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DevOps&SRE Library

DevOps&SRE Library

前往频道在 Telegram

Библиотека статей по теме DevOps и SRE. Реклама: @ostinostin Контент: @mxssl РКН: https://www.gosuslugi.ru/snet/67704b536aa9672b963777b3

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📈 Telegram 频道 DevOps&SRE Library 的分析概览

频道 DevOps&SRE Library (@devopslibrary) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 19 395 名订阅者,在 技术与应用 类别中位列第 6 952,并在 俄罗斯 地区排名第 34 902

📊 受众指标与增长动态

невідомо 创建以来,项目保持高速增长,吸引了 19 395 名订阅者。

根据 10 六月, 2026 的最新数据,频道保持稳定运转。过去 30 天订阅人数变化为 154,过去 24 小时变化为 7,整体触达仍然可观。

  • 认证状态: 未认证
  • 互动率 (ER): 平均受众互动率为 15.22%。内容发布后 24 小时内通常能获得 7.12% 的反应,占订阅者总量。
  • 帖子覆盖: 每篇帖子平均可获得 2 949 次浏览,首日通常累积 1 380 次浏览。
  • 互动与反馈: 受众积极参与,单帖平均反应数为 1
  • 主题关注点: 内容集中在 kubernete, cluster, infrastructure, storage, configuration 等核心主题上。

📝 描述与内容策略

作者将该频道定位为表达主观观点的平台:
Библиотека статей по теме DevOps и SRE. Реклама: @ostinostin Контент: @mxssl РКН: https://www.gosuslugi.ru/snet/67704b536aa9672b963777b3

凭借高频更新(最新数据采集于 11 六月, 2026),频道始终保持新鲜度与高覆盖。分析显示受众积极互动,使其成为 技术与应用 类别中的关键影响点。

19 395
订阅者
+724 小时
+67
+15430
帖子存档
A Comprehensive Comparison of Cloud Backup Tools
This blog post is a comparison of personal, accessible, cloud backup options.
https://www.ybrikman.com/blog/2026/02/03/computer-backup-options

Why the OpenTelemetry Batch Processor is Going Away (Eventually)
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

Create readable terraform plans for pull request reviews with tfplan2md
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

Scaling Terraform Across many Teams: A Native Framework for Platform Engineering
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

Unconventional PostgreSQL Optimizations
Creative ideas for speeding up queries in PostgreSQL
https://hakibenita.com/postgresql-unconventional-optimizations

OpenTelemetry Collector vs agent: How to choose the right telemetry approach https://www.cncf.io/blog/2026/02/02/opentelemetry-collector-vs-agent-how-to-choose-the-right-telemetry-approach

“You Had One Job”: Why Twenty Years of DevOps Has Failed to Do it
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

Is the future of MySQL PostgreSQL (Or MariaDB, or TiDB, or ...)? https://stokerpostgresql.blogspot.com/2026/01/is-future-of-mysql-postgresql-or.html

Introduction to Buffers in PostgreSQL
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

How OpenAI Scales Postgres to Power 800 Million ChatGPT Users
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

10 Elasticsearch Production Issues (and How Postgres Avoids Them)
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

The future of software engineering is SRE
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

prek
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

Hi! My good friend is looking for a colleague to join their team. You can check the details of the position and apply here: https://jobs.ashbyhq.com/perplexity/7bce0fcf-eef6-41aa-9243-896f07a0316e If you have additional questions about the position, you can send them to alena@perplexity.ai.

zerobrew
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

whosthere
Local Area Network discovery tool with a modern Terminal User Interface (TUI) written in Go. Discover, explore, and understand your LAN in an intuitive way. Whosthere performs unprivileged, concurrent scans using mDNS and SSDP scanners. Additionally, it sweeps the local subnet by attempting TCP/UDP connections to trigger ARP resolution, then reads the ARP cache to identify devices on your Local Area Network. This technique populates the ARP cache without requiring elevated privileges. All discovered devices are enhanced with OUI lookups to display manufacturers when available. Whosthere provides a friendly, intuitive way to answer the question every network administrator asks: "Who's there on my network?"
https://github.com/ramonvermeulen/whosthere

Kubernetes в продакшене: от CI/CD до безопасности и отказоустойчивости 👩‍💻 Курс по Kubernetes: автоматизируйте инфраструкту
Kubernetes в продакшене: от CI/CD до безопасности и отказоустойчивости 👩‍💻 Курс по Kubernetes: автоматизируйте инфраструктуру и подготовьтесь к CKA/CKAD Пройдите тест и забронируйте место на курсе от OTUS. А так же и получите скидку 🎁 до 15.02.2026 - подробности у менеджера. ➡ ПРОЙТИ ТЕСТ: https://vk.cc/cUeS0D Курс «Инфраструктурная платформа на основе Kubernetes» научит проектировать и запускать платформы для цифровых продуктов: IaC, механизмы K8s, экосистему инструментов и эксплуатацию кластеров. Программа от Express 42 ориентирована на практику и подходит техлидам, архитекторам ПО, разработчикам, DevOps и администраторам. 🎁 Бонус — курс в записи на выбор: - Elastic/OpenSearch Advanced - Углубленное изучение языка Java - GitOps Реклама. ООО «Отус онлайн-образование», ОГРН 1177746618576, erid: 2VtzqwTHk9Z

Owning a $5M data center
These days it seems you need a trillion fake dollars, or lunch with politicians to get your own data center. They may help, but they’re not required. At comma we’ve been running our own data center for years. All of our model training, metrics, and data live in our own data center in our own office. Having your own data center is cool, and in this blog post I will describe how ours works, so you can be inspired to have your own data center too.
https://blog.comma.ai/datacenter

graft
Graft is a CLI tool that brings the Overlay Pattern (similar to Kustomize) to Terraform. It acts as a JIT (Just-In-Time) Compiler, allowing you to apply declarative patches to third-party modules at build time. With Graft, you can treat upstream modules (e.g., from the Public Registry) as immutable base layers and inject your own logic on top—without the maintenance nightmare of forking.
https://github.com/ms-henglu/graft