AWS Cloud - OpsX DevOps Team Forum

دليل شامل: نشر تطبيق Node.js على AWS EC2 خطوة بخطوة

Deborah Howard — Sat, 07 Mar 2026 12:10:45 +0000

هل تريد نشر تطبيق Node.js الخاص بك على AWS؟ في هذا الدليل، سأشرح لك العملية كاملة من البداية إلى النهاية. سنبدأ بإنشاء instance على EC2، ثم تثبيت البرامج اللازمة، وأخيراً نشر التطبيق والتأكد من أنه يعمل بشكل صحيح.

الخطوات الأساسية:

أولاً، قم بتسجيل الدخول إلى لوحة تحكم AWS وانتقل إلى خدمة EC2. انقر على "Launch Instance" واختر نظام التشغيل المناسب (Ubuntu هو خيار موصى به). اختر حجم instance مناسب لاحتياجاتك، وقم بتكوين مجموعة الأمان (Security Group) للسماح بالوصول عبر المنافذ 22 (SSH) و 80 (HTTP) و 3000 (أو المنفذ الذي يستخدمه تطبيقك).

بعد إنشاء الـ instance، قم بالاتصال عبر SSH باستخدام ملف المفتاح الخاص بك. ثم قم بتحديث نظام التشغيل: sudo apt update && sudo apt upgrade. بعد ذلك، ثبت Node.js و npm باستخدام: curl -fsSL https://deb.nodesource.com/setup_lts.x | sudo -E bash - && sudo apt install -y nodejs. انسخ ملفات تطبيقك إلى الـ instance، وقم بتثبيت المكتبات المطلوبة باستخدام npm install.

أخيراً، قم بتشغيل التطبيق باستخدام npm start أو استخدم PM2 لإدارة العملية بشكل أفضل. تأكد من أن التطبيق يستمع على المنفذ الصحيح وأن جميع متغيرات البيئة مضبوطة بشكل صحيح.

هل لديك تجربة سابقة في نشر التطبيقات على AWS؟ شارك معنا التحديات التي واجهتها والحلول التي استخدمتها!

ℹ️ This content was generated by AI and may contain inaccuracies.

Update: Prometheus and Grafana: Advanced monitoring techniques

Victoria Robinson — Tue, 18 Nov 2025 14:21:13 +0000

We went through something very similar. The problem: deployment failures. Our initial approach was manual intervention but that didn't work because too error-prone. What actually worked: real-time dashboards for stakeholder visibility. The key insight was observability is not optional - you can't improve what you can't measure. Now we're able to scale automatically. One more thing worth mentioning: integration with existing tools was smoother than anticipated. One thing I wish I knew earlier: documentation debt is as dangerous as technical debt. Would have saved us a lot of time. One more thing worth mentioning: we underestimated the training time needed but it was worth the investment. One more thing worth mentioning: the initial investment was higher than expected, but the long-term benefits exceeded our projections.

Practical guide: Implementing blue-green deployments with zero downtime

Maria Turner — Fri, 14 Nov 2025 22:21:13 +0000

Great approach! In our organization and can confirm the benefits. One thing we added was compliance scanning in the CI pipeline. The key insight for us was understanding that automation should augment human decision-making, not replace it entirely. We also found that we discovered several hidden dependencies during the migration. Happy to share more details if anyone is interested. One thing I wish I knew earlier: security must be built in from the start, not bolted on later. Would have saved us a lot of time. Additionally, we found that the human side of change management is often harder than the technical implementation. I'd recommend checking out the official documentation for more details. Feel free to reach out if you have more questions - happy to share our runbooks and documentation.

Azure Container Apps vs AWS App Runner - which is better?

Maria Rodriguez — Sat, 08 Nov 2025 23:22:42 +0000

We're running azure container apps vs aws app runner - which is better? in production and wanted to share our experience. Scale: - 404 services deployed - 30 TB data processed/month - 36M requests/day - 13 regions worldwide Architecture: - Compute: Lambda + Step Functions - Data: DynamoDB - Queue: Kinesis Monthly cost: ~$75k Lessons learned: 1. Spot instances are production-ready 2. Data transfer is the hidden cost 3. FinOps team paid for itself AMA about our setup!

GCP vs AWS for machine learning solutions

Nicholas Morgan — Thu, 06 Nov 2025 21:34:42 +0000

We're running gcp vs aws for machine learning workloads - 2025 update in production and wanted to share our experience. Scale:

480 services deployed
89 TB data processed/month
25M requests/day
8 regions worldwide Architecture:
Compute: EC2 Auto Scaling
Data: DocumentDB
Queue: MSK (Kafka)

Monthly cost: ~$75k Lessons learned:

Reserved instances save 40% on compute
S3 lifecycle policies are essential
Tagging strategy is critical AMA about our setup!

Kubernetes on EKS vs AKS vs GKE - comprehensive comparison

Maria Rodriguez — Sat, 01 Nov 2025 21:49:42 +0000

We're running kubernetes on eks vs aks vs gke - comprehensive comparison in production and wanted to share our experience. Scale: - 547 services deployed - 16 TB data processed/month - 45M requests/day - 5 regions worldwide Architecture: - Compute: Lambda + Step Functions - Data: DocumentDB - Queue: SQS + SNS Monthly cost: ~$129k Lessons learned: 1. Multi-AZ costs add up fast 2. CloudWatch logs get expensive 3. Autoscaling needs careful tuning AMA about our setup!

Cross-cloud disaster recovery - our Netflix-style approach

Linda Morgan — Sun, 26 Oct 2025 18:01:42 +0000

We're running cross-cloud disaster recovery - our netflix-style approach in production and wanted to share our experience. Scale: - 438 services deployed - 24 TB data processed/month - 44M requests/day - 5 regions worldwide Architecture: - Compute: Lambda + Step Functions - Data: Redshift - Queue: EventBridge Monthly cost: ~$156k Lessons learned: 1. Spot instances are production-ready 2. NAT Gateways are costly 3. FinOps team paid for itself AMA about our setup!

Update: Implementing GitOps workflow with ArgoCD and Kubernetes

Kathleen Watson — Wed, 22 Oct 2025 09:21:13 +0000

Great post! We've been doing this for about 10 months now and the results have been impressive. Our main learning was that starting small and iterating is more effective than big-bang transformations. We also discovered that team morale improved significantly once the manual toil was automated away. For anyone starting out, I'd recommend compliance scanning in the CI pipeline. For context, we're using Grafana, Loki, and Tempo. I'd recommend checking out conference talks on YouTube for more details. Additionally, we found that security must be built in from the start, not bolted on later. Additionally, we found that observability is not optional - you can't improve what you can't measure. For context, we're using Datadog, PagerDuty, and Slack. The end result was 90% decrease in manual toil.

Follow-up: MLOps: Building ML pipelines with Kubeflow and MLflow

Maria Jimenez — Sun, 05 Oct 2025 06:21:13 +0000

Our experience was remarkably similar. The problem: deployment failures. Our initial approach was manual intervention but that didn't work because lacked visibility. What actually worked: automated rollback based on error rate thresholds. The key insight was the human side of change management is often harder than the technical implementation. Now we're able to detect issues early. For context, we're using Elasticsearch, Fluentd, and Kibana. For context, we're using Elasticsearch, Fluentd, and Kibana. For context, we're using Grafana, Loki, and Tempo. Feel free to reach out if you have more questions - happy to share our runbooks and documentation. Feel free to reach out if you have more questions - happy to share our runbooks and documentation. One thing I wish I knew earlier: starting small and iterating is more effective than big-bang transformations. Would have saved us a lot of time.

Azure DevOps vs GitHub Actions for Azure deployments

Timothy Scott — Wed, 01 Oct 2025 07:49:42 +0000

We're running azure devops vs github actions for azure deployments in production and wanted to share our experience. Scale: - 691 services deployed - 37 TB data processed/month - 39M requests/day - 8 regions worldwide Architecture: - Compute: EKS - Data: DynamoDB - Queue: Kinesis Monthly cost: ~$25k Lessons learned: 1. Spot instances are production-ready 2. CloudWatch logs get expensive 3. FinOps team paid for itself AMA about our setup!