fa
Feedback
DevOps & SRE notes

DevOps & SRE notes

رفتن به کانال در Telegram

Helpful articles and tools for DevOps&SRE WhatsApp: https://whatsapp.com/channel/0029Vb79nmmHVvTUnc4tfp2F For paid consultation (RU/EN), contact: @tutunak All ways to support https://telegra.ph/How-support-the-channel-02-19

نمایش بیشتر

📈 تحلیل کانال تلگرام DevOps & SRE notes

کانال DevOps & SRE notes (@devops_sre_notes) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 12 732 مشترک است و جایگاه 9 995 را در دسته فناوری و برنامه‌ها و رتبه 2 933 را در منطقه الولايات المتحدة الأمريكية دارد.

📊 شاخص‌های مخاطب و پویایی

از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 12 732 مشترک جذب کرده است.

بر اساس آخرین داده‌ها در تاریخ 23 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 209 و در ۲۴ ساعت گذشته برابر 7 بوده و همچنان دسترسی گسترده‌ای حفظ شده است.

  • وضعیت تأیید: تأیید نشده
  • نرخ تعامل (ER): میانگین تعامل مخاطب 14.26% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 3.58% واکنش نسبت به کل مشترکان کسب می‌کند.
  • دسترسی پست‌ها: هر پست به طور میانگین 1 812 بازدید دریافت می‌کند. در اولین روز معمولاً 455 بازدید جمع‌آوری می‌شود.
  • واکنش‌ها و تعامل: مخاطبان به‌طور فعال حمایت می‌کنند؛ میانگین واکنش به هر پست 7 است.
  • علایق موضوعی: محتوا بر موضوعات کلیدی مانند kubernete, cluster, author, engineering, monitoring تمرکز دارد.

📝 توضیح و سیاست محتوایی

نویسنده این فضا را محل بیان دیدگاه‌های شخصی توصیف می‌کند:
Helpful articles and tools for DevOps&SRE WhatsApp: https://whatsapp.com/channel/0029Vb79nmmHVvTUnc4tfp2F For paid consultation (RU/EN), contact: @tutunak All ways to support https://telegra.ph/How-support-the-channel-02-19

به لطف به‌روزرسانی‌های پرتکرار (آخرین داده در تاریخ 24 ژوئن, 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.

12 732
مشترکین
+724 ساعت
+217 روز
+20930 روز
آرشیو پست ها
Shopify discovered that deeply nested, high-cardinality GraphQL queries were bottlenecking not on I/O, but on CPU-bound field resolver execution driven by GraphQL’s standard depth-first traversal model. To solve this, Shopify built "GraphQL Cardinal," a breadth-first execution engine that resolves each field once across all objects rather than recursively per object, vastly reducing platform overhead and resolving N+1 issues more efficiently. https://shopify.engineering/faster-breadth-first-graphql-execution

A utility for fetching Kubernetes Manifest documents from a running cluster. This utility can be run inside or outside a Kubernetes cluster, and utilizes a config file to determine what kind of objects to detect. Manifests files are stored in an output directory in the format: <outputDir>/<kind>/<namespace>/<name>.yaml https://github.com/grafana/k8s-manifest-tail

Airbnb migrated its high-volume metrics infrastructure to adopt the OpenTelemetry Protocol (OTLP) and Prometheus. To do so without massive disruption, they implemented a dual-emit strategy in their shared metrics libraries. They encountered and solved specific performance bottlenecks regarding high-cardinality data and replaced their legacy Veneur aggregator with a custom-sharded vmagent setup. Crucially, they developed a "zero injection" technique to solve systemic undercounting issues when translating StatsD-style counters into Prometheus cumulative counters. https://medium.com/airbnb-engineering/building-a-high-volume-metrics-pipeline-with-opentelemetry-and-vmagent-c714d6910b45

The article explores the newly introduced CloudWatch Logs delivery feature for Amazon EKS Auto Mode. https://shinyaz.com/en/blog/2026/03/19/eks-auto-mode-enhanced-logging

The primary bottleneck in software delivery is no longer writing code (thanks to AI-assisted development) but rather post-commit infrastructure operations, which are traditionally built for human interaction rather than machine autonomy. It positions Crossplane and Kubernetes-native control planes as the necessary solution, advocating for "API-first infrastructure." https://www.cncf.io/blog/2026/03/20/crossplane-and-ai-the-case-for-api-first-infrastructure/

ING tackled developer portal sprawl (60+ disparate tools) by adopting Backstage.io as their unified front-end standard. The talk outlines their specific architectural choices and governance models to scale Backstage without it becoming a monolithic bottleneck or crashing due to community plugins. - To prevent a single bad plugin from crashing the portal, ING separates core services (like the software catalog, which handles hundreds of thousands of entities and has dedicated DB tuning) from community/external plugins, running them on separate instances. - To avoid costly rewrites of legacy services, internal teams can use a backend proxy plugin to connect existing backend tools into the Backstage UI. - Built a custom plugin to solve ownership issues in complex, cross-domain workflows. - Because anyone can contribute, ING enforces a "Contribution Plugin" workflow - They drove adoption by focusing heavily on Developer Experience (local setups, playgrounds) while simultaneously having their Technology Standards Board mandate Backstage for all new internal UI initiatives. https://tldrecap.tech/posts/2026/backstagecon-europe/ing-backstage-scaling-developer-platform/

CLI tool for linting and testing Helm charts https://github.com/helm/chart-testing

The new DNSTracking feature in the Red Hat network observability operator 1.11, which now captures DNS query names directly via eBPF without additional configuration. https://developers.redhat.com/articles/2026/04/09/how-dns-name-tracking-enhances-network-observability#

When you have a special math to calculate your uptime, you always have 100%.
When you have a special math to calculate your uptime, you always have 100%.

Repost from N/a
kagent runs your agents where your workloads already live — on Kubernetes. Deploy, observe, and govern AI agents with the tools your platform team already trusts. Open source. Production grade. Built by the founders of Istio. https://github.com/kagent-dev/kagent

I found a good example of why autoscaling based only on CPU utilization can cause an outage. About a week ago, Twingate had an incident that affected us as a client. They've published a postmortem, and it's a good example of why CPU isn't a good metric to rely on when autoscaling your services.
The incident was triggered by elevated network latency affecting communication paths used by the Authorization service. As requests took longer to complete, individual service instances were able to process fewer requests than normal.

This reduction in throughput exposed a limitation in our auto-scaling configuration, which primarily relied on CPU utilization to determine service capacity requirements.
So, from the CPU utilization perspective, everything was OK, but the number of processed requests decreased. https://status.twingate.com/incidents/49qvqk7swjpq

Networking within container orchestration can often seem like a black box to developers. This explanation aims to demystify Kubernetes CNI providers and how they manage connectivity. https://medium.com/@csinclair11/demystifying-kubernetes-cni-providers-5ed79569c797

The article details how to implement production-grade distributed tracing for complex multi-agent AI workflows using OpenTelemetry. https://developers.redhat.com/articles/2026/04/06/distributed-tracing-agentic-workflows-opentelemetry#

Many organizations are looking for more efficient logging solutions than the traditional stack. This comparison highlights a modern alternative to ELK that aims to reduce complexity and resource usage. https://osuite.io/articles/modern-alternative-to-elk

kro | Kube Resource Orchestrator https://github.com/kubernetes-sigs/kro

This informative post details a clever method for securing Grafana dashboards when using Google Cloud Identity-Aware Proxy. You will learn how to seamlessly integrate these two powerful technologies for enhanced access control. https://www.vidbregar.com/blog/grafana-gcp-iap

Managing expenses in the cloud requires a strategic approach beyond just looking at bills. A senior engineer shares valuable insight into optimizing costs effectively in this detailed read. https://medium.com/@razkevich8/cloud-cost-optimization-a-senior-engineers-guide-d49ed4606de1

A popular & widely deployed Open Source Container Native Storage platform for Stateful Persistent Applications on Kubernetes. https://github.com/openebs/openebs

The observability market is shifting from volume-based data ingestion to a value-driven model due to the unsustainable costs of scaling cloud-native and AI workloads. Driven by innovations like Chronosphere’s "Logs 2.0" and its subsequent acquisition by Palo Alto Networks, the industry is prioritizing "signal discipline"—retaining only actionable telemetry—and integrating observability directly into broader AI and security platforms. https://siliconangle.com/2026/02/05/observability-cost-ai-scale-chronosphere-opensourcesummit/

One left
One left