DevOps & SRE notes
前往频道在 Telegram
Helpful articles and tools for DevOps&SRE youtube: https://www.youtube.com/@DevOpsAndSRENotes
显示更多📈 Telegram 频道 DevOps & SRE notes 的分析概览
频道 DevOps & SRE notes (@devops_sre_notes) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 13 326 名订阅者，在 技术与应用 类别中位列第 9 202，并在 美国 地区排名第 2 711 位。
📊 受众指标与增长动态
自 невідомо 创建以来，项目保持高速增长，吸引了 13 326 名订阅者。
根据 05 十月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 47，过去 24 小时变化为 2，整体触达仍然可观。
- 认证状态： 未认证
- 互动率 (ER)： 平均受众互动率为 16.21%。内容发布后 24 小时内通常能获得 4.46% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 2 161 次浏览，首日通常累积 594 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 3。
- 主题关注点： 内容集中在 kubernete, cluster, author, engineering, monitoring 等核心主题上。
📝 描述与内容策略
作者将该频道定位为表达主观观点的平台：
“Helpful articles and tools for DevOps&SRE
youtube: https://www.youtube.com/@DevOpsAndSRENotes”
凭借高频更新（最新数据采集于 06 十月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为 技术与应用 类别中的关键影响点。
13 326
订阅者
+224 小时
+117 天
+4730 天
帖子存档
13 332
Realtime log viewer with web UI, tail -f for logs with a web interface browser.
https://github.com/logdyhq/logdy-core
13 332
Securing every Kubernetes workload at scale — LinkedIn Engineering
https://www.linkedin.com/blog/engineering/infrastructure/securing-every-kubernetes-workload-at-scale
13 332
Failure is inevitable: Learning from a large outage, and building for reliability in depth at Datadog — Datadog Engineering
https://www.datadoghq.com/blog/engineering/rethinking-reliability/
13 332
A Kubernetes operator designed to intelligently manage resource overcommit on pod resource requests.
https://github.com/InditexTech/k8s-overcommit-operator
13 332
A practical guide to building a local multi-cluster platform engineering lab with vind, Sveltos, and Argo CD. It covers GitOps, label-based deployments, drift correction, and the networking issues you’ll encounter along the way.
https://itnext.io/local-platform-engineering-on-your-laptop-vind-sveltos-and-argocd-2b3e1341ebe7
13 332
I always use this service before every deployment. I’ve had 100% uptime ever since: https://deploytarot.com/
13 332
Mercado Libre runs its observability platform, O11y events, on ClickHouse Cloud to answer granular, business-level questions like why a specific payment failed.
The team built O11y events to take troubleshooting from days to minutes, with full business-flow visibility and high-cardinality filtering on identifiers like payment and user IDs.
Migrating to ClickHouse Cloud increased query performance by 50x and delivered up to 89% data compression, enabling them to scale from 7 million spans per minute to 400 million and growing in ClickHouse.
https://clickhouse.com/blog/mercado-libre-observability-on-clickhouse-cloud
13 332
A fast, structural YAML diff tool — in a single-dependency binary
https://github.com/szhekpisov/diffyml
