This content originally appeared on DEV Community and was authored by Tandap Noel Bansikah
Introduction
Serverless computing has revolutionized how we build and deploy applications. In this comprehensive guide, I'll walk you through building a complete serverless Todo API using AWS services and Terraform. We'll cover everything from basic concepts to production-ready deployment, including comprehensive documentation and best practices.
What is Serverless Computing?
Serverless computing is a cloud computing model where you don't manage servers directly. Instead, the cloud provider automatically handles server provisioning, scaling, and maintenance. You only pay for the actual compute time your code uses.
Key Benefits:
- No server management: No need to provision, configure, or maintain servers
- Auto-scaling: Automatically scales up or down based on demand
- Pay-per-use: Only pay for actual execution time
- High availability: Built-in redundancy and fault tolerance
- Faster development: Focus on code, not infrastructure
Our Project: Serverless Todo API
This is the what we are going to build ,a complete serverless Todo API with the following architecture:
Technologies Used:
- AWS Lambda: Serverless compute for business logic
- API Gateway: RESTful HTTP endpoints
- DynamoDB: Serverless NoSQL database
- Terraform: Infrastructure as Code
- Python: Lambda function runtime
Project Overview
Our Todo API provides three main endpoints:
-
GET /tasks- List all tasks -
POST /tasks- Create a new task -
DELETE /tasks/{id}- Delete a specific task
Key Features:
- RESTful API design: Clean, standard HTTP endpoints
- Automatic scaling: Handles traffic spikes without manual intervention
- Pay-per-use pricing: Cost-effective for variable workloads
- Comprehensive error handling: Robust error management and responses
- Complete documentation: Extensive guides and examples
- Infrastructure as Code: Reproducible deployments with Terraform
Architecture Deep Dive
1. AWS Lambda
Lambda is the heart of our serverless application. It runs our Python code in response to HTTP requests from API Gateway.
Configuration:
- Runtime: Python 3.11
- Memory: 128 MB
- Timeout: 30 seconds
- Handler:
lambda_function.lambda_handler
Example Lambda Function:
import json
import boto3
import uuid
from datetime import datetime
def lambda_handler(event, context):
try:
http_method = event['httpMethod']
path = event['path']
if http_method == 'GET' and path == '/tasks':
return get_all_tasks()
elif http_method == 'POST' and path == '/tasks':
return create_task(event)
elif http_method == 'DELETE' and path.startswith('/tasks/'):
task_id = path.split('/')[-1]
return delete_task(task_id)
else:
return create_response(404, {'error': 'Endpoint not found'})
except Exception as e:
return create_response(500, {'error': 'Internal server error'})
2. Amazon API Gateway
API Gateway acts as the front door for our application, handling HTTP requests and routing them to Lambda.
Features:
- RESTful API endpoints
- Automatic HTTPS
- CORS support
- Request/response transformation
- Built-in monitoring
3. Amazon DynamoDB
DynamoDB is our serverless NoSQL database that automatically scales based on demand.
Configuration:
- Billing Mode: Pay-per-request
- Primary Key:
id(String) - No minimum capacity requirements
Data Structure:
{
"id": "uuid-here",
"title": "Task title",
"description": "Task description",
"completed": false,
"created_at": "2025-08-23T11:05:09.335843",
"updated_at": "2025-08-23T11:05:09.335843"
}
Infrastructure as Code with Terraform
We use Terraform to define and manage our infrastructure. This ensures:
- Reproducible deployments
- Version control for infrastructure
- Easy environment management
- Cost tracking and optimization
Key Terraform Files:
-
main.tf- Provider configuration -
lambda.tf- Lambda function and IAM roles -
api_gateway.tf- API Gateway configuration -
dynamodb.tf- DynamoDB table -
variables.tf- Input variables -
outputs.tf- Output values
Example Terraform Configuration:
# Lambda function
resource "aws_lambda_function" "todo_api" {
filename = local.lambda_zip_path
function_name = "${var.project_name}-${var.environment}"
role = aws_iam_role.lambda_role.arn
handler = "lambda_function.lambda_handler"
runtime = var.lambda_runtime
timeout = var.lambda_timeout
memory_size = var.lambda_memory_size
environment {
variables = {
DYNAMODB_TABLE = aws_dynamodb_table.tasks.name
ENVIRONMENT = var.environment
}
}
}
Deployment Process
1. Prerequisites
# Install required tools
brew install terraform # macOS
pip install awscli
aws configure
2. Deploy Infrastructure
# Clone the repository
git clone https://github.com/bansikah22/aws-serverless-demo.git
cd aws-serverless-demo
# Deploy using our automated script
./scripts/deploy.sh
3. Test the API
# Get the API URL from Terraform output
API_URL=$(terraform output -raw api_gateway_url)
# Test creating a task
curl -X POST $API_URL \
-H "Content-Type: application/json" \
-d '{"title": "Learn Serverless", "description": "Build amazing apps"}'
# List all tasks
curl $API_URL
Cost Optimization
One of the biggest advantages of serverless is cost optimization. Our setup uses pay-per-request pricing, which provides significant cost benefits compared to traditional server-based architectures.
Cost Benefits:
- No idle costs: You only pay when your code is actually running
- Automatic scaling: No need to provision capacity for peak loads
- Pay-per-use: Charges are based on actual usage, not reserved capacity
- Free tier: AWS provides generous free tiers for development and testing
- Predictable pricing: Costs scale linearly with usage
Cost Optimization Strategies:
- Use appropriate memory allocation for Lambda functions
- Implement efficient database queries in DynamoDB
- Monitor usage patterns to optimize performance
- Leverage AWS free tier for development and testing
Security Best Practices
1. IAM Roles and Policies
We follow the principle of least privilege:
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"dynamodb:GetItem",
"dynamodb:PutItem",
"dynamodb:DeleteItem",
"dynamodb:Scan"
],
"Resource": "arn:aws:dynamodb:region:account:table/tasks"
}
]
}
2. API Gateway Security
- HTTPS only (automatic)
- CORS headers configured
- Request validation
- Rate limiting capabilities
3. DynamoDB Security
- Encryption at rest (automatic)
- Encryption in transit (TLS)
- IAM-based access control
Monitoring and Observability
CloudWatch Integration
- Lambda Logs: Automatic logging with structured data
- API Gateway Metrics: Request count, latency, error rates
- DynamoDB Metrics: Read/write capacity, throttled requests
Example Monitoring Setup:
# View Lambda logs
aws logs tail /aws/lambda/serverless-todo-api-dev --follow
# Get API Gateway metrics
aws cloudwatch get-metric-statistics \
--namespace AWS/ApiGateway \
--metric-name Count \
--dimensions Name=ApiName,Value=serverless-todo-api-dev
Performance Characteristics
Response Times:
Performance varies based on several factors including memory allocation, code complexity, and AWS region. Here are typical characteristics:
- Cold Start: Initial request may take 100-500ms (depends on runtime and memory)
- Warm Start: Subsequent requests typically 10-100ms
- Database Operations: DynamoDB operations usually 1-20ms
- Total Response Time: Varies based on complexity, typically 50-500ms
Factors Affecting Performance:
- Memory allocation: More memory = faster execution
- Code optimization: Efficient algorithms and minimal dependencies
- AWS region: Closer regions have lower latency
- Database query complexity: Simple queries are faster
- Concurrent requests: Lambda can handle multiple requests simultaneously
Scalability:
- Lambda: Automatically scales from 0 to thousands of concurrent executions
- API Gateway: Handles millions of requests per second
- DynamoDB: Scales automatically based on demand without manual intervention
Monitoring Performance:
# View Lambda execution times
aws logs tail /aws/lambda/serverless-todo-api-dev --follow
# Check API Gateway metrics
aws cloudwatch get-metric-statistics \
--namespace AWS/ApiGateway \
--metric-name Latency \
--dimensions Name=ApiName,Value=serverless-todo-api-dev
Development Workflow
1. Local Development
# Create Lambda package
./scripts/create_lambda_package.sh
# Plan Terraform changes
make plan
# Deploy changes
make deploy
2. Testing
# Test API endpoints
make test API_URL=https://your-api-url/dev
# Run linting and formatting
make format
make lint
3. Cleanup
# Destroy infrastructure
make destroy
Lessons Learned
1. Infrastructure as Code is Essential
Terraform made our deployments reproducible and version-controlled. We can easily recreate the entire infrastructure in different environments.
2. Documentation is Key
We created comprehensive documentation including:
- Architecture explanations
- API testing guides
- Makefile usage
- Contributing guidelines
3. Error Handling Matters
Proper error handling in Lambda functions is crucial for production applications. We implemented comprehensive error handling with appropriate HTTP status codes.
4. Monitoring is Critical
CloudWatch integration provides valuable insights into application performance and helps identify issues quickly.
Future Enhancements
Potential Improvements:
- Authentication: Add AWS Cognito for user management
- Caching: Implement CloudFront for static content
- CI/CD: Automated deployment pipeline with GitHub Actions
- Frontend: React/Vue.js web application
- Advanced Features: Task categories, search, filtering
- Monitoring: Enhanced CloudWatch dashboards
Conclusion
Building a serverless application with AWS and Terraform provides numerous benefits:
- Cost-effective: Pay only for what you use
- Scalable: Automatic scaling based on demand
- Maintainable: Infrastructure as Code
- Secure: Built-in security features
- Fast: Quick development and deployment
Our Todo API demonstrates how to build a production-ready serverless application with proper documentation, testing, and deployment practices.
Get Started
Ready to build your own serverless application? Check out our complete project:
The repository includes:
- Complete source code: Ready-to-deploy Lambda functions and Terraform configurations
- Comprehensive documentation: Detailed guides for every aspect of the project
- Deployment scripts: Automated deployment and testing scripts
- Testing guides: Complete API testing workflows and examples
- Architecture explanations: In-depth explanations of serverless concepts
Resources
- AWS Lambda Documentation
- Amazon API Gateway Documentation
- Amazon DynamoDB Documentation
- Terraform Documentation
- Serverless Framework
Happy coding! 🚀
Feel free to reach out if you have questions or want to contribute to the project!
This content originally appeared on DEV Community and was authored by Tandap Noel Bansikah
Tandap Noel Bansikah | Sciencx (2025-08-23T12:06:03+00:00) Building a Serverless Todo API with AWS and Terraform: A Complete Learning Guide. Retrieved from https://www.scien.cx/2025/08/23/building-a-serverless-todo-api-with-aws-and-terraform-a-complete-learning-guide/
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