Introduction
In modern software engineering, the shift toward serverless architecture provides unparalleled scalability and cost efficiency. AWS Lambda makes it easier to deploy lightweight, event-driven functions, while tools like Terraform simplify infrastructure as code (IaC) processes. In this blog, I'll walk you through the creation of an AWS Lambda-powered application, highlighting the use of Terraform, shell scripts, and custom integrations with services like Twitter, Google Sheets, and Brevo (formerly Sendinblue).
The complete project codebase is available on GitHub: Python-Tech_Bot (AWS Lambda Implementation).
Why AWS?
When building this application, I chose AWS for the following reasons:
- Scalability: AWS Lambda automatically scales to handle the load, making it a great fit for applications with variable traffic.
- Integration: AWS offers seamless integration with other services like EventBridge, DynamoDB, and S3.
- Cost Efficiency: Lambda’s pay-as-you-go model minimizes costs, especially for small or infrequent workloads.
- Global Availability: AWS’s infrastructure ensures that applications remain highly available and responsive.
Application Overview
The application includes:
- AWS Lambda to run Python-based serverless functions.
- Terraform for provisioning infrastructure.
- Shell scripts to streamline packaging and deployment processes.
- Twitter API for automated posting.
- Google Sheets API for dynamic data storage and retrieval.
- Brevo Messaging API to send alerts and notifications.
Setting Up Infrastructure with Terraform
Using Terraform allowed me to standardize the infrastructure. Here's a breakdown of the core resources defined in my Terraform files:
- IAM Role and Policies: The Lambda function required permissions to log events and access external APIs like Twitter and Google Sheets.
- Lambda Function and Layer: The function handled logic, while the Lambda Layer packaged dependencies.
- EventBridge Rule: Configured to invoke the Lambda every 8 hours.
Here’s the Terraform configuration snippet for the EventBridge rule:
resource "aws_cloudwatch_event_rule" "every_8_hours" {
name = "every-8-hours-rule"
schedule_expression = "rate(8 hours)"
}
resource "aws_cloudwatch_event_target" "lambda_target" {
rule = aws_cloudwatch_event_rule.every_8_hours.name
target_id = "lambda-target"
arn = aws_lambda_function.tech_job_bot.arn
}
resource "aws_lambda_permission" "allow_eventbridge" {
statement_id = "AllowEventBridgeInvoke"
action = "lambda:InvokeFunction"
function_name = aws_lambda_function.tech_job_bot.function_name
principal = "events.amazonaws.com"
source_arn = aws_cloudwatch_event_rule.every_8_hours.arn
}
Accelerating Deployment with Shell Scripts
To save time and reduce errors, I created shell scripts to automate Lambda packaging and deployment.
Packaging Lambda Layer:
#!/bin/bash
# build.sh - Automates Lambda Layer packaging
cd lambda/part_time_pulse
pip install -r layer_requirements.txt -t python/
zip -r lambda_layer.zip python
rm -rf python
Packaging the Lambda Function:
#!/bin/bash
# build_app.sh - Packages Lambda application
cd lambda/part_time_pulse
zip application.zip main.py
These scripts ensure that every deployment is consistent, removing the possibility of missing dependencies or misconfigured files.
Application Logic: Rate Limiting and Backoff
The Lambda function interacts with the Twitter and Google Sheets APIs, both of which have strict rate limits. To manage these effectively, I implemented rate-limiting and exponential backoff logic.
Example: Rate Limiting with Backoff
import time
import requests
def rate_limited_request(url, headers, max_retries=5):
for attempt in range(max_retries):
response = requests.get(url, headers=headers)
if response.status_code == 429: # Rate limit exceeded
wait_time = 2 ** attempt
print(f"Rate limit hit. Retrying in {wait_time} seconds...")
time.sleep(wait_time)
else:
return response.json()
raise Exception("Max retries reached. Request failed.")
This code ensures that the application gracefully handles API rate limits without manual intervention.
Brevo Messaging for Notifications
To send alerts and updates, I integrated Brevo’s Messaging API. This was crucial for notifying users of critical updates or application results.
Example: Sending a Notification
from brevo_api import SendEmail
def send_notification(recipient_email, subject, message):
email = SendEmail(api_key="YOUR_BREVO_API_KEY")
email.send_email(
to=recipient_email,
subject=subject,
message=message
)
Google Sheets for Dynamic Data
Google Sheets acts as a lightweight database, storing data like user information or configuration settings. Using the gspread library, I could fetch and update sheet data in real time.
Example: Reading from Google Sheets
import gspread
gc = gspread.service_account(filename='credentials.json')
sheet = gc.open("PartTimePulse Data").sheet1
def get_data_from_sheet():
rows = sheet.get_all_records()
return rows
Automating Social Media with Twitter
To post updates to Twitter, I used the tweepy library, which offers an easy-to-use interface for interacting with Twitter's API.
Example: Posting a Tweet
import tweepy
def post_tweet(api_key, api_secret, access_token, access_secret, message):
auth = tweepy.OAuthHandler(api_key, api_secret)
auth.set_access_token(access_token, access_secret)
api = tweepy.API(auth)
api.update_status(message)
This feature enables the application to post scheduled updates, boosting user engagement on social media platforms.
Lessons Learned
Terraform for Modular and Scalable IaC: The modular design in Terraform allowed me to abstract reusable components, making infrastructure replication and scaling straightforward. Organizing resources with modules improves maintainability and fosters team collaboration.
Optimized CI/CD with Shell Automation: Automating deployment pipelines with shell scripts not only eliminated manual errors but also paved the way for future enhancements like integrating CI/CD tools (e.g., Jenkins, GitHub Actions). These scripts act as a lightweight prelude to advanced automation systems.
Advanced API Rate-Limiting Strategies: Handling API rate limits required implementing exponential backoff and retries while ensuring state persistence across retries. Extending this with dynamic rate adjustments, based on API response headers (like
Retry-After), could further optimize request throughput.Enhanced API Integrations for Business Agility: Leveraging APIs like Google Sheets and Brevo demonstrated how external tools can serve as lightweight, cost-effective solutions for managing business processes. Moving forward, transitioning to fully managed databases or messaging systems might offer better control over scaling and latency.
Serverless Architecture Observability: AWS CloudWatch Logs became invaluable tools for debugging and performance monitoring in a serverless context. Understanding cold starts, execution latency, and API throttling bottlenecks provided insights for refining both infrastructure and application logic.
Security at Every Layer: IAM roles and least-privilege access principles ensured secure interactions between AWS services and external APIs. Token management strategies for third-party APIs were essential for preventing unauthorized access and maintaining compliance.
Cost Monitoring for Scalability: Lambda’s pay-as-you-go model requires diligent monitoring of execution time and invocation frequency to prevent runaway costs. Adding cost tracking tools like AWS Budgets or third-party platforms ensures financial predictability while scaling.
Conclusion
By leveraging AWS Lambda, Terraform, and a suite of API integrations, this application delivers efficient and scalable functionality. Whether you're scheduling social media posts, managing data dynamically, or sending user notifications, the tools and techniques covered in this blog can serve as a foundation for your next serverless project.
Have questions or thoughts about this project? Let’s discuss in the comments below!
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