13.2. How to Configure AWS Lambda: Configuring the AWS Workbench

Página 43

Amazon Web Services (AWS) is a cloud services platform that offers a wide range of products for compute, storage, databases, analytics, networking, mobile devices, developer tools, management tools, IoT, security and corporate applications. AWS Lambda is a service that allows you to run your code without provisioning or managing servers.

Setting up AWS Lambda starts with creating a Lambda function. To do this, you need to have an AWS account. If you don't already have one, you can create one at https://aws.amazon.com/. After creating and logging into your account, go to the AWS Lambda console and click 'Create function'.

On the 'Create Role' page, you have the option of using a role template or creating one from scratch. For this course, we will create a function from scratch. Select 'Author from Scratch' and give your role a name. Under 'Permissions', select 'Create a new function with basic Lambda permissions'. Click 'Create role' to finish.

After creating the role, you will be taken to the role configuration page. Here, you can configure the AWS workbench. In 'Designer' you can add triggers and layers to your function. A trigger is an AWS resource or developer endpoint that launches the function. A layer is a distribution of libraries, a custom runtime, or other resources.

To add a trigger, click 'Add trigger' in the Designer. Select the service you want to use as a trigger. For this course, we will use API Gateway. After selecting the API Gateway, you can configure the trigger settings. Click 'Add' to finish.

To add a layer, click 'Add layer' in the Designer. You can select an AWS tier or provide your own tier. For this course, we will use the AWS Python layer. After selecting the Python layer, click 'Add' to finish.

In the 'Function code' section, you can write your Python code. AWS Lambda supports several programming languages, including Python. You can write your code directly in the AWS Lambda console or you can upload a .zip or .jar file. You can also specify an Amazon S3 bucket that contains your code.

Under 'General Settings' you can configure memory, timeout and environment variables for your function. The memory allocated to your function determines the amount of CPU, the rate at which the function can access disk storage, and the network bandwidth. The timeout determines how long AWS Lambda allows your function to run before terminating. Environment variables allow you to provide dynamic settings for your function.

In 'Network Settings', you can configure the VPC, subnets, and security groups for your role. A VPC is a virtual network dedicated to your AWS account. Subnets allow you to divide your network into one or more networks. Security groups act as a firewall for your resource, controlling inbound and outbound traffic.

After configuring the AWS workbench, click 'Save' to save your settings. Now your Lambda function is ready to use. You can test your function by clicking 'Test' in the AWS Lambda console. You can monitor the execution of your function using AWS CloudWatch.

In summary, setting up AWS Lambda involves creating a Lambda function, configuring the AWS workbench, adding triggers and layers, writing code, configuring general and network settings, and testing of its function. With AWS Lambda, you can run your code without needing to provision or manage servers, making it an ideal option for backend development.

Next page of the Free Ebook:

4413.3. How to Configure AWS Lambda: Creating a Lambda Function