DevOps&SRE Library
Библиотека статей по теме DevOps и SRE. Реклама: @ostinostin Контент: @mxssl РКН: https://www.gosuslugi.ru/snet/67704b536aa9672b963777b3
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30 Avgust, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 135 ga, so‘nggi 24 soatda esa 1 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 13.71% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 6.94% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 2 709 marta ko‘riladi; birinchi sutkada odatda 1 372 ta ko‘rish yig‘iladi.
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📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Библиотека статей по теме DevOps и SRE.
Реклама: @ostinostin
Контент: @mxssl
РКН: https://www.gosuslugi.ru/snet/67704b536aa9672b963777b3”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 31 Avgust, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
Tuckr is a dotfile manager inspired by Stow and Git. Tuckr aims to make dotfile management less painful. It follows the same model as Stow, symlinking files onto $HOME. It works on all the major OSes (Linux, Windows, BSDs and MacOS). Tuckr aims to bring the simplicity of Stow to a dotfile manager with a very small learning curve. To achieve that goal Tuckr tries to only cover what is directly needed to manage dotfiles and nothing else. We won't wrap git, rm, cp or reimplement the functionality that are perfeclty covered by other utilities in the system unless it greatly impacts usability.https://github.com/RaphGL/Tuckr
This blog post is a comparison of personal, accessible, cloud backup options.https://www.ybrikman.com/blog/2026/02/03/computer-backup-options
This article explains why OpenTelemetry no longer recommends the batch processor for production durability-sensitive pipelines. It compares in-memory batching with exporter-level persistent queues and shows how the newer approach improves recovery during collector restarts.https://www.dash0.com/blog/why-the-opentelemetry-batch-processor-is-going-away-eventually
This article introduces tfplan2md, a tool that converts Terraform JSON plans into clearer markdown summaries for pull request reviews. It focuses on making plan output easier to understand in Azure DevOps and GitHub workflows.https://levelup.gitconnected.com/create-readable-terraform-plans-for-pull-request-reviews-with-tfplan2md-ea646e00e59b
This write-up presents a pure Terraform framework where 50+ teams deploy infrastructure using simple tfvars while platform teams maintain reusable building blocks. It highlights native lookup patterns, automated PR updates, and significant boilerplate reduction without adding preprocessing layers.https://dev.to/jverhoeks/-scaling-terraform-across-many-teams-a-native-framework-for-platform-engineering-3n0b
Creative ideas for speeding up queries in PostgreSQLhttps://hakibenita.com/postgresql-unconventional-optimizations
I think the entire DevOps movement was a mighty, twenty year battle to achieve one thing: a single feedback loop connecting devs with prod. On those grounds, it failed.https://www.honeycomb.io/blog/you-had-one-job-why-twenty-years-of-devops-has-failed-to-do-it
The work around RegreSQL led me to focus a lot on buffers. If you are a casual PostgreSQL user, you have probably heard about adjusting shared_buffers and followed the good old advice to set it to 1/4 of available RAM. But after we went a little bit too enthusiastic about them on a recent Postgres FM episode I've been asked what that's all about. Buffers are one of those topics that easily gets forgotten. And while they are a foundation block of PostgreSQL's performance architecture, most of us treat them as a black box. This article is going to attempt to change that.https://boringsql.com/posts/introduction-to-buffers
For years, PostgreSQL has been one of the most critical, under-the-hood data systems powering core products like ChatGPT and OpenAI’s API. As our user base grows rapidly, the demands on our databases have increased exponentially, too. Over the past year, our PostgreSQL load has grown by more than 10x, and it continues to rise quickly.https://openai.com/index/scaling-postgresql
Elasticsearch may work great in initial testing and development but Production is a different story. This blog is about what happens after you ship: the JVM tuning, the shard math, the 3 AM pages, the sync pipelines that break silently. The stuff your ops team lives with. After years of teams running Elasticsearch in production, certain patterns keep emerging. The same issues show up in blog posts, Stack Overflow questions, and incident reports. We've compiled ten of the most common ones below, with references to the engineers who've documented them. We’ve also added images to make it easy to quickly skim through it and compare the challenges against Postgres. TLDR: With great power comes great operational complexity.https://www.tigerdata.com/blog/10-elasticsearch-production-issues-how-postgres-avoids-them
When code gets cheap operational excellence wins. Anyone can build a greenfield demo, but it takes engineering to run a service.https://swizec.com/blog/the-future-of-software-engineering-is-sre
pre-commit is a framework to run hooks written in many languages, and it manages the language toolchain and dependencies for running the hooks.https://github.com/j178/prek
alena@perplexity.ai.zerobrew applies uv's model to Mac packages. Packages live in a content-addressable store (by sha256), so reinstalls are instant. Downloads, extraction, and linking run in parallel with aggressive HTTP caching. It pulls from Homebrew's CDN, so you can swap brew for zb with your existing commands. This leads to dramatic speedups, up to 5x cold and 20x warm.https://github.com/lucasgelfond/zerobrew
