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 19 870 مشترک است و جایگاه 6 592 را در دسته فناوری و برنامهها و رتبه 33 022 را در منطقه روسيا دارد.
📊 شاخصهای مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 19 870 مشترک جذب کرده است.
بر اساس آخرین دادهها در تاریخ 08 نوامبر, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر -26 و در ۲۴ ساعت گذشته برابر -5 بوده و همچنان دسترسی گستردهای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 12.80% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 6.49% واکنش نسبت به کل مشترکان کسب میکند.
- دسترسی پستها: هر پست به طور میانگین 2 544 بازدید دریافت میکند. در اولین روز معمولاً 1 289 بازدید جمعآوری میشود.
- واکنشها و تعامل: مخاطبان بهطور فعال حمایت میکنند؛ میانگین واکنش به هر پست 0 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 09 نوامبر, 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.
Alaz is an open-source Ddosify eBPF agent that can inspect and collect Kubernetes (K8s) service traffic without the need for code instrumentation, sidecars, or service restarts. This is possible due to its use of eBPF technology. Alaz can create a Service Map that helps identify golden signals and problems like: - High latencies between K8s services - Detect 5xx HTTP status codes - Detect Idle / Zombie services - Detect slow SQL querieshttps://github.com/ddosify/alaz
The local volume static provisioner manages PersistentVolume lifecycle for pre-allocated disks by detecting and creating PVs for each local disk on the host, and cleaning up the disks when released. It does not support dynamic provisioning.https://github.com/kubernetes-sigs/sig-storage-local-static-provisioner
A node autoscaler project for Proxmox allowing a Kubernetes cluster to dynamically scale across a Proxmox cluster.https://github.com/lupinelab/kproximate
Реклама. ООО «Отус онлайн-образование», ОГРН 1177746618576, www.otus.ru, erid: 2VtzqvpoGdbNote: This blog post demonstrates how to create a lightweight Internal Developer Platform without relying on Backstage, while still empowering you and your developers with a self-service approach. By utilizing GitOps with Argo CD and leveraging Kubernetes labels, this method offers a streamlined and efficient solution for managing and deploying your infrastructure.https://itnext.io/build-a-lightweight-internal-developer-platform-with-argo-cd-and-kubernetes-labels-4c0e52c6c0f4
Retina is a cloud-agnostic, open-source Kubernetes network observability platform that provides a centralized hub for monitoring application health, network health, and security. It provides actionable insights to cluster network administrators, cluster security administrators, and DevOps engineers navigating DevOps, SecOps, and compliance use cases.https://github.com/microsoft/retina
jnv is designed for navigating JSON, offering an interactive JSON viewer and jq filter editor.https://github.com/ynqa/jnv
OpenStatus is open-source synthetic monitoring platform with beautiful status page and incident management. We are building it publicly for everyone to see our progress. We believe great softwares are built this way.https://github.com/openstatusHQ/openstatus
Реклама. ООО «Отус онлайн-образование», ОГРН 1177746618576, www.otus.ru, erid : 2Vtzqw1wpJ1This blog post describes Uber’s journey towards utilizing hardware efficiently via better load balancing. The work described here lasted over a year, involved engineers across multiple teams, and delivered significant efficiency savings. The article covers the technical solutions and our discovery process to get to them–in many ways, the journey was harder than the destination.https://www.uber.com/en-HR/blog/load-balancing-handling-heterogeneous-hardware
Grafana Mimir is our open source, horizontally scalable, multi-tenant time series database, which allows us to ingest beyond 1 billion active series. Mimir ingesters use consistent hashing, a distributed hashing technique for data replication. This technique guarantees a minimal number of relocation of time series between available ingesters when some ingesters are added or removed from the system. Unfortunately, we noticed that the consistent hashing algorithm previously used by Mimir ingesters caused an uneven distribution of time series between ingesters, with load distribution differences going up to 25%. As a consequence, some ingesters were overwhelmed, while the others were underused. In order to solve this problem, we came up with a novel algorithm, called spread-minimizing token generation strategy, that allows us to benefit from the consistent hashing on one side and from an almost perfect load distribution on the other side. Uniform load balancing optimizes network performance and reduces latency as the demand is equally distributed among ingesters. This allows for better usage of compute resources, which leads to more consistent performance. In this blog post, we introduce our new algorithm and show how it improved ingesters load balancing in some of our production clusters for Grafana Cloud Metrics (which is powered by Mimir) to the degree that it’s now almost perfect.https://grafana.com/blog/2024/03/07/how-we-improved-ingester-load-balancing-in-grafana-mimir-with-spread-minimizing-tokens
Measuring developer productivity is a difficult challenge. Conventional metrics focused on development cycle time and throughput are limited, and there aren't obvious answers for where else to turn. Qualitative metrics offer a powerful way to measure and understand developer productivity using data derived from developers themselves. Organizations should prioritize measuring developer productivity using data from humans, rather than data from systems.https://martinfowler.com/articles/measuring-developer-productivity-humans.html
