DevOps&SRE Library
Библиотека статей по теме DevOps и SRE. Реклама: @ostinostin Контент: @mxssl РКН: https://www.gosuslugi.ru/snet/67704b536aa9672b963777b3
نمایش بیشتر📈 تحلیل کانال تلگرام DevOps&SRE Library
کانال DevOps&SRE Library (@devopslibrary) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 19 385 مشترک است و جایگاه 6 952 را در دسته فناوری و برنامهها و رتبه 34 902 را در منطقه روسيا دارد.
📊 شاخصهای مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 19 385 مشترک جذب کرده است.
بر اساس آخرین دادهها در تاریخ 10 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 154 و در ۲۴ ساعت گذشته برابر 7 بوده و همچنان دسترسی گستردهای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 15.22% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 7.12% واکنش نسبت به کل مشترکان کسب میکند.
- دسترسی پستها: هر پست به طور میانگین 2 949 بازدید دریافت میکند. در اولین روز معمولاً 1 380 بازدید جمعآوری میشود.
- واکنشها و تعامل: مخاطبان بهطور فعال حمایت میکنند؛ میانگین واکنش به هر پست 1 است.
- علایق موضوعی: محتوا بر موضوعات کلیدی مانند kubernete, cluster, infrastructure, storage, configuration تمرکز دارد.
📝 توضیح و سیاست محتوایی
نویسنده این فضا را محل بیان دیدگاههای شخصی توصیف میکند:
“Библиотека статей по теме DevOps и SRE.
Реклама: @ostinostin
Контент: @mxssl
РКН: https://www.gosuslugi.ru/snet/67704b536aa9672b963777b3”
به لطف بهروزرسانیهای پرتکرار (آخرین داده در تاریخ 11 ژوئن, 2026)، کانال همواره بهروز و دارای دسترسی بالاست. تحلیلها نشان میدهد مخاطبان بهطور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته فناوری و برنامهها تبدیل کردهاند.
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
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
Реклама. ООО «Отус онлайн-образование», ОГРН 1177746618576, erid: 2VtzqwTHk9ZThese 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 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
اکنون در دسترس! پژوهش تلگرام ۲۰۲۵ — مهمترین بینشهای سال 
