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Among all the new features and services that AWS announced during the re:Invent 2020, my favorites were definitely the AWS Lambda updates. And there were many! For example, your code execution is no longer rounded up to the nearest 100ms of duration for billing — you are now billed on a per millisecond. On top of that, AWS increased the Lambda’s memory capacity to 10 GB, and correspondingly the CPU capacity up to 6 vCPUs . But today, I want to dig deeper into something even more exciting for me. Namely, from now on, AWS Lambda doesn’t require packaging your code and dependencies to a zip file. Instead, you can now do it with a Docker container image that can be up to 10 GB in size.
Personally, I consider this a game-changer for many serverless use cases. And here’s why.
Until recently, the only way of creating a serverless function on AWS was to select your specific language and runtime (ex. Python 3.8), then making sure that you install all your custom dependencies inside of your project directory (or adding site-packages from a Python’s virtual environment to your zip package) and finally, compressing all that into a zip package. If your zip file is bigger than 50MB, you would also have to upload the code to S3 and reference it in your function definition. All that is doable. Many developers (me including) used to create their own methods to make it easier, such as using Lambda layers, site-packages from a virtual environment, and building shell scripts for deployment.
On the surface, it seems like not much changes — instead of zipping your code, you now define your dependencies inside a Dockerfile. But there is more to it, as defining your runtime environment in a container image gives you much more control over your environment compared to what you get with predefined runtimes and zipping dependencies.
A zip file with a predefined runtime environment has its limits: what if you would like to use a specific Python environment that has been reviewed by your company’s security team? Or what if you need some additional OS-level package? With the container image support, you can do that since a Docker container has no restrictions in the base image and packages you choose to install. This makes “serverless” accessible to a wider audience, and the development of FaaS (Function as a Service) becomes much easier.
In theory, it’s even possible to create custom images for other programming languages, although this requires implementing a custom runtime and is more involved.
The interface of AWS Lambda now looks as follows:
You can now use your own custom environment packaged as a container image.
Note: at the time of writing, only Linux containers are supported.
Let’s build a simple ETL example. Here is a project structure that we will use:
My requirements.txt contains only: pandas==1.1.0.
The actual code, demonstrated below, is just a simple ETL example counting exam scores of Harry Potter’s characters, but you can use it as a scaffold for your use case:
Now to the fun part: the Dockerfile that will define all our code dependencies so that we don’t need to zip our code!💪🏻
Usually, your base image for Python 3.8 would start with FROM python:3.8 in order to use the official Python image from the Dockerhub. However, to make it usable with AWS Lambda, your base image must include the Lambda Runtime API. To make it easier for us, AWS prepared many base images that we can use, such as the one defined in line 3 in the Dockerfile presented above. You can find all AWS Lambda images in the public ECR repository as well as in the Dockerhub registry:
Base images for AWS Lambda.
The best part of developing your Lambda functions with a container image is the dev/prod environment parity. You can easily test your code locally with Docker before deploying your code to AWS. Your local containerized environment is identical to the one you will be using later in production. This is possible due to a web-server environment called Lambda Runtime Interface Emulator (RIE) (you can find out more about it here), which has been open-sourced by AWS. This emulator is already baked into all Lambda images (amazon/aws-lambda-*) that you can find on Dockerhub or in the ECR public image repository.
Run the following commands from the project directory that contains Dockerfile:
Then, in a new terminal window, run:
Here is what I’m getting as output:
Local execution looks good. Let’s deploy it to AWS.
We can now run the following commands to create an ECR repository and push our container image to ECR:
Now that our image is deployed, we can use it in our Lambda function:
Deploying a Lambda function with a container image from ECR — GIF made by the author
Note that we didn’t have to select the runtime environment since it’s all already defined in our container image. We tested the function from the AWS management console and saw that we got the same result as when tested locally.
By now, you may be convinced that running containerized workloads with AWS Lambda has a myriad of advantages, and you may want to use it now much more extensively. However, I encourage you to think ahead about observability and approach the serverless workloads with an architect’s foresight.
Imagine that you migrated several data pipelines from a container orchestration solution to AWS Lambda. How do you know which of those pipelines failed and why? Sure, AWS offers native support for logging and alerting via Amazon CloudWatch. Still, to be completely honest, AWS services for monitoring and observability require some extra work to set up proper alerting, configure log groups, and set up everything to ensure tracing with X-Ray. Then, we also need to decide on metrics to track and build CloudWatch dashboards to visualize this data.
You can considerably improve the developer experience by using tools such as Dashbird, which allows you to easily add observability to your existing serverless workloads without any changes to your code or infrastructure. All you need to do is to create an IAM role that will grant Dashbird cross-account permission to communicate with your AWS resources. Once that’s configured, you can immediately start enjoying all benefits of the platform, such as automated alert notifications, visualizations of your metrics, and actionable insights based on the AWS Well-Architected Framework to improve performance, save costs, and enhance the security of your cloud resources.
Actionable insights gathered by leveraging Dashbird.
When using a container image rather than a zip package for your serverless function deployments, you’ll get the following benefits:
I’m quite happy about all the new AWS Lambda features. As a huge proponent of containerized applications, I prefer that option over zipping the code for a serverless deployment. These days, developing self-contained microservices has become easier than ever before due to the existence of so many platforms and services to run containers at scale. And if you want to ensure observability and enterprise-grade monitoring of your serverless containers, Dashbird is a great option to consider: https://dashbird.io/.
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In this article we’ll go through the ins and outs of AWS Lambda pricing model, how it works, what additional charges you might be looking at and what’s in the fine print.
Dashbird was born out of our own need for an enhanced serverless debugging and monitoring tool, and we take pride in being developers.
Dashbird gives us a simple and easy to use tool to have peace of mind and know that all of our Serverless functions are running correctly. We are instantly aware now if there’s a problem. We love the fact that we have enough information in the Slack notification itself to take appropriate action immediately and know exactly where the issue occurred.
Thanks to Dashbird the time to discover the occurrence of an issue reduced from 2-4 hours to a matter of seconds or minutes. It also means that hundreds of dollars are saved every month.
Great onboarding: it takes just a couple of minutes to connect an AWS account to an organization in Dashbird. The UI is clean and gives a good overview of what is happening with the Lambdas and API Gateways in the account.
I mean, it is just extremely time-saving. It’s so efficient! I don’t think it’s an exaggeration or dramatic to say that Dashbird has been a lifesaver for us.
Dashbird provides an easier interface to monitor and debug problems with our Lambdas. Relevant logs are simple to find and view. Dashbird’s support has been good, and they take product suggestions with grace.
Great UI. Easy to navigate through CloudWatch logs. Simple setup.
Dashbird helped us refine the size of our Lambdas, resulting in significantly reduced costs. We have Dashbird alert us in seconds via email when any of our functions behaves abnormally. Their app immediately makes the cause and severity of errors obvious.
End-to-end observability and real-time error tracking for AWS applications.