than no cache… is a broken one. 😅 ---- If you want to learn backend development through real-world project implementations, follow me or DM me — I’ll personally guide you. 🚀 ---- #Redis #Caching #Scalability #PerformanceEngineering #Microservices #BackendEngineering #BackendDevelopment #SystemDesign #LinkedIn #LinkedInLearning

𝐖𝐡𝐲 𝐂𝐡𝐨𝐨𝐬𝐢𝐧𝐠 𝐭𝐡𝐞 𝐑𝐢𝐠𝐡𝐭 𝐂𝐚𝐜𝐡𝐢𝐧𝐠 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝐂𝐚𝐧 𝐌𝐚𝐤𝐞 𝐨𝐫 𝐁𝐫𝐞𝐚𝐤 𝐘𝐨𝐮𝐫 𝐒𝐲𝐬𝐭𝐞𝐦 Caching is one of those things that seems simple - until it isn’t. Many teams rush to add Redis, in-memory caches, or CDN layers, thinking speed is the end goal. But the real challenge isn’t just caching - it’s caching smartly. Here’s what experience teaches you over time 👇 𝐅𝐨𝐫 𝐫𝐞𝐚𝐝-𝐡𝐞𝐚𝐯𝐲 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 – Cache aggressively, but focus on cache invalidation. Nothing hurts trust more than stale or inconsistent data. 𝐅𝐨𝐫 𝐰𝐫𝐢𝐭𝐞-𝐡𝐞𝐚𝐯𝐲 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 – Consider the cost of synchronization. Over-caching can create more bottlenecks than it solves. 𝐀𝐭 𝐭𝐡𝐞 𝐝𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐥𝐞𝐯𝐞𝐥 – Query caching helps, but only when query patterns are stable. Dynamic or ad-hoc queries can actually degrade performance. 𝐀𝐭 𝐭𝐡𝐞 𝐂𝐃𝐍 𝐨𝐫 𝐞𝐝𝐠𝐞 𝐥𝐞𝐯𝐞𝐥 – It’s fantastic for static or semi-static content, but remember that one wrong TTL can turn debugging into a nightmare. “Sometimes the fastest system isn’t the one that caches everything - it’s the one that caches smartly.” In your experience, which caching approach has worked best for large-scale systems? #Java #SpringBoot #Redis #Caching #SystemDesign #PerformanceEngineering #FullStackDevelopment #Microservices

Since the release of Redis 8, we get support for RedisJSON and RedisSearch enabling us to convert our old and boring key-value store into a full document-querying machine just like any DocumentDB+Elasticsearch does it. 🆁🅴🅳🅸🆂🅹🆂🅾🅽? RedisJSON lets you store, tweak, and fetch semi-structured JSON docs natively. NO MORE awkward serialization hacks. Think updating nested fields like: $.𝐮𝐬𝐞𝐫.𝐬𝐤𝐢𝐥𝐥𝐬[𝟐] is now atomic. It’s schema-free flexibility meets Redis’s in-memory blitz, clocking sub-millisecond operations even on complex nests. 🆁🅴🅳🅸🆂🆂🅴🅰🆁🅲🅷? Layer this onto RedisSearch and boom, you’ve got full-text search, numeric filters, tag matching, faceted aggregations. Index paths like: $.𝐧𝐚𝐦𝐞 𝐀𝐒 𝐭𝐞𝐱𝐭 𝐒𝐎𝐑𝐓𝐀𝐁𝐋𝐄 $.𝐭𝐚𝐠𝐬 𝐀𝐒 𝐭𝐚𝐠 And query them like Lucene-inspired DSL: @𝐬𝐤𝐢𝐥𝐥𝐬:{𝐏𝐲𝐭𝐡𝐨𝐧} @𝐬𝐚𝐥𝐚𝐫𝐲:[𝟕𝟎𝟎𝟎 +𝐢𝐧𝐟] Instant hits with relevance, sorting and pagination. No separate clusters, no cold-start lags. 🆆🅷🆈 🅲🅰🆁🅴? Fast prototype an e-commerce search in hours. This scales via Redis Cluster, persists reliably, and integrates seamlessly crushing 80% of use cases without have to deal with stacks like (DocumentDB + Elasticsearch). — ♻️ Repost to help others become better engineers. 👤 Follow Serjeel Ranjan for more. #Redis #RedisJSON #RedisSearch #NoSQL #Database #BackendDevelopment #DataEngineering #RealTimeData #SoftwareArchitecture #OpenSource #InMemoryDatabase #ScalableSystems

🧠 Learning Log: Redis Queues & Pub/Sub System 🚀 Today, I explored Redis — an in-memory data structure store that’s not just a database, but also a message broker for building real-time systems like LeetCode ⚡ ⚙️ Key Takeaways 💾 Redis Basics Keeps all data in memory, giving ultra-fast access. Supports persistence through: 🔹 RDB – periodic snapshots 🔹 AOF – logs every write operation for recovery 🧱 Redis in Action 💡 As a Database SET mykey "Hello" GET mykey DEL mykey 🌀 As a Queue (LPUSH / BRPOP) Built a simple Express (Producer) + Worker (Consumer) system for task handling. LPUSH problems 1 BRPOP problems 0 📡 As a Pub/Sub System Enabled real-time communication between services 👇 PUBLISH problems_done "{id:1, status:'TLE'}" SUBSCRIBE problems_done 💻 Tech Stack Used Node.js + Express + Redis Official redis client for Node.js 🧠 Next Step: Build a WebSocket server that listens to Redis Pub/Sub and sends live updates to users — just like real-time submissions on LeetCode 🚀 Redis made me realize how real-time event-driven systems are built in the backend — from task queues to instant notifications 🔥 #Redis #Backend #PubSub #Queues #NodeJS #100xDevs #SystemDesign #LearningJourney

🚀 Just wrapped up a project implementing Pagination with Redis Caching! In this project, I built a full-stack setup where the backend efficiently serves paginated data using Node.js, Express, and Prisma (PostgreSQL). To boost performance, I integrated Redis for caching frequently requested pages — reducing response times significantly. ⚡ 🧩 Tech Stack: Backend: Node.js + Express Database: PostgreSQL (via Prisma ORM) Cache Layer: Redis Frontend: React 💡 Key Features: ✅ Server-side pagination for large datasets ✅ Caching with Redis for faster data retrieval ✅ Clean API integration with the frontend ✅ Improved user experience with smooth data loading 📈 Outcome: Data that initially took ~500ms to load now loads in under 50ms from cache! It was a great learning experience in optimizing backend performance and understanding how caching strategies can drastically improve scalability. #NodeJS #Redis #Prisma #PostgreSQL #React #FullStackDevelopment #WebDevelopment #Backend #PerformanceOptimization #Pagination #Caching #LearningJourney

𝗛𝗼𝘄 𝗖𝗮𝗰𝗵𝗶𝗻𝗴 𝗪𝗼𝗿𝗸𝘀 (Using Redis) 𝗜𝗺𝗮𝗴𝗶𝗻𝗲 𝘁𝗵𝗶𝘀 — Your backend handles thousands of requests per second. Every time someone opens the app, it fetches user data from the database. 𝗡𝗼𝘄 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺: Databases are slow when hit repeatedly for the same data. That’s where caching saves the day 🚀 Here’s how it works 👇 1️⃣ Client → API Request A user requests some data, say profile info. 2️⃣ API → Redis Cache (Check)   • If the data is already in cache → 🎯 Cache Hit → data returned instantly.   • If the data is not in cache → ⚠️ Cache Miss → API goes to the database. 3️⃣ Database → API → Redis (Store) The API fetches fresh data from the DB and stores it in Redis for next time. 4️⃣ Next Request → Redis Future requests get the same data in milliseconds — no DB load. ✅ Why use Redis?   • Extremely fast (in-memory).   • Ideal for caching frequently accessed data.   • Reduces DB calls, improves latency, and scales better. ⚙️ In short: Cache = Speed Database = Accuracy Together = Optimal performance for your backend 𝗙𝗼𝗹𝗹𝗼𝘄 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝗯𝗮𝗰𝗸𝗲𝗻𝗱 & 𝘀𝘆𝘀𝘁𝗲𝗺 𝗱𝗲𝘀𝗶𝗴𝗻 𝗯𝗿𝗲𝗮𝗸𝗱𝗼𝘄𝗻𝘀 🔥 #SystemDesign #Redis #BackendDevelopment #SoftwareEngineering #Caching #Performance #linkedin

From Slow to Scalable — Lessons from Optimizing APIs with FastAPI + PostgreSQL A while back, one of our APIs was struggling — response times were sitting around 600–700 ms, even with caching turned on. Instead of throwing more servers at it, I decided to dig a little deeper. Here’s what really helped us turn things around 👇 1️⃣ PostgreSQL Indexing: Adding a few composite indexes on the right fields shaved off almost 40 % of the query time. 2️⃣ Async + Connection Pooling (FastAPI): Switching to async endpoints and tuning max_connections helped handle concurrent requests much better. 3️⃣ Redis Caching: Rather than caching everything, we focused only on the heavy joins and user-specific queries — simple, but super effective. After these tweaks, our average latency dropped to under 200 ms, and the app handled 2× the traffic — no extra hardware needed. 💡 Sometimes performance isn’t about scaling out — it’s about understanding your data flow and how async really works. If you’ve done any FastAPI or PostgreSQL performance tuning, I’d love to hear what made the biggest difference for you 👇 Let’s swap a little backend wisdom ⚙️ #python #fastapi #backend #microservices #postgresql #softwareengineering #scalability #webdevelopment