A Serverless Architecture is a method of developing and deploying applications and services without the need to maintain infrastructure. One of the essential solutions for executing application code while creating Serverless Applications is AWS Lambda.
In this article, you will gain information about AWS Lambda. You will also gain a holistic understanding of its working, its use cases, its benefits and limitations, and the steps involved in its implementation. Read along to find out in-depth information.
What is AWS Lambda?
It is a Serverless, Event-Driven Compute Service offered by Amazon as part of Amazon Web Services. It is a computing service that runs code in response to events and maintains the computing resources required by that code automatically. Lambda functions can do anything from providing web pages and processing Data Streams to using APIs and connecting with other AWS services.
It executes code responding to AWS service events, such as adding/deleting files in an S3 bucket, making an HTTP request to the Amazon API gateway, etc.
It allows you to concentrate on your core product and business logic rather than handling Operating System (OS) Access Control, Patching, Right-sizing, Provisioning, Scaling, and so on.
How does it Work?
Each Lambda function has a container of its own for execution. When a function is written, Lambda packages it into a new container, executed on an AWS-managed multi-tenant cluster of machines. Each function’s container is assigned the requisite RAM and CPU capacity before the functions start running. When the functions finish running, the RAM allocated at the start is multiplied by the amount of time for which the function was running. Customers are then charged depending on the allotted memory and the length of time it takes the function to finish.
AWS manages the whole infrastructure layer of AWS Lambda. Customers don’t have much visibility into how the system works, but they also don’t have to worry about maintaining the underlying computers, avoiding network conflicts, and so on; AWS handles all of that for them. Users can also save time on operational tasks as the service is fully managed.
Why Makes AWS Lambda a Key Part of the Serverless Architecture?
When it comes to developing Serverless architecture, it is one of the must-have options for running the application code.
To build a complete Serverless stack, you’ll need:
- A Computational Service
- A Database Service
- An HTTP Gateway Service
On AWS, Lambda serves as the primary computing service. It also links with numerous other AWS services and, together with API Gateway, DynamoDB, and RDS, serves as the foundation for Serverless solutions for the users of AWS products.
Lambda supports several of the most common programming languages and runtimes, making it suitable for a wide spectrum of Serverless developers.
Lambda allows you to run an application without having to set up or manage servers. It is a computer platform that is based on events. When an event trigger is configured, the code is performed in Lambda.
Getting Started with AWS Lambda
The steps involved in creating, deploying, and securing AWS Lambda functions are as follows:
We two approaches covered are as follows:
1) Creating AWS Lambda Functions using the AWS Console
You can use the AWS Lambda Console for creating your first function.
The steps are as follows:
- Step 1: In the AWS console, choose the “Lambda” option.
- Step 2: When you’re in the Lambda Management Console, click on the “Create Function” button.
- Step 3: Add a name for your new function and choose the desired runtime.
- Step 4: Now, click the “Create Function” button to confirm the settings.
Now, the function is created. You can now work on the function code and deploy the function directly in the Lambda Console.
2) How to Design AWS Lambda Functions using a Serverless Framework?
It is suggested that you get started with AWS Lambda by utilizing the Serverless Framework. With the Serverless Framework, you can create Lambda functions on your local PC using familiar tools and deploy them to AWS in seconds. This approach places your function’s code and configuration in the same Git repository which makes collaboration, change tracking, and Lambda function deployment easy.
The steps for creating AWS Lambda Functions using the Serverless Framework are as follows:
- Step 1: Install Serverless Framework on your machine.
- Step 2: Now, you can create a new service.
- Step 3: Add your function’s resources to the serverless.yml file. The AWS Intro documentation provides an example of this file and a list of options you can configure there.
- Step 4: Add code to your service. The procedures for creating your functions are in the Specific AWS Provider Documentation.
- Step 5: Now, you need to deploy to AWS.
Your function will now be deployed, and the URL of the function’s endpoint will appear in your console.
Implementing AWS Lambda Functions in Production
Once your Lambda functions have been deployed, you will most likely want to secure and monitor them in order to appropriately serve production traffic.
1) Monitoring Lambda Functions with AWS CloudWatch
After you’ve deployed your function, you may use CloudWatch to monitor your Lambda applications. CloudWatch has a few default metrics that are available to you as soon as the function is deployed. To access these, you can navigate to the Lambda interface in the AWS console, choose “Functions,” and then select the “Monitoring” option. You can view the metrics directly in the Lambda interface or go to CloudWatch for additional information. The specific steps can be referred to in the Lambda metrics Documentation. Using CloudWatch Alarms, you can set up basic alerts on metrics.
2) Securing the Lambda Functions
Many of the functions you deploy will almost certainly need connectivity to other services or your internal infrastructure. You need to secure these credentials.
3) Unconventional Monitoring and Security with the Serverless Framework
If you used Serverless Framework to deploy your function, it is already secured and monitored. You can simply navigate to the Serverless Dashboard to get the most recent metrics and security events for your function.
To use the metrics and the built-in Secrets Management Mechanism, you must first run serverless login before deploying your Serverless application.
Use Cases
Lambda Functions and Triggers are the foundational components of AWS Lambda applications. A Lambda function represents a code/runtime framework that processes events, whereas a Trigger is the AWS service or application that calls the function.
Because of the architecture of Lambda, it can provide significant benefits over traditional Cloud Computing setups for applications where:
- Individual tasks run for a short period of time.
- Each task is generally self-contained.
- The workload of the application has a large difference between the lowest and highest levels.
The common use cases are as follows:
- Scalable APIs: When creating APIs, a single Lambda function execution can serve a single HTTP request. Using Amazon API Gateway, different parts of the API can be routed to separate Lambda functions. It dynamically scales individual functions based on demand, allowing various portions of your API to scale differently based on current usage levels. This enables API installations to be both cost-effective and flexible.
- Data Processing: Lambda functions are designed for event-based data processing. It is simple to combine AWS Lambda with data sources such as Amazon DynamoDB and trigger a Lambda function for specific types of data events. For example, you might use Lambda to conduct some task every time a DynamoDB item is created or updated, making it an excellent fit for things like notifications, counters, and analytics.
- File Processing: Suppose you have a Photo Sharing Application. People may use your application to upload images and the application stores these user photos in an Amazon S3 bucket. Then, on the user’s profile page, your application generates a thumbnail version of each user’s images. In this case, you may write a Lambda function that generates thumbnails automatically.
- Websites: Suppose you’re building a website and want to host the backend logic on Lambda. You may use Amazon API Gateway as the HTTP endpoint to invoke your Lambda function over HTTP. Your web client can then call the API, and API Gateway will route the request to Lambda.
- Mobile Applications: Suppose you have a custom mobile app that generates events. To process events published by your custom application, you may create a Lambda function. For example, within your own mobile application, you may configure a Lambda function to process clicks.
Task Automation Using AWS Lambda
AWS Lambda, with its Event-driven approach and flexibility, is an excellent choice for automating a variety of business operations that do not require the use of a full server at all times. This may involve conducting scheduled jobs that undertake infrastructure cleanup, processing data from forms provided on your website, or transferring data across datastores on demand.
It enables you to effortlessly automate your IT processes thus helping to save your time so that you can focus on developing business logic. There are no servers to maintain or provision; all you have to do is upload your code, and everything needed to execute and scale it is handled for you with high availability. You pay for the computations that you utilize.
It can be used to automate repetitive processes by triggering them with events or by executing them on a fixed schedule. You can automate the firmware updation of hardware devices, the start and stop of Amazon EC2 instances, the scheduling of security group upgrades, and the automation of your test and deployment pipeline.
Supported Languages and Runtimes
AWS Lambda does not currently support all programming languages, but it does support a handful of the most common programming languages and runtimes. The languages that are supported are as follows:
| Node.js Runtimes |
| Name | Identifier | SDK for JavaScript | Operating System | Architectures |
| Node.js 14 | nodejs14.x | 2.1001.0 | Amazon Linux 2 | x86_64, arm64 |
| Node.js 12 | nodejs12.x | 2.1001.0 | Amazon Linux 2 | x86_64, arm64 |
| Node.js 10 | nodejs10.x | 2.1001.0 | Amazon Linux 2 | x86_64 |
| Python Runtimes |
| Name | Identifier | AWS SDK for Python | Operating System | Architectures |
| Python 3.9 | python3.9 | boto3-1.18.55 botocore-1.21.55 | Amazon Linux 2 | x86_64, arm64 |
| Python 3.8 | python3.8 | boto3-1.18.55 botocore-1.21.55 | Amazon Linux 2 | x86_64, arm64 |
| Python 3.7 | python3.7 | boto3-1.18.55 botocore-1.21.55 | Amazon Linux | x86_64 |
| Python 3.6 | python3.6 | boto3-1.18.55 botocore-1.21.55 | Amazon Linux | x86_64 |
| Python 2.7 | python2.7 | boto3-1.17.100 botocore-1.20.100 | Amazon Linux | x86_64 |
| Ruby Runtimes |
| Name | Identifier | SDK for Ruby | Operating System | Architectures |
| Ruby 2.7 | ruby2.7 | 3.0.1 | Amazon Linux 2 | x86_64, arm64 |
| Ruby 2.5 | ruby2.5 | 3.0.1 | Amazon Linux | x86_64 |
| Java Runtimes |
| Name | Identifier | JDK | Operating System | Architectures |
| Java 11 | java11 | amazon-corretto-11 | Amazon Linux 2 | x86_64, arm64 |
| Java 8 | java8.al2 | amazon-corretto-8 | Amazon Linux 2 | x86_64, arm64 |
| Java 8 | java8 | amazon-corretto-8 | Amazon Linux | x86_64 |
| Go Runtimes |
| Name | Identifier | Operating System | Architectures |
| Go 1.x | go1.x | Amazon Linux | x86_64 |
| .NET Runtimes |
| Name | Identifier | Operating System | Architectures |
| .NET Core 3.1 | dotnetcore3.1 | Amazon Linux 2 | x86_64, arm64 |
| .NET Core 2.1 | dotnetcore2.1 | Amazon Linux | x86_64 |
You may implement a Custom Runtime to use other languages in Lambda. The Lambda execution environment offers a runtime interface for receiving invocation events and providing answers. A custom runtime can be deployed with your function code or as a layer.
| Custom Runtime |
| Name | Identifier | Operating System | Architectures |
| Custom Runtime | provided.al2 | Amazon Linux 2 | x86_64, arm64 |
| Custom Runtime | provided | Amazon Linux | x86_64 |
AWS maintains all of these runtimes, which are available in an Amazon Linux or Amazon Linux 2 environment. AWS provides an SDK for each supported language that makes it easy to write your Lambda functions and unify them with other AWS services.
A couple more runtimes are still in the pre-release phase. These runtimes are created as part of AWS Labs and are not referenced in the official documentation:
The C++ runtime may also be used to create custom runtimes. If your language isn’t supported by default, you can see the AWS documentation for details on how to develop a Custom Runtime.
What are the Benefits of using AWS Lambda?
The advantages are as follows:
1) Minimized Cost
You pay only for the compute resource your functions use, plus any network traffic generated ie., it follows the pay-as-you-go model. None of the prices you pay at the end of the month goes towards any unused minutes of server time, as your cost is solely a function of the time your application used.
2) Accommodate Variety of Use Cases
The advantages of AWS Cloud serve a wide range of use cases exceptionally well, beginning with daily Tasks, Notifications, Processing S3 Objects, Automated Chatbots, and many more.
3) Fully Managed Infrastructure
Since your functions run on the managed AWS infrastructure, you don’t need to worry about the underlying servers—AWS handles this for you. This can result in considerable savings on operational tasks such as operating system upgrades and network layer management.
4) Automatic Scalability
AWS Lambda generates instances of your function as requested. It makes no difference if you require 3 requests one minute and 2,000 the next; the nature of serverless allows for any unexpected increase in use. It simply performs everything by itself. You just pay for the time each function runs.
5) Firm Integration with other AWS Products
It interacts with services like DynamoDB, S3, and API Gateway, allowing you to create fully functional apps within your Lambda functions.
Limitations of AWS Lambda
Some of the limitations are as follows:
1) Cold Start Time
When a function is initiated in response to an event, there may be some delay between when the event occurs and when the function executes. If your function hasn’t been utilized in the previous 15 minutes, latency might be as high as 5-10 seconds, making Lambda unsuitable for latency-critical applications.
2) Function Limits
Some of the limitations of the AWS Functions are as follows:
- Execution Time: After running for 15 minutes, a Lambda function will time out. This limitation cannot be altered. If your function normally takes more than 15 minutes to run, AWS Lambda may not be the best solution for your requirements.
- Memory Availability: The amount of RAM accessible to Lambda functions ranges from 128MB to 3,008MB in 64MB increments.
- Code Package Size: A zipped Lambda code package should not be greater than 50MB, and the unzipped version should not be larger than 250MB.
- Concurrency: By default, all functions inside a single AWS account are restricted to 1,000 concurrent executions.
- Payload Size: The maximum payload size that Amazon API Gateway can handle when used to trigger Lambda functions in response to HTTP requests (i.e. when developing a web application) is 10MB.
3) Not Always Cost-effective
You only pay for the function runtime you utilize (plus any associated charges like network traffic). This can result in considerable cost reductions for specific usage patterns, such as cron jobs or other on-demand operations. However, when the load on your application rises, the cost grows correspondingly and may wind up being greater than the cost of equivalent infrastructure on AWS EC2 or other cloud providers.
4) Restricted Number of Supported Runtimes
Although it enables the addition of custom runtimes, doing so can be time-consuming. If the programming language version you’re using isn’t supported by Lambda, you can use AWS EC2 or any other Cloud provider.
Pricing
Lambda does not charge for server units. Instead, customers are charged based on:
- The number of requests AWS served
- The time it takes those functions to run your code (duration)
AWS begins counting a request as soon as it begins to execute in response to an invoke call or event notification. It then computes the time elapsed between the start of the execution of your Lambda function (your code) and its termination or return.
Billing is done in 1-millisecond increments with the service meters. However, the actual cost per duration is determined by the amount of memory allocated to a function.
When you select the amount of memory you want for your function in the AWS Lambda resource model, Lambda will assign the appropriate CPU, RAM, and other computing resources to your function. That happens every time you adjust your memory requirements.
Lambda counts two things when it comes to Provisioned Concurrency:
- The quantity of concurrency you specify.
- The time you configure it for.
If you activate Provisioned Concurrency for your functions, you’ll additionally be charged for responses and duration. The amount you pay for each request and length may vary depending on your location, though most regions get the same prices.
Every AWS account comes with a certain amount of executions as part of the AWS Free Tier. Unlike some other services, the Lambda free tier is not restricted to 12 months. Existing and new accounts both receive 1 million AWS Lambda requests per month, as well as 400,000 GB-seconds per month – Lambda’s measure of function runtime and memory allocated to a function.
The prices excluding the free tier are as follows:
The overall cost for each execution will be the sum of all applicable factors, including the cost per request, memory and run time, and network traffic.
Conclusion
In this article, you have learned about AWS Lambda. This article also provided information on its benefits, limitations, use cases, and ways of implementing AWS Lambda Functions.
Explore the concept of Data Pipeline Lambda and learn how AWS Lambda can enhance your data processing capabilities with our comprehensive guide. Create performant serverless functions with Rust on AWS Lambda, leveraging Rust’s efficiency. Explore details at AWS Rust Lambda.
Hevo Data, a No-code Data Pipeline provides you with a consistent and reliable solution to manage data transfer between a variety of sources and a wide variety of Desired Destinations with a few clicks.
Frequently Asked Questions (FAQs)
1) Is AWS Lambda Open-Source?
No, it is a proprietary system that’s only available within AWS.
2) Is AWS Lambda an API?
AWS Lambda provides an API via which you may do a variety of operations on your functions, such as deploying a new version of the function’s code, seeing the function’s layers, and deleting functions. The API reference docs include a detailed overview of the AWS Lambda API. You may also utilize it to create your own APIs.
3) Can AWS Lambda call other AWS Lambda Functions?
Yes. There’s an option to call another function using the Lambda API (invoked using the AWS SDK in your function code). Another option is to utilize a queue service, such as Amazon SNS, to generate an SNS message from one function and then use that message as an event to trigger the second function. Another alternative is to utilize AWS Step Functions to build a process with many functions.
4) What kind of code can run on AWS Lambda?
AWS Lambda makes it simple to carry out a variety of activities in the Cloud. For example, it can be used to build mobile backends that retrieve and transform data from Amazon DynamoDB, handlers that compress or transform objects as they are uploaded to Amazon S3, auditing and reporting API calls made to any Amazon Web Service, and server-less streaming data processing using Amazon Kinesis.
Share your experience of understanding, in the comment section below! We would love to hear your thoughts.
Manisha Jena is a data analyst with over three years of experience in the data industry and is well-versed with advanced data tools such as Snowflake, Looker Studio, and Google BigQuery. She is an alumna of NIT Rourkela and excels in extracting critical insights from complex databases and enhancing data visualization through comprehensive dashboards. Manisha has authored over a hundred articles on diverse topics related to data engineering, and loves breaking down complex topics to help data practitioners solve their doubts related to data engineering.
