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Every morning
Weekly
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The article explains how finite server resources—CPU, RAM, and bandwidth—can be overwhelmed by sudden traffic spikes, leading to queue buildup and latency spikes. When request arrival rates outpace processing capacity, servers enter a "death spiral" where resource contention degrades performance and goodput collapses. Load shedding is presented as an admission‑control pattern that deliberately rejects excess requests, typically with HTTP 503, to keep the system responsive for the majority of users. The piece targets junior developers, emphasizing the shift from functional coding to resilient system design.
The article breaks down hashing, encryption, and tokenization, explaining how each technique transforms data to protect it. It highlights hashing as a one‑way function ideal for password storage, encryption as a reversible process that secures data in transit, and tokenization...
AI systems are inherently non‑deterministic, producing different answers for the same prompt, which makes traditional unit testing ineffective. This variability leads to hallucinations—confidently fabricated facts—that can cascade through downstream processes and cause costly business errors. The article argues that reliability...
The article contrasts choreography and orchestration as two core patterns for managing communication in event‑driven microservice architectures on AWS. Choreography relies on decentralized broadcasting via Amazon SNS and rule‑based routing with Amazon EventBridge, keeping services loosely coupled. Orchestration centralizes workflow...
The article explains how database indexes, built on B‑Tree structures, can accelerate query performance by up to 1,000×. It contrasts full table scans, which require linear O(N) reads of every row, with indexed lookups that use sorted pointers to jump...
The post explains why distributed systems constantly encounter failures and introduces three core resilience patterns—Retry, Circuit Breaker, and Timeout. It details how transient errors can be mitigated with retries, how circuit breakers prevent cascading outages, and how timeouts avoid indefinite...
The post explains why a slow‑responding service can cripple a distributed system more than a hard crash. A sluggish component holds onto threads, sockets, and memory, causing resource starvation while health checks appear normal. In contrast, a crash instantly frees...
The article explains semantic caching as a solution for high‑cost LLM API usage, where traditional exact‑match caches fail because natural‑language queries vary in phrasing. By converting queries into embeddings and performing similarity search, systems can retrieve previously generated answers for...
The article breaks down the three dominant compute models—virtual machines, containers, and serverless—highlighting their evolution and core trade‑offs. It explains how VMs provide strong isolation at the cost of heavyweight OS overhead, containers streamline deployment but add orchestration complexity, and...
Amazon’s system design interviews embed Leadership Principles (LPs) throughout, meaning candidates are evaluated on ownership, customer obsession, frugality, and more alongside technical skills. Interviewers receive 1‑3 specific LPs to assess, and they ask explicit behavioral questions while watching for implicit...
The post demystifies large language models (LLMs) by framing them as massive next‑word prediction engines rather than knowledge databases. It explains core concepts such as tokenization, showing that 1,000 tokens roughly equal 750 words, and how embeddings turn tokens into...
The post argues that traditional, provisioned infrastructure is over‑engineered for early‑stage projects and promotes a serverless “Indie Hacker Stack” that scales to zero. By using Vercel’s edge compute, Supabase’s managed database, and Upstash’s serverless cache, developers can launch globally‑distributed apps...
System reliability engineering addresses hardware degradation, software bugs, and network partitions that can trigger cascading outages. The article distinguishes reliability from mere availability and stresses the need to eliminate single points of failure. It introduces Service Level Indicators, Objectives, and...
Modern microservice architectures often suffer cascading failures when a single downstream component slows or crashes, causing synchronous calls to block threads and exhaust memory. The blog explains how synchronous communication forces services to wait for network responses, leading to system-wide...
Modern microservices rely on asynchronous messaging to avoid cascading failures. The article contrasts Kafka and RabbitMQ, outlining each broker’s architecture, delivery guarantees, and typical use cases. RabbitMQ is described as a smart‑broker with a push model and fine‑grained routing, while...
