# fault-tolerance Published articles for fault-tolerance. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Thursday Notes on Architecture Trade-offs and High-Performing Teams DevFeed: [Thursday Notes on Architecture Trade-offs and High-Performing Teams]() Original publisher: [Read original article]() Author: Dr Milan Milanović Published: 2026-09-10T15:00:57Z Content type: opinion Language: en Sources: [Tech World With Milan Newsletter]() Topics: [Architecture & Design](), [Microservice]() Tags: [architecture](), [fault-tolerance](), [security](), [software-architecture](), [split](), [team-topologies](), [transactions]() ### AI overview This issue of Thursday Notes discusses architecture as a search for acceptable trade-offs, including reasons to split or merge services. It also explains how teams cycle through forming, storming, norming, and performing, and how leadership should adapt to each stage. ### Source excerpt Hey, Milan here. ## Designing Resilient APIs: Failure-Handling Patterns for Distributed Systems DevFeed: [Designing Resilient APIs: Failure-Handling Patterns for Distributed Systems]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-03-16T00:00:00Z Content type: tutorial Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [distributed-systems](), [Resilience](), [API](), [Architecture & Design]() Tags: [api-design](), [apis](), [async](), [distributed-systems](), [fault-tolerance](), [idempotency](), [jitter](), [observability](), [outages](), [reliability](), [resilience](), [retries](), [systems]() ### AI overview A practical guide to designing resilient APIs in distributed systems. It covers fail-fast validation, graceful degradation, idempotency for safe retries, and bounded retry policies using exponential backoff, jitter, attempt limits, and total time budgets. ### Source excerpt Resilience is failing in controlled ways rather than not failing. Validate and fail fast at the boundary, degrade gracefully by serving cached or reduced responses, require an idempotency key for side-effecting operations, and bound retries with exponential backoff, jitter, an attempt cap, and a total time budget. Unbounded retries amplify outages. ## Temporal raises $300M Series D at a $5B valuation as AI drives demand for Durable Execution DevFeed: [Temporal raises $300M Series D at a $5B valuation as AI drives demand for Durable Execution]() Original publisher: [Read original article]() Author: Allanah Hughes Published: 2026-02-17T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [distributed-systems](), [Artificial Intelligence](), [Orchestration](), [Cloud](), [Disaster Recovery](), [backends]() Tags: [agentic-ai](), [ai](), [ai-agents](), [announcements](), [backends](), [cloud](), [disaster-recovery](), [distributed-systems](), [fault-tolerance](), [orchestration]() ### AI overview Temporal announces a $300 million Series D at a $5 billion post-money valuation, led by Andreessen Horowitz. The company says demand for durable execution is increasing as AI and other long-running production workflows require state preservation and recoverable failures. ### Source excerpt Temporal raises $300M Series D at a $5B valuation as AI drives demand for Durable Execution. ## On Metastable Failures and Interactions Between Systems DevFeed: [On Metastable Failures and Interactions Between Systems]() Original publisher: [Read original article]() Author: Aleksey Charapko Published: 2025-12-24T21:26:49Z Content type: opinion Language: en Sources: [Aleksey Charapko]() Topics: [systems](), [retry](), [clients](), [servers](), [Latency]() Tags: [clients](), [fault-tolerance](), [latency](), [load](), [metastabiilty](), [other-thoughts](), [performance](), [retry](), [server](), [signal](), [state](), [systems](), [timeout]() ### AI overview The article explains metastable failures as self-sustaining performance failures caused by positive feedback loops. It uses retry storms to show how overload, latency, timeouts, and client retries can reinforce one another, and examines how ambiguous timeout signals make recovery more difficult. ### Source excerpt I've written about metastable failures before. The topic has been picked up by a few different teams since the, all analyzing metastable failures more, while I apparently has been slacking off... Anyway, Metastable failures are self-sustaining performance failures that arise in systems due to a positive feedback loop triggered by an initial problem. This positive [...] ## Understanding fault tolerance in distributed systems DevFeed: [Understanding fault tolerance in distributed systems]() Original publisher: [Read original article]() Author: Lauren Bennett Published: 2025-01-08T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [distributed-systems](), [systems](), [Availability](), [Cloud](), [Replication](), [backups]() Tags: [availability](), [backups](), [cloud](), [data](), [distributed](), [distributed-systems](), [downtime](), [failover](), [fault-tolerance](), [hardware](), [outage](), [replication](), [services](), [software](), [systems](), [temporal-concepts](), [user]() ### AI overview An explanation of fault tolerance in distributed systems, including how it keeps systems operating during failures and limits disruptions, downtime, and data loss. It describes redundancy, replication, failover mechanisms, graceful degradation, and the relationship between high availability and fault tolerance. ### Source excerpt Discover what fault tolerance is and how it ensures reliable systems with key principles and examples in cloud environments. ## High availability and disaster recovery with Temporal Cloud DevFeed: [High availability and disaster recovery with Temporal Cloud]() Original publisher: [Read original article]() Author: Meagan Speare Published: 2024-03-27T06:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Availability](), [Disaster Recovery](), [Self-hosted](), [distributed-systems]() Tags: [availability](), [cloud](), [disaster-recovery](), [fault-tolerance](), [reliability](), [self-hosting](), [temporal-concepts]() ### AI overview The article explains the operational challenges of self-hosting Temporal at scale, including maintaining multiple independently scalable services and their supporting database. It describes how Temporal Cloud provides managed high availability, fault tolerance, reliability, and disaster-recovery capabilities for mission-critical and high-scale applications. ### Source excerpt Learn how Temporal Cloud supports high availability and disaster recovery to keep your applications reliable at scale. ## Media processing Workflows DevFeed: [Media processing Workflows]() Original publisher: [Read original article]() Author: Nir Padmanabhan Published: 2021-07-19T07:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [distributed-systems](), [Orchestration](), [Internet of things](), [data](), [API](), [Microservice](), [Remote Procedure Call (RPC)]() Tags: [api](), [distributed](), [fault-tolerance](), [iot](), [processing](), [reliability](), [rpc](), [temporal-concepts](), [workflows]() ### AI overview This tutorial presents a reliable, fault-tolerant workflow for processing video footage from IoT devices. It describes triggers, vendor API requests, data downloads and transformation, downstream processing, and failure conditions such as connectivity loss, API downtime, worker crashes, and hardware failures, using Temporal as the workflow technology. ### Source excerpt Dive into a media processing problem that requires high reliability and fault tolerance, and see how using Temporal can help build a robust solution. ## High availability with nats-streaming-server (fault-tolerance) DevFeed: [High availability with nats-streaming-server (fault-tolerance)]() Original publisher: [Read original article]() Author: Carlos Alexandro Becker Published: 2019-07-25T00:00:00Z Content type: tutorial Language: en Sources: [Carlos Becker]() Topics: [distributed-systems](), [Availability](), [Cloud](), [Google Cloud Platform (GCP)](), [Back end]() Tags: [cloud](), [fault-tolerance](), [google-cloud](), [guide](), [high-availability](), [server]() ### AI overview This tutorial describes deploying a fault-tolerant, highly available nats-streaming-server. It explains why clustering was too slow for the author's needs, how large messages and leader re-elections affected performance, and how Google Cloud Filestore provided the shared filesystem required for fault-tolerant mode. ### Source excerpt I wanted to set up a fault tolerant nats-streaming-server, but couldn't find a "quick" guide on how to do it - so here we are. ## High availability with nats-streaming-server (clustering) DevFeed: [High availability with nats-streaming-server (clustering)]() Original publisher: [Read original article]() Author: Carlos Alexandro Becker Published: 2019-05-16T00:00:00Z Content type: tutorial Language: en Sources: [Carlos Becker]() Topics: [Streaming](), [clustering](), [Availability](), [Raft]() Tags: [clustering](), [fault-tolerance](), [high-availability](), [leader-election](), [raft](), [streaming]() ### AI overview A practical guide to configuring a high-availability nats-streaming-server cluster. It compares fault-tolerance and clustering approaches, then demonstrates clustering NATS and nats-streaming-server with three nodes, RAFT leader election, replicated writes, and peer configuration. ### Source excerpt I wanted to set up a high available nats-streaming-server cluster, but couldn't find a "quick" guide on how to do it.