AWS Lambda

AWS Lambda

AWS Lambda is a serverless compute service that responds to the events triggered by a user and automatically manages underlying computing for the resources. Lambda can extend other AWS services to create customized backend services, which can operate as per AWS scale, performance, and security. Lambda has been designed to automatically handle a host of events, such as hypertext requests received through payment gateways, changes made in services, tables update in Amazon DynamoDB, and a host of other activities.

Components of AWS Lambda

Lambda consists of runtimes, which supports switching between languages and frameworks, as per requirements. It provides a management console and API, which aids in managing and invoking functions. 

Some of the key components of Lambda’s architecture can be broadly classified into the following categories:

  • Programming Model

    The model is a common source for all runtimes, which defines an interface between the actual code and the runtime code. The runtime is customized by the user as to which method to run, by defining a handler in the function configuration, and the runtime runs the method. The handler contains the invocation event and context, which could be name or request ID. The handler keeps on executing tasks one after the other as per events sent by the runtime.

  • Scaling

    Lambda is efficient at running code and scales automatically in response to the events that keep coming in. When the volume of events increases, Lambda runs additional instances to scale up according to the incoming load. When the incoming traffic lowers, the additional instances stop. Therefore, the customer needs to pay only for the part of the function performed. 

  • Concurrency Controls

    Concurrency refers to the settings that are meant to keep the applications in a production environment, highly available, and responsive. For efficient concurrency utilization, a reserved portion of the account is made available, which can be used by a function at any given point in time. 

  • Asynchronous Invocation

    There can be two ways to invoke a function - synchronous or asynchronous. In synchronous invocation, the user needs to wait for the function to process the event and return a response. In asynchronous invocation, Lambda puts the events in queues, for processing and returns a response immediately. In case of an error in any of the queued events, Lambda applies retrials, if a function returns an error. Such error handling can also be configured in AWS Lambda to send a record of any invocation to the destination.

  • Event source mappings

    An event source mapping is a repository that reads items from Amazon SQS (Simple Queue Service) queue or an Amazon database stream and sends them to the user's function in batches. Each event has the potential to contain thousands of items for processing a function.

  • Destinations

    This is the location that can be referred to as an AWS resource keeping a record of function. Separate destinations can be configured in Lambda to send records of invocation to a queue, topic, or function. The record contains details about the events function responses and enlists the reason as to why a record was sent. 

  • Function blueprints 

    A blueprint in the context of Lambda is a container image that represents the sample code to show how Lambda is used with an AWS service or any third-party application. 

  • Testing and deployment tools

    In Lambda, there is a rich ecosystem available for authoring, building, and deploying Lambda functions using AWS along with community tools, such as Docker command-line interface (CLI).

Tools for AWS Lambda

To manage and invoke Lambda resources, we have the following tools at our disposal, in addition to the Lambda console:

  • AWS CLI (Command Line Interface): Installing AWS CLI can facilitate the management of Lambda functions through the command line with appropriate Lambda API operations. However, few functionalities may not be available in the Lambda console and can only be accessed through AWS CLI or AWS SDKs. 
  • AWS SAM (Serverless Application Model): This model lets the users define serverless applications at a higher level by abstracting some common tasks such as function role creation, which makes it easier to write templates. 
  • AWS SAM CLI: This is a separate command-line tool that lets a user manage and test AWS serverless applications. AWS SAM CLI provides a host of commands to validate templates and run applications in a Docker container. 

Code Authoring Tools:

Lambda can be used for authoring function code in any language supported by Lambda. Some of the tools that are available for authoring code are Lambda console, Eclipse integrated development environment (IDE), and Visual Studio IDE. 

We list down some of the tools that are supported by Lambda as follows:

  • Node.js - With Node.js users can integrate Lambda console, Visual Studio with IDE plugin, and have their own customized authoring environment. 
  • Java - Java can be used with Eclipse by configuring AWS toolkit for Eclipse, IntelliJ by having AWS toolkit for JetBrains.
  • C# - For working with C#, users have .NET Core and Visual Studio IDE plugin.

Similarly, other languages that can be integrated with the Lambda platform are Python, Ruby, Go, PowerShell, which can be used by installing respective plugins.

AWS Lambda thus offers a host of features that makes it a highly customizable environment. We, therefore, summarize our discussion on AWS Lambda by listing the few key takeaways-

  • AWS Lambda is very easy to start with, it allows adding custom logic to AWS resources such as Amazon S3 buckets, and Amazon DynamoDB tables, which allows easy computation of data on the cloud.
  • AWS Lambda services can be used to create new back-end services for applications that can address real-time capabilities with the help of Lambda or custom API endpoints such as Amazon API gateway.
  • Automatic scaling is a very important feature that can automatically scale according to the rate of incoming requests. 
  • A shared file system, Amazon Elastic File System, enables secure read, write to support large data volumes at any scale. There is no need for writing code and the team can focus more on business logic.


AWS Lambda provides the ease to manage application development with the help of cost optimization, automatic scaling, support for integration of languages, and so on. Thus Lambda has occupied an important space in the AWS market and helps in security, governance, and monitoring of various activities.