AWS Lambda

AWS Lambda

AWS Lambda is a serverless computing service that allows you to run code in response to events and has the added benefit of automatically managing the underlying compute resources. Among these events may be a change in status or an update, such as a user adding an item to his or her shopping cart on an ecommerce website, among others.

Why use Lambda?

AWS Lambda is a serverless computing service that allows you to run code in response to events and has the added benefit of automatically managing the underlying compute resources.In addition to a user adding an item to their shopping cart on an e-commerce website, these events include a change in status or an update. It’s possible to utilize AWS Lambda to develop your own back-end services that function at the same high levels of scalability as other AWS services, or to add custom logic to other AWS services, performance, and security as AWS. An Amazon API Gateway request, object adjustments in Amazon S3, table updates in Amazon DynamoDB, and AWS Step Functions state transitions all trigger AWS Lambda to automatically run code. A high-availability infrastructure is provided by Lambda to run your code, which manages your computing resources. Server and operating system maintenance, capacity provisioning, automatic scaling, security patch distribution, and code and log monitoring and archiving are just a few of the facets involved. Only the code must be supplied.

How it Works

AWS Lambda is an event-driven computing service that can run code for almost any application or backend service and have no need to provision and manage servers.More than 200 services and software-as-a-service (SaaS) applications can trigger AWS Lambda.

AWS Lambda functions can be activated by various event sources across the AWS ecosystem. These functions can be set to execute in response to modifications in Amazon DynamoDB tables or when there are changes to objects in Amazon S3. Additionally, they can be invoked by HTTP requests managed through Amazon API Gateway, or by data being streamed via Amazon Kinesis for serverless processing. Other sources that can initiate Lambda functions include interactions with CloudFront, messages from SQS or SNS, events from Alexa Smart Home or AWS IoT, logs and events captured by CloudWatch, and traffic through an Application Load Balancer.



File Processing:

AWS Lambda data processing

Using Amazon Simple Storage Service (Amazon S3) or an Amazon EFS file system, you can provide massively parallel shared access for large-scale file processing to AWS Lambda data processing in real time.

Stream Processing:

AWS Stream Processing

Real-time streaming analysis of application activity, transaction order processing, and clickstream analysis are all possible with the support of AWS Lambda and Amazon Kinesis.

Web Applications:

Amazon Web Applications

Create powerful web applications that can automatically scale up and down by making use of the scalability and high availability that are given by AWS Lambda and other services supplied by AWS.

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IoT Backends

IoT Backends

A serverless backend can be constructed with AWS Lambda for web, mobile, Internet of Things, and API calls from third parties.

Mobile Backends


AWS Lambda Mobile Backends

AWS Lambda and Amazon API Gateway backends can be used to authenticate and handle API requests. To swiftly connect your backend to your frontends on iOS, Android, Web and React Native, you can utilize AWS Amplify.



Augment other AWS services by utilizing custom logic

AWS Lambda offers a multitude of capabilities that empower users to leverage custom logic and enhance their cloud computing experience. By seamlessly integrating custom logic into Amazon S3 buckets and DynamoDB tables, Lambda enables the application of computation as data flows through the cloud. The service simplifies the process by allowing users to determine crucial parameters such as memory size, timeout period, and IAM role when submitting or generating code through the Lambda interface.

Create your own unique backend services

AWS Lambda facilitates the creation of personalized backend services through its intuitive API, which can be seamlessly activated on demand via Amazon API Gateway. This innovative approach eliminates the need to process custom events on client devices, ensuring a consistent user experience across platforms while streamlining implementation and maintenance.

Bring your own code 

This service is versatile, accommodating various programming languages, tools, frameworks, and libraries without requiring users to learn new technologies. Developers can seamlessly integrate diverse resources into their functions by packaging third-party libraries, including native ones, in Lambda Layers for efficient management and sharing. Additionally, Lambda’s Runtime API empowers users to write functions in many programming languages, expanding the realm of possibilities for cloud computing applications.

The languages supported natively include Node.js, which has versions 14 and 12 compatible with the Amazon Linux 2 OS. Python is supported in versions 3.6 and 3.7 on Amazon Linux, and version 3.8 on Amazon Linux 2 OS. For Go developers, AWS Lambda accommodates Go runtime 1.x on Amazon Linux 2 OS.

For .NET developers, C# is available through the .NET Core 3.1 runtime on Amazon Linux 2. Ruby developers can use Ruby 2.7, also on Amazon Linux 2 OS. Java is extensively supported with Java 8 running on Amazon Linux OS and both Java 8 (Java8.al2) and Java 11 on Amazon Linux 2 OS.

In cases where developers need to use a language not natively supported, AWS Lambda allows the deployment of custom runtimes. This includes two options: `provided.al2` and `provided`, for Amazon Linux 2 and Amazon Linux OSes, respectively. These custom runtimes enable the use of any Docker container image as a runtime, allowing virtually any programming language to be used.

Automation of administration

It automates administrative tasks, enabling users to focus on creating robust and fault-tolerant backend services without the hassle of scaling or server management. The service ensures continuous optimization by updating the underlying operating system, while built-in logging and monitoring through Amazon CloudWatch offer comprehensive oversight of applications.

Built-in tolerance for failures

Lambda excels in safeguarding code against outages through multiple Availability Zones within each AWS Region. This ensures consistent operational performance and availability, with no scheduled downtimes or maintenance windows to interrupt service. Additionally, Lambda’s support for container images streamlines the packaging and deployment of applications, allowing enterprises to leverage existing container image tools and workflows seamlessly.

Container images can be used to package and deliver applications

AWS Lambda allows functions to be packaged and deployed as container images, making it easier for clients to build Lambda-based applications using existing container image tools and workflows. A few of the benefits Lambda offers consumers include its ease of use, quick setup times, and high availability. With Lambda and containerized applications, enterprises can employ a standardized set of technologies, making central governance tasks like security scanning and image signing simpler.

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Automatic scalability

Lambda is a powerful tool for building chatbots, providing both scalability and flexibility. With Lambda, your code can handle an unlimited number of requests, ensuring that your chatbot can effectively handle high volumes of user interactions. As soon as an event occurs, Lambda automatically starts running your code, eliminating the need to worry about scalability issues. This means that whether you have a few users or millions of users, Lambda can dynamically scale to handle the increasing demand, ensuring a seamless experience for your users. Runtimes play a crucial role in Lambda applications by allowing functions written in different languages to be executed in the same base environment, ensuring compatibility and smooth operation across various programming languages. Layers provide a structured approach to managing your chatbot’s code, enabling easier maintenance and organization of your chatbot’s components. An event source triggers your chatbot’s function.

Developing highly scalable, secure Lambda-based serverless applications that interface with relational databases is made easier when thousands of concurrent connections to relational databases are managed efficiently. The ability to run a wide range of tasks at a relatively low cost is one of the key advantages of Lambda. This makes it an attractive option for organizations looking to optimize their infrastructure expenses while benefiting from the scalability and flexibility it offers.

Additionally, with Amazon Elastic File System (EFS) for AWS Lambda, it is possible to read, write, and persist massive amounts of data in a safe manner at any scale, with very low latency. EFS for Lambda is ideally suited for a wide range of use cases, such as processing or backing up vast volumes of data, as well as importing large reference files or models.

Serverless computing with Lambda is not only about running code; it includes built-in autoscaling and a pricing model that charges only for the compute time you consume. This means no charges when your code is not running, providing a cost-efficient solution to manage variable workloads without wasting resources on idle capacity.

  • Function: The piece of code you upload on Lambda is called a function. This function is the heart of your chatbot, enabling it to handle an unlimited number of requests seamlessly.
  • Runtimes: Runtimes play a crucial role in Lambda applications by allowing functions written in different languages to be executed in the same base environment. This ensures compatibility and smooth operation across various programming languages.
  • Layers: Layers provide a structured approach to managing your chatbot’s code. By separating in-development function code from unchanging code and resources, layers enable easier maintenance and organization of your chatbot’s components.
  • Event Source: An event source triggers your chatbot’s function. Whether it’s an AWS service or a custom trigger, Lambda ensures that your chatbot responds promptly to any incoming events, enhancing its responsiveness and user experience.
  • Downstream Resources: After being triggered, your Lambda function may call downstream resources, such as other AWS services, to fulfill its tasks. This seamless integration with external resources allows your chatbot to access additional functionalities and data as needed.

Use a web browser to access relational databases

It is possible to employ fully controlled connection pools for relational databases, which are otherwise unavailable. Developing highly scalable, secure Lambda-based serverless applications that interface with relational databases is made easier when thousands of concurrent connections to relational databases are managed efficiently. MySQL and Aurora databases are presently supported by RDS Proxy. You can set up RDS Proxy for your serverless apps if you’re utilising the Amazon RDS or AWS Lambda consoles.

Relational databases should be connected

Relational databases can benefit from Amazon RDS Proxy’s fully managed connection pools. Scalability and security can be achieved with Lambda-based serverless applications that interface with relational databases even when thousands of connections to such databases are active. MySQL and Aurora databases are both supported by RDS Proxy at the time of this writing. For serverless apps, you can utilize RDS Proxy in conjunction with Amazon RDS or AWS Lambda.

Control over performance at the granular level

Provisioned Concurrency gives you more control over serverless application performance. In order to respond in less than ten milliseconds, the Provisioned Concurrency functionality must be enabled. For any AWS Lambda application that needs more control over the time it takes for functions to start, Provisioned Concurrency is a great option to explore. Your application’s concurrency needs can be readily set and changed. You can increase, decrease, or fully turn it off depending on how much demand there is. It is possible to achieve consistent performance for latency-sensitive applications without having to rewrite your code or manage computing resources by using Provisioned Concurrency.

Connect to networked file systems

With Amazon Elastic File System (EFS) for AWS Lambda, it is possible to read, write, and persist massive amounts of data in a safe manner at any scale, with very low latency. Processing the information does not need the creation of code or the downloading of it to any kind of temporary storage. This not only saves you time but also makes the code simpler, allowing you to focus more on the reasoning behind your business. EFS for Lambda is ideally suited for a wide range of use cases, such as processing or backing up vast volumes of data, as well as importing large reference files or models. Other use cases include: You can move files between serverless instances or container-based apps if you use EFS for AWS Lambda. Additionally, you can use AWS Lambda to carry out machine learning (ML) inference if you use this service.

Update workloads

Updating workloads to deploy to AWS Lambda involves a shift in mindset and a reevaluation of traditional deployment strategies. While there may be skepticism surrounding the suitability of AWS Lambda for various workloads, it is indeed possible to leverage this service for the cost-effective execution of diverse tasks. To begin with, the process of updating workloads for deployment on Lambda necessitates a fundamental shift in thinking. Traditional approaches where web pages are served separately from backend data processing need to be reconsidered. Lambda offers the capability to handle both aspects within a unified service, enabling the seamless execution of code for various purposes.

When updating workloads for Lambda deployment, it is crucial to bear in mind the cost-efficient nature of this service. The ability to run a wide range of tasks at a relatively low cost is one of the key advantages of Lambda. This makes it an attractive option for organizations looking to optimize their infrastructure expenses while benefiting from the scalability and flexibility it offers. Additionally, it is important to note some of the limitations of AWS Lambda that users should consider. These limitations include a limited disk space of 512 MB, a default deployment package size of 50 MB, memory ranging from 128 to 3008 MB, a maximum execution time of 15 minutes for a function, and body payload sizes for request and response capped at 6 MB. Furthermore, the event request body is restricted to a size of up to 128 KB. Despite these constraints, the cost-effectiveness and versatility of Lambda continue to make it an appealing choice for various organizations seeking to streamline their operations.

To successfully update workloads for Lambda deployment, it is essential to assess and analyze the specific requirements of your application or system. Identifying tasks that can be offloaded to Lambda allows for more efficient resource utilization and improved overall performance. By selecting the appropriate functions and services offered by AWS Lambda, workloads can be effectively migrated and updated.

Potential Issues:


AWS Lambda, while powerful, presents some limitations that may impact its suitability for certain applications:

Latency Issues

Due to AWS Lambda’s serverless nature, there are inherent latency issues, particularly with cold starts. When a function hasn’t been invoked for a while (typically over 15 minutes), it goes into a dormant state. Reactivating or “warming up” the function can introduce a delay ranging from a few milliseconds to several seconds. This latency can be problematic for time-sensitive applications where immediate response is crucial.

Duration Constraint

AWS Lambda is designed for short-term execution, with a maximum limit for running an instance or function set at 15 minutes. This constraint makes it unsuitable for long-running processes which require prolonged execution times beyond the prescribed limit.

Concurrency and Scaling Limitations

There is an upper cap on the number of concurrent executions that AWS Lambda can handle, which is set at 1,000. For applications requiring higher simultaneous processing, this can be a significant bottleneck, affecting the ability to scale operations effectively during peak times.

Cost Management

While AWS Lambda can be cost-effective due to its pay-as-you-go pricing model, managing these costs can be tricky. Without careful planning and optimization, it’s easy for expenses to escalate, especially if functions are triggered excessively or run for near the maximum time allowed. This makes it essential for users to understand and optimize their usage to keep costs under control.


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Respond to Amazon CloudFront requests by executing code.

Our code can be executed in various AWS locations around the world using Lambda@Edge in response to events generated by Amazon CloudFront, such as content requests from origin servers or viewers. Because of this, it becomes easier to give more personalized and customized content to your customers while reducing the amount of time it takes to do so. In addition to CloudFront events, AWS Lambda can be triggered by a wide range of other events within the AWS ecosystem. These include inserting, updating, and removing data from Dynamo DB tables, incorporating push alerts into SNS, searching for log history in CloudTrail, object modifications in S3 buckets, Amazon SNS notifications, processing CloudTrail logs, and using API Gateway methods. This versatility allows you to automate tasks, process data, and respond to various events efficiently and effectively using AWS Lambda.

Coordinate different functions

Amazon Web Services’ Step Functions can be used to orchestrate many AWS Lambda functions for long-running or complex processes. Using the Step Functions library’s sequential, parallel, branching, and error-handling steps, programmers can activate a collection of LambdA functions. Step Functions and Lambda can be used to construct long-running processes for apps and backends.

Security model that is integrated

AWS Lambda’s built-in SDK integrates with AWS Identity and Access Management (IAM) to ensure safe code access to other AWS services (IAM). An Amazon VPC (Virtual Private Cloud) is the default setting for Amazon Web Services Lambda (VPC). AWS Lambda resource access can be routed through your own VPC if you want to use custom security groups and network access control lists. This allows Lambda functions to safely and securely access your VPC resources. Compliance with SOC, HIPAA, PCI and ISO standards can be achieved with AWS Lambda.

Controls are based on trust and integrity.

Code signing for AWS Lambda allows you to ensure that only unaltered code published by authorized developers is deployed in your Lambda functions by using the Code Signing API. You only need to build digitally signed code artefacts and configure your Lambda functions to check the signatures when the code is being deployed. To maximize the speed and agility of your application development while simultaneously guaranteeing that strict security standards are adhered to while working in large groups.

Pay as you go

Lambda’s cost is influenced by several factors, including the chosen concurrency settings. When you deploy Provisioned Concurrency, the costs are impacted because it ensures that a specified number of instances are always warm and ready to respond to requests immediately. This leads to charges based both on the number of requests you make and the duration of each request’s processing time.

The cost of execution time is heavily influenced by the amount of memory you allocate to your Lambda function. When setting up a function in AWS Lambda, you specify the amount of memory you deem necessary. Based on the memory size specified, AWS allocates proportional CPU and other computing resources necessary for your function.

Lambada charges you based on the number of requests for your functions and the time your code executes. You are billed for the total number of requests served and the duration of code execution. Each request is counted as soon as AWS Lambda begins to execute in response to an invoke call or event notification. The duration is carefully calculated from the moment your Lambda function starts until it terminates or returns, providing a precise measurement of execution time. This cost is calculated by multiplying the number of requests by the duration of execution, rounded up to the nearest 100 milliseconds. AWS Lambda enhances this accuracy by metering billing in 1-millisecond increments, ensuring you only pay for the exact amount of compute time you use. Additionally, the actual price per duration depends on the amount of memory you allocate to each function, allowing for flexible pricing that matches your specific usage and needs.

In essence, when Provisioned Concurrency is enabled, you not only pay for the actual use (requests and duration) but also for the prepared instances maintained on standby. The rate for both requests and duration typically varies by region. Generally, rates in many locations around the US, Europe, and Asia are lower compared to some locations such as Cape Town, South Africa.

For a more specific comparison, whilst locations like North Virginia in the US or Stockholm in Europe might charge around $0.20 per 1 million requests and $0.0000166667 per GB-second for duration, Cape Town’s rates are notably higher at $0.27 per 1 million requests and $0.0000221 per GB-second. Additionally, some other regions, such as Milan, may have different rates from the general pricing structure observed in most regions.

Adaptable resource model

According to how much memory you specify, AWS Lambda distributes CPU, network, and disc I/O resources proportionally.

Synchronize Lambda with your favourite tools

Monitoring, security, and governance are simplified by AWS Lambda extensions. A secure and isolated execution environment is provided by Lambda when your function is invoked. Lambda extensions operate alongside your function code in the same environment as your function code. Your function logs, metrics, and traces can be sent anywhere using Lambda extensions. Security agents can be implemented with minimal operational overhead and no performance impact using the Lambda execution environment.

Pricing performance can be improved by up to 34% using Graviton2 functions.

Functions running on Graviton2, AWS’s Arm-based processing architecture, beat x86-based functions by as much as 34%. Serverless online and mobile backends, data, and media processing are all included here. In terms of performance, affordability, and power efficiency, Graviton2 functions are AWS’s finest today.

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