AWS Lambda Deployment

This guide will walk you through the process of deploying Data Neuron as a serverless application on AWS Lambda.

Prerequisites

• An AWS account
• AWS CLI installed and configured
• Serverless Framework installed (npm install -g serverless)
• Python 3.8 or later

Deployment Steps

1. Prepare Your Project

   Create a new directory for your Lambda function:

   mkdir data-neuron-lambda && cd data-neuron-lambda

2. Install Dependencies

   Create a requirements.txt file with the following content:

   dataneuron[mysql]  # Replace 'mysql' with your specific database requirement

3. Create a Serverless Configuration

   Create a serverless.yml file:

   service: data-neuron-api
   
   provider:
     name: aws
     runtime: python3.8
     stage: ${opt:stage, 'dev'}
     region: ${opt:region, 'us-east-1'}
   
   functions:
     app:
       handler: dataneuron.lambda_handler
       events:
         - http:
             path: /{proxy+}
             method: ANY
       environment:
         CLAUDE_API_KEY: ${env:CLAUDE_API_KEY}
         # Add any other environment variables your app needs
   
   plugins:
     - serverless-python-requirements
   
   custom:
     pythonRequirements:
       dockerizePip: non-linux

4. Deploy

   Deploy your function to AWS Lambda:

   serverless deploy

Considerations for Serverless Deployment

• Cold Starts: Be aware of cold start times for Lambda functions. Consider using provisioned concurrency for frequently accessed functions.
• Database Connections: Ensure your database configuration is set up to handle serverless environments, potentially using connection pooling.
• Environment Variables: Use AWS Lambda environment variables to store sensitive information like API keys and database credentials.
• Timeouts: Configure appropriate timeouts for your Lambda function based on expected query complexity.
• Dependencies: Certain database adapters or libraries may require additional setup. For example, MSSQL adapters or the wkhtmltopdf library for PDF generation may need to be included as Lambda Layers.

Monitoring and Logging

• Use AWS CloudWatch to monitor your Lambda function's performance and logs.
• Implement proper error handling and logging in your application code.