DevOps&SRE Library
Библиотека статей по теме DevOps и SRE. Реклама: @ostinostin Контент: @mxssl РКН: https://www.gosuslugi.ru/snet/67704b536aa9672b963777b3
Mostrar más📈 Análisis del canal de Telegram DevOps&SRE Library
El canal DevOps&SRE Library (@devopslibrary) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 19 414 suscriptores, ocupando la posición 6 932 en la categoría Tecnologías y Aplicaciones y el puesto 34 727 en la región Rusia.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 19 414 suscriptores.
Según los últimos datos del 19 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 123, y en las últimas 24 horas de -3, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 14.85%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 7.26% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 2 883 visualizaciones. En el primer día suele acumular 1 409 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 1.
- Intereses temáticos: El contenido se centra en temas clave como kubernete, cluster, infrastructure, storage, configuration.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Библиотека статей по теме DevOps и SRE.
Реклама: @ostinostin
Контент: @mxssl
РКН: https://www.gosuslugi.ru/snet/67704b536aa9672b963777b3”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 20 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Tecnologías y Aplicaciones.
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
Реклама. ООО «Отус онлайн-образование», ОГРН 1177746618576, erid: 2VtzquePyj3Set up a development environment on any infrastructure, with a single command.https://github.com/daytonaio/daytona
Ingestr is a command-line application that allows you to ingest data from any source into any destination using simple command-line flags, no code necessary.https://github.com/bruin-data/ingestr
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