Consistent Hashing for Scalable Distributed Systems

𝐂𝐨𝐬𝐭 vs 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞? 𝐈𝐭’𝐬 𝐍𝐨𝐭 𝐚 𝐓𝐫𝐚𝐝𝐞-𝐨𝐟𝐟 — 𝐈𝐭’𝐬 𝐚 𝐃𝐞𝐬𝐢𝐠𝐧 𝐂𝐡𝐨𝐢𝐜𝐞 In large-scale systems, cost and performance are often treated as competing priorities. But in reality — the best architectures optimize both. From my experience, true optimization doesn’t come from cutting costs at the end. It comes from making smarter engineering decisions from the start. Here are a few strategies that consistently move the needle 👇 🔹 Right-Sizing Infrastructure Provision based on actual workload patterns — not assumptions. Overprovisioning wastes money, underprovisioning hurts performance. Balance is key. 🔹 Smart Caching Strategy Use Redis or in-memory caching to reduce latency and offload repeated database reads. The fastest query is the one you don’t make. 🔹 Tiered Storage Architecture Not all data needs the same speed. Leverage hot, warm, and cold storage tiers (S3 / GCP lifecycle policies) to optimize both cost and access. 🔹 Observability That Drives Action Tools like Datadog and Splunk aren’t just for monitoring — they help you detect bottlenecks early and optimize continuously. #Java #Microservices #CloudArchitecture #CostOptimization #PerformanceTuning #GCP #SpringBoot #Redis #Datadog #FullStackDevelopment #C2C #W2 #C2H

Modern distributed systems must handle high request volumes while maintaining low latency and high throughput. One of the most effective strategies to achieve this is implementing an efficient caching layer within the system architecture. 1️⃣ Reducing Database Load Caching stores frequently requested data in memory-based systems like Redis or Memcached. Instead of querying the database repeatedly, the application retrieves data directly from the cache, significantly reducing database I/O operations and improving response times. 2️⃣ Improving Application Performance By serving data from an in-memory cache, applications can reduce latency from milliseconds to microseconds. Techniques like cache-aside, write-through, and write-back caching strategies help maintain consistency while optimizing performance. 3️⃣ Scalable System Architecture In high-scale architectures, caching works alongside CDNs, load balancers, and microservices. Distributed caching clusters allow applications to scale horizontally while maintaining fast data access across multiple services. Efficient caching strategies are critical for building high-performance, scalable, and resilient software systems capable of handling large-scale workloads. #SoftwareEngineering #SystemDesign #Caching #DistributedSystems #BackendEngineering

One thing distributed systems teach you very quickly is this; “Did the operation fail… or did the response fail?” At small scale we think in simple request/response terms. Client sends request ➝ server processes it ➝ response comes back. Simple. But once networks, retries, timeouts, and message queues enter the picture, things get messy. Imagine this scenario 👇 User pays for an order API processes payment Database writes the transaction Network drops the response From the client’s perspective ❌ the request failed. So what happens next? Retry. Now your system receives the same payment request again. If the backend blindly processes it, the user just got charged twice 💳💳 Nothing technically broke. The system behaved exactly the way it was written. This is why distributed systems rely heavily on something called idempotency. The idea is simple: ➡️ The same request can run multiple times ➡️ But the final result stays the same In practice this usually means attaching a unique key to the request. Something like this: "Idempotency-Key: payment_482193" The service stores that key along with the result. If the request shows up again, the system doesn’t process it twice. It simply returns the original result. In a Go service it might look something like the code below; A few things worth noting here: GetOrLock is a single atomic operation , Redis SET NX or a DB row with SELECT FOR UPDATE. The pending record goes in before the charge, not after. And duplicates return the original result, not just nil. Because in distributed systems , Networks drop responses, Clients retry requests, Messages get duplicated The question is never if it happens. Only whether your system handles it correctly, or just quietly handles it wrong.

𝗡𝗲𝘄 𝗯𝗹𝗼𝗴 𝗽𝗼𝘀𝘁: 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁 𝗛𝗮𝘀𝗵𝗶𝗻𝗴 I’ve just published a new article where I break down consistent hashing, a key concept behind scalable distributed systems. In this post, I explain: - What consistent hashing is and why it matters - How it helps distribute data efficiently - Why it’s widely used in systems like caching, load balancing, and distributed storage If you’re interested in backend systems, distributed architectures, or just want to strengthen your fundamentals, this might be useful for you 👇 🇬🇧 English version: https://lnkd.in/ebSEG_ES 🇪🇸 Versión en español: https://lnkd.in/eCjfejQd I’d love to hear your thoughts or feedback! 💬 #DistributedSystems #BackendDevelopment #Scalability #SoftwareEngineering #ComputerScience

🚀 Scaling a distributed system? ---Consistent Hashing--- from my regular reads 🛠️ In dynamic distributed systems, compute or storage nodes are routinely added, removed, or fail. If you use traditional modulo hashing, adding or removing a single node forces almost all objects to be remapped, causing massive cache invalidations, huge data migrations, and system outages. Enter Consistent Hashing: a family of stateless mapping techniques designed specifically for minimal disruption. Here are the core concepts you need to know for high-scale system design: 🔹 Minimal Disruption: When a node joins or leaves the cluster, only a small, bounded fraction of keys move. This smooth evolution prevents "thundering herd" recovery traffic and contains failures locally. 🔹 The Identifier Ring: Many systems map nodes and keys onto a large circular identifier space, where a key is assigned to the next active "successor" node moving clockwise around the ring. 🔹 Virtual Nodes (vnodes): Basic ring hashing can cause uneven load distribution. Massive systems like Amazon's Dynamo and Apache Cassandra fix this by assigning multiple "virtual" tokens to a single physical node, which evenly distributes the load and accommodates servers with different hardware capacities. 🔹 Rendezvous Hashing (HRW): Instead of building a ring, this algorithm assigns keys to the server with the highest pseudo-random score. It offers an optimal disruption bound—if a server fails, only the keys mapped to that specific server are reassigned. Understanding these trade-offs is foundational for building elastic caches, network load balancers, and NoSQL databases. #SystemDesign #ConsistentHashing #DistributedSystems #SoftwareArchitecture #BackendEngineering #CloudComputing #TechLeadership

Scaling Your Systems for Maximum Performance ???? Best practices to scale your systems such that they will serve a higher load without compromising performance is covered in this article. PRACTICES FOR SCALING SERVICES Stateless Services: Designing services to be stateless means you can quickly distribute them across several instances and continue being very efficient. Distribution of traffic: Use a load balancer, such as NGINX, or AWS ELB. Scalability: Add more instances to scale out rather than scaling up a single machine. Implement caches (Redis, Memcached) to minimise database load and hasten quick responses. Async Processing : Offload jobs from your system by making use of message queues like Kafka and RabbitMQ for efficient execution in an asynchronous manner. Sharding : Shard large databases into smaller, hence more manageable, pieces for performance betterment. Database Replication Guarantee availability is high by performing master-slave or master-master database replication. Auto Scaling Make use of cloud-based auto-scaling solutions like AWS and Azure for automatic scaling based on demand. With these, you can ensure your systems are very robust and scalable enough to be able to meet any level of growth that you may require! Did i miss any other important point ?

Scaling Your Systems for Maximum Performance ???? Best practices to scale your systems such that they will serve a higher load without compromising performance is covered in this article. PRACTICES FOR SCALING SERVICES Stateless Services: Designing services to be stateless means you can quickly distribute them across several instances and continue being very efficient. Distribution of traffic: Use a load balancer, such as NGINX, or AWS ELB. Scalability: Add more instances to scale out rather than scaling up a single machine. Implement caches (Redis, Memcached) to minimise database load and hasten quick responses. Async Processing : Offload jobs from your system by making use of message queues like Kafka and RabbitMQ for efficient execution in an asynchronous manner. Sharding : Shard large databases into smaller, hence more manageable, pieces for performance betterment. Database Replication Guarantee availability is high by performing master-slave or master-master database replication. Auto Scaling Make use of cloud-based auto-scaling solutions like AWS and Azure for automatic scaling based on demand. With these, you can ensure your systems are very robust and scalable enough to be able to meet any level of growth that you may require! Did i miss any other important point ?

Today’s session in our System Design cohort with Piyush Garg was all about Consistent Hashing and its real-world applications. I learned how consistent hashing helps in distributing data efficiently across servers, and more importantly, how it minimizes re-distribution when nodes are added or removed — making systems more scalable and resilient. Along the way, I also explored some important concepts and tools: • Valkey – an open-source in-memory data store • Memcache – widely used for high-performance distributed caching • LRU (Least Recently Used) – a cache eviction strategy • Read-through cache – where cache automatically loads data on a miss • Virtual Nodes – improve load balancing in consistent hashing • AWS DynamoDB – a fully managed NoSQL database built for scale • Hypervisor – software that enables virtualization by running multiple VMs on a single machine One key takeaway: Good system design is not just about scaling, but about scaling efficiently with the right trade-offs. Really enjoying diving deeper into these concepts and connecting them with real-world systems. #SystemDesign #ConsistentHashing #Caching #DistributedSystems #LearningInPublic

Your system handles traffic well. But one popular user can still slow it down for everyone. *** Consistent Hashing *** ~ What It Is Consistent Hashing is a technique used to distribute data or requests across multiple nodes with minimal rebalancing. Instead of assigning keys directly to servers, both servers and keys are mapped onto a hash ring. When nodes are added or removed, only a small portion of data needs to move. ~ What Problem It Solves In distributed systems, scaling clusters up or down can cause massive data reshuffling. Traditional hashing redistributes almost all keys when nodes change, causing cache misses and heavy data migration. Consistent hashing ensures that most keys stay on the same nodes even when the cluster topology changes. ~ Real-World Example Imagine a large caching layer storing user sessions. If a new server is added to handle increased traffic, you don’t want to invalidate the entire cache. Systems like Apache Cassandra and caching platforms used by companies like Amazon rely on consistent hashing to distribute workload efficiently across nodes. Trade-offs -> Requires careful virtual node configuration -> Can still create hotspots if keys are unevenly distributed -> Operational complexity in large clusters -> Minimal data movement during scaling events If you added two new servers to your cluster today — would your system rebalance smoothly or invalidate most of your cache? #SystemDesign #DistributedSystems #Scalability #BackendEngineering #DistributedHashing #CloudArchitecture #TechLeadership

𝗜𝘀 𝘆𝗼𝘂𝗿 𝘀𝘆𝘀𝘁𝗲𝗺 𝘀𝗹𝗼𝘄𝗶𝗻𝗴 𝗱𝗼𝘄𝗻 𝗮𝘀 𝘁𝗿𝗮𝗳𝗳𝗶𝗰 𝗴𝗿𝗼𝘄𝘀? 𝗕𝗲𝗳𝗼𝗿𝗲 𝘆𝗼𝘂 𝘀𝗰𝗮𝗹𝗲 𝘆𝗼𝘂𝗿 𝘀𝗲𝗿𝘃𝗲𝗿𝘀, 𝗿𝗲𝘃𝗶𝘀𝗶𝘁 𝘆𝗼𝘂𝗿 𝗰𝗮𝗰𝗵𝗶𝗻𝗴 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆. 🚀 Caching can improve performance by 10x, but the wrong pattern can lead to stale data or system crashes. Here are 5 common strategies every engineer should know: 1️⃣ 𝗖𝗮𝗰𝗵𝗲 𝗔𝘀𝗶𝗱𝗲 (𝗟𝗮𝘇𝘆 𝗟𝗼𝗮𝗱𝗶𝗻𝗴) - Application checks the cache first. - If data is missing (cache miss), it fetches from the DB and updates the cache. 2️⃣ 𝗪𝗿𝗶𝘁𝗲 𝗧𝗵𝗿𝗼𝘂𝗴𝗵 - Data is written to the cache and database simultaneously. - Benefit: Ensures the cache is always consistent with the DB. 3️⃣ 𝗥𝗲𝗮𝗱 𝗧𝗵𝗿𝗼𝘂𝗴𝗵 - The application interacts only with the cache layer. - The cache automatically retrieves missing data from the DB. 4️⃣ 𝗪𝗿𝗶𝘁𝗲 𝗕𝗮𝗰𝗸 (𝗪𝗿𝗶𝘁𝗲 𝗕𝗲𝗵𝗶𝗻𝗱) - Data is written to the cache first; DB updates happen asynchronously later. - Benefit: Extremely fast, but requires strong failure handling. 5️⃣ 𝗪𝗿𝗶𝘁𝗲 𝗔𝗿𝗼𝘂𝗻𝗱 - Writes go directly to the database, bypassing the cache. - The cache is only updated during the next read. 𝗧𝗵𝗲 𝗕𝗼𝘁𝘁𝗼𝗺 𝗟𝗶𝗻𝗲: Choosing a strategy isn't just about speed. You must consider: ✅ Data freshness vs. staleness tolerance. ✅ Read-heavy vs. Write-heavy workloads. ✅ Cache failure recovery plans. I’ve also written a detailed 𝗠𝗲𝗱𝗶𝘂𝗺 𝗔𝗿𝘁𝗶𝗰𝗹𝗲 explaining this concept with real-world examples. 𝗟𝗶𝗻𝗸 𝗶𝗻 𝘁𝗵𝗲 𝗰𝗼𝗺𝗺𝗲𝗻𝘁𝘀 👇 GIF: Neo Kim #AWS #CloudComputing #DevOps #SystemDesign #CloudArchitect #SoftwareEngineering #InfrastructureAsCode #TechCommunity #Serverless #BackendDevelopment