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#redis

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24 articles

API Rate Limiting at Scale: Redis-Based Strategies

API Rate Limiting at Scale with Redis Rate limiting is essential for protecting your APIs from abuse, ensuring fair usage, and preventing cascading failures. Redis is the ideal store for rate limiting because of its spee…

Apr 20, 20262 min read

Deep Dive

#redis#api-gateway#rate-limiting

AWSAdvanced

Cloud Data Infrastructure: 5 Strategies for Cost Optimization

Cloud Data Infrastructure: Cutting the Bill Building high-performance data infrastructure on AWS, Azure, or GCP is easy; doing it affordably is the real challenge. As your traffic grows, data costs can quickly become you…

Apr 20, 20262 min read

Deep Dive

#cloud-computing#cost-optimization#aws

DatabasesAdvanced

Distributed Caching at Scale: Mitigating the Thundering Herd

Distributed Caching at Scale In a distributed system, caching is often the difference between a sub-100ms response and a total system collapse. However, most developers treat Redis as a simple "key-value bucket." At scal…

Apr 20, 20263 min read

Deep DiveBackend Systems Mastery

#caching#redis#distributed-systems

System DesignAdvanced

Distributed Data Observability: Metrics That Actually Matter

Distributed Data Observability: High-Signal Metrics In a distributed data system, CPU and RAM are "low-signal" metrics. A system can have 10% CPU usage and still be completely stalled. To truly understand the health of y…

Apr 20, 20262 min read

Deep Dive

#observability#monitoring#distributed-systems

System DesignAdvanced

Distributed Locking: Redis Redlock vs. Zookeeper vs. Database Constraints

Distributed Locking: Coordinating at Scale In a distributed system, multiple instances of a service often need to access a shared resource (like an inventory item or a single-use coupon) simultaneously. Standard language…

Apr 20, 20263 min read

PlaybookDistributed Systems Fundamentals

#distributed-systems#locking#redis

System DesignExpert

HyperLogLog at Scale: Billion-Cardinality Estimation

HyperLogLog at Scale Counting unique users (cardinality) for billions of events is a classic trade-off problem. Do you want 100% accuracy (massive memory) or high speed with 99% accuracy (tiny memory)? 1. The Cardinality…

Apr 20, 20262 min read

Deep DivePerformance & Optimization Mastery

#hyperloglog#analytics#redis

DatabasesAdvanced

Redis in Production: 5 Common Pitfalls and How to Avoid Them

Redis in Production: Common Gotchas Redis is incredibly fast, but its single-threaded nature means that a single mistake can bring down your entire application's performance. Here are 5 common pitfalls developers face wh…

Apr 20, 20262 min read

Deep Dive

#redis#performance#production-best-practices

DatabasesAdvanced

Redis Internals: Event Loop, Data Structures, and Persistence

Redis Internals: The Speed of Single-Threading Redis is often categorized as just a "cache," but its internal architecture makes it a powerful data structure server. Let's dive into the core technologies that make Redis…

Apr 20, 20262 min read

Deep Dive

#redis#databases#event-loop

DatabasesAdvanced

Redis Lua Scripting: Ensuring Atomicity in Complex Workflows

Redis Lua Scripting: The Power of Atomicity In distributed systems, race conditions are a constant threat. While Redis offers simple commands like INCR and SETNX, complex workflows often require checking multiple keys or…

Apr 20, 20262 min read

Deep Dive

#redis#databases#lua

System DesignAdvanced

System Design: Designing a Distributed Lock Manager

System Design: Designing a Distributed Lock Manager (DLM) In a microservices architecture, multiple instances of a service often need to access a shared resource (like an inventory item or a single-use coupon) simultaneo…

Apr 20, 20263 min read

Deep Dive

#system-design#distributed-locking#redis

System DesignAdvanced

System Design: Designing a Global Distributed Rate Limiter

System Design Masterclass: Designing a Distributed Rate Limiter In a distributed environment, a single malicious script, a misconfigured client, or a massive traffic spike can easily overwhelm your backend servers, bring…

Apr 20, 20266 min read

Case StudyBackend Systems Mastery

#system-design#rate-limiting#redis

System DesignAdvanced

System Design: Designing a Real-time Gaming Leaderboard (Massive Scale)

System Design: Designing a Real-time Gaming Leaderboard A leaderboard is a core feature in competitive gaming and fitness apps. While a simple SQL ORDER BY works for 100 users, doing it for 100 million users with real-ti…

Apr 20, 20263 min read

Deep Dive

#system-design#gaming#leaderboard

System DesignAdvanced

System Design: Designing Nearby Friends (Real-time Geospatial Streams)

System Design: Designing Nearby Friends (Real-time Geospatial) Designing a "Nearby Friends" feature (like Snapchat's Snap Map or Facebook's Nearby Friends) is a unique challenge. Unlike Yelp or Uber, where the entities (…

Apr 20, 20263 min read

Deep Dive

#system-design#geospatial#real-time

System DesignAdvanced

System Design: Designing Ticketmaster (High-Concurrency Booking)

System Design: Designing Ticketmaster The primary challenge of a system like Ticketmaster or Booking.com is not storage, but Concurrency. How do you ensure that when 100,000 users try to book the same 10 front-row seats,…

Apr 20, 20263 min read

Deep Dive

#system-design#ticketmaster#concurrency

System DesignAdvanced

System Design: Designing Twitter (Timeline and News Feed)

System Design: Designing Twitter (Timeline and News Feed) Twitter (now X) is a massive real-time messaging system. The core technical challenge is not storing the tweets, but delivering them to millions of followers' tim…

Apr 20, 20263 min read

Case Study

#system-design#twitter#scalability

DatabasesAdvanced

Vector Search in NoSQL: Redis and MongoDB as Vector Databases

Vector Search in NoSQL: The AI Evolution With the rise of Large Language Models (LLMs), Vector Search has become a critical requirement for Retrieval-Augmented Generation (RAG). While specialized databases like Pinecone…

Apr 20, 20262 min read

Deep Dive

#redis#mongodb#vector-search

System DesignAdvanced

System Design: Building a Distributed Cache

At small scale, cache looks like a simple optimization. Put Redis in front of the database, cache a few expensive reads, and enjoy lower latency. At production scale, a distributed cache stops being a helper library and…

Apr 10, 202614 min read

Deep Dive

#system design#distributed cache#redis

JavaAdvanced

Spring Boot Timeouts: The Production Guide for HTTP, DB, Redis, and Kafka

Timeouts are one of the highest-return production settings in a Spring Boot service. Without them, slow dependencies turn into stuck threads, connection pool exhaustion, retry storms, cascading failures, and deploys that…

Apr 8, 20269 min read

Deep Dive

#spring boot#timeouts#java

DatabasesAdvanced

Cache Invalidation Patterns: TTL, Write-Through, Cache-Aside, and Event-Driven Eviction

Cache invalidation is hard because the cache is not the source of truth. It is a copy, and every copy has a consistency problem. The real question is not "how do we make cache perfectly consistent?" The better question i…

Apr 7, 20265 min read

Deep Dive

#cache invalidation#redis#caching

System DesignAdvanced

WebSocket and SSE for Real-Time Systems: Architecture and Production Patterns

Real-time features — live notifications, collaborative editing, live dashboards, streaming data — require pushing data from server to client without the client repeatedly polling. HTTP polling wastes resources and adds l…

May 4, 20257 min read

Deep Dive

#websocket#sse#real-time

DatabasesAdvanced

Redis Caching Strategy at Scale: Beyond Simple Key-Value

Every senior engineer has fought a caching bug that looked simple and turned out to be a distributed systems problem. Cache stampedes, thundering herds, avalanche failures — these happen at scale and they are expensive.…

May 3, 202511 min read

Deep Dive

#redis#caching#java

System DesignAdvanced

System Design: Real-Time Chat Application at Scale

Real-time chat systems are among the most architecturally interesting distributed systems. They require persistent connections at massive scale, exactly-once message delivery guarantees, presence detection across million…

Mar 17, 202511 min read

Deep Dive

#system design#websocket#real-time

JavaAdvanced

Spring Boot Performance Tuning: From 200 to 2000 RPS

A Spring Boot application that handles 200 RPS on Day 1 often has the same underlying hardware capacity to handle 2000 RPS — the gap is how you use it. Most performance bottlenecks in Java web services follow predictable…

Mar 13, 202513 min read

Deep Dive

#spring boot#java#performance

DatabasesAdvanced

Redis Beyond Cache: Sorted Sets, Streams, and Pub/Sub Patterns

Most teams use Redis for one thing: caching. They store objects with a TTL, check the cache before hitting the database, and call it a day. This barely scratches the surface. Redis is a data structure server. Its real po…

Jan 22, 202513 min read

Deep Dive

#redis#cache#java