Designing High-Performance Systems: A Guide

How Redis + Smart Diffing Helped Us Cut Down Heavy DB Load While building a system with React Flow + Graph Database, we faced a big challenge: - The Problem Every time a user made a small change on the frontend, the entire JSON was sent to the backend. The backend recalculated diffs and updated the graph DB again and again. This led to slow queries, high DB load, and poor performance. - The Solution At session start, we store the initial state in Redis cache. From frontend → we now send only the changed JSON, not the whole thing. Redis keeps the session in sync and only writes to the DB when: Session TTL expires, or User logs out. Added xxHash validation on both ends to ensure data integrity. - The Impact Queries became super fast Data loading improved drastically DB load reduced massively Sometimes the smartest optimization is not more infra, but the right use of cache + hashing + efficient data flow. - Have you solved similar frontend–backend sync problems? Would love to hear your approaches! #Redis #ReactFlow #GraphDatabase #SystemDesign #Backend #Performance

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

🔎 Why is Redis so fast? Let’s break it down technically. When developers hear “Redis is blazing fast,” it’s not hype — it’s engineering. Here’s why: ⚡ 1. In-Memory Database Redis stores its entire dataset in RAM. Memory access ≈ nanoseconds Disk access ≈ milliseconds That’s a million times faster on raw access speed alone. ⚡ 2. Single-Threaded with Event Loop Instead of using multiple threads and locks, Redis uses a single-threaded event loop with I/O multiplexing. No race conditions No lock contention Predictable execution (every command runs atomically) This design dramatically reduces complexity while improving performance under high concurrency. ⚡ 3. Optimized Data Structures Redis isn’t just “key-value.” It uses specialized structures: Hash tables for lookups Lists for queues Sets / Sorted Sets for rankings HyperLogLog for approximations Each structure is implemented with efficiency in mind, minimizing CPU cycles and memory overhead. 📌 Takeaway for engineers: Redis’s speed comes from smart trade-offs: keep it in-memory, keep it single-threaded, and optimize the core structures. It’s a great reminder that in system design, simplicity is often the fastest path to performance. Have you used Redis only for caching, or have you explored advanced use cases like pub/sub or real-time analytics? #Redis #SystemDesign #Database #Caching #BackendEngineering Image From: Jaffery Richman (Redis Supported Data Types)

🚀 Understanding How Redis Works — and Why It’s Called a Persistent Database  Redis often gets mistaken as just an in-memory cache , but it’s actually a persistent NoSQL database designed for speed, scalability, and durability. Let’s decode how it works under the hood 👇   🔧 How Redis Works   Redis stores data in RAM, which makes read/write operations blazingly fast (often under a millisecond).   It uses a key-value model and supports advanced data structures like lists, sets, sorted sets, hashes, bitmaps, and streams — all directly in memory.  When a client sends a command, Redis performs the operation entirely in memory and returns the result instantly. Its single-threaded event loop and optimized data encoding keep latency ultra-low.   💾 Why Redis Is Called a Persistent DB   Even though Redis is in-memory, it can persist data to disk. There are two main ways:   - RDB Snapshots – Creates point-in-time binary snapshots of your dataset periodically.   - AOF (Append Only File) – Logs every write operation to disk sequentially, allowing Redis to replay operations on restart.  This combination ensures that data isn’t lost even if the server restarts — earning Redis the title of a *persistent* database.   🧠 Internal Memory Management   Redis has a highly efficient memory allocator (jemalloc) that minimizes fragmentation.   It also supports:   - LRU/LFU eviction policies. to remove least-used keys when memory is full.   - Memory compression and object sharing to reduce overhead.   - Active defragmentation to reclaim memory without blocking operations.  The result? A system that balances in-memory performance with on-disk reliability — perfect for caching, session stores, leaderboards, and real-time analytics.  💡 Takeaway: Redis isn’t just fast; it’s *intelligently fast*. Its hybrid approach to persistence and memory management makes it one of the most trusted data stores in modern backend architecture.  #Redis #BackendDevelopment #Databases #SystemDesign #Caching #SpringBoot #Java #SoftwareEngineering  

🧠 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

Databases: The Backbone of Full Stack Applications Every full stack project eventually comes down to one critical decision: where and how to store data. Picking the right database can define system performance, scalability, and maintainability. Relational strength: PostgreSQL and MySQL shine for transactional integrity and complex queries. NoSQL flexibility: MongoDB and DynamoDB handle semi-structured data and scale horizontally with ease. In-memory speed: Redis accelerates caching, session management, and leaderboard-style workloads. Hybrid strategy: Polyglot persistence — using the right tool for the right job — avoids one-size-fits-all bottlenecks. Cloud-native edge: Managed services like AWS Aurora, RDS, and Cosmos DB reduce operational overhead and improve reliability. Recent highlight: I redesigned a retail system’s data layer by combining PostgreSQL for transactions, Redis for caching, and MongoDB for catalog data, boosting query response times by 65% during high-traffic events. #FullStackDeveloper #Databases #PostgreSQL #MongoDB #Redis #AWS #Java #SpringBoot #Scalability #CloudEngineering need a reallistic photo

Most projects start simple. Then someone adds Redis for sessions. Another adds Elasticsearch for search. A third adds RabbitMQ for jobs. Before the first user signs up, the architecture already looks like a bowl of spaghetti 🍝...and the infra bill could fund a small startup. That’s when I realized something important: Postgres isn’t just a database anymore — it’s a platform. It can handle: - Structured and unstructured data (JSONB) - Background jobs (pg_cron) - Search and AI queries (pgvector + FTS) - APIs (PostGraphile) - Even client sync (ElectricSQL) You don’t always need more tools. You just need to unlock what Postgres can already do. 🔥 Writing about this in detail: “The Database That Became a Platform: Why Postgres Might Be All You Need.” If you’ve ever fought with tool sprawl — this post is for you. https://lnkd.in/d-Pz_sCa #TechLeadership #StartupEngineering #DevTools #SaaSDevelopment #BuildInPublic #Postgres #DatabaseEngineering #SoftwareArchitecture

⚙️ How MongoDB Decides What to Forget 💚 Inside the WiredTiger eviction engine where memory meets discipline. Every byte in MongoDB’s memory exists under policy. 𝗡𝗼𝘁𝗵𝗶𝗻𝗴 𝗶𝘀 𝗿𝗮𝗻𝗱𝗼𝗺. 𝗡𝗼𝘁𝗵𝗶𝗻𝗴 𝗶𝘀 “𝗷𝘂𝘀𝘁 𝗰𝗮𝗰𝗵𝗲𝗱.” WiredTiger’s eviction engine runs as a continuous feedback system a 𝗹𝗶𝘃𝗲 𝗻𝗲𝗴𝗼𝘁𝗶𝗮𝘁𝗶𝗼𝗻 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗥𝗔𝗠, 𝗜/𝗢 𝗯𝗮𝗻𝗱𝘄𝗶𝗱𝘁𝗵, 𝗮𝗻𝗱 𝗱𝘂𝗿𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗴𝘂𝗮𝗿𝗮𝗻𝘁𝗲𝗲𝘀. 𝗜𝘁 𝗱𝗼𝗲𝘀𝗻’𝘁 𝗰𝗹𝗲𝗮𝗻; 𝗶𝘁 𝗴𝗼𝘃𝗲𝗿𝗻𝘀. When the cache approaches its eviction_target, internal workers (evict_lru_worker, WT_PAGE_INDEX, WT_REF) begin scanning B-trees, assigning scores based on access recency, reuse probability, and mutation cost. Dirty pages are written through the WiredTiger Journal and the Durable History Store; clean pages are dropped instantly. Every decision feeds telemetry back into a PID-like control loop that constantly tunes itself against real-time pressure. When the cache fills faster than it can drain, flow control steps in backpressure propagates all the way to the client layer. Write throughput slows, replication catches up, and equilibrium is restored. Memory, disk, and replication behave as a single feedback organism sharing one timebase. 𝗠𝗼𝗻𝗴𝗼𝗗𝗕 𝗱𝗼𝗲𝘀𝗻’𝘁 𝗮𝗶𝗺 𝗳𝗼𝗿 𝘀𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆; 𝗶𝘁 𝗮𝗶𝗺𝘀 𝗳𝗼𝗿 𝗯𝗼𝘂𝗻𝗱𝗲𝗱 𝗶𝗻𝘀𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 controlled oscillation between volatility and persistence. That oscillation is what makes latency predictable under chaos. Eviction 𝗶𝘀𝗻’𝘁 𝗮 𝗰𝗹𝗲𝗮𝗻𝘂𝗽 𝗰𝘆𝗰𝗹𝗲. It’s a systemic act of self-governance, a runtime decision framework embedded deep in C and shared locks a storage engine regulating its own volatility so higher layers can stay deterministic. And the deeper you study it, the more you realize: 𝗧𝗵𝗲 𝗵𝗮𝗿𝗱𝗲𝘀𝘁 𝗽𝗮𝗿𝘁 𝗼𝗳 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗿𝗲𝗹𝗶𝗮𝗯𝗹𝗲 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗶𝘀𝗻’𝘁 𝘄𝗿𝗶𝘁𝗶𝗻𝗴 𝗱𝗮𝘁𝗮 𝗶𝘁’𝘀 𝗸𝗻𝗼𝘄𝗶𝗻𝗴 𝗲𝘅𝗮𝗰𝘁𝗹𝘆 𝘄𝗵𝗲𝗻 𝘁𝗼 𝗹𝗲𝘁 𝗴𝗼. 📘 Full deep-dive on Medium: https://lnkd.in/d6sXQKTF #MongoDB #MongoDBChampion #WiredTiger #DatabaseInternals #SystemDesign #EngineeringLeadership #ControlTheory #LowLevelEngineering #DatabaseArchitecture #Replication #Caching #MemoryManagement #ConcurrencyControl #DistributedSystems #SystemsDesign #StorageEngine #Observability #Telemetry #TechnicalArchitecture #SoftwarePerformance #EngineeringExcellence

You might not have terabytes of financial data or millions of daily queries... but you can start thinking like Zerodha engineers today. Most of us use PostgreSQL as a black box. We CREATE TABLE, we SELECT, we move on. But behind those commands lies an elegant system of pages, tuples, and smart engineering choices that make PostgreSQL the powerhouse it is. In my latest deep-dive, I unpack: How you can tune before you scale, and build performance into your mindset This one isn’t about “Postgres tips.” It’s about how engineers at scale think differently. Ask yourself: - What would I denormalize if performance mattered more than purity? - What can I cache without bringing in Redis? - How can I tune before I scale? Think about it, experiment! That's how we grow as Engineers! Read it here: https://lnkd.in/gRNdyEZK #engineering #systemdesign #software #storage

Redis is insanely fast but ask it to do a range query and you quickly see its limits. Redis distributes keys using a hash-based sharding model. That means each key (user:101, user:106, user:115) is hashed and sent to a different node. It’s perfect for O(1) lookups you know exactly where your key lives. But hold on there is a catch. When you ask for a range say, user:100–120 those keys are spread all over the cluster. Now your query has to jump between multiple shards, collect responses, and merge them. No locality, no ordering just chaos for range scans. On the other hand, distributed KV stores like TiKV or Cassandra organize data by ordered key ranges. Each node owns a continuous slice of the keyspace. Node 1 [user:100–110 ] Node 2 [ user:111–120] So a range query touches just a few nodes data locality wins. This is one of those subtle architecture trade-offs. Redis optimizes for speed and simplicity hash partitioning. TiKV/Cassandra optimize for ordered reads and range queries. As a Solution Architect, understanding this helps you pick the right tool for the right pattern because every design decision is a trade-off, not a silver bullet. #SystemDesign #DistributedSystems #Redis #DatabaseInternals #SoftwareArchitecture #BackendEngineering #Scalability #PerformanceEngineering #TechInsights #EngineeringLeadership