In this post, we’ll look at 12 “pearls” – bite-sized code snippets – that demonstrate some fun ways you can program the cloud using Pulumi. In my introductory post, I mentioned a few of my “favorite things”. Now let’s dive into a few specifics, from multi-cloud to cloud-specific, spanning containers, serverless, and infrastructure, and generally highlighting why using familiar languages is so empowering for cloud scenarios. Since Pulumi lets you do infrastructure-as-code from the lowest-level to the highest, we will cover a lot of interesting ground in short order.
If you’ve been following the blog, you know that Pulumi is great for building serverless applications, container-based applications, and a combination of the two. But, did you know that you can manage any cloud resource in AWS, Azure, or Google Cloud Platform?
Setting up the infrastructure to serve a static website doesn’t sound like it would be all that difficult, but when you consider HTTPS certificates, content distribution networks, and attaching it to a custom domain, integrating all the components can be quite daunting.
Fortunately this is a task where Pulumi really shines. Pulumi’s code-centric approach not only makes configuring cloud resources easier to do and maintain, but it also eliminates the pain of integrating multiple products together.
This isn’t a hypothetical benefit of using the Pulumi programming model. We use a setup similar to the one described in this post for powering our own static websites, like www.pulumi.com and get.pulumi.com.
Two weeks ago Amazon added Simple Queue Service (SQS) as a supported event source for Lambda. SQS is one of AWS’s oldest services, providing access to a powerful message queue that can do things like guarantee messages will be delivered at least once, or messages that will be processed in the same order they were received in. Adding SQS as a supported event source for Lambda means that now it’s possible to use SQS in a serverless computing infrastructure, where Lambdas are triggered in response to messages added to your SQS queue. Now, instead of needing some sort of Service dedicated to polling your SQS queue, or creating Simple Notification Service (SNS) notifications from your messages, you can instead just directly trigger whatever Lambda you want.
Here at Pulumi we are (perhaps unsurprisingly!) huge fans of using Pulumi to manage our cloud infrastructure and services. We author our infrastructure in strongly-typed programming languages, which allows us to to benefit from rich tooling - documenting and factoring our infrastructure using the same software engineering practices we apply to our application code. This also allows us to create reusable abstractions which accelerate our ability to deliver new features and services, and our ability to standardize and refactor infrastructure patterns across our services with relative ease.
Like other users, we use Pulumi at a variety of levels of abstraction. We use Pulumi for raw infrastructure provisioning, defining the core networking layer for our AWS-based backend infrastructure. And we use Pulumi to define how our application services are deployed into ECS using just a few lines of code. Pulumi hosts and manages static content for www.pulumi.com and get.pulumi.com. We use Pulumi to define the CloudWatch dashboards connected to our infrastructure. And for monitoring, Pulumi defines metrics and notifications/alarms in PagerDuty and Slack.
Best of all, we’ve been able to take things we’ve learned from these use
cases, and others we’ve worked with beta users on over the last few
months (thank you!), and factor common patterns out into reusable
@pulumi/cloud for ourselves and
others to build upon.
In this post, we’ll do a deeper dive into each of these use cases, highlighting unique aspects of how we use Pulumi itself, and some of our engineering processes around how we integrate Pulumi into the rest of our toolchain.
Pulumi makes it easy to build cloud applications that use a combination of containers, lambdas, and connected data services and infrastructure: Colada apps.
An example of a Colada app is extracting a thumbnail from a video. A serverless function can only run for 5 minutes, so we’ll run a container in AWS Fargate to do the video processing.
In this app, a Lambda function is triggered whenever a new video is uploaded to S3. This function launches a task in Fargate that uses FFmpeg to extract a video thumbnail. A second Lambda function is triggered when a new thumbnail has been created.
Containers are a great way to deploy applications to the cloud, especially with new execution models like AWS Fargate. Pulumi makes it easy to deploy production Docker containers, handling details such as creating a container registry instance in ECR, creating task definitions in ECS, and configuring a load balancer. With Pulumi, deploying a container to production is almost as easy as running it locally! In this blog post, we’ll deploy a simple Docker container running NGINX.