DevOps&SRE Library
Библиотека статей по теме DevOps и SRE. Реклама: @ostinostin Контент: @mxssl РКН: https://www.gosuslugi.ru/snet/67704b536aa9672b963777b3
Show more📈 Analytical overview of Telegram channel DevOps&SRE Library
Channel DevOps&SRE Library (@devopslibrary) in the English language segment is an active participant. Currently, the community unites 19 870 subscribers, ranking 6 592 in the Technologies & Applications category and 33 022 in the Russia region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 19 870 subscribers.
According to the latest data from 08 November, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -26 over the last 30 days and by -5 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 12.80%. Within the first 24 hours after publication, content typically collects 6.49% reactions from the total number of subscribers.
- Post reach: On average, each post receives 2 544 views. Within the first day, a publication typically gains 1 289 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 0.
- Thematic interests: Content is focused on key topics such as kubernete, cluster, infrastructure, storage, configuration.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“Библиотека статей по теме DevOps и SRE.
Реклама: @ostinostin
Контент: @mxssl
РКН: https://www.gosuslugi.ru/snet/67704b536aa9672b963777b3”
Thanks to the high frequency of updates (latest data received on 09 November, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.
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
