How VRRP Eliminates the Hidden Single Point of Failure in Load Balancer Setups

·1 views

A common infrastructure design uses multiple backend servers behind a load balancer to prevent downtime, but the load balancer itself can become a single point of failure if it crashes. Running a second load balancer does not solve the problem on its own, because DNS still points clients to the first machine's fixed IP address. Switching DNS during an outage is unreliable due to TTL caching by resolvers, operating systems, and browsers, resulting in prolonged downtime. The solution is a Virtual IP (VIP), an address that no single machine permanently owns and can move between servers without any client-side changes. VRRP (Virtual Router Redundancy Protocol), defined in RFC 5798, is the protocol that allows two or more machines to coordinate ownership of a VIP and automatically elect a new owner when the active node fails.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

How to Collect Meaningful Bug Reports During Google Play Closed Testing

Google reduced its closed testing requirement from 20 to 12 testers in December 2024, making production access more attainable for personal Android developer accounts created after November 13, 2023. However, simply recruiting testers is not enough, as low engagement or absent crash logs can flag an app during Google Play's production review. Developers are advised to reduce friction by integrating in-app shake-to-report tools that auto-capture screenshots and device logs, replacing manual email-based reporting. Structuring testers' sessions around specific user journeys — such as offline mode simulation or payment flows — helps surface edge cases and permission errors more reliably. Platforms like PeerPlay offer reciprocal and paid testing tiers to help indie developers secure consistent, quality feedback before submitting for production access.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

AgentInspect Tool Lets Developers Audit AI Agent Execution Paths, Not Just Answers

A developer explored AgentInspect, a TypeScript library designed to verify whether AI agents follow correct execution paths — such as actually calling a weather tool rather than skipping it and returning a fabricated answer. The tool records agent traces locally as JSONL files and applies deterministic, rule-based checks on model calls, tool calls, and their order. In a test using Vercel AI SDK with mock language models, two scenarios produced identical final answers, but AgentInspect correctly flagged the run that bypassed the weather tool as a process failure. The experiment also compared AgentInspect with more established tools like Promptfoo, LangSmith, and DeepEval, noting that AgentInspect feels early-stage but is already practical for CI pipeline checks. The author was prompted to try the library after receiving a cold email from its developer, highlighting growing interest in agent observability beyond simple output evaluation.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Developer Builds AI-Powered Lost-and-Found Pet App Using Google AI Studio

A developer created PetFinder, an AI-powered web app designed to match lost and found pets by analyzing their photos and details. The project was submitted as part of the DEV Education Track challenge focused on building apps with Google AI Studio. PetFinder allows users to report missing or found pets, upload images, and receive AI-generated match suggestions along with shareable posters. The app was built primarily through iterative prompting rather than manual coding, with the developer refining features through follow-up instructions. The experience highlighted that clearly communicating requirements to an AI tool can be just as critical as writing code directly.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

12% of LLM API Calls Returned No Answer, Exposing Hidden Costs in AI Pipelines

An analysis of 5,087 LLM API calls made between late June and early September 2026 found that 622 calls — about 12.2% — failed to return any answer, a rate the authors describe as normal for production environments. The largest failure category was 503 errors, meaning the upstream provider lacked capacity, while the second largest was 400 errors caused by malformed client requests. Critically, these two failure types require different handling: 503 errors are worth retrying, while 400 errors will never succeed regardless of how many retries are attempted. Timeouts and client-side cancellations proved uniquely costly, as tokens were already being generated when those calls stopped, and any retry would bill the full input prompt again from scratch. The authors recommend retrying only on 429 and 5xx status codes, and capping retry budgets by token count rather than by number of attempts to avoid runaway costs on large prompts.

0 comments Read more at DEV Community