How To

Almost one year ago, I talked about AsyncAPI 2.6 and how confusing its publish and subscribe operations can be in my Understanding AsyncAPI’s publish & subscribe semantics with an example post.

Since then, a new 3.0 version of AsyncAPI has been released with breaking changes and a totally new send and receive operations.

In this blog post, I want to revisit the example from last year and show how to rewrite it for AsyncAPI 3.0 with the new send and receive operations.

One challenge with any distributed system, including Workflows, is ensuring that requests sent from one service to another are processed exactly once, when needed; for example, when placing a customer order in a shipping queue, withdrawing funds from a bank account, or processing a payment.

In this blog post, we’ll provide an example of a website invoking Workflows, and Workflows in turn invoking a Cloud Function. We’ll show how to make sure both Workflows and the Cloud Function logic only runs once. We’ll also talk about how to invoke Workflows exactly once when using HTTP callbacks, Pub/Sub messages, or Cloud Tasks.

Everyone is excited about generative AI (gen AI) nowadays and rightfully so. You might be generating text with PaLM 2 or Gemini Pro, generating images with ImageGen 2, translating code from language to another with Codey, or describing images and videos with Gemini Pro Vision.

No matter how you’re using gen AI, at the end of the day, you’re calling an endpoint either with an SDK or a library or via a REST API. Workflows, my go-to service to orchestrate and automate other services, is more relevant than ever when it comes to gen AI.

As you orchestrate more services with Workflows, the workflow gets more complicated with more steps, jumps, iterations, parallel branches. When the workflow execution inevitably fails at some point, you need to debug and figure out which step failed and why. So far, you only had an execution summary with inputs/outputs and logs to rely on in your execution debugging. While this was good enough for basic workflows, it didn’t provide step level debugging information.

Workflows is a versatile service in orchestrating and automating a wide range of use cases: microservices, business processes, Data and ML pipelines, IT operations, and more. It can also be used to automate deployment of containerized applications on Kubernetes Engine (GKE) and this got even easier with the newly released (in preview) Kubernetes API Connector.

The new Kubernetes API connector enables access to GKE services from Workflows and this in turn enables Kubernetes based resource management or orchestration, scheduled Kubernetes jobs, and more.

Introduction

Eventarc helps users build event-driven architectures without having to implement, customize, or maintain the underlying infrastructure.

Eventarc has added support (in public preview) for delivering events to internal HTTP endpoints in a Virtual Private Cloud (VPC) network. Customers, especially large enterprises, often run compute (typically GKE or GCE) on VPC-private IPs, often behind internal load balancers. This launch will enable these services to consume Eventarc events.

Internal HTTP endpoints can be an internal IP address or fully qualified DNS name (FQDN) for any HTTP endpoint in the VPC network. Examples of destinations that can be targeted via the internal HTTP endpoints include Compute Engine VMs with internal IPs, services fronted by an L7 Internal Load Balancer, Private Service Connect endpoints, Google Kubernetes Engine Ingress, Google Kubernetes Engine Services, Cloud Run behind an internal Application Load Balancer and any destinations registered with Cloud DNS via DNS record.

What languages are supported in WebAssembly running outside the browser? This is a question I often hear people ask. It’s has a complicated answer because:

1. WebAssembly outside the browser needs WASI and not all languages have WASI support in their toolchain.
2. Even if WASI is supported well in a language, WASI has its own limitations that you need to take into account.

In short, you can’t take any code written in any language and expect to compile and run it as a Wasm+Wasi module right now. Documentation on what’s supported is patchy or misleading at times. Unfortunately, you often need to try things out before knowing what works and what doesn’t.

In my previous posts, I talked about how you can run WebAssembly (Wasm) outside the browser with Wasi and run it in a Docker container with runwasi. The Wasi specification allows Wasm modules access to things like the filesystem and environment variables (and I showed how in this blog post) but networking and threading are not implemented yet. This is severely limiting if you want to run HTTP based microservices on Wasm for example.

Introduction

In my previous post, I talked about how you can use a parent workflow to execute child workflows in parallel for faster overall processing time and easier detection of errors. Another useful pattern is to use a Cloud Tasks queue to create Workflows executions and that’s the topic of this post.

When your application experiences a sudden surge of traffic, it’s natural to want to handle the increased load by creating a high number of concurrent workflow executions. However, Google Cloud’s Workflows enforces quotas to prevent abuse and ensure fair resource allocation. These quotas limit the maximum number of concurrent workflow executions per region, per project, for example, Workflows currently enforces a maximum of 2000 concurrent executions by default. Once this limit is reached, any new executions beyond the quota will fail with an HTTP 429 error.

Introduction

There are numerous scenarios where you might want to execute tasks in parallel. One common use case involves dividing data into batches, processing each batch in parallel, and combining the results in the end. This approach not only enhances the speed of the overall processing but it also allows for easier error detection in smaller tasks.

On the other hand, setting up parallel tasks, monitoring them, handling errors in each task, and combining the results in the end is not trivial. Thankfully, Google Cloud’s Workflows can help. In this post, we will explore how you can use a parent workflow to set up and execute parallel child workflows.