Mikhail Shilkov

We are excited to announce the beta release of a next generation Microsoft Azure provider for Pulumi. Azure has been a rapidly growing cloud platform among Pulumi users over the last year, and with the next generation Azure provider, we are doubling down on providing the best support possible for the Azure platform in Pulumi. We designed the new provider to expose the entire API surface of Azure to developers and operators, now and forever.

The new Azure provider for Pulumi (azure-nextgen) works directly with the Azure Resource Manager (ARM) platform instead of depending on a handwritten layer as with the previous provider. This approach ensures higher quality and higher fidelity with the Azure platform.

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Because Pulumi uses general-purpose programming languages to provision cloud resources, you can take advantage of native tools and perform automated tests of your infrastructure. The full power of each language is available, including access to libraries and frameworks for testing.

This blog post takes a deeper dive into mock-based unit testing of Pulumi programs written in C# and F#.

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We are happy to announce the release of a new major version of the Pulumi Azure provider. Pulumi Azure 2.0 is based on the 2.0 release of the upstream provider and brings several improvements and breaking changes.

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Google Cloud Run is the latest addition to the serverless compute family. While it may look similar to existing services of public cloud, the feature set makes Cloud Run unique: Docker as a deployment package enables using any language, runtime, framework, or library that can respond to an HTTP request. Automatic scaling, including scale to zero, means you pay for what you consume with no fixed cost and no management overhead.

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AWS Lambda cold starts (the time it takes for AWS to assign a worker to a request) are a major frustration point of many serverless programmers. In this article, we will take a look at the problem of latency-critical serverless applications, and how Provisioned Concurrency impacts the status-quo. Concurrency Model of AWS Lambda Despite being serverless, AWS Lambda uses lightweight containers to process incoming requests. Every container, or worker, can process only a single request at any given time.

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Azure Functions is a managed service for serverless applications in the Azure cloud. More broadly, Azure Functions is a runtime with multiple hosting possibilities. KEDA (Kubernetes-based Event-Driven Autoscaling) is an emerging option to host this runtime in Kubernetes.

In the first part of this post, I compare KEDA with cloud-based scaling and outline the required components. In the second part, I define infrastructure as code to deploy a sample KEDA application to an Azure Kubernetes Service (AKS) cluster.

The result is a fully working example and a high-level idea of how it works. Kubernetes expertise is not required!

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In a previous blog post, I shared how easy it is to create a globally distributed, highly-available, low-latency application with Azure Functions, Azure Cosmos DB, and Pulumi.

Today, I want to show how the same approach can be generalized for any cloud compute service, including containers, virtual machines, and serverless functions.

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In this post, we’ll take a look at 10 “pearls”—bite-sized code snippets—that demonstrate using Pulumi to build serverless applications with Azure Functions and infrastructure as code. These pearls are organized into four categories, each demonstrating a unique scenario:

• Function App Deployment: Deploy an existing Azure Functions application using infrastructure as code.
• HTTP Functions as Callbacks: Mix JavaScript or TypeScript functions with your infrastructure definition to produce strongly-typed, self-contained, serverless HTTP endpoints.
• Cloud Event Handling: Leverage a variety of event sources available to Azure Functions with lightweight event handlers.
• Data Flows with Function Bindings: Take advantage of function bindings—declarative connectors to Azure services.

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Every non-trivial application relies on configuration values that may depend on the current execution environment. Some of these values contain sensitive information that shouldn’t be shared publicly. In general, the fewer parties that have access to those secret values, the safer the application will be—in fact, in an ideal world, no one would be granted direct access to those secrets.

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Pulumi is excellent at connecting multiple cloud components into a cohesive application. In my previous post, I introduced the way to mix JavaScript or TypeScript serverless functions directly into the cloud infrastructure programs.

Today, I will build a serverless application with both the data store and the HTTP endpoint located close to end users to ensure prompt response time. The entire application runs on top of managed Azure services and is defined as a single Pulumi program in TypeScript.

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