Performance Testing AWS Lambda Functions with Cucumber

Performance testing is critical to ensure that your AWS Lambda functions can handle the expected load and perform efficiently. By leveraging Cucumber, a popular BDD (Behavior Driven Development) tool, you can write clear, human-readable tests that describe how your Lambdas should perform under various conditions. This blog post will guide you through the process of setting up performance tests for AWS Lambda functions using Cucumber, with detailed explanations and relevant code examples.

Why Performance Testing for AWS Lambda?

AWS Lambda allows you to run code without provisioning or managing servers. However, it is essential to ensure that your Lambda functions perform well under load to maintain application performance and user satisfaction. Performance testing helps you identify bottlenecks, optimize resource allocation, and ensure that your functions scale effectively.

Why Use Cucumber?

Cucumber provides a way to write tests in a natural language style, which makes it easier to understand and communicate the test scenarios. Using Gherkin syntax, you can define the expected behavior of your Lambda functions in a format that is easy to read and write.

Setting Up the Environment

To get started, you need to set up your development environment. Make sure you have the following installed:

Node.js: To run your Lambda functions locally.
AWS CLI: To interact with AWS services.
Serverless Framework: To deploy and manage your Lambda functions.
Cucumber: To write and execute your performance tests.
Gatling: To simulate load on your Lambda functions.

Step-by-Step Guide

1. Create a Lambda Function

First, create a simple AWS Lambda function. For this example, let’s create a function that processes an order.

// orderProcessor.js

exports.handler = async (event) => {
  const order = event.order;
  // Simulate order processing
  await new Promise(resolve => setTimeout(resolve, 100));
  
  return {
    statusCode: 200,
    body: JSON.stringify({ message: 'Order processed successfully', orderId: order.id }),
  };
};

2. Deploy the Lambda Function

Use the Serverless Framework to deploy your Lambda function.

# serverless.yml

service: order-service

provider:
  name: aws
  runtime: nodejs14.x

functions:
  orderProcessor:
    handler: orderProcessor.handler
    events:
      - http:
          path: process-order
          method: post

Deploy the function:

serverless deploy

3. Write Cucumber Tests

Next, create a Cucumber test to define the performance criteria for your Lambda function.

# features/performance.feature

Feature: Performance testing for order processing Lambda

  Scenario: Processing a large number of orders
    Given the following orders
      | id  | product  | quantity |
      | 1   | product1 | 10       |
      | 2   | product2 | 20       |
      | ... | ...      | ...      |
    When the orders are processed
    Then the response should be successful
    And the processing time should be less than 200ms per order

4. Implement Cucumber Steps

Implement the steps in your Cucumber tests.

// features/step_definitions/performanceSteps.js

const { Given, When, Then } = require('@cucumber/cucumber');
const axios = require('axios');
const assert = require('assert');

let orders;
let responses;
let startTime;
let endTime;

Given('the following orders', (dataTable) => {
  orders = dataTable.hashes();
});

When('the orders are processed', async () => {
  startTime = new Date().getTime();
  responses = await Promise.all(orders.map(order => 
    axios.post('https://your-api-endpoint/process-order', { order })
  ));
  endTime = new Date().getTime();
});

Then('the response should be successful', () => {
  responses.forEach(response => {
    assert.strictEqual(response.status, 200);
  });
});

Then('the processing time should be less than {int}ms per order', (maxTime) => {
  const totalProcessingTime = endTime - startTime;
  const avgProcessingTime = totalProcessingTime / orders.length;
  assert(avgProcessingTime < maxTime, `Average processing time was ${avgProcessingTime}ms`);
});

5. Simulate Load Using Gatling

Gatling is a powerful tool for simulating load on your Lambda functions. Create a simple Gatling script to simulate concurrent requests.

// OrderProcessorSimulation.scala

import io.gatling.core.Predef._
import io.gatling.http.Predef._

class OrderProcessorSimulation extends Simulation {

  val httpProtocol = http
    .baseUrl("https://your-api-endpoint") 

  val orderProcessorScenario = scenario("Order Processor Load Test")
    .exec(
      http("Process Order")
        .post("/process-order")
        .body(StringBody("""{"order": {"id": 1, "product": "product1", "quantity": 10}}""")).asJson
        .check(status.is(200))
    )

  setUp(
    orderProcessorScenario.inject(
      atOnceUsers(100), 
      rampUsers(1000) during (10 minutes)
    )
  ).protocols(httpProtocol)
}

Run the Gatling simulation to generate load and verify the performance of your Lambda function.

Conclusion

Performance testing AWS Lambda functions with Cucumber allows you to define and verify performance criteria in a clear and understandable way. By combining Cucumber for test definitions and Gatling for load simulation, you can ensure that your Lambda functions perform well under various conditions.

If you found this article helpful and are interested in integrating Cucumber Automation Framework with AWS Lambda, I suggest you check out some of the other articles I’ve written for this series:

If you’re looking for a deeper dive into some of the concepts and specifics discussed in my article, feel free to reach out to me directly or as always you can checkout the official AWS Lambda Developer Documentation for more information.