AWS Lambda Tutorial for Beginners: Complete Tutorial

With the development of cloud technology, AWS Lambda has become the most widely used serverless architecture. One of the examples of cloud service providers that have been using the AWS platform is Netflix. The article will discuss AWS lambda tutorial for beginners.

The AWS Lambda allows the running of code without the requirement of creating the code, managing it, or in fact, paying for running the code. The user must submit the code for running the function and has to pay for the time AWS takes to run the code. That’s all that is required to be paid by the user. The code submitted can access any other services of AWS or can also run by itself.

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One of the powers that AWS Lambda offers is scalability. AWS will scale the code for the user. However, it depends on how many requests it receives. If you are more interested in learning about AWS Lambda, the article best suits you. We are going to discuss the various concepts associated with architecture for the AWS lambda tutorial for beginners.

AWS Lambda

The AWS Lambda is a platform provided as a part of the Amazon web services (AWS) by Amazon, which will be an AWS lambda tutorial for beginners. It is a computing platform that is serverless. The computing platform runs any code that is in response to the events. It then automatically manages all the resources that are required by the code to run. Amazon introduced the serverless platform in 2014 November.

The users who use the platform create functions and any self-contained applications through one of the languages supported by the platform. The functions are then uploaded to the AWS Lambda and executed by the platform efficiently. The officially supporting languages of AWS Lambda are Python, Java, Ruby, Node.js, Go, and C#. The functions of Lambda are capable of performing any computing task such as web page service, processing data streams to the API, and integrating the functions to other services of the AWS.

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One of the benefits of having a serverless platform is that there is no requirement to maintain the servers by yourself to run the created functions. The platform of AWS is fully managed and will do all the services for you. It also manages the infrastructure for the users. Therefore, through the serverless term, it doesn’t mean that no servers are present. However, it means that the operating systems, servers, and the infrastructure associated with the server is all maintained, and the users need to focus only on writing the codes.

Lambda Deployment and Monitoring

Lambda Deployment

Deploying AWS Lambda functions makes the code accessible and executable by the AWS infrastructure, which will be a helpful AWS Lambda tutorial for beginners. There are several ways to deploy Lambda functions:

1. AWS Management Console

Developers can use the AWS Management Console to manually create and upload Lambda function code packages in supported languages like Python, Node.js, Java, etc.

2. AWS CLI (Command Line Interface)

The AWS CLI allows developers to package and deploy Lambda functions from the command line, streamlining the deployment process and enabling automation.

3. AWS SDKs (Software Development Kits)

AWS SDKs provide language-specific libraries that facilitate programmatically deploying Lambda functions.

4. Infrastructure as Code (IaC) Tools

Infrastructure as Code tools like AWS CloudFormation or AWS Serverless Application Model (SAM) templates can define and deploy Lambda functions as part of larger application stacks.

Lambda Monitoring

Monitoring Lambda functions is crucial to ensure performance, identify potential issues, and optimize resource usage. AWS provides various monitoring and logging capabilities for Lambda functions:

1. Amazon CloudWatch Metrics

AWS Lambda automatically generates various CloudWatch metrics, such as function invocations, errors, duration, and throttling. These metrics allow developers to track the health and performance of their Lambda functions.

2. CloudWatch Logs

Lambda automatically streams logs to CloudWatch Logs, providing developers with insights into the execution and debugging of Lambda functions.

3. X-Ray Tracing

AWS X-Ray can be integrated with Lambda functions to trace and analyze the entire request/response workflow, helping developers identify bottlenecks and optimize performance.

4. Custom Metrics and Logging

Developers can also instrument their Lambda functions to emit custom metrics and logs for specific monitoring needs.

Working of the AWS Lambda

AWS Lambda functions in the following manner:

• Every Lambda function runs inside its own space. After the creation of a function, it is packaged by Lambda into a container.
• The container is then executed on a cluster of machines that are managed by the service of AWS.
• Before running the function, the container for every function is allocated a RAM necessary to run it and the CPU’s capacity.
• Once execution of the function stops, the RAM, which was allocated at the start of running the function, is multiplied by the time required for running itself.
• This is how the charges are decided for the customers. It is based on the allocated memory and the run time required for completely running the code.

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Amazon Web Services manages the whole infrastructure of the computing platform. There is, however, no visibility to the customers as to the operation of the system. But, there is no need for them to worry regarding the system update, failure in network connectivity, etc., as AWS itself manages everything.

Also, because AWS manages the whole service, the users save a lot of time for other operational tasks. Also, because there is no need to maintain the infrastructure, the users can develop the application’s code.

Another important property of the AWS architecture is that the users can run different instances of the same function in the same account of AWS. Also, different instances from different functions can be executed from the same account.

There are many AWS Lambda tutorials available on the internet that will give you an idea of the working of the AWS architecture.

AWS: An Essential Part of Serverless Computing

For completing a serverless slack, the requirements are:

• Computing service
• Service of a database
• Gateway service such as an HTTP

The primary requirement of AWS, which is the computing service, is filled in by Lambda. It also integrates with other services of AWS along with the API gateway such as RDS and DynamoDB. All of this together is considered the basis for serverless solutions for the users using AWS. Also, for many serverless developers, Lambda seems to be a good fit as it supports a wide variety of runtimes and languages.

Advantages of the AWS Lambda

There are a few advantages that the AWS Lambda provides over the other servers that are maintained in the cloud. 

1. Pay per use

This is one of the biggest advantages of AWS’s architecture, making it an AWS lambda tutorial for beginners. In AWS Lambda, we only pay for the time required to compute the tasks. Also, in addition to this, if sometimes there is any generation of network traffic. Because of this, the architecture of AWS is considered to be cost-effective.

2. Fully managed infrastructure

The infrastructures of AWS where the customers run the functions are all managed by the AWS. The customers don’t have to think about the servers that are working behind the architecture. For the benefit of the customers, the management is done by the AWS itself. Therefore, it results in a lot of saving as no operational tasks are required by the customer, such as updating the operating system or managing the network layers.

3. Automatic scaling

Instances of the functions are automatically created as requested in the AWS.

4. Integration with other services in the AWS

Services such as DynamDB, API gateway, S3 are allowed to integrate with the AWS Lambda.

Because of all these advantages that the architecture provides, it has been used for many different tasks. Below are some of the tasks that are done through the use of AWS Lambda. The traditional system of cloud computing is left behind with the benefits provided by the AWS platform. Different tasks that can be performed over the traditional system of cloud computing are:

• The tasks run for a shorter duration of time compared to the traditional cloud computing system.
• The tasks are mostly self-contained.

A few of the cases of the common use of API are:

• Scalable API: While building an API through using the AWS Lambda, a single request of HTTP can be served by a single execution of the function of Lambda. The API gateway of amazon can route the different API parts with the Lambda functions. Individual functions are automatically scaled through the AWS function as per the demand. Therefore, different API parts can scale as per the usage level. As a result, effective setups of API can be done, which are also flexible. AWS API gateway tutorial will help in the understanding of the concepts in more detail.
• Data processing: The functions of Lambda are optimized for event-based data processing.

AWS Lambda Triggers and Event Sources

AWS Lambda functions are executed in response to certain events or triggers. These triggers are referred to as event sources. AWS Lambda supports various event sources, allowing developers to build serverless applications that react to events in real-time. Here are some common AWS Lambda triggers and event sources that can be an AWS lambda tutorial for beginners:

1. API Gateway Events

HTTP requests through Amazon API Gateway can trigger AWS Lambda. This allows you to create RESTful APIs and execute Lambda functions when specific endpoints are accessed.

2. S3 Bucket Events

AWS Lambda can automatically process objects as they are uploaded to or deleted from an Amazon S3 bucket. This enables you to build serverless data processing pipelines or automate file operations.

3. DynamoDB Stream Events

Changes to a DynamoDB table, such as new item creations, updates, or deletions, can trigger AWS Lambda. This enables you to build real-time data processing applications.

4. CloudWatch Events

AWS Lambda can respond to events generated by CloudWatch. This includes actions like scheduled tasks, system events, and custom events published to CloudWatch.

5. Kinesis Stream Events

AWS Lambda can consume and process records from an Amazon Kinesis data stream, which allows you to build real-time streaming data applications.

6. Cognito Events

AWS Lambda can be triggered by events related to Amazon Cognito, such as user sign-ups, authentications, and account updates.

7. IoT Events

AWS Lambda can respond to events from AWS IoT Core, allowing you to build IoT applications that react to sensor data and device events.

8. Alexa Skill Kit Events

AWS Lambda can process voice requests and build custom skills for Amazon Alexa.

9. Step Functions

AWS Lambda functions can be orchestrated using AWS Step Functions, which allow you to create complex workflows and manage stateful interactions between Lambda functions.

Programming Languages Supported by AWS

A lot of popular languages are supported by the AWS platform. Below is the list of the languages that are being supported by the platform:

• Node.js 8.10
• Node.js 10.x
• Node.js 12.x
• Python 2.7
• Python 3.6
• Python 3.7
• Python 3.8
• Ruby 2.5
• Java 8
• Java 11
• Go 1.x
• C# — .NET Core 1.0
• C# — .NET Core 2.1
• PowerShell Core 6.0

AWS is involved in maintaining these runtimes, which are provided in an environment of Amazon Linux 2 or Amazon Linux. In addition, an SDK is provided by AWS for every language that it supports. This makes the user comfortable and finds it easy to write the functions of Lambda and then integrate with the other services from AWS.

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AWS Lambda Tutorial

1. Account of AWS

This is the first thing that needs to be created for creating the Lambda function. 

2. Creating a Lambda

The function can be set up once the customer is in the console. The “Lambda” entry under the menu “compute” is to be used. 

3. Finishing the Lambda creation

Python programming is used for entering the code in the console.

4. Configuration of the function

 The function has to be generated.

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Conclusion

AWS architecture is used to carry out the activities such as operational and administrative on behalf of the customers. A lot of benefits are offered by the AWS platforms, which are discussed in the article, which will be an AWS lambda tutorial for beginners. Also, there are a lot of uses provided by AWS Lambda, such as the operation of serverless websites; conversion of documents rapidly; backend cleaning; processing of the objects of S3 that are uploaded; and many others. Examples of AWS Lambda include Netflix, Localytics, REA group, and many others. With these advantages, AWS Lambda has increased among enterprises worldwide. It is one of the most versatile technologies that range from the core platforms of cloud computing to legacy applications. A whole lot of opportunities lie ahead in the use of AWS computing services.