Richard Seroter's Architecture Musings

Category: Cloud

  • Let’s compare the CLI experiences offered by AWS, Microsoft Azure, and Google Cloud Platform

    Let’s compare the CLI experiences offered by AWS, Microsoft Azure, and Google Cloud Platform

    Real developers use the CLI, or so I’m told. That probably explains why I mostly use the portal experiences of the major cloud providers. But judging from the portal experiences offered by most clouds, they prefer you use the CLI too. So let’s look at the CLIs.

    Specifically, I evaluated the cloud CLIs with an eye on five different areas:

    1. API surface and patterns. How much of the cloud was exposed via CLI, and is there a consistent way to interact with each service?
    2. Authentication. How do users identify themselves to the CLI, and can you maintain different user profiles?
    3. Creating and viewing services. What does it feel like to provision instances, and then browse those provisioned instances?
    4. CLI sweeteners. Are there things the CLI offers to make using it more delightful?
    5. Utilities. Does the CLI offer additional tooling that helps developers build or test their software?

    Let’s dig in.

    Disclaimer: I work for Google Cloud, so obviously I’ll have some biases. That said, I’ve used AWS for over a decade, was an Azure MVP for years, and can be mostly fair when comparing products and services. Please call out any mistakes I make!

    AWS

    You have a few ways to install the AWS CLI. You can use a Docker image, or install directly on your machine. If you’re installing directly, you can download from AWS, or use your favorite package manager. AWS warns you that third party repos may not be up to date. I went ahead and installed the CLI on my Mac using Homebrew.

    API surface and patterns

    As you’d expect, the AWS CLI has wide coverage. Really wide. I think there’s an API in there to retrieve the name of Andy Jassy’s favorite jungle cat. The EC2 commands alone could fill a book. The documentation is comprehensive, with detailed summaries of parameters, and example invocations.

    The command patterns are relatively consistent, with some disparities between older services and newer ones. Most service commands look like:

    aws [service name] [action] [parameters]

    Most “actions” start with create, delete, describe, get, list, or update.

    For example:

    aws elasticache create-cache-cluster --engine redis
aws kinesis describe-stream --stream-name seroter-stream
aws kinesis describe-stream --stream-name seroter-stream
aws qldb delete-ledger --name seroterledger
aws sqs list-queues

    S3 is one of the original AWS services, and its API is different. It uses commands like cp, ls, and rm. Some services have modify commands, others use update. For the most part, it’s intuitive, but I’d imagine most people can’t guess the commands.

    Authentication

    There isn’t one way to authenticate to the AWS CLI. You might use SSO, an external file, or inline access key and ID, like I do below.

    The CLI supports “profiles” which seems important when you may have different access to default values based on what you’re working on.

    Creating and viewing service instances

    By default, everything the CLI does occurs in the region of the active profile. You can override the default region by passing in a region flag to each command. See below that I created a new SQS queue without providing a region, and it dropped it into my default one (us-west-2). By explicitly passing in a target region, I created the second queue elsewhere.

    The AWS Console shows you resources for a selected region. I don’t see obvious ways to get an all-up view. A few services, like S3, aren’t bound by region, and you see all resources at once. The CLI behaves the same. I can’t view all my SQS queues, or databases, or whatever, from around the world. I can “list” the items, region by region. Deletion behaves the same. I can’t delete the above SQS queue without providing a region flag, even though the URL is region-specific.

    Overall, it’s fast and straightforward to provision, update, and list AWS services using the CLI. Just keep the region-by-region perspective in mind!

    CLI sweeteners

    The AWS CLI gives you control over the output format. I set the default for my profile to json, but you can also do yaml, text, and table. You can toggle this on a request by request basis.

    You can also take advantage of command completion. This is handy, given how tricky it may be to guess the exact syntax of a command. Similarly, I really like you can be prompted for parameters. Instead of guessing, or creating giant strings, you can go parameter by parameter in a guided manner.

    The AWS CLI also offers select opportunities to interact with the resources themselves. I can send and receive SQS messages. Or put an item directly into a DynamoDB table. There are a handful of services that let you create/update/delete data in the resource, but many are focused solely on the lifecycle of the resource itself.

    Finally, I don’t see a way to self-update from within the CLI itself. It looks like you rely on your package manager or re-download to refresh it. If I’m wrong, tell me!

    Utilities

    It doesn’t look like the CLI ships with other tools that developers might use to build apps for AWS.

    Microsoft Azure

    The Microsoft Azure CLI also has broad coverage and is well documented. There’s no shortage of examples, and it clearly explains how to use each command.

    Like AWS, Microsoft offers their CLI in a Docker image. They also offer direct downloads, or access via a package manager. I grabbed mine from Homebrew.

    API surface and patterns

    The CLI supports almost every major Azure service. Some, like Logic Apps or Blockchain, only show up in their experimental sandbox.

    Commands follow a particular syntax:

    az [service name] [object] create | list | delete | update [parameters]

    Let’s look at a few examples:

    az ad app create --display-name my-ad-app
az cosmosdb list --resource-group group1
az postgres db show --name mydb --resource-group group1 --server-name myserver
az service bus queue delete --name myqueue --namespace-name mynamespace --resource-group group1

    I haven’t observed much inconsistency in the CLI commands. They all seem to follow the same basic patterns.

    Authentication

    Logging into the CLI is easy. You can simply do az login as I did below—this opens a browser window and has you sign into your Azure account to retrieve a token—or you can pass in credentials. Those credentials may be a username/password, service principal with a secret, or service principal with a client certificate.

    Once you log in, you see all your Azure subscriptions. You can parse the JSON to see which one is active, and will be used as the default. If you wish to change the default, you can use az account set --subscription [name] to pick a different one.

    There doesn’t appear to be a way to create different local profiles.

    Creating and viewing service instances

    It seems that most everything you create in Azure goes into a resource group. While a resource group has a “location” property, that’s related to the metadata, not a restriction on what gets deployed into it. You can set a default resource group (az configure --defaults group=[name]) or provide the relevant input parameter on each request.

    Unlike other clouds, Azure has a lot of nesting. You have a root account, then a subscription, and then a resource group. And most resources also have parent-child relationships you must define before you can actually build the thing you want.

    For example, if you want a service bus queue, you first create a namespace. You can’t create both at the same time. It’s two calls. Want a storage blob to upload videos into? Create a storage account first. A web application to run your .NET app? Provision a plan. Serverless function? Create a plan. This doesn’t apply to everything, but just be aware that there are often multiple steps involved.

    The creation activity itself is fairly simple. Here are commands to create Service Bus namespace and then a queue

    az servicebus namespace create --resource-group mydemos --name seroter-demos --location westus
az servicebus queue create --resource-group mydemos --namespace-name seroter-demos --name myqueue

    Like with AWS, some Azure assets get grouped by region. With Service Bus, namespaces are associated to a geo. I don’t see a way to query all queues, regardless of region. But for the many that aren’t, you get a view of all resources across the globe. After I created a couple Redis caches in my resource group, a simple az redis list --resource-group mydemos showed me caches in two different parts of the US.

    Depending on how you use resource groups—maybe per app or per project, or even by team—just be aware that the CLI doesn’t retrieve results across resource groups. I’m not sure the best strategy for viewing subscription-wide resources other than the Azure Portal.

    CLI sweeteners

    The Azure CLI has some handy things to make it easier to use.

    There’s a find function for figuring out commands. There’s output formatting to json, tables, or yaml. You’ll also find a useful interactive mode to get auto-completion, command examples, and more. Finally, I like that the Azure CLI supports self-upgrade. Why leave the CLI if you don’t have to?

    Utilities

    I noticed a few things in this CLI that help developers. First, there’s an az rest command that lets you call Azure service endpoints with authentication headers taken care of for you. That’s a useful tool for calling secured endpoints.

    Azure offers a wide array of extensions to the CLI. These aren’t shipped as part of the CLI itself, but you can easily bolt them on. And you can create your own. This is a fluid list, but az extension list-available shows you what’s in the pool right now. As of this writing, there are extensions for preview AKS capabilities, managing Azure DevOps, working with DataBricks, using Azure LogicApps, querying the Azure Resource Graph, and more.

    Google Cloud Platform

    I’ve only recently started seriously using the GCP CLI. What’s struck me most about the gcloud tool is that it feels more like a system—dare I say, platform—than just a CLI. We’ll talk more about that in a bit.

    Like with other clouds, you can use the SDK/CLI within a supported Docker image, package manager, or direct download. I did a direct download, since this is also a self-updating CLI, so I didn’t want to create a zombie scenario with my package manager.

    API surface and patterns

    The gcloud CLI has great coverage for the full breadth of GCP. I can’t see any missing services, including things launched two weeks ago. There is a subset of services/commands available in the alpha or beta channels, and are fully integrated into the experience. Each command is well documented, with descriptions of parameters, and example calls.

    CLI commands follow a consistent pattern:

    gcloud [service] create | delete | describe | list | update [parameters]

    Let’s see some examples:

    gcloud bigtable instances create seroterdb --display-name=seroterdb --cluster=serotercluster --cluster-zone=us-east1-a
gcloud pubsub topics describe serotertopic
gcloud run services update --memory=1Gi
gcloud spanner instances delete myspanner

    All the GCP services I’ve come across follow the same patterns. It’s also logical enough that I even guessed a few without looking anything up.

    Authentication

    A gcloud auth login command triggers a web-based authorization flow.

    Once I’m authenticated, I set up a profile. It’s possible to start with this process, and it triggers the authorization flow. Invoking the gcloud init command lets me create a new profile/configuration, or update an existing one. A profile includes things like which account you’re using, the “project” (top level wrapper beneath an account) you’re using, and a default region to work in. It’s a guided processes in the CLI, which is nice.

    And it’s a small thing, but I like that when it asks me for a default region, it actually SHOWS ME ALL THE REGION CODES. For the other clouds, I end up jumping back to their portals or docs to see the available values.

    Creating and viewing service instances

    As mentioned above, everything in GCP goes into Projects. There’s no regional affinity to projects. They’re used for billing purposes and managing permissions. This is also the scope for most CLI commands.

    Provisioning resources is straightforward. There isn’t the nesting you find in Azure, so you can get to the point a little faster. For instance, provisioning a new PubSub topic looks like this:

    gcloud pubsub topics create richard-topic

    It’s quick and painless. PubSub doesn’t have regional homing—it’s a global service, like others in GCP—so let’s see what happens if I create something more geo-aware. I created two Spanner instances, each in different regions.

    gcloud spanner instances create seroter-db1 --config=regional-us-east1 --description=ordersdb --nodes=1
gcloud spanner instances create seroter-db2 --config=regional-us-west1 --description=productsdb --nodes=1

    It takes seconds to provision, and then querying with gcloud spanner instances list gives me all Spanner database instances, regardless of region. And I can use a handy “filter” parameter on any command to winnow down the results.

    The default CLI commands don’t pull resources from across projects, but there is a new command that does enable searching across projects and organizations (if you have permission). Also note that Cloud Storage (gsutil) and Big Query (bq) use separate CLIs that aren’t part of gcloud directly.

    CLI sweeteners

    I used one of the “sweeteners” before: filter. It uses a simple expression language to return a subset of results. You’ll find other useful flags for sorting and limiting results. Like with other cloud CLIs, gcloud lets you return results as json, table, csv, yaml, and other formats.

    There’s also a full interactive shell with suggestions, auto-completion, and more. That’s useful as you’re learning the CLI.

    gcloud has a lot of commands for interacting with the services themselves. You can publish to a PubSub topic, execute a SQL statement against a Spanner database, or deploy and call a serverless Function. It doesn’t apply everywhere, but I like that it’s there for many services.

    The GCP CLI also self-updates. We’ll talk about it more in the section below.

    Utilities

    A few paragraphs ago, I said that the gcloud CLI felt more like a system. I say that, because it brings a lot of components with it. When I type in gcloud components list, I see all the options:

    We’ve got the core SDK and other GCP CLIs for Big Query, but also a potpourri of other handy tools. You’ve got Kubernetes development tools like minikube, Skaffold, Kind, kpt, and kubectl. And you get a stash of local emulators for cloud services like Bigtable, Firestore, Spanner, PubSub and Spanner.

    I can install any or all of these, and upgrade them all from here. A gcloud components update command update all of them, and, shows me a nice change log.

    There are other smaller utility functions included in gcloud. I like that I have commands to configure Docker to work with Google Container Registry, Or fetch Kubernetes cluster credentials and put them into my active profile. And print my identity token to inject into the auth headers of calls to secure endpoints.

    Wrap

    To some extent, each CLI reflects the ethos of their cloud. The AWS CLI is dense, powerful, and occasionally inconsistent. The Azure CLI is rich, easy to get started with, and 15% more complicated than it should be. And the Google Cloud CLI is clean, integrated, and evolving. All of these are great. You should use them and explore their mystery and wonder.

  • First look: Triggering Google Cloud Run with events generated by GCP services

    First look: Triggering Google Cloud Run with events generated by GCP services

    When you think about “events” in an event-driven architecture, what comes to mind? Maybe you think of business-oriented events like “file uploaded”, “employee hired”, “invoice sent”, “fraud detected”, or “batch job completed.” You might emit (or consume) these types of events in your application to develop more responsive systems. 

    What I find even more interesting right now are the events generated by the systems beneath our applications. Imagine what your architects, security pros, and sys admins could do if they could react to databases being provisioned, users getting deleted, firewall being changed, or DNS zone getting updated. This sort of thing is what truly enables the “trust, but verify” approach for empowered software teams. Let those teams run free, but “listen” to things that might be out of compliance.

    This week, the Google Cloud team announced Events for Cloud Run, in beta this September. What this capability does is let you trigger serverless containers when lifecycle events happen in most any Google Cloud service. These lifecycle events are in the CloudEvents format, and distributed (behind the scenes) to Cloud Run via Google Cloud PubSub. For reference, this capability bears some resemblance to AWS EventBridge and Azure Event Grid. In this post, I’ll give you a look at Events for Cloud Run, and show you how simple it is to use.

    Code and deploy the Cloud Run service

    Developers deploy containers to Cloud Run. Let’s not get ahead of ourselves. First, let’s build the app. This app is Seroter-quality, and will just do the basics. I’ll read the incoming event and log it out. This is a simple ASP.NET Core app, with the source code in GitHub. 

    I’ve got a single controller that responds to a POST command coming from the eventing system. I take that incoming event, serialize from JSON to a string, and print it out. Events for Cloud Run accepts either custom events, or CloudEvents from GCP services. If I detect a custom event, I decode the payload and print it out. Otherwise, I just log the whole CloudEvent.

    namespace core_sample_api.Controllers
{
    [ApiController]
    [Route("")]
    public class Eventsontroller : ControllerBase
    {
        private readonly ILogger<Eventsontroller> _logger;
        public Eventsontroller(ILogger<Eventsontroller> logger)
        {
            _logger = logger;
        }
        [HttpPost]
        public void Post(object receivedEvent)
        {
            Console.WriteLine("POST endpoint called");
            string s = JsonSerializer.Serialize(receivedEvent);
            //see if custom event with "message" root property
            using(JsonDocument d = JsonDocument.Parse(s)){
                JsonElement root = d.RootElement;
                if(root.TryGetProperty("message", out JsonElement msg)) {
                    Console.WriteLine("Custom event detected");
                    JsonElement rawData = msg.GetProperty("data");
                    //decode
                    string data = System.Text.Encoding.UTF8.GetString(Convert.FromBase64String(rawData.GetString()));
                    Console.WriteLine("Data value is: " + data);
                }
            }
            Console.WriteLine("Data: " + s);
        }
    }
}

    After checking all my source code into GitHub, I was ready to deploy it to Cloud Run. Note that you can use my same repo to continue on this example!

    I switched over to the GCP Console, and chose to create a new Cloud Run service. I picked a region and service name. Then I could have chosen either an existing container image, or, continuous deployment from a git repo. I chose the latter. First I picked my GitHub repo to get source from.

    Then, instead of requiring a Dockerfile, I picked the new Cloud Buildpacks support. This takes my source code and generates a container for me. Sweet. 

    After choosing my code source and build process, I kept the default HTTP trigger. After a few moments, I had a running service.

    Add triggers to Cloud Run

    Next up, adding a trigger. By default, the “triggers” tab shows the single HTTP trigger I set up earlier. 

    I wanted to show custom events in addition to CloudEvents ones, so I went to the PubSub dashboard and created a new queue that would trigger Cloud Run.

    Back in the Cloud Run UX, I added a new trigger. I chose the trigger type of “com.google.cloud.pubsub.topic.publish” and picked the Topic I created earlier. After saving the trigger, I saw it show up in the list.

    After this, I wanted to trigger my Cloud Run service with CloudEvents. If you’re receiving events from Google Cloud services, you’ll have to enable Data Access Logs so that events can be spun up from Cloud Logs. I’m going to listen for events from Cloud Storage and Cloud Build, so I turned on audit logging for each.

    All that was left to define the final triggers. For Cloud Storage, I chose the storage.create.bucket trigger.

    I wanted to react to Cloud Build, so that I could see whenever a build started.

    Terrific. Now I was ready to test. I sent in a message to PubSub to trigger the custom event.

    I checked the logs for Cloud Run, and almost immediately saw that the service ran, accepted the event, and logged the body.

    Next, I tested Cloud Storage by adding a new bucket.

    Almost immediately, I saw a CloudEvent in the log.

    Finally, I kicked off a new Build pipeline, and saw an event indicating that Cloud Run received a message, and logged it.

    If you care about what happens inside the systems your apps depend on, take a look at the new Events for Cloud Run and start tapping into the action.

  • I’m looking forward these 8 sessions at Google Cloud Next ’20 OnAir (Week 7)

    I’m looking forward these 8 sessions at Google Cloud Next ’20 OnAir (Week 7)

    It’s here. After six weeks of OTHER topics, we’re up to week seven of Google Cloud Next OnAir, which is all about my area: app modernization. The “app modernization” bucket in Google Cloud covers lots of cool stuff including Cloud Code, Cloud Build, Cloud Run, GKE, Anthos, Cloud Operations, and more. It basically addresses the end-to-end pipeline of modern apps. I recently sketched it out like this:

    I think this the biggest week of Next, with over fifty breakout sessions. I like that most of the breakouts so far have been ~20 minutes, meaning you can log in, set playback speed to 1.5x, and chomp through lots of topic quickly. 

    Here are eight of the sessions I’m looking forward to most:

    1. Ship Faster, Spend Less By Going Multi-Cloud with Anthos. This is the “keynote” for the week. We’re calling out a few product announcements, highlighting some new customers, and saying keynote-y things. You’ll like it.
    2. GKE Turns 5: What’s New? All Kubernetes aren’t the same. GKE stands apart, and the team continues solving customer problems in new ways. This should be a great look back, and look ahead.
    3. Cloud Run: What’s New? To me, Cloud Run has the best characteristics of PaaS, combined with the the event-driven, scale-to-zero of serverless functions. This is the best place I know of to run custom-built apps in the Google Cloud (or anywhere, with Anthos).
    4. Modernize Legacy Java Apps Using Anthos. Whoever figures out how to unlock value from existing (Java) apps faster, wins. Here’s what Google Cloud is doing to help customers improve their Java apps and run them on a great host.
    5. Running Anthos on Bare Metal and at the Edge with Major League Baseball (MLB). Baseball’s back, my Slam Diego Padres are fun again, and Anthos is part of the action. Good story here.
    6. Getting Started with Anthos, Anthos Deep Dive: Part One, Anthos Deep Dive: Part Two. Am I cheating by making three sessions into one entry? Fine, you caught me. But this three part trilogy is a great way to grok Anthos and understand its value.
    7. Develop for Cloud Run in the IDE with Cloud Code. Cloud Code extends your IDE to support Google Cloud, and Cloud Run is great. Combine the two, and you’ve got some good stuff.
    8. Event-Driven Microservices with Cloud Run. You’re going to enjoy this one, and seeing what’s now possible.

    I’m looking forward to this week. We’re sharing lots of fun progress, and demonstrating some fresh perspectives on what app modernization should look like. Enjoy watching!

  • I’m looking forward these 7 sessions at Google Cloud Next ’20 OnAir (Weeks 5 + 6)

    Nearly halfway through the “Summer of Google”, there’s been some significant announcements, and plenty of interesting talks. We’ve shared the names of some new Google Cloud customers—Deutsche Bank, Goldman Sachs, Spotify, Best Buy and more—and announced some pretty cool stuff—think Confidential VMs, BigQuery Omni, a Certificate Authority Service, a new undersea cable, a cloud migration program, plus the usual barrage of new things we’re constantly shipping for you. The conference is still free to sign up for, and you can catch up on everything you’ve missed.

    Week five content is available on August 11, and week six material is binge-able on August 18. Week five is all about Data Analytics and the focus of week six is Data Management and Databases.

    Here’s what looks good to me in week five:

    These sessions look appealing in week six:

    It was hard to just pick a few talks! Check those out, and stay tuned for a final look at the last three weeks of this summer extravaganza.

  • Think all Kubernetes look alike? Look for differences in these six areas.

    Think all Kubernetes look alike? Look for differences in these six areas.

    I feel silly admitting that I barely understand what happens in the climactic scene of the 80s movie Trading Places. It has something to do with short-selling commodities—in this case, concentrated orange juice. Let’s talk about commodities, which Investopedia defines as:

    a basic good used in commerce that is interchangeable with other goods of the same type. Commodities are most often used as inputs in the production of other goods or services. The quality of a given commodity may differ slightly, but it is essentially uniform across producers.

     Our industry has rushed to declare Kubernetes a commodity, but is it? It is now a basic good used as input to other goods and services. But is uniform across producers? It seems to me that the Kubernetes API is commoditized and consistent, but the platform experience isn’t. Your Kubernetes experience isn’t uniform across Google Kubernetes Engine (GKE), AWS Elastic Kubernetes Service (EKS), Azure Kubernetes Service (AKS), VMware PKS, Red Hat OpenShift, Minikube, and 130+ other options. No, there are real distinctions that can impact your team’s chance of success adopting it. As you’re choosing a Kubernetes product to use, pay upfront attention to provisioning, upgrades, scaling/repair, ingress, software deployment, and logging/monitoring. 

    I work for Google Cloud, so obviously I’ll have some biases. That said, I’ve used AWS for over a decade, was an Azure MVP for years, and can be mostly fair when comparing products and services.

    1. Provisioning

    Kubernetes is a complex distributed system with lots of moving parts. Multi-cluster has won out as a deployment strategy (versus one giant mega cluster segmented by namespace), which means you’ll provision Kubernetes clusters with some regularity.

    What do you have to do? How long does it take? What options are available? Those answers matter! 

    Kubernetes offerings don’t have identical answers to these questions:

    • Do you want clusters in a specific geography?
    • Should clusters get deployed in an HA fashion across zones?
    • Can you build a tiny cluster (small machine, single node) and a giant cluster?
    • Can you specify the redundancy of the master nodes? Is there redundancy?
    • Do you need to choose a specific Kubernetes version? 
    • Are worker nodes provisioned during cluster build, or do you build separately and attach to the cluster?
    • Will you want persistent storage for workloads?
    • Are there “special” computing needs, including large CPU/memory nodes, GPUs, or TPUs?
    • Are you running Windows containers in the cluster?

    As you can imagine, since GKE is the original managed Kubernetes, there’s lots of options for you when building clusters. Or, you can do a one-click install of a “starter” cluster, which is pretty great.

    2. Upgrades

    You got a cluster running? Cool! Day 2 is usually where the real action’s at. Let’s talk about upgrades, which are a fact of life for clusters. What gets upgraded? Namely the version of Kubernetes, and the configuration/OS of the nodes themselves. The level of cluster management amongst the various providers is not uniform.

    GKE supports automated upgrades of everything in the cluster, or you can trigger it manually. Either way, you don’t do any of the upgrade work yourself. Release channels are pretty cool, too. DigitalOcean looks somewhat similar to GKE, from an upgrade perspective. AKS offers manually triggered upgrades. AWS offers kinda automated or extremely manual (i.e. creating new node groups or using Cloud Formation), depending on whether you used managed or unmanaged worker nodes.

    3. Scaling / Repairs

    Given how many containers you can run on a good-sized cluster, you may not have to scale your cluster TOO often. But, you may also decide to act in a “cloudy” way, and purposely start small and scale up as needed.

    Like with most any infrastructure platform, you’ll expect to scale Kubernetes environments (minus local dev environments) both vertically and horizontally. Minimally, demand that your Kubernetes provider can scale clusters via manual commands. Increasingly, auto-scaling of the cluster is table-stakes. And don’t forget scaling of the pods (workloads) themselves. You won’t find it everywhere, but GKE does support horizontal pod autoscaling and vertical pod autoscaling too.

    Also, consider how your Kubernetes platform handles the act of scaling. It’s not just about scaling the nodes or pods. It’s how well the entire system swells to absorb the increasing demand. For instance, Bayer Crop Science worked with Google Cloud to run a 15,000 node cluster in GKE. For that to work, the control planes, load balancers, logging infrastructure, storage, and much more had to “just work.” Understand those points in your on-premises or cloud environment that will feel the strain.

    Finally, figure out what you want to happen when something goes wrong with the cluster. Does the system detect a down worker and repair/replace it? Most Kubernetes offerings support this pretty well, but do dig into it!

    4. Ingress

    I’m not a networking person. I get the gist, and can do stuff, but I quickly fall into the pit of despair. Kubernetes networking is powerful, but not simple. How do containers, pods, and clusters interact? What about user traffic in and out of the cluster? We could talk about service meshes and all that fun, but let’s zero in on ingress. Ingress is about exposing “HTTP and HTTPS routes from outside the cluster to services within the cluster.” Basically, it’s a Layer 7 front door for your Kubernetes services.

    If you’re using Kubernetes on-premises, you’ll have some sort of load balancer configuration setup available, maybe even to use with an ingress controller. Hopefully! In the public cloud, major providers offer up their load-balancer-as-a-service whenever you expose a service of type “LoadBalancer.” But, you get a distinct load balancer and IP for each service. When you use an ingress controller, you get a single route into the cluster (still load balanced, most likely) and the traffic is routed to the correct pod from there. Microsoft, Amazon, and Google all document their way to use ingress controllers with their managed Kubernetes.

    Make sure you investigate the network integrations and automation that comes with your Kubernetes product. There are super basic configurations (that you’ll often find in local dev tools) all the way to support for Istio meshes and ingress controllers.

    5. Software Deployment

    How do you get software into your Kubernetes environment? This is where the commoditization of the Kubernetes API comes in handy! Many software products know how to deploy containers to a Kubernetes environment.

    Two areas come to mind here. First, deploying packaged software.  You can use Helm to deploy software to most any Kubernetes environment. But let’s talk about marketplaces. Some self-managed software products deliver some form of a marketplace, and a few public clouds do. AWS has the AWS Marketplace for Containers. DigitalOcean has a nice little marketplace for Kubernetes apps. In the Google Cloud Marketplace, you can filter by Kubernetes apps, and see what you can deploy on GKE, or in Anthos environments. I didn’t notice a way in the Azure marketplace to find or deploy Kubernetes-targeted software.

    The second area of software deployment I think about relates to CI/CD systems for custom apps. Here, you have a choice of 3rd party best-of-breed tools, or whatever your Kubernetes provider bakes in. AWS CodePipeline or CodeDeploy can deploy apps to ECS (not EKS, it seems). Azure Pipelines looks like it deploys apps directly to AKS. Google Cloud Build makes it easy to deploy apps to GKE, App Engine, Functions, and more.

    When thinking about software deployment, you could also consider the app platforms that run atop a Kubernetes foundation, like Knative and in the future, Cloud Foundry. These technologies can shield you from some of the deployment and configuration muck that’s required to build a container, deploy it, and wire it up for routing.

    6. Logging/Monitoring

    Finally, take a look at what you need from a logging and monitoring perspective. Most any Kubernetes system will deliver some basic metrics about resource consumption—think CPU, memory, disk usage—and maybe some Kubernetes-specific metrics. From what I can tell, the big 3 public clouds integrate their Kubernetes services with their managed monitoring solutions. For example, you get visibility into all sorts of GKE metrics when clusters are configured to use Cloud Operations.

    Then there’s the question of logging. Do you need a lot of logs, or is it ok if logs rotate often? DigitalOcean rotates logs when they reach 10MB in size. What kind of logs get stored? Can you analyze logs from many clusters? As always, not every Kubernetes behaves the same!

    Plenty of other factors may come into play—things like pricing model, tenancy structure, 3rd party software integration, troubleshooting tools, and support community come to mind—when choosing a Kubernetes product to use, so don’t get lulled into a false sense of commoditization!

  • I’m looking forward these 6 sessions at Google Cloud Next ’20 OnAir (Weeks 3 + 4)

    Google Cloud’s 9-week conference is underway. Already lots of fun announcements and useful sessions in this free event. I wrote a post about what I was looking forward to watching during weeks one and two, and I’m now thinking about the upcoming weeks three and four. The theme for week three is “infrastructure” and week four’s topic is “security.”

    For week three (starting July 28), I’m looking forward to watching (besides the keynote):

    For week four (starting August 4), I’d like to watch:

    Should be another educational two weeks. Join in and learn some new stuff.

  • I’m looking forward these 4 sessions at Google Cloud Next ’20 OnAir (Weeks 1 & 2)

    Like every other tech vendor, Google’s grand conference plans for 2020 changed. Instead of an in-person mega-event for cloud aficionados, we’re doing a (free!) nine week digital event called Google Cloud Next OnAir. Each week, starting July 14th, you get on-demand breakout sessions about a given theme. And there are ongoing demo sessions, learning opportunities, and 1:1s with Google Experts. Every couple weeks, I’ll highlight a few talks I’m looking forward to.

    Before sharing my week one and week two picks, a few words on why you should care about this event. More than any other company, Google defines what’s “next” for customers and competitors alike. Don’t believe me? I researched a few of the tech inflection points of the last dozen years and Google’s fingerprints are all over them! If I’m wrong with any of this, don’t hesitate to correct me in the comments.

    In 2008, Google launched Google App Engine (GAE), the first mainstream platform-as-a-service offering that introduced a compute model that obfuscated infrastructure. GAE (and Heroku) sparked an explosion of other products like Azure Web Apps, Cloud Foundry, AWS Elastic Beanstalk and others. I’m also fairly certain that the ideas behind PaaS led to the serverless+FaaS movement as well.

    Google created Go and released the new language in 2009. It remains popular with developers, but has really taken off with platform builders. A wide range of open-source projects use Go, including Docker, Kubernetes, Terraform, Prometheus, Hugo, InfluxDB, Jaeger, CockroachDB, NATS, Cloud Foundry, etcd, and many more. It seems to be the language of distributed systems and the cloud.

    Today, we think of data processing and AI/ML when we think of cloud computing, but that wasn’t always the case. Google had the first cloud-based data warehouse (BigQuery, in 2010) and AI/ML as a service by a cloud provider (Translate API, in 2011). And don’t forget Spanner, the distributed SQL database unveiled by Google in 2012 that inspired a host of other database vendors. We take for granted that every major public cloud now offers data warehouses, AI/ML services, and planet-scale databases. Seems like Google set the pace.

    Keep going? Ok. The components emerging as the heart of the modern platform? Looks like container orchestration, service mesh, and serverless runtime. The CNCF 2019 survey shows that Kubernetes, Istio, and Knative play leading roles. All of those projects started at Google, and the industry picked up on each one. While, we were first to offer a managed Kubernetes service (GKE in 2015),  now you find everyone offering one. Istio and Knative are also popping up all over. And our successful effort to embed all those technologies into a software-driven managed platform (Anthos, in 2019) may turn out to be the preferred model vs. a hyperconverged stack of software plus infrastructure.

    Obviously, additional vendors and contributors have moved our industry forward over the past dozen years: Docker with approachable containerization, Amazon with AWS Lambda, social coding with GitHub, infrastructure-as-code from companies like HashiCorp, microservice and chaos engineering ideas from the likes of Netflix, and plenty of others. But I contend that no single vendor has contributed more value for the entire industry than Google. That’s why Next OnAir matters, as we’ll keep sharing tech that makes everything better.

    Ok, enough blather. The theme of week one is “industry insights.” There’s also the analyst summit and partner summit, both with great content for those audiences. The general sessions I definitely want to watch:

    Week two starts July 21, and has a theme of “productivity & collaboration.” A couple sessions stood out to me:

    Next OnAir has a tremendous list of speakers, including customers and Google engineers sharing their insight. It’s free to sign up, you can watch at your leisure, and maybe, get a glimpse of what’s next.

  • Google Cloud’s support for Java is more comprehensive than I thought

    Google Cloud’s support for Java is more comprehensive than I thought

    Earlier this year, I took a look at how Microsoft Azure supports  Java/Spring developers. With my change in circumstances, I figured it was a good time to dig deep into Google Cloud’s offerings for Java developers. What I found was a very impressive array of tools, services, and integrations. More than I thought I’d find, honestly. Let’s take a tour.

    Local developer environment

    What stuff goes on your machine to make it easier to build Java apps that end up on Google Cloud?

    Cloud Code extension for IntelliJ

    Cloud Code is a good place to start. Among other things, it delivers extensions to IntelliJ and Visual Studio Code. For IntelliJ IDEA users, you get starter templates for new projects, snippets for authoring relevant YAML files, tool windows for various Google Cloud services, app deployment commands, and more. Given that 72% of Java developers use IntelliJ IDEA, this extension helps many folks.

    Cloud Code in VS Code

    The Visual Studio Code extension is pretty great too. It’s got project starters and other command palette integrations, Activity Bar entries to manage Google Cloud services, deployment tools, and more. 4% of Java devs use Visual Studio Code, so, we’re looking out for you too. If you use Eclipse, take a look at the Cloud Tools for Eclipse.

    The other major thing you want locally is the Cloud SDK. Within this little gem you get client libraries for your favorite language, CLIs, and emulators. This means that as Java developers, we get a Java client library, command line tools for all-up Google Cloud (gcloud), Big Query (bq) and Storage (gsutil), and then local emulators for cloud services like Pub/Sub, Spanner, Firestore, and more. Powerful stuff.

    App development

    Our machine is set up. Now we need to do real work. As you’d expect, you can use all, some, or none of these things to build your Java apps. It’s an open model.

    Java devs have lots of APIs to work with in the Google Cloud Java client libraries, whether talking to databases or consuming world-class AI/ML services. If you’re using Spring Boot—and the JetBrains survey reveals that the majority of you are—then you’ll be happy to discover Spring Cloud GCP. This set of packages makes it super straightforward to interact with terrific managed services in Google Cloud. Use Spring Data with cloud databases (including Cloud Spanner and Cloud Firestore), Spring Cloud Stream with Cloud Pub/Sub, Spring Caching with Cloud Memorystore, Spring Security with Cloud IAP, Micrometer with Cloud Monitoring, and Spring Cloud Sleuth with Cloud Trace.  And you get the auto-configuration, dependency injection, and extensibility points that make Spring Boot fun to use. Google offers Spring Boot starters, samples, and more to get you going quickly. And it works great with Kotlin apps too.

    Emulators available via gcloud

    As you’re building Java apps, you might directly use the many managed services in Google Cloud Platform, or, work with the emulators mentioned above. It might make sense to work with local emulators for things like Cloud Pub/Sub or Cloud Spanner. Conversely, you may decide to spin up “real” instance of cloud services to build apps using Managed Service for Microsoft Active Directory, Secret Manager, or Cloud Data Fusion. I’m glad Java developers have so many options.

    Where are you going to store your Java source code? One choice is Cloud Source Repositories. This service offers highly available, private Git repos—use it directly or mirror source code code from GitHub or Bitbucket—with a nice source browser and first-party integration with many Google Cloud compute runtimes.

    Building and packaging code

    After you’ve written some Java code, you probably want to build the project, package it up, and prepare it for deployment.

    Artifact Registry

    Store your Java packages in Artifact Registry. Create private, secure artifact storage that supports Maven and Gradle, as well as Docker and npm. It’s the eventual replacement of Container Registry, which itself is a nice Docker registry (and more).

    Looking to build container images for your Java app? You can write your own Dockerfiles. Or, skip docker build|push by using our open source Jib as a Maven or Gradle plugin that builds Docker images. Jib separates the Java app into multiple layers, making rebuilds fast. A new project is Google Cloud Buildpacks which uses the CNCF spec to package and containerize Java 8|11 apps.

    Odds are, your build and containerization stages don’t happen in isolation; they happen as part of a build pipeline. Cloud Build is the highly-rated managed CI/CD service that uses declarative pipeline definitions. You can run builds locally with the open source local builder, or in the cloud service. Pull source from Cloud Source Repositories, GitHub and other spots. Use Buildpacks or Jib in the pipeline. Publish to artifact registries and push code to compute environments. 

    Application runtimes

    As you’d expect, Google Cloud Platform offers a variety of compute environments to run your Java apps. Choose among:

    • Compute Engine. Pick among a variety of machine types, and Windows or Linux OSes. Customize the vCPU and memory allocations, opt into auto-patching of the OS, and attach GPUs.
    • Bare Metal. Choose a physical machine to host your Java app. Choose from machine sizes with as few as 16 CPU cores, and as many as 112.
    • Google Kubernetes Engine. The first, and still the best, managed Kubernetes service. Get fully managed clusters that are auto scaled, auto patched, and auto repaired. Run stateless or stateful Java apps.
    • App Engine. One of the original PaaS offerings, App Engine lets you just deploy your Java code without worrying about any infrastructure management.
    • Cloud Functions. Run Java code in this function-as-a-service environment.
    • Cloud Run. Based on the open source Knative project, Cloud Run is a managed platform for scale-to-zero containers. You can run any web app that fits into a container, including Java apps. 
    • Google Cloud VMware Engine. If you’re hosting apps in vSphere today and want to lift-and-shift your app over, you can use a fully managed VMware environment in GCP.

    Running in production

    Regardless of the compute host you choose, you want management tools that make your Java apps better, and help you solve problems quickly. 

    You might stick an Apigee API gateway in front of your Java app to secure or monetize it. If you’re running Java apps in multiple clouds, you might choose Google Cloud Anthos for consistency purposes. Java apps running on GKE in Anthos automatically get observability, transport security, traffic management, and SLO definition with Anthos Service Mesh.

    Anthos Service Mesh

    But let’s talk about day-to-day operations of Java apps. Send Java app logs to Cloud Logging and dig into them. Analyze application health and handle alerts with Cloud Monitoring. Do production profiling of your Java apps using Cloud Profiler. Hunt for performance problems via distributed tracing with Cloud Trace. And if you need to, debug in production by analyzing the running Java code (in your IDE!) with Cloud Debugger.

    Modernizing existing apps

    You probably have lots of existing Java apps. Some are fairly new, others were written a decade ago. Google Cloud offers tooling to migrate many types of existing VM-based apps to container or cloud VM environments. There’s good reasons to do it, and Java apps see real benefits.

    Migrate for Anthos takes an existing Linux or Windows VM and creates artifacts (Dockerfiles, Kubernetes YAML, etc) to run that workload in GKE. Migrate for Compute Engine moves your Java-hosting VMs into Google Compute Engine.

    All-in-all, there’s a lot to like here if you’re a Java developer. You can mix-and-match these Google Cloud services and tools to build, deploy, run, and manage Java apps.

  • Build and deploy secure containers to a serverless runtime using Google Cloud Buildpacks and this six-line file.

    Build and deploy secure containers to a serverless runtime using Google Cloud Buildpacks and this six-line file.

    I rarely enjoy the last mile of work. Sure, there’s pleasure in seeing something reach its conclusion, but when I’m close, I just want to be done! For instance, when I create a Pluralsight course—my new one, Cloud Foundry: The Big Picture just came out—I enjoy the building part, and dread the record+edit portion. Same with writing software. I like coding an app to solve a problem. Then, I want a fast deployment so that I can see how the app works, and wrap up. Ideally, each app I write doesn’t require a unique set of machinery or know-how. Thus, I wanted to see if I could create a *single* Google Cloud Build deployment pipeline that shipped any custom app to the serverless Google Cloud Run environment.

    Cloud Build is Google Cloud’s continuous integration and delivery service. It reminds me of Concourse in that it’s declarative, container-based, and lightweight. It’s straightforward to build containers or non-container artifacts, and deploy to VMs, Kubernetes, and more. The fact that it’s a hosted service with a great free tier is a bonus. To run my app, I don’t want to deal with configuring any infrastructure, so I chose Google Cloud Run as my runtime. It just takes a container image and offers a fully-managed, scale-to-zero host. Before getting fancy with buildpacks, I wanted to learn how to use Build and Run to package up and deploy a Spring Boot application.

    First, I generated a new Spring Boot app from start.spring.io. It’s going to be a basic REST API, so all I needed was the Web dependency.

    I’m not splitting the atom with this Java code. It simply returns a greeting when you hit the root endpoint.

    @RestController
@SpringBootApplication
public class HelloAppApplication {

	public static void main(String[] args) {
		SpringApplication.run(HelloAppApplication.class, args);
	}

	@GetMapping("/")
	public String SayHello() {
		return "Hi, Google Cloud Run!";
	}
}

    Now, I wanted to create a pipeline that packaged up the Boot app into a JAR file, built a Docker image, and deployed that image to Cloud Run. Before crafting the pipeline file, I needed a Dockerfile. This file offers instructions on how to assemble the image. Here’s my basic one:

    FROM openjdk:11-jdk
ARG JAR_FILE=target/hello-app-0.0.1-SNAPSHOT.jar
COPY ${JAR_FILE} app.jar
ENTRYPOINT ["java", "-Djava.security.edg=file:/dev/./urandom","-jar","/app.jar"]

    On to the pipeline. A build configuration isn’t hard to understand. It consists of a series of sequential or parallel steps that produce an outcome. Each step runs in its own container image (specified in the name attribute), and if needed, there’s a simple way to transfer state between steps. My cloudbuild.yaml file for this Spring Boot app looked like this:

    steps:
# build the Java app and package it into a jar
- name: maven:3-jdk-11
  entrypoint: mvn
  args: ["package", "-Dmaven.test.skip=true"]
# use the Dockerfile to create a container image
- name: gcr.io/cloud-builders/docker
  args: ["build", "-t", "gcr.io/$PROJECT_ID/hello-app", "--build-arg=JAR_FILE=target/hello-app-0.0.1-SNAPSHOT.jar", "."]
# push the container image to the Registry
- name: gcr.io/cloud-builders/docker
  args: ["push", "gcr.io/$PROJECT_ID/hello-app"]
#deploy to Google Cloud Run
- name: 'gcr.io/cloud-builders/gcloud'
  args: ['run', 'deploy', 'seroter-hello-app', '--image', 'gcr.io/$PROJECT_ID/hello-app', '--region', 'us-west1', '--platform', 'managed']
images: ["gcr.io/$PROJECT_ID/hello-app"]

    You’ll notice four steps. The first uses the Maven image to package my application. The result of that is a JAR file. The second step uses a Docker image that’s capable of generating an image using the Dockerfile I created earlier. The third step pushes that image to the Container Registry. The final step deploys the container image to Google Cloud Run with an app name of seroter-hello-app. The final “images” property puts the image name in my Build results.

    As you can imagine, I can configure triggers for this pipeline (based on code changes, etc), or execute it manually. I’ll do the latter, as I haven’t stored this code in a repository anywhere yet. Using the terrific gcloud CLI tool, I issued a single command to kick off the build.

    gcloud builds submit --config cloudbuild.yaml .

    After a minute or so, I have a container image in the Container Registry, an available endpoint in Cloud Run, and a full audit log in Cloud Build.

    Container Registry:

    Cloud Run (with indicator that app was deployed via Cloud Build:

    Cloud Build:

    I didn’t expose the app publicly, so to call it, I needed to authenticate myself. I used the “gcloud auth print-identity-token” command to get a Bearer token, and plugged that into the Authorization header in Postman. As you’d expect, it worked. And when traffic dies down, the app scales to zero and costs me nothing.

    So this was great. I did all this without having to install build infrastructure, or set up an application host. But I wanted to go a step further. Could I eliminate the Dockerization portion? I have zero trust in myself to build a good image. This is where buildpacks come in. They generate well-crafted, secure container images from source code. Google created a handful of these using the CNCF spec, and we can use them here.

    My new cloudbuild.yaml file looks like this. See that I’ve removed the steps to package the Java app, and build and push the Docker image, and replaced them with a single “pack” step.

    steps:
# use Buildpacks to create a container image
- name: 'gcr.io/k8s-skaffold/pack'
  entrypoint: 'pack'
  args: ['build', '--builder=gcr.io/buildpacks/builder', '--publish', 'gcr.io/$PROJECT_ID/hello-app-bp:$COMMIT_SHA']
#deploy to Google Cloud Run
- name: 'gcr.io/cloud-builders/gcloud'
  args: ['run', 'deploy', 'seroter-hello-app-bp', '--image', 'gcr.io/$PROJECT_ID/hello-app-bp:latest', '--region', 'us-west1', '--platform', 'managed']

    With the same gcloud command (gcloud builds submit --config cloudbuild.yaml .) I kicked off a new build. This time, the streaming logs showed me that the buildpack built the JAR file, pulled in a known-good base container image, and containerized the app. The result: a new image in the Registry (21% smaller in size, by the way), and a fresh service in Cloud Run.

    I started out this blog post saying that I wanted a single cloudbuild.yaml file for *any* app. With Buildpacks, that seemed possible. The final step? Tokenizing the build configuration. Cloud Build supports “substitutions” which lets you offer run-time values for variables in the configuration. I changed my build configuration above to strip out the hard-coded names for the image, region, and app name.

    steps:
# use Buildpacks to create a container image
- name: 'gcr.io/k8s-skaffold/pack'
  entrypoint: 'pack'
  args: ['build', '--builder=gcr.io/buildpacks/builder', '--publish', 'gcr.io/$PROJECT_ID/$_IMAGE_NAME:$COMMIT_SHA']
#deploy to Google Cloud Run
- name: 'gcr.io/cloud-builders/gcloud'
  args: ['run', 'deploy', '$_RUN_APPNAME', '--image', 'gcr.io/$PROJECT_ID/$_IMAGE_NAME:latest', '--region', '$_REGION', '--platform', 'managed']

    Before trying this with a new app, I tried this once more with my Spring Boot app. For good measure, I changed the source code so that I could confirm that I was getting a fresh build. My gcloud command now passed in values for the variables:

    gcloud builds submit --config cloudbuild.yaml . --substitutions=_IMAGE_NAME="hello-app-bp",_RUN_APPNAME="seroter-hello-app-bp",_REGION="us-west1"

    After a minute, the deployment succeeded, and when I called the endpoint, I saw the updated API result.

    For the grand finale, I want to take this exact file, and put it alongside a newly built ASP.NET Core app. I did a simple “dotnet new webapi” and dropped the cloudbuild.yaml file into the project folder.

    After tweaking the Program.cs file to read the application port from the platform-provided environment variable, I ran the following command:

    gcloud builds submit --config cloudbuild.yaml . --substitutions=_IMAGE_NAME="hello-app-core",_RUN_APPNAME="seroter-hello-app-core",_REGION="us-west1"

    A few moments later, I had a container image built, and my ASP.NET Core app listening to requests in Cloud Run.

    Calling that endpoint (with authentication) gave me the API results I expected.

    Super cool. So to recap, that six line build configuration above works for your Java, .NET Core, Python, Node, and Go apps. It’ll create a secure container image that works anywhere. And if you use Cloud Build and Cloud Run, you can do all of this with no mess. I might actually start enjoying the last mile of app development with this setup.

  • Google Cloud Pub/Sub has three unique features that help it deliver value for app integrators

    Google Cloud Pub/Sub has three unique features that help it deliver value for app integrators

    This week I’m once again speaking at INTEGRATE, a terrific Microsoft-oriented conference focused on app integration. This may be the last year I’m invited, so before I did my talk, I wanted to ensure I was up-to-speed on Google Cloud’s integration-related services. One that I was aware of, but not super familiar with, was Google Cloud Pub/Sub. My first impression was that this was a standard messaging service like Amazon SQS or Azure Service Bus. Indeed, it is a messaging service, but it does more.

    Here are three unique aspects to Pub/Sub that might give you a better way to solve integration problems.

    #1 – Global control plane with multi-region topics

    When creating a Pub/Sub topic—an object that accepts a feed of messages—you’re only asked one major question: what’s the ID?

    In the other major cloud messaging services, you select a geographic region along with the topic name. While you can, of course, interact with those messaging instances from any other region via the API, you’ll pay the latency cost. Not so with Pub/Sub. From the documentation (bolding mine):

    Cloud Pub/Sub offers global data access in that publisher and subscriber clients are not aware of the location of the servers to which they connect or how those services route the data.

    …

    Pub/Sub’s load balancing mechanisms direct publisher traffic to the nearest GCP data center where data storage is allowed, as defined in the Resource Location Restriction section of the IAM & admin console. This means that publishers in multiple regions may publish messages to a single topic with low latency. Any individual message is stored in a single region. However, a topic may have messages stored in many regions. When a subscriber client requests messages published to this topic, it connects to the nearest server which aggregates data from all messages published to the topic for delivery to the client.

    That’s a fascinating architecture, and it means you get terrific publisher performance from anywhere.

    #2 – Supports both pull and push subscriptions for a topic

    Messaging queues typically store data until it’s retrieved by the subscriber. That’s ideal for transferring messages, or work, between systems. Let’s see an example with Pub/Sub.

    I first used the Google Cloud Console to create a pull-based subscription for the topic. You can see a variety of other settings (with sensible defaults) around acknowledgement deadlines, message retention, and more.

    I then created a pair of .NET Core applications. One pushes messages to the topic, another pulls messages from the corresponding subscription. Google created NuGet packages for each of the major Cloud services—which is better than one mega-package that talks to all services—that you can see listed here on GitHub. Here’s the Pub/Sub package that added to both projects.

    The code for the (authenticated) publisher app is straightforward, and the API was easy to understand. 

    using Google.Cloud.PubSub.V1;
using Google.Protobuf;

...

 public string PublishMessages()
 {
   PublisherServiceApiClient publisher = PublisherServiceApiClient.Create();
            
   //create messages
   PubsubMessage message1 = new PubsubMessage {Data = ByteString.CopyFromUtf8("Julie")};
   PubsubMessage message2 = new PubsubMessage {Data = ByteString.CopyFromUtf8("Hazel")};
   PubsubMessage message3 = new PubsubMessage {Data = ByteString.CopyFromUtf8("Frank")};

   //load into a collection
   IEnumerable<PubsubMessage> messages = new PubsubMessage[] {
      message1,
      message2,
      message3
    };
            
   //publish messages
   PublishResponse response = publisher.Publish("projects/seroter-anthos/topics/seroter-topic", messages);
            
   return "success";
 }

    After I ran the publisher app, I switched to the web-based Console and saw that the subscription had three un-acknowledged messages. So, it worked.

    The subscribing app? Equally straightforward. Here, I asked for up to ten messages associated with the subscription, and once I processed then, I sent “acknowledgements” back to Pub/Sub. This removes the messages from the queue so that I don’t see them again.

    using Google.Cloud.PubSub.V1;
using Google.Protobuf;

...

  public IEnumerable<String> ReadMessages()
  {
     List<string> names = new List<string>();

     SubscriberServiceApiClient subscriber = SubscriberServiceApiClient.Create();
     SubscriptionName subscriptionName = new SubscriptionName("seroter-anthos", "sub1");

     PullResponse response = subscriber.Pull(subscriptionName, true, 10);
     foreach(ReceivedMessage msg in response.ReceivedMessages) {
         names.Add(msg.Message.Data.ToStringUtf8());
     }

     if(response.ReceivedMessages.Count > 0) {
         //ack the message so we don't receive it again
         subscriber.Acknowledge(subscriptionName, response.ReceivedMessages.Select(m => m.AckId));
     }

     return names;
 }

    When I start up the subscriber app, it reads the three available messages in the queue. If I pull from the queue again, I get no results (as expected).

    As an aside, the Google Cloud Console is really outstanding for interacting with managed services. I built .NET Core apps to test out Pub/Sub, but I could have done everything within the Console itself. I can publish messages:

    And then retrieve those message, with an option to acknowledge them as well:

    Great stuff.

    But back to the point of this section, I can use Pub/Sub to create pull subscriptions and push subscriptions. We’ve been conditioned by cloud vendors to expect distinct services for each variation in functionality. One example is with messaging services, where you see unique services for queuing, event streaming, notifications, and more. Here with Pub/Sub, I’m getting a notification service and queuing service together. A “push” subscription doesn’t wait for the subscriber to request work; it pushes the message to the designated (optionally, authenticated) endpoint. You might provide the URL of an application webhook, an API, a function, or whatever should respond immediately to your message.

    I like this capability, and it simplifies your architecture.

    #3 – Supports message replay within an existing subscription and for new subscriptions

    One of the things I’ve found most attractive about event processing engines is the durability and replay-ability functionality. Unlike a traditional message queue, an event processor is based on a durable log where you can rewind and pull data from any point. That’s cool. Your event streaming engine isn’t a database or system of record, but a useful snapshot in time of an event stream. What if you could get the queuing semantics you want, with that durability you like from event streaming? Pub/Sub does that.

    This again harkens back to point #2, where Pub/Sub absorbs functionality from other specialized services. Let me show you what I found.

    When creating a subscription (or editing an existing one), you have the option to retain acknowledged messages. This keeps these messages around for whatever the duration is for the subscription (up to seven days).

    To try this out, I sent in four messages to the topic, with bodies of “test1”, “test2”, “test3”, and “test4.” I then viewed, and acknowledged, all of them.

    If I do another “pull” there are no more messages. This is standard queuing behavior. An empty queue is a happy queue. But what if something went wrong downstream? You’d typically have to go back upstream and resubmit the message. Because I saved acknowledged messages, I can use the “seek” functionality to replay!

    Hey now. That’s pretty wicked. When I pull from the subscription again, any message after the date/time specified shows up again.

    And I get unlimited bites at this apple. I can choose to replay again, and again, and again. You can imagine all sorts of scenarios where this sort of protection can come in handy.

    Ok, but what about new subscribers? What about a system that comes online and wants a batch of messages that went through the system yesterday? This is where snapshots are powerful. They store the state of any unacknowledged messages in the subscription, and any new messages published after the snapshot was taken. To demonstrate this, I sent in three more messages, with bodies of “test 5”, “test6” and “test7.” Then I took a snapshot on the subscription.

    I read all the new messages from the subscription, and acknowledged them. Within this Pub/Sub subscription, I chose to “replay”, load the snapshot, and saw these messages again. That could be useful if I took a snapshot pre-deploy of code changes, something went wrong, and I wanted to process everything from that snapshot. But what if I want access to this past data from another subscription?

    I created a new subscription called “sub3.” This might represent a new system that just came online, or even a tap that wants to analyze the last four days of data. Initially, this subscription has no associated messages. That makes sense; it only sees messages that arrived after the subscription was created. From this additional subscription, I chose to “replay” and selected the existing snapshot.

    After that, I went to my subscription to view messages, and I saw the three messages from the other subscription’s snapshot.

    Wow, that’s powerful. New subscribers don’t always have to start from scratch thanks to this feature.

    It might be worth it for you to take an extended look at Google Cloud Pub/Sub. It’s got many of the features you expect from a scaled cloud service, with a few extra features that may delight you.