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 19 405 suscriptores, ocupando la posición 6 933 en la categoría Tecnologías y Aplicaciones y el puesto 34 700 en la región Rusia.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 19 405 suscriptores.
Según los últimos datos del 21 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 89, y en las últimas 24 horas de -4, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 14.66%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 7.20% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 2 845 visualizaciones. En el primer día suele acumular 1 398 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 22 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.
In distributed systems, failures and latency issues are inevitable. Services can fail due to overloaded servers, network issues, bugs, and various other factors. As engineers building distributed systems, we need strategies to make our services robust and resilient in the face of such failures. One useful technique is using retries.https://www.codereliant.io/retries-backoff-jitter
The microservices architecture adds more moving parts to the overall system, and this doesn’t come for free. The cost of fully embracing microservices is only worth paying if it can be amortized across dozens of development teams.https://robertovitillo.com/costs-of-microservices
This week, we're taking another significant step forward as we get into the critical stack of monitoring and alerting. Now, it's time to equip yourself with the knowledge and tools needed to keep an eye on systems, analyze performance, and respond quickly to any issues that may come up.https://www.codereliant.io/sre-interview-prep-plan-week-3
In this blog post, we aim to expand on the first 5 lessons shared by Google's Site Reliability Engineering team, offering a closer look at practical implementation examples.https://www.codereliant.io/20-sre-lessons-from-google-part1
2VtzqvBXnuEWe often get questions like: - How much data can I put in an Elasticsearch cluster? - How many nodes can an Elasticsearch cluster have? - What’s the biggest cluster that you’ve seen? And while the 14-year-old in me is proud to say that we’ve done 24/7 support for clusters of 1000+ nodes holding many PB of data, I am quick to add that: 1. It doesn’t mean it’s a good idea to have clusters that big. 2. Such generic questions deserve more nuanced answers. Which is exactly what this blog post does. And it applies to OpenSearch as well as for Elasticsearch. And for the most part, to Solr (where the cluster state is stored in Zookeeper).https://sematext.com/blog/elasticsearch-scaling-cluster-state
We've added the ability to lint Dockerfiles on demand in Depot. This post covers the top 10 most common Dockerfile linting issues we've seen flowing through Depot.https://depot.dev/blog/dockerfile-linting-issues
Argo Workflows provides an excellent platform for infrastructure automation, and has replaced Jenkins as my go tool for running scheduled or event-driven automation tasks. In growing my experience with Argo Workflows, I’ve killed clusters, broken workflows and generally made a mess of things. I’ve also built a lot of workflows that needed refactoring as they became difficult to maintain. This blog post aims to share some of the lessons I’ve learned, and some of the patterns I’ve developed, to help you avoid the same mistakes I’ve made.https://hodgkins.io/argo-workflow-proven-patterns-from-production
A Terraform Provider for Namecheap domain DNS configuration.https://github.com/namecheap/terraform-provider-namecheap
Scratch is an open-source alternative to BigQuery, Redshift, and Snowflake. Runs on Clickhouse.https://github.com/scratchdata/ScratchDB
RedisInsight is a visual tool that provides capabilities to design, develop and optimize your Redis application. Query, analyse and interact with your Redis data.https://github.com/RedisInsight/RedisInsight
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