Prerequisites

Before we start on our exciting journey to create a serverless retail solution, let's ensure we have all the necessary tools:

1. AWS Account: Ensure you have an AWS account with the required permissions to create resources.
2. Terraform: Install Terraform on your local machine for infrastructure management.
3. Barcode Scanner: A barcode scanner is essential for simulating a retail environment, even if it's virtual for testing purposes.

With these prerequisites set, we can proceed to build our endless aisle experience.

Project Scenario

Imagine a bustling retail environment where both associates and customers are on the lookout for that perfect product. Our objective is to provide them with an endless aisle—a digital realm of products that transcends the limitations of physical shelves. Visualize a scenario where a store associate, equipped with a barcode scanner, reveals a world of options for a customer in search of a specific item.

Solution Walkthrough

Step 1: User Authentication

The process begins when a store associate logs into the application using their credentials. The application supports scanning barcodes or SKUs to retrieve product information.

Step 2: Front-end Application

The front-end application converts the scanned SKU into a product number and calls the Get Item API.

# Front-end Application Code

def invoke_get_item_api(product_number):

headers = {"Content-Type": "application/json"}

data = {"product_number": product_number}

try:

response = requests.post(api_url, headers=headers, json=data)

if response.status_code == 200:

return response.json()

else:

response.raise_for_status()

except Exception as e:

print(f"Error invoking Get Item API: {e}")

return None

# Example usage

product_number = "123456"

response = invoke_get_item_api(product_number)

In this code snippet, the function invoke_get_item_api manages the interaction with the Get Item API. It takes a product number as input, constructs a JSON payload, sends a POST request to the API endpoint, and processes the response.

Step 3: Get Item API

The Get Item API is implemented as an AWS Lambda function that manages the API calls.

# Terraform Code for Get Item Lambda

resource "aws_lambda_function" "get_item_lambda" {

function_name = "get-item-lambda"

runtime = "python3.8"

handler = "get_item.handler"

filename = "lambda_code/get_item_lambda.zip"

role = aws_iam_role.lambda_execution_role.arn

memory_size = 256

timeout = 10

tags = {

Name = "GetItemLambda"

}

}

The above Terraform code sets up the Lambda function's configurations, including the runtime, handler, and the location of the function's code.

Step 4: DynamoDB for Partner Information

Amazon DynamoDB will be utilized to store metadata related to partners, such as partner_id and partner_name.

# Terraform Code for DynamoDB Table

resource "aws_dynamodb_table" "partners_table" {

name = "partners_table"

billing_mode = "PAY_PER_REQUEST"

hash_key = "partner_id"

attribute {

name = "partner_id"

type = "S"

}

attribute {

name = "partner_name"

type = "S"

}

tags = {

Name = "PartnersTable"

}

}

This code will create a DynamoDB table to manage partner information effectively.

Explore the process of building serverless applications using Terraform in the video above.

Step 5: Get Item Lambda Processing

The Get Item Lambda function retrieves partner information from the DynamoDB table and processes the API calls.

import boto3

import json

import requests

def fetch_partner_info(partner_id):

dynamodb = boto3.resource('dynamodb')

table = dynamodb.Table("partners_table")

response = table.get_item(Key={'partner_id': partner_id})

return response.get('Item', {})

This function fetches partner information from the DynamoDB table based on the partner_id provided.

Step 6-9: Order Processing

Orders will be sent to an Amazon Simple Queue Service (SQS) queue called create-order-queue.

# Terraform Code for SQS Queue

resource "aws_sqs_queue" "create_order_queue" {

name = "create-order-queue"

max_message_size = 262144

message_retention_seconds = 345600

}

The above code sets up an SQS queue with appropriate configurations for order handling.

The second video will provide comprehensive insights into serverless architecture with Terraform on AWS.

Conclusion

This serverless retail solution for an endless aisle on AWS offers a scalable and adaptable architecture. Utilizing Terraform for infrastructure provisioning ensures that the management is straightforward and reproducible. By following this guide, you will be equipped to implement an efficient endless aisle solution that integrates seamlessly with partner APIs, significantly enhancing the retail experience for both customers and associates.

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Feel free to reach out for further assistance.