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 19 390 مشترک است و جایگاه 6 923 را در دسته فناوری و برنامهها و رتبه 34 735 را در منطقه روسيا دارد.
📊 شاخصهای مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 19 390 مشترک جذب کرده است.
بر اساس آخرین دادهها در تاریخ 23 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 66 و در ۲۴ ساعت گذشته برابر -12 بوده و همچنان دسترسی گستردهای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 14.63% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 7.14% واکنش نسبت به کل مشترکان کسب میکند.
- دسترسی پستها: هر پست به طور میانگین 2 837 بازدید دریافت میکند. در اولین روز معمولاً 1 384 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 24 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.
Gamedays are one of the most effective ways we proactively uncover gaps in our systems and processes. At Datadog, we regularly run a variety of gamedays to intentionally stress our platforms and learn how our systems and teams respond under real-world conditions. These exercises help us surface hidden vulnerabilities, strengthen our operational readiness, and continually raise the bar for our infrastructure. During one such gameday, a simulated zonal failure introduced targeted disruptions in an availability zone on a staging environment by inducing network latency, which exposed a weakness in our PostgreSQL architecture. Several of our Kubernetes-based PostgreSQL clusters had primary or writer nodes running in the affected availability zone. As network latency spiked, those primaries could no longer communicate reliably with their replicas. Replication lag quickly grew, writes stalled, and applications began serving stale data. Because no replica was sufficiently up to date, failover wasn’t safe and the clusters were effectively stuck. We rely on PostgreSQL as the backend database for many Datadog products, and this architecture has served us well under normal conditions. But the gameday revealed an uncomfortable truth: In the face of certain network failures, our setup prioritized availability over durability in ways that left us with no safe recovery path. In practice, this meant the primary continued accepting writes even while replication to replicas was delayed due to elevated network latency. The system remained writable, but replication lag continued to grow, and replicas drifted further behind the primary. As a result, failover candidates could no longer be promoted safely without risking data loss. We were left with only one viable option: wait for latency to subside and for replicas to catch up. We set out to fix this failure mode. Our goal was to make failover both automatic and safe, without compromising PostgreSQL’s performance characteristics more than necessary. To do this, we rearchitected our PostgreSQL deployment to use synchronous replication for failover candidates, coordinated by Patroni, an open source high-availability manager. In this post, we’ll walk through how we redesigned our Kubernetes-based PostgreSQL clusters for failover safety, how we balanced durability against latency, and what we learned while validating this approach through benchmarking and failure testing.https://www.datadoghq.com/blog/engineering/postgresql-ha-kubernetes
How Airbnb built a Kubernetes sidecar to deliver dynamic configuration reliably at scale.https://medium.com/airbnb-engineering/sitar-agent-building-a-reliable-dynamic-configuration-sidecar-at-scale-b7e00c152068
We investigated why firmware updates were causing our core servers to take four hours to reboot.https://blog.cloudflare.com/optimizing-core-unit-boot-time
Spontaneous swarming of responders might seem like a nuisance that breaks our tidy mental models of incident response, but it's actually very powerful.https://greatcircle.com/blog/2026/03/24/swarming-is-a-feature
If you serve LLMs on Kubernetes without inference-aware routing, your load balancer is likely wasting inference capacity. Generic HTTP traffic management blindly routes requests, assuming the backends in your cluster are interchangeable. But your model-serving backends are stateful and unevenly prepared to handle any given request. As a result, requests are often routed to the backend that’s not the one best suited to respond. Migrating to Gateway API gives you a more capable foundation for traffic management and opens the door to inference-aware routing. The Kubernetes Gateway API’s Inference Extension routes requests based on backend serving state, which tends to make better use of cluster capacity and reduce request latency. In this post, we’ll look at how the Inference Extension works, the routing strategies it enables, and the signals you can use to monitor whether inference-aware routing is behaving as intended in production.https://www.datadoghq.com/blog/llm-routing-kubernetes-inference-extension/
Practically all of my work happens inside a terminal. Git, kubectl, tmux, ssh'ing into a server, open practically the entire day. Something I use that much has to be fast. Any lag in opening a new tab, typing a character or hitting tab for a completion is something I feel hundreds of times a day. It's death by a thousand cuts.https://mijndertstuij.nl/posts/life-is-too-short-for-a-slow-terminal
Long-running, fault-tolerant SQL functions for teams that already keep their state in Postgres and want to stop stitching together cron jobs, workers, queues, and status tables to make background work reliable. Define the workflow in SQL, let pg_durable checkpoint each step, and resume after crashes, restarts, or failed steps. Durable execution is now a standard industry pattern, and pg_durable brings it inside Postgres with no extra service infrastructure required. Part of our mission to bring compute close to data.https://github.com/microsoft/pg_durable
Zero-config, fast io_uring-based HTTPS server. zeroserve serves a website packaged as a tarball, and handles hot-reload via SIGHUP.https://github.com/losfair/zeroserve
sem is a semantic version control tool that works on top of Git. It parses your code with tree-sitter, extracts every function, class, and method as an entity, and diffs at the entity level instead of lines. This means you see "function blahh was modified" instead of "lines x-y changed."https://github.com/Ataraxy-Labs/sem
A Golang-based Redis operator that will make/oversee Redis standalone, cluster, replication, and sentinel mode setup on top of Kubernetes. It can create Redis setups with best practices on Cloud as well as the bare metal environment. Also, it provides an in-built monitoring capability using redis-exporter.https://github.com/OT-CONTAINER-KIT/redis-operator
The fix took us down a rabbit hole of Next.js caching internals, Kubernetes networking, and a Redis Pub/Sub setup.https://strapi.io/blog/fixing-isr-revalidation-across-kubernetes-replicas-on-strapi
Base64 is a reversible encoding, not a security mechanism.https://segfaultpw.substack.com/p/sre-secrets-management-in-kubernetes
Kubermatic just released SecureGuard — an open-source secrets management platform built on OpenBao and External Secrets Operator.https://dmuix.medium.com/i-setup-kubermatic-secureguard-before-it-even-existed-03137e825c3a
اکنون در دسترس! پژوهش تلگرام ۲۰۲۵ — مهمترین بینشهای سال 
