AWS Lambda SnapStart: Cold Start Zero

🚀 Demystifying AWS Lambda – The Power of Serverless Computing AWS Lambda is a serverless computing service that lets you run code without provisioning or managing servers. It automatically scales based on demand and charges only for the compute time you use. 🔑 Key Concepts to Know: Serverless → Focus on writing code while AWS manages infrastructure. Event-driven → Functions run in response to events (S3 uploads, DB changes, API calls). Function as a Service (FaaS) → Execute small, independent functions on demand. Function → Your business logic packaged with configuration. Runtime → Execution environment (Python, Node.js, Java, .NET, etc.). Handler → Entry point for your function (filename.method_name). Event → JSON input representing the trigger data. Context → Runtime info (request ID, memory, timeout). Trigger → AWS service/resource that invokes your function (API Gateway, S3, DynamoDB, CloudWatch). 🖼️ Hands‑on with AWS Lambda + S3: Building an Image Resizer I recently walked through a practical lab where AWS Lambda automatically resizes images uploaded to S3. Here’s the workflow 👇 🔧 Step‑by‑Step Setup Step 1: Create S3 Buckets Source bucket → incoming uploads Destination bucket → resized thumbnails (region: us-east-1) Step 2: Create Lambda Function Name: ImageResizerFunction Runtime: Python 3.12 Architecture: x86_64 Step 3: Add Pillow Library via Layer Attach public ARN: arn:aws:lambda:us-east-1:770693421928:layer:Klayers-p312-Pillow:4 Step 4: Grant IAM Permissions Attach AmazonS3FullAccess (lab use; production should be restricted). Step 5: Write the Code Lambda downloads the image, resizes to 128×128 thumbnail, and uploads to the destination bucket. Step 6: Configure S3 Trigger Trigger on All object create events in source bucket. Add suffix .jpg to avoid non‑image files. Step 7: Test It! Upload a large .jpg to the source bucket. Within seconds, a resized thumbnail appears in the destination bucket. #AWS #Lambda #Serverless #CloudComputing #DevOps #FaaS

🚨 AWS Lambda Cold Start — The Hidden Latency Trap in Serverless Serverless feels magical… until your first request suddenly takes seconds. 😅 If you’re using AWS Lambda, you’ve likely faced this: --- ## ❄️ What is a Cold Start? When a new request comes in and no execution environment is ready: text Request → Create Container → Init Runtime → Load Code → Execute ⏱️ Result: High latency (cold start delay) --- ## 🔥 Why does it hurt? - First user gets slow response - Spikes in traffic = unpredictable latency - Worse with Java / heavy frameworks --- ## ⚡ Solution: Provisioned Concurrency > “Keep Lambda instances pre-warmed” text Pre-warmed Containers (Ready) │ ▼ Request → Direct Execution → Fast Response 🚀 No container creation No runtime initialization 👉 No cold start (within limits) --- ## ⚠️ But here’s the catch text Provisioned = 5 instances Requests = 8 │ ▼ 5 → Fast (warm) 3 → Cold start ❄️ 👉 It’s not elimination, it’s controlled mitigation --- ## 🧠 Key Takeaway > “Cold start is the cost of serverless abstraction — you trade infra management for startup latency.” --- ## 💡 When should you use it? ✔️ Latency-sensitive APIs ✔️ User-facing endpoints ✔️ Critical workflows ❌ Not needed for async / batch jobs --- Curious — how are you handling cold starts in your systems? Have you tried Provisioned Concurrency or SnapStart? 👇 #AWS #Lambda #Serverless #SystemDesign #BackendEngineering #CloudComputinf #Java #SystemDesign

For years, the AWS Lambda Handler Cookbook was missing one thing I kept putting off: real, production-grade CRUD across multiple functions with a single, unified Swagger. v9.6.0 finally fixes that, thanks to the event handler alpha feature in Powertools for AWS Lambda's event handler. What's new in v9.6.0: 🔧 Create, get, and delete order APIs as micro Lambda functions over DynamoDB 📄 Unified OpenAPI schema generated across all endpoints 🔍 Automated API breaking changes detection in CI 📑 Swagger published to GitHub Pages and always in sync with the code What you get overall in the cookbook template: 🏗️ Production-ready serverless project in Python with CDK infrastructure 🧪 Five testing strategies: unit, integration, infrastructure, security, and E2E ⚙️ CI/CD with GitHub Actions across dev, staging, and production environments 📊 CloudWatch dashboards and alarms with SNS notifications out of the box 🔒 WAF protection, input validation with Pydantic, and idempotent API design 🏷️ Feature flags and dynamic configuration via AppConfig 📈 Business KPI metrics and distributed tracing with Powertools for AWS Lambda Thanks to Leandro Cavalcante Damascena for developing the Powertools OpenAPI feature that enabled the unified schema. I hope you merge it soon :) 🔗 https://lnkd.in/dZe74TCc #AWSLambda #Serverless #AWS #OpenAPI #PowertoolsForAWS #PlatformEngineering

3 weeks ago, I didn’t even know what 𝐀𝐏𝐈 𝐆𝐚𝐭𝐞𝐰𝐚𝐲 was. Today, I shipped a fully serverless URL shortener on AWS , built with Mohan Merugu. 🚀☁️ And honestly? This project taught me more than any course ever could. Here's how it actually went: 𝐖𝐞𝐞𝐤 1 — "How hard can it be?" We set up DynamoDB, wrote the Lambda functions in Python, connected everything to API Gateway. It worked. We felt like geniuses. 𝐖𝐞𝐞𝐤 2 — Reality check. CORS errors. IAM permission issues. CloudFront cache not refreshing. The kind of bugs that don't make sense until suddenly they do. 𝐖𝐞𝐞𝐤 3 — It clicked. Everything connected. User pastes a URL → Lambda shortens it → DynamoDB stores it → short link redirects perfectly. Zero servers managed. What we shipped: ⚙️ lambda_shorten.py — generates a 6-char short code + writes to DynamoDB 🔁 lambda_redirect.py — fetches the URL and returns HTTP 302 redirect 🌍 S3 + CloudFront frontend — fast UI, HTTPS, globally accessible 🔐 iam_policy.json — strict least-privilege access (security-first) This is project #2 in my cloud journey (Azure was #1). Each one teaches something the docs never tell you. 📂 The full repo is public on GitHub. Every file — Lambda functions, IAM policy, frontend, README — is open for anyone who wants to learn how a real cloud project is structured. If you're just starting out, use it. That's exactly why we made it public. 🔗 https://lnkd.in/d7a6dMvm #AWS #Serverless #CloudComputing #BuildInPublic #OpenSource #Lambda #Python #StudentDeveloper #hackerhaibhai

I just finished building a completely asynchronous, bare-metal SaaS architecture to solve a classic enterprise problem: querying massive FinOps datasets without timing out the user’s browser. ☁️ I built out a "Cloud FinOps Analyzer" designed to crunch through 5,000,000 rows of distributed billing logs. To handle the load without API degradation, I completely decoupled the architecture. Here is how the pipeline runs on the backend: The Gateway: A FastAPI server instantly receives the request and drops it into a queue, returning a 202 tracking ID to the frontend. The Broker: A Redis message queue deployed natively on a 3-node Kubernetes (K3s) cluster running on Proxmox. The Compute: Python Celery workers distributed across the K3s cluster pick up the job and execute the heavy GROUP BY aggregations against a dedicated bare-metal PostgreSQL node. The UI: Vanilla JS actively polls the status endpoint until the worker finishes, rendering the dashboard. The biggest win? Swapping standard SQL inserts for binary COPY streams via psycopg to seed those 5 million rows in under two minutes. It is incredibly satisfying to watch the terminal logs light up across different physical nodes when a job drops into the queue! #DevOps #SRE #Kubernetes #FastAPI #Python #Proxmox #PostgreSQL #FinOps

💥 I made a small mistake in Kubernetes… and it broke everything. While deploying MongoDB + Mongo Express on Kubernetes, I hit a scary error: 👉 CreateContainerConfigError At first, I thought something was wrong with my containers, YAML, or even Minikube setup. But the real issue? ❌ Just ONE mismatched name. I used: mongo-secret But actually created: mongodb-secret That’s it. One tiny inconsistency = entire app fails. 👉 What I learned from this: • Kubernetes is VERY strict about naming • Secrets & ConfigMaps must match EXACTLY • kubectl describe pod is your best debugging weapon 👉 After fixing it, everything worked perfectly: ✔ MongoDB connected ✔ Mongo Express UI running ✔ Services communicating via internal DNS If you're learning Kubernetes, don’t just watch tutorials — 👉 build real projects and break things. That’s where real learning happens. 📖 I wrote a full step-by-step guide here: https://lnkd.in/gVCzAhHs 💻 Full source code: https://lnkd.in/g3vgsvQh #Kubernetes #DevOps #MongoDB #CloudNative #Backend #LearningInPublic #100DaysOfCode

Anyone can build an app that works when things go right. I wanted to build a system that survives when things go wrong. Most portfolio projects often end with simple interactions like "user clicks a button, a database updates." I aimed to create something that truly breaks, recovers, and scales. Over the past few weeks, I developed a fully serverless AWS event-driven system that simulates an end-to-end factory production line. https://lnkd.in/dGHN7Tud Instead of a monolithic backend, I designed an event-driven flow where state changes dictate the next action, eliminating manual orchestration and relying solely on events. The Architecture & The "Why": - API Gateway + Cognito (JWT): Securing and throttling the edge. - DynamoDB + Streams: The source of truth, where a payment update automatically triggers the next phase via Streams. - SQS + DLQ: The shock absorbers, decoupling the storefront from the factory floor to prevent traffic spikes from crashing the processing engine. - EventBridge (Scheduler): The watchdog, monitoring for edge cases, such as orders stuck in production for over 24 hours. - SNS: Real-time alerting for inventory drops and factory delays. - Lambda (Python): The stateless glue that holds the business logic together. This project forced me to confront the realities of distributed systems: handling failures gracefully, avoiding tight coupling, and keeping cloud costs near $0 for idle workloads. My next optimization will be implementing ElastiCache to enhance read-heavy paths. I am focusing my work on architectures that not only function but also survive failure. For those building in the serverless space: How do you prefer to manage complex, multi-step workflows without creating a tangled web of dependencies? Step Functions, or pure event choreography? #AWS #Serverless #EventDriven #SoftwareArchitecture #CloudComputing #EventDrivenArchitecture #DistributedSystems #Microservices #SystemDesign #BackendEngineering #AmazonWebServices #CloudNative #AWSLambda #DynamoDB #CloudArchitecture #Python #PythonDeveloper #BackendDeveloper #Coding #SoftwareEngineering #Scalability #Resilience #FinOps

🚀 Flask App Deployed on AWS Elastic Beanstalk (No CLI Required!) Excited to share my latest project where I deployed a Python Flask web application using AWS Elastic Beanstalk, entirely through the AWS Management Console ☁️ 🧑💻 Project by: Avdhoot Lad 🔧 Tech Stack: Python (Flask) Gunicorn (WSGI server) AWS Elastic Beanstalk AWS EC2 🏗️ Architecture (No CLI) User → EC2 → Flask App (Gunicorn) (Managed automatically by Elastic Beanstalk) ✨ Key Highlights: ✅ Simple deployment without any CLI commands ✅ AWS handles EC2 provisioning, environment setup, scaling & monitoring ✅ Production-ready setup with Gunicorn ✅ Easy updates via Upload and Deploy in console 📌 Learnings: How to deploy apps via AWS Console Understanding Elastic Beanstalk environment management Deploying production-ready Python applications 📸 Attached: Custom architecture diagram for No CLI deployment Next steps: 🔹 Add custom domain + HTTPS 🔹 Implement CI/CD pipeline #AWS #Flask #ElasticBeanstalk #CloudComputing #Python #DevOps #WebDevelopment #LearningByDoing

🚀 Serverless in Action: AWS Lambda + S3 Event Trigger As part of my DevOps learning, I built a simple AWS Lambda function that gets triggered whenever a file is uploaded to an S3 bucket. 👉 The function extracts key details like: Bucket name File name File size Here’s a simplified version of the script 👇 import json def lambda_handler(event, context): kp = event['Records'][0]['s3'] bucket = kp['bucket']['name'] file_name = kp['object']['key'] file_size = kp['object']['size'] print(f"Bucket: {bucket}") print(f"File: {file_name}") print(f"Size: {file_size} bytes") 💡 What I learned: ✅ How S3 event notifications trigger Lambda functions ✅ How to parse JSON event structures in Python ✅ Real-time processing of uploaded files ✅ Basics of serverless architecture 🔧 Next steps: Add error handling & logging (CloudWatch) Decode S3 object keys properly Integrate with SNS/Slack for alerts Extend to automate workflows (e.g., file validation, ETL) Serverless is powerful — no servers to manage, just focus on logic 🚀 How are you using AWS Lambda in your projects? #AWS #Lambda #Serverless #DevOps #CloudComputing #Python #S3

Weekend learning update from our System Design Masterclass. This week was even more exciting than usual because we had 3 lectures instead of 2. Along with our regular weekend sessions, we had an extra lecture on Friday focused on building an AWS image pipeline, which made the whole week even more enjoyable. In our system design sessions, we covered: • Database indexing and the trade-offs between clustered and non-clustered indexes • Sharding and replication strategies • Consistent hashing in wide-column databases like Apache Cassandra • Apache Kafka and other queue use cases • Caching strategies, best practices, and common do’s and don’ts • TinyURL / URL shortener design strategies The extra Friday lecture was a hands-on session where we explored an AWS image pipeline: • Upload an image • Convert it to grayscale • Process and store it What I like most about these classes is that they connect theory with practical engineering decisions. They help me understand how systems scale, where trade-offs matter, and how real-world architectures are designed. My next step is to rebuild and test the same image pipeline on my local machine using LocalStack, so I can learn the full flow by implementing it myself. Big credit to Instructor Umer Farooq for making complex topics easy to understand and genuinely engaging. #SystemDesign #SoftwareEngineering #DistributedSystems #ApacheKafka #Caching #Cassandra #AWS #LocalStack #BackendDevelopment #Nodejs #LearningInPublic