What is AWS Lambda?​

AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers. You just upload your code, and Lambda handles the rest—scaling, patching, high availability, etc.

Key Concepts

  • Event-driven: Lambda runs your code in response to events such as HTTP requests (API Gateway), file uploads (S3), or database changes (DynamoDB).
  • Stateless: No persistent state. Each invocation is independent.
  • Runtime support: Supports Python, Node.js, Java, Go, .NET Core, Ruby, and custom runtimes via Lambda Runtime API.
  • Execution model: Your function runs inside a container for the duration of the invocation.

Advantages

  • No Server Management – You don’t manage infrastructure.
  • Auto Scaling – Scales automatically with the number of events.
  • Pay-as-you-go – Charged only for execution time and number of requests.
  • Quick Deployment – Easy to deploy, update, and rollback.
  • Built-in Monitoring – CloudWatch integration.
  • Integrates with many AWS services – API Gateway, S3, DynamoDB, SNS, SQS, EventBridge, and more.

Limitations

  • Runtime Duration – Max 15 minutes per invocation
  • Memory – 128 MB to 10 GB (affects CPU power too)
  • Disk Space (/tmp) – 512 MB of temporary storage
  • Package Size – 250 MB unzipped (50 MB zipped for direct upload)
  • Concurrency – 1,000 concurrent executions by default (can be increased)
  • Cold Start – Delay when function is invoked after a period of inactivity
  • State Management – Stateless by design, use external storage (e.g., S3, DynamoDB)

Real-World Use Cases & Scenarios

1. Real-Time File Processing

Event Source: S3
Use Case: Resize image, transcode video, extract metadata when files are uploaded to S3.

2. Serverless Web APIs

Event Source: API Gateway
Use Case: Build RESTful APIs without servers using Lambda + API Gateway + DynamoDB.

3. Chatbot Backend

Event Source: Lex or API Gateway
Use Case: Process user messages, call third-party APIs, return response via Lambda.

4. Data Validation / ETL

Event Source: Kinesis / DynamoDB Streams / S3
Use Case: Stream-based validation, filtering, and transformation.

5. Notifications & Alerts

Event Source: CloudWatch / SNS / EventBridge
Use Case: Send alerts based on system metrics, logs, or events.

6. CRON Jobs

Event Source: CloudWatch Scheduled Events
Use Case: Scheduled tasks like nightly DB cleanup, data sync.

7. Authentication & Authorization

Event Source: API Gateway (custom authorizer)
Use Case: JWT token verification, user-based access control logic.

8. Email Parsing

Event Source: SES (Simple Email Service)
Use Case: Read incoming emails, parse contents, and store data.

9. IoT Data Processing

Event Source: AWS IoT Core
Use Case: Process telemetry data from smart devices.

10. Machine Learning Inference

Event Source: API Gateway / EventBridge
Use Case: Run lightweight ML models in Lambda (use external models if large).

Lambda Lifecycle and Architecture

1. Invocation Models

  • Synchronous: API Gateway, ALB

  • Asynchronous: S3, SNS, EventBridge

  • Streaming: Kinesis, DynamoDB Streams, MSK

2. Execution Context

  • Reused for performance (warm start)

  • Each invocation gets its own memory and CPU slice

3. Error Handling

  • Retries for async calls

  • DLQs (Dead Letter Queues)

  • Destinations (success/failure)

Best Practices

  • Keep functions small and focused.

  • Avoid large dependencies.

  • Use environment variables for configuration.

  • Monitor with CloudWatch Metrics, Logs, and X-Ray.

  • Manage permissions with IAM roles.

  • Externalize state (don’t store in /tmp or assume reuse).

  • Secure Lambda with least-privilege policies.

Resources