Scaling Serverless Architectures: Implementing Scalable API Solutions

This lab focuses on Task Statement 3.2: Implement solutions that are scalable to meet business requirements from the AWS Certified DevOps Engineer - Professional (DOP-C02) exam. You will build a loosely coupled, serverless architecture using Amazon API Gateway and AWS Lambda, then explore how to configure and monitor scaling behaviors.

[!WARNING] Remember to run the teardown commands at the end of the lab to avoid ongoing charges.

Prerequisites

An AWS Account with administrative access.
AWS CLI installed and configured with appropriate credentials.
Basic familiarity with Python (for the Lambda function code).
Terminal/Command line access.

Learning Objectives

Deploy a serverless REST API using Amazon API Gateway and AWS Lambda.
Configure Lambda Concurrency to manage scaling limits and protect downstream resources.
Implement API Gateway Throttling to manage request rates and meet business SLAs.
Verify scaling behavior through active testing.

Architecture Overview

Loading Diagram...

Figure 1 — Mermaid diagram

Step-by-Step Instructions

Step 1: Create an IAM Execution Role for Lambda

Before creating the function, we need a role that allows Lambda to write logs to CloudWatch.

bash

# 1. Create the trust policy file
echo '{"Version": "2012-10-17","Statement": [{"Effect": "Allow","Principal": {"Service": "lambda.amazonaws.com"},"Action": "sts:AssumeRole"}]}' > trust-policy.json

# 2. Create the IAM Role
aws iam create-role --role-name brainybee-lab-lambda-role --assume-role-policy-document file://trust-policy.json

# 3. Attach the basic execution policy
aws iam attach-role-policy --role-name brainybee-lab-lambda-role --policy-arn arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole

▶Console alternative

Navigate to IAM > Roles > Create role. Select Lambda as the service. Add the AWSLambdaBasicExecutionRole managed policy. Name it brainybee-lab-lambda-role.

Step 2: Create the Lambda Function

We will create a simple Python function that simulates a processing delay to observe scaling behavior.

bash

# 1. Create the function code
echo 'import json
import time
def lambda_handler(event, context):
    time.sleep(0.5) # Simulate work
    return {"statusCode": 200, "body": json.dumps("Scaling Success!")}' > lambda_function.py

# 2. Package the code
zip function.zip lambda_function.py

# 3. Deploy the function (Replace <ACCOUNT_ID> with your actual ID)
aws lambda create-function --function-name brainybee-scaling-func \
--zip-file fileb://function.zip --handler lambda_function.lambda_handler --runtime python3.9 \
--role arn:aws:iam::<YOUR_ACCOUNT_ID>:role/brainybee-lab-lambda-role

Step 3: Create the API Gateway

Now we create a REST API to expose our Lambda function via an HTTP endpoint.

bash

# 1. Create the REST API
aws apigateway create-rest-api --name "ScalingLabAPI"

# 2. Note the "id" from the output. We will call this <API_ID>.
# Get the Root Resource ID
aws apigateway get-resources --rest-api-id <API_ID>

# 3. Create a resource and method (using Root Resource ID <ROOT_ID>)
aws apigateway put-method --rest-api-id <API_ID> --resource-id <ROOT_ID> --http-method GET --authorization-type "NONE"

# 4. Set the Lambda integration
aws apigateway put-integration --rest-api-id <API_ID> --resource-id <ROOT_ID> --http-method GET --type AWS_PROXY \
--integration-http-method POST --uri arn:aws:apigateway:<YOUR_REGION>:lambda:path/2015-03-31/functions/arn:aws:lambda:<YOUR_REGION>:<YOUR_ACCOUNT_ID>:function:brainybee-scaling-func/invocations

Step 4: Configure Scaling Limits (Reserved Concurrency)

To ensure this function doesn't consume all account-wide concurrency, we will set a limit.

bash

aws lambda put-function-concurrency --function-name brainybee-scaling-func --reserved-concurrent-executions 5

[!NOTE] By setting reserved concurrency to 5, we guarantee this function can always scale to 5 instances, but never more, protecting our backend if this were a database-heavy task.

Checkpoints

Deployment Check: Run aws lambda get-function --function-name brainybee-scaling-func. You should see the configuration and the concurrency limit of 5.
API Check: Use the AWS Console to "Test" the API Gateway method. It should return a 200 OK with "Scaling Success!".
Permissions Check: Ensure you have added the Lambda permission to allow API Gateway to invoke it:
bash
aws lambda add-permission --function-name brainybee-scaling-func --statement-id apigateway-test \ --action lambda:InvokeFunction --principal apigateway.amazonaws.com

Concept Review

When implementing scalable solutions, DevOps engineers must balance Throughput (requests per second) against Latency (time per request).

Compiling TikZ diagram…

⏳

Running TeX engine…

This may take a few seconds

Figure 2 — TikZ diagram

Feature	Purpose in Scaling	Metric to Watch
Reserved Concurrency	Prevents one function from starving others.	`ConcurrentExecutions`
Provisioned Concurrency	Eliminates "Cold Starts" for low-latency needs.	`ProvisionedConcurrencyUtilization`
API Throttling	Protects API from spikes at the entry point.	`429 Too Many Requests`

Troubleshooting

Error	Likely Cause	Fix
`403 Forbidden`	Missing Lambda permissions.	Run the `add-permission` command in the Checkpoints section.
`429 Too Many Requests`	Hit API Gateway or Lambda scaling limits.	Increase Reserved Concurrency or check API Stage settings.
`504 Gateway Timeout`	Lambda took longer than 29s (API limit).	Optimize Lambda code or use asynchronous patterns.

Stretch Challenge

Implement Provisioned Concurrency: Update the Lambda function to use Provisioned Concurrency of 2. This requires creating a function Alias first. Research how this differs from Reserved Concurrency in terms of "Cold Start" performance.

Cost Estimate

Service	Usage	Estimated Monthly Cost (Free Tier)
AWS Lambda	1 Million Requests/mo	$0.00 (Free Tier Forever)
API Gateway	1 Million Requests/mo	$0.00 (12 months free)
CloudWatch Logs	5GB Ingestion	$0.00 (Free Tier)

Clean-Up / Teardown

[!IMPORTANT] Always delete resources to keep your account clean and avoid unexpected costs.

bash

# 1. Delete the API Gateway
aws apigateway delete-rest-api --rest-api-id <API_ID>

# 2. Delete the Lambda Function
aws lambda delete-function --function-name brainybee-scaling-func

# 3. Detach and delete the IAM Role
aws iam detach-role-policy --role-name brainybee-lab-lambda-role --policy-arn arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole
aws iam delete-role --role-name brainybee-lab-lambda-role

# 4. Remove local files
rm lambda_function.py function.zip trust-policy.json

Scaling Serverless Architectures: Implementing Scalable API Solutions

[!WARNING] Remember to run the teardown commands at the end of the lab to avoid ongoing charges.

Prerequisites

An AWS Account with administrative access.
AWS CLI installed and configured with appropriate credentials.
Basic familiarity with Python (for the Lambda function code).
Terminal/Command line access.

Learning Objectives

Deploy a serverless REST API using Amazon API Gateway and AWS Lambda.
Configure Lambda Concurrency to manage scaling limits and protect downstream resources.
Implement API Gateway Throttling to manage request rates and meet business SLAs.
Verify scaling behavior through active testing.

Architecture Overview

Loading Diagram...

Figure 1 — Mermaid diagram

Step-by-Step Instructions

Step 1: Create an IAM Execution Role for Lambda

Before creating the function, we need a role that allows Lambda to write logs to CloudWatch.

bash

# 1. Create the trust policy file
echo '{"Version": "2012-10-17","Statement": [{"Effect": "Allow","Principal": {"Service": "lambda.amazonaws.com"},"Action": "sts:AssumeRole"}]}' > trust-policy.json

# 2. Create the IAM Role
aws iam create-role --role-name brainybee-lab-lambda-role --assume-role-policy-document file://trust-policy.json

# 3. Attach the basic execution policy
aws iam attach-role-policy --role-name brainybee-lab-lambda-role --policy-arn arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole

▶Console alternative

Navigate to IAM > Roles > Create role. Select Lambda as the service. Add the AWSLambdaBasicExecutionRole managed policy. Name it brainybee-lab-lambda-role.

Step 2: Create the Lambda Function

We will create a simple Python function that simulates a processing delay to observe scaling behavior.

bash

# 1. Create the function code
echo 'import json
import time
def lambda_handler(event, context):
    time.sleep(0.5) # Simulate work
    return {"statusCode": 200, "body": json.dumps("Scaling Success!")}' > lambda_function.py

# 2. Package the code
zip function.zip lambda_function.py

# 3. Deploy the function (Replace <ACCOUNT_ID> with your actual ID)
aws lambda create-function --function-name brainybee-scaling-func \
--zip-file fileb://function.zip --handler lambda_function.lambda_handler --runtime python3.9 \
--role arn:aws:iam::<YOUR_ACCOUNT_ID>:role/brainybee-lab-lambda-role

Step 3: Create the API Gateway

Now we create a REST API to expose our Lambda function via an HTTP endpoint.

bash

# 1. Create the REST API
aws apigateway create-rest-api --name "ScalingLabAPI"

# 2. Note the "id" from the output. We will call this <API_ID>.
# Get the Root Resource ID
aws apigateway get-resources --rest-api-id <API_ID>

# 3. Create a resource and method (using Root Resource ID <ROOT_ID>)
aws apigateway put-method --rest-api-id <API_ID> --resource-id <ROOT_ID> --http-method GET --authorization-type "NONE"

# 4. Set the Lambda integration
aws apigateway put-integration --rest-api-id <API_ID> --resource-id <ROOT_ID> --http-method GET --type AWS_PROXY \
--integration-http-method POST --uri arn:aws:apigateway:<YOUR_REGION>:lambda:path/2015-03-31/functions/arn:aws:lambda:<YOUR_REGION>:<YOUR_ACCOUNT_ID>:function:brainybee-scaling-func/invocations

Step 4: Configure Scaling Limits (Reserved Concurrency)

To ensure this function doesn't consume all account-wide concurrency, we will set a limit.

bash

aws lambda put-function-concurrency --function-name brainybee-scaling-func --reserved-concurrent-executions 5

[!NOTE] By setting reserved concurrency to 5, we guarantee this function can always scale to 5 instances, but never more, protecting our backend if this were a database-heavy task.

Checkpoints

Deployment Check: Run aws lambda get-function --function-name brainybee-scaling-func. You should see the configuration and the concurrency limit of 5.
API Check: Use the AWS Console to "Test" the API Gateway method. It should return a 200 OK with "Scaling Success!".
Permissions Check: Ensure you have added the Lambda permission to allow API Gateway to invoke it:
bash
aws lambda add-permission --function-name brainybee-scaling-func --statement-id apigateway-test \ --action lambda:InvokeFunction --principal apigateway.amazonaws.com

Concept Review

When implementing scalable solutions, DevOps engineers must balance Throughput (requests per second) against Latency (time per request).

Compiling TikZ diagram…

⏳

Running TeX engine…

This may take a few seconds

Figure 2 — TikZ diagram

Feature	Purpose in Scaling	Metric to Watch
Reserved Concurrency	Prevents one function from starving others.	`ConcurrentExecutions`
Provisioned Concurrency	Eliminates "Cold Starts" for low-latency needs.	`ProvisionedConcurrencyUtilization`
API Throttling	Protects API from spikes at the entry point.	`429 Too Many Requests`

Troubleshooting

Error	Likely Cause	Fix
`403 Forbidden`	Missing Lambda permissions.	Run the `add-permission` command in the Checkpoints section.
`429 Too Many Requests`	Hit API Gateway or Lambda scaling limits.	Increase Reserved Concurrency or check API Stage settings.
`504 Gateway Timeout`	Lambda took longer than 29s (API limit).	Optimize Lambda code or use asynchronous patterns.

Stretch Challenge

Cost Estimate

Service	Usage	Estimated Monthly Cost (Free Tier)
AWS Lambda	1 Million Requests/mo	$0.00 (Free Tier Forever)
API Gateway	1 Million Requests/mo	$0.00 (12 months free)
CloudWatch Logs	5GB Ingestion	$0.00 (Free Tier)

Clean-Up / Teardown

[!IMPORTANT] Always delete resources to keep your account clean and avoid unexpected costs.

bash

# 1. Delete the API Gateway
aws apigateway delete-rest-api --rest-api-id <API_ID>

# 2. Delete the Lambda Function
aws lambda delete-function --function-name brainybee-scaling-func

# 3. Detach and delete the IAM Role
aws iam detach-role-policy --role-name brainybee-lab-lambda-role --policy-arn arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole
aws iam delete-role --role-name brainybee-lab-lambda-role

# 4. Remove local files
rm lambda_function.py function.zip trust-policy.json