DevOps & SRE notes – Telegram
DevOps & SRE notes
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Helpfull articles and tools for DevOps&SRE

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As the complexity of modern software systems grows, the meaning and practice of "observability" have become increasingly muddled. In this personal essay, Charity Majors argues that it's time to "version" observability—differentiating the traditional metrics-logs-traces approach (Observability 1.0) from a new, more flexible model built on wide, structured log events (Observability 2.0).

https://charity.wtf/2024/08/07/is-it-time-to-version-observability-signs-point-to-yes/
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Designing a robust network architecture for K3s multi-cluster environments can be challenging, especially when integrating Layer 2 and BGP routing on Unifi UDM devices. In this guide, David Elizondo walks through practical considerations and strategies for planning private RFC 1918 address spaces and achieving effective communication between clusters using tools like Cilium and native routing.

https://medium.com/@david-elizondo/planning-a-k3s-multi-cluster-network-with-l2-and-bgp-on-unifi-udm-ae4480a7b4f7
Learning from unexpected service failures can be a catalyst for long-term improvement, as Tines software engineer Shayon Mukherjee shares in this blog post. The story reveals how a Redis upgrade exposed a hidden point of failure in their webhook system, ultimately leading to stronger resilience and more comprehensive testing practices.

https://www.tines.com/blog/engineering-incidents-improvement/
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Slow container startup times can cripple the productivity of Kubernetes teams managing large Docker images—sometimes dragging deployments out for hours. In this feature, Kazakov Kirill shares a practical strategy for pre-warming nodes and leveraging image caching, dramatically reducing cold starts and disk pressure during mass pod rollouts in Amazon EKS clusters.

https://hackernoon.com/how-to-optimize-kubernetes-for-large-docker-images
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Tail-based sampling unlocks deeper insights into distributed systems by allowing OpenTelemetry users to prioritize traces that matter most, such as those with errors or slow responses. This guide explains how tail-based sampling works, its differences from head-based sampling, and provides a practical walkthrough for setting up a two-tier OpenTelemetry Collector architecture that intelligently filters traces for more actionable observability.

https://itnext.io/empower-your-observability-tail-based-sampling-for-better-tracing-with-opentelemtry-243ca2cc55d1
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Achieving end-to-end visibility for Python data pipelines is essential for ensuring quality and reliability in modern data architectures. This hands-on walkthrough from Elastic Observability Labs explains how to implement OpenTelemetry (OTEL) in your Python ETL noscripts—covering automatic instrumentation, manual tracing, performance metrics, and anomaly-driven alerting—to proactively monitor, troubleshoot, and optimize your entire pipeline lifecycle using Elastic’s platform.

https://www.elastic.co/observability-labs/blog/monitor-your-python-data-pipelines-with-otel
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While GitOps has brought consistency and innovation to Kubernetes deployments, its reliance on git-based workflows and tools like ArgoCD and Flux still leaves important challenges unsolved. This article explores both the real-world progress and the limitations of GitOps, from deployment strategies and multi-cluster rollouts to issues around permissions, secrets management, and the need for solutions that go beyond git as the sole source of truth.

https://itnext.io/realizing-the-potential-of-gitops-263051baff04
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Meeting customers’ rising expectations for security, speed, and personalization demands a new approach to computing infrastructure, which is exactly where distributed cloud comes in. This feature explains why developers must look beyond traditional centralized cloud models—adopting distributed cloud computing to optimize performance, comply with data regulations, and deliver truly customized services at scale.

https://thenewstack.io/why-developers-need-to-care-about-distributed-cloud-computing/
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Upgrading from Node.js 18 to 20 brought unexpected performance impacts to a Kubernetes-deployed service, as detailed in this technical recap. The experience-driven story reveals how changing memory reservations on Kubernetes pods can shrink Node.js heap spaces—specifically the "new space"—triggering heavier garbage collection and higher CPU load, and how adjusting the --max-semi-space-size parameter restored both speed and stability.

https://deezer.io/node-js-20-upgrade-a-journey-through-unexpected-heap-issues-with-kubernetes-27ae3d325646
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