From 159bfd927de196034333ea504d6bcbbbd5c46e79 Mon Sep 17 00:00:00 2001 From: Daniel Edgar Date: Tue, 23 Jul 2024 11:29:20 -0400 Subject: [PATCH] feat: Contribute an NGINX Gateway Fabric example and article Signed-off-by: Daniel Edgar --- README.md | 1 + examples/nginx-gateway-fabric/ARTICLE.md | 323 ++++++++++++++++++ examples/nginx-gateway-fabric/README.md | 3 + .../analysis-success-rate.yaml | 29 ++ .../nginx-gateway-fabric/canary-service.yaml | 12 + examples/nginx-gateway-fabric/gateway.yaml | 10 + examples/nginx-gateway-fabric/httproute.yaml | 18 + .../images/canary-deployments.png | Bin 0 -> 218943 bytes .../images/canary-rollout-red-rollback-ui.png | Bin 0 -> 55040 bytes .../images/canary-rollout-red-rollback.png | Bin 0 -> 141543 bytes .../images/canary-rollout-red-ui.png | Bin 0 -> 56348 bytes .../images/canary-rollout-red.png | Bin 0 -> 141543 bytes .../canary-rollout-yellow-complete-ui.png | Bin 0 -> 56054 bytes .../images/canary-rollout-yellow-complete.png | Bin 0 -> 135848 bytes .../images/canary-rollout-yellow.png | Bin 0 -> 120608 bytes .../images/demo-diagram.png | Bin 0 -> 139219 bytes .../images/initial-rollout-blue.png | Bin 0 -> 81609 bytes .../images/ngf-high-level.png | Bin 0 -> 138656 bytes .../images/resource-model.png | Bin 0 -> 125607 bytes .../nginx-gateway-fabric/images/ui-blue.png | Bin 0 -> 51602 bytes .../nginx-gateway-fabric/images/ui-yellow.png | Bin 0 -> 52186 bytes .../images/yellow-analysis.png | Bin 0 -> 118633 bytes examples/nginx-gateway-fabric/rollout.yaml | 57 ++++ .../nginx-gateway-fabric/stable-service.yaml | 12 + 24 files changed, 465 insertions(+) create mode 100644 examples/nginx-gateway-fabric/ARTICLE.md create mode 100644 examples/nginx-gateway-fabric/README.md create mode 100644 examples/nginx-gateway-fabric/analysis-success-rate.yaml create mode 100644 examples/nginx-gateway-fabric/canary-service.yaml create mode 100644 examples/nginx-gateway-fabric/gateway.yaml create mode 100644 examples/nginx-gateway-fabric/httproute.yaml create mode 100644 examples/nginx-gateway-fabric/images/canary-deployments.png create mode 100644 examples/nginx-gateway-fabric/images/canary-rollout-red-rollback-ui.png create mode 100644 examples/nginx-gateway-fabric/images/canary-rollout-red-rollback.png create mode 100644 examples/nginx-gateway-fabric/images/canary-rollout-red-ui.png create mode 100644 examples/nginx-gateway-fabric/images/canary-rollout-red.png create mode 100644 examples/nginx-gateway-fabric/images/canary-rollout-yellow-complete-ui.png create mode 100644 examples/nginx-gateway-fabric/images/canary-rollout-yellow-complete.png create mode 100644 examples/nginx-gateway-fabric/images/canary-rollout-yellow.png create mode 100644 examples/nginx-gateway-fabric/images/demo-diagram.png create mode 100644 examples/nginx-gateway-fabric/images/initial-rollout-blue.png create mode 100644 examples/nginx-gateway-fabric/images/ngf-high-level.png create mode 100644 examples/nginx-gateway-fabric/images/resource-model.png create mode 100644 examples/nginx-gateway-fabric/images/ui-blue.png create mode 100644 examples/nginx-gateway-fabric/images/ui-yellow.png create mode 100644 examples/nginx-gateway-fabric/images/yellow-analysis.png create mode 100644 examples/nginx-gateway-fabric/rollout.yaml create mode 100644 examples/nginx-gateway-fabric/stable-service.yaml diff --git a/README.md b/README.md index 07df616af..c73fb4acc 100644 --- a/README.md +++ b/README.md @@ -18,6 +18,7 @@ The following examples are provided: | [Preview Stack Testing](examples/preview-testing) | Rollout which launches an experiment that tests a preview stack (which receives no production traffic) | | [Canary with istio (1)](examples/istio) | Rollout which uses host-level traffic splitting during update | | [Canary with istio (2)](examples/istio-subset) | Rollout which uses subset-level traffic splitting during update | +| [Canary Analysis with NGINX Gateway Fabric](examples/nginx-gateway-fabric) | Rollout which performs canary analysis as part of the update. Uses the prometheus metric provider, and requires the [Argo Rollouts Gateway API Plugin](https://rollouts-plugin-trafficrouter-gatewayapi.readthedocs.io/en/latest/) | Before running an example: diff --git a/examples/nginx-gateway-fabric/ARTICLE.md b/examples/nginx-gateway-fabric/ARTICLE.md new file mode 100644 index 000000000..7a4065df8 --- /dev/null +++ b/examples/nginx-gateway-fabric/ARTICLE.md @@ -0,0 +1,323 @@ +# Powering Progressive Deployment in Kubernetes + +Recently, the Kubernetes community celebrated the platform's 10th year in existence. Inarguably, it has come a long way since its early days at Google and has established itself as the de-facto platform for delivering cloud-native modern applications. However, will it intrinsically solve all your delivery challenges out-of-the-box? In this article, we will be leveraging ecosystem tooling to improve the quality and frequency of your application deployments in Kubernetes. + +In your application modernization journey, you may have discovered there are still inherent complexities that remain unsolved for you. For instance, you may find it relatively easy to set up your first production cluster, deploy your applications to it, and start accepting live traffic to it. You may wonder: + +- How is your application performing? +- What are users experiencing when they consume your applications and APIs? +- When you inevitably need to update your application, how do you do so while minimizing disruption? + +If you can identify with at least one of the above concerns, you are not alone. By the end of this article, you will have an understanding of how to: + +- **Enhance Traffic Management:** The ability to make dynamic routing adjustments during deployments. +- **Increased Availability and Reliability:** How to achieve high availability and reliability through intelligent traffic handling and failover mechanisms. +- **Improved User Experience:** Leverage progressive delivery patterns to minimize downtime. +- **Efficient Rollback Mechanisms:** Easily roll back changes in the event of issues, ensuring quick recovery and minimal impact on users. + +To achieve the above, we need to understand and employ proven patterns that assist us in improving our delivery pipelines. + +## Deployment Patterns + +The practice of software development has benefited from a wealth of "design patterns" - repeatable blueprints for successfully and sustainably building applications. Ostensibly, there also exist patterns for the deployment and operation of these applications. You may have heard of deployment patterns such as "Blue-green", "Canary deployments", and "A/B Testing" which help route and shape application traffic over time. While these deployment patterns seem promising, until the last several years, you had been left to your own devices (no pun intended) to implement them. However, proxies such as NGINX, Envoy and service meshes such as Istio have been able to assist in the execution of these patterns as application rollout strategies. While these solutions fit neatly into a Kubernetes environment and can route and process traffic into the applications residing in the cluster, many of them have their own specific behaviors and differences in configuration. + +Fortunately, the Kubernetes community responded to this shared need in an effort to ultimately unify such disparate implementations moving forward. Specifically, the [SIG-NETWORK](https://github.com/kubernetes/community/tree/master/sig-network) community released a common specification for modern application delivery services in Kubernetes called [Gateway API](https://gateway-api.sigs.k8s.io/). Gateway API is an official Kubernetes project focused on L4 and L7 routing in Kubernetes. This project represents the next generation of Kubernetes Ingress, Load Balancing, and Service Mesh APIs. From the outset, it has been designed to be generic, expressive, and role-oriented. + +The overall resource model focuses on 3 separate personas and corresponding resources that they are expected to manage: + +![Gateway API resources](images/resource-model.png "Gateway API Resources (credit: k8s.io)") + +We will explore using some of these resources later in this article. + +This new specification has been supported by a number of projects and vendors, most notably the NGINX Gateway Fabric for Kubernetes (NGF). +At KubeCon 2023, F5 NGINX [unveiled](https://www.f5.com/company/blog/nginx/watch-nginx-gateway-fabric-at-kubecon-north-america-2023) 1.0 of NGF, supporting traffic splitting patterns such as Canary and Blue-green intrinsically. + +![NGF high-level architecture](images/ngf-high-level.png "NGF high-level architecture") + +While the capability to execute these application rollouts in a vendor-neutral way is a win for the community, some challenges remain: + +- How can we be assured that our applications are working after deploying new versions? +- What if we want to orchestrate the progressive introduction of application changes rather than a "big-bang" deployment? +- If my deployment is not successful, how will I know if I need to roll back? + +We are getting there. Read on... + +## Progressive Delivery + +Progressive delivery has emerged as a preferred approach for modern applications for a number of reasons. It can be a means to orchestrate a gradual feature rollout for an application. It can help reduce risk by deploying changes only to a subset or test group of users first. You can also use progressive delivery to gather early feedback on a new application deployment and other use cases. This is not meant to be an exhaustive list of use cases, but merely an introduction. + +## A Solution + +NGINX is the most widely used API gateway, application proxy, and web server in the world. It has been very popular in the Kubernetes community, as the de-facto Ingress Controller. With NGINX's Gateway Fabric packaging, this workhorse has renewed its reputation for seamless integration with Kubernetes. + +How do we orchestrate progressive delivery? There is more we need. + +Our friends at the Argo Project (famous for their GitOps platform, ArgoCD) released Argo Rollouts back in 2019, focused on this progressive delivery problem. Argo Rollouts enables the orchestration of Canary deployments, Blue-green deployments, and experimentation with traffic splitting. + +![Canary deployments](images/canary-deployments.png "Canary Deployments (credit:argoproj.io)") + +However, in order for ingress proxies and service meshes to execute the traffic shaping portion of application rollouts, directly extending the Argo Rollouts code was necessary. As a consequence, this added complexity, and slowed down contributions. As you might imagine, the amount of effort required to develop and maintain all these vendor and implementation-specific extensions has been a chore to say the least. + +Fortunately, on April 5, 2023, the Argo Project [announced](https://blog.argoproj.io/argo-rollouts-1-5-release-candidate-2bd93720e411) that a plugin system for Argo Rollouts had been developed. This would enable future extensibility without having to contribute to the core Argo Rollouts project itself. This was a welcome addition, and would set up the Argo Project for yet another important innovation. + +To much celebration, the Argo Project [announced](https://blog.argoproj.io/argo-rollouts-now-supports-version-1-0-of-the-kubernetes-gateway-api-acc429729e42) on June 20, 2024 that Argo Rollouts now supports ingresses, gateways and service meshes that implement the Kubernetes Gateway API for progressive delivery. Unsurprisingly, NGINX Gateway Fabric, powered by the most popular proxy in the world, is one of the [supported providers](https://gateway-api.sigs.k8s.io/implementations/). + +## The Details + +Without further ado, let's set this solution stack up for a test drive. You are going to need a Kubernetes cluster, ideally [version 1.25 or greater](https://github.com/nginxinc/nginx-gateway-fabric#technical-specifications) so we can take advantage of features in the latest version of the Gateway API. While the installation could be performed entirely by script or using something like Argo CD, we will be performing each step manually for learning purposes. + +Here is the overall architecture of the demo environment. We will deploy and configure everything you see here step-by-step. + +![demo diagram](images/demo-diagram.png) + +Local tools needed: + +- [Helm](https://helm.sh/docs/intro/install/) +- [kubeconfig](https://kubernetes.io/docs/tasks/tools/#kubectl) (set up with a config valid for your test cluster) +- [git](https://git-scm.com/downloads) + +We will begin by installing NGINX Gateway Fabric (NGF). We will be using the NGINX Plus edition of NGF to take advantage of its extended metrics and seamless configuration reload capabilities. + +If you instead wish to use the NGINX Open Source edition of NGF, you will need to update the Helm installation command with the guidance [here](https://docs.nginx.com/nginx-gateway-fabric/installation/installing-ngf/helm/#for-nginx). Additionally, you will need to use different metrics in the [analysis template](analysis-success-rate.yaml), as upstream metrics are only available in the NGINX Plus edition. + +> Note: It is not recommended to perform the demo steps in a production Kubernetes cluster without prior validation in a less-critical environment. + +### Installation + +1. Create the namespace for NGF: + + ```shell + kubectl create namespace nginx-gateway + ``` + +1. Create a Secret to pull the NGF container from the F5 private registry. The secret is based on the contents of the trial JWT from MyF5. If you do not have a trial JWT, you can request one [here](https://www.f5.com/trials/free-trial-connectivity-stack-kubernetes). + + ```shell + kubectl create secret docker-registry nginx-plus-registry-secret --docker-server=private-registry.nginx.com --docker-username=`cat the_full_path_to_you_jwt_here` --docker-password=none -n nginx-gateway + ``` + +1. The Kubernetes Gateway API is not included in clusters by default. We need to install its Custom Resource Definitions (CRDs) in order to use it: + + ```shell + kubectl kustomize "https://github.com/nginxinc/nginx-gateway-fabric/config/crd/gateway-api/standard?ref=v1.3.0" | kubectl apply -f - + ``` + +1. Next, we will install NGF via its Helm chart: + + ```shell + helm install ngf oci://ghcr.io/nginxinc/charts/nginx-gateway-fabric --set nginx.image.repository=private-registry.nginx.com/nginx-gateway-fabric/nginx-plus --set nginx.plus=true --set serviceAccount.imagePullSecret=nginx-plus-registry-secret -n nginx-gateway --version 1.3.0 + ``` + +1. Run the following command to wait until NGF has been verified as deployed: + + ```shell + kubectl wait --timeout=5m -n nginx-gateway deployment/ngf-nginx-gateway-fabric --for=condition=Available + ``` + +1. We will use Prometheus metrics to inform Argo Rollouts of the state of our application's health during rollouts. Add the Prometheus community helm chart, then update the Helm repo: + + ```shell + helm repo add prometheus-community https://prometheus-community.github.io/helm-charts + helm repo update + ``` + +1. Install Prometheus on your cluster in its own namespace: + + ```shell + helm install prometheus prometheus-community/prometheus -n prometheus --create-namespace --set server.global.scrape_interval=15s + ``` + +1. Create a namespace for Argo Rollouts, and install it using manifests from the project's GitHub repo: + + ```shell + kubectl create namespace argo-rollouts + kubectl apply -n argo-rollouts -f https://github.com/argoproj/argo-rollouts/releases/latest/download/install.yaml + ``` + +1. Install the Argo Rollouts CLI using the [instructions](https://argoproj.github.io/argo-rollouts/installation/#kubectl-plugin-installation) for your client platform. + +1. Install the Gateway API Plugin for Argo Rollouts: + + ```shell + kubectl apply -f gateway-plugin.yml -n argo-rollouts + ``` + +1. Restart the Argo Rollouts controller so that it detects the presence of the plugin. + + ```shell + kubectl rollout restart deployment -n argo-rollouts argo-rollouts + ``` + +1. Then, check the controller logs. You should see a line for loading the plugin: + + ```shell + time="XXX" level=info msg="Downloading plugin argoproj-labs/gatewayAPI from: https://github.com/argoproj-labs/rollouts-plugin-trafficrouter-gatewayapi/releases/download/v0.2.0/gateway-api-plugin-linux-amd64" + time="YYY" level=info msg="Download complete, it took 7.792426599s" + ``` + +### Demo Setup + +Now that our cluster services are in place, we will now use NGF and Argo Rollouts to perform a Canary deployment from the [Argo Rollouts demo application](https://github.com/argoproj/rollouts-demo). This type of deployment assumes we will start with a known good or "stable" deployment into our cluster. Once this is deployed, we can update the deployment to introduce new versions of an application's pods in a gradual manner. I will highlight how this works along the way. + +1. With git, clone the demo repo: + + ```shell + git clone https://github.com/argoproj/rollouts-demo.git + ``` + +1. Change directory to the repo's `examples/nginx-gateway-fabric` directory: + + ```shell + cd rollouts-demo/examples/nginx-gateway-fabric + ``` + +1. Apply the `Gateway` manifest: + + ```shell + kubectl apply -f gateway.yaml + ``` + + > Note: This is a resource that has been added by the Kubernetes Gateway API, and will be used by NGF to accept requests coming into the cluster. + + > Note: This and all additional resources are deployed into the default namespace. If you need to install into a different namespace, add `-n namespace_name_here` to each of the kubectl commands. + +1. Apply the `HTTPRoute` manifest: + + ```shell + kubectl apply -f httproute.yaml + ``` + + > Note: This resource was *also* added by the Kubernetes Gateway API. This resource will reference the `Gateway` object, and contains layer 7 routing rules to direct the traffic to the configured Kubernetes services. + +1. Next, deploy two services: a service to represent the "stable" version of our application, and one to represent our "canary" version: + + ```shell + kubectl apply -f stable-service.yaml + kubectl apply -f canary-service.yaml + ``` + + ***Why do we need two different services that contain the same app selector?*** + When implementing a Canary deployment, Argo Rollouts requires two different Kubernetes services: A "stable" service, and a "canary" service. The "stable" service will direct traffic to the initial deployment of the application. In subsequent (or "canary") deployments, Argo Rollouts will transparently configure the "canary" service to use the endpoints exposed by the pods referenced in the new deployment. NGF will use both of these services to split traffic between these two defined services based on the Argo Rollout rules. + +1. Now we will deploy the `AnalysisTemplate` resource, provided by Argo Rollouts. This resource contains the rules to assess a deployment's health, and how to interpret this data. In this demo, we will be using Prometheus query to examine the canary service's upstream pods for the absence of 4xx and 5xx HTTP response codes as an indication of its health. + + ```shell + kubectl apply -f analysis-success-rate.yaml + ``` + +1. Finally, deploy the app itself. This step introduces the `Rollout` resource, provided by Argo Rollouts. This resource is simply an extension of the familiar `Deployment` resource, but contains additional objects to instruct Argo Rollouts in how to deploy subsequent versions of this application, while monitoring their health. + + ```shell + kubectl apply -f rollout.yaml + ``` + +1. Use `kubectl` to verify that there are 5 replicas of the application running: + + ```shell + kubectl get pods + ``` + +### Monitoring the Stable Rollout + +1. Now that the application is fully deployed, use Argo Rollouts' `kubectl` plugin to observe the initial state of the application. Open a new shell window, and run the following: + + ```shell + kubectl argo rollouts get rollout rollouts-demo -w + ``` + + > Note: Leave this running in its own window, as we will use it through the rest of the demo. + + You should see the following: + + ![initial rollout](images/initial-rollout-blue.png) + + In the image above, you will observe several things (in no particular order): + + - The rollout of the "stable" version of the application was successful, with 5 replicas of the `rollouts-demo:blue` image created. + - There are 8 steps to this progressive rollout. Refer to the `rollout.yaml` file to see the configured stages. + - You should see that the rollout is `Progressing`, awaiting a canary rollout. + +1. Open a new shell window, and set up a port forward to the NGF pod's NGINX container: + + ```shell + NGINX_POD=`kubectl get pods --selector=app.kubernetes.io/name=nginx-gateway-fabric -n nginx-gateway -o=name` + kubectl port-forward $NGINX_POD -n nginx-gateway 8080:80 + ``` + + > Note: In a production scenario, we would not have used a port forward. Rather, we would have likely used a `Service` type `LoadBalancer` to reach the NGF instance. However, this requires additional setup, and varies greatly depending on where your cluster is hosted. + +1. Open a browser to `http://localhost:8080`. You will be presented with the demo application: + + ![stable rollout ui blue](images/ui-blue.png) + + > Note: In the grid, each blue square represents a connection being made through NGF to one of the demo pods hosted in the rollout deployment. Since we are using the `rollouts-demo:blue` image, the service responds with that color. The area at the bottom represents the portion of responses by a particular color over time. + + > Important: Certain browsers like Chrome will put a tab to sleep if the tab is not the actively selected one. As a result, if you switch to another tab, the HTTP calls to the demo app will cease until the tab has been activated once again. + +### Initiating a Canary Rollout + +Now that we have a stable version of our rollout running, we need to introduce an application change. The demo project contains different variants of the color-producing services, so we will leverage these so you can easily see what is happening. To update the deployment, you can either update the `rollout.yaml` file to use a different image, or use the Argo Rollouts CLI. We will do the latter. + +1. In the original shell window you opened, use the CLI to start a Canary rollout of the `rollouts-demo:yellow` image: + + ```shell + kubectl argo rollouts set image rollouts-demo "*=argoproj/rollouts-demo:yellow" + ``` + +1. Switch to the shell window that is monitoring the rollout status. You should see something similar to the following: + + ![canary rollout yellow](images/canary-rollout-yellow.png) + + > Note: The canary rollout has begun, and has paused at the 30% traffic stage, as directed by the rollout rules in `rollout.yaml`. + +1. Switch back to your browser and you should see something similar to this: + + ![canary rollout ui yellow](images/ui-yellow.png) + +1. Switch back to the rollout status shell window. + + ![alt text](images/yellow-analysis.png) + + > Note: The AnalysisRun is continually running. Why? Since this canary rollout has only *partially* progressed, Argo Rollouts continues to monitor the results of the Prometheus query configured in the AnalysisTemplate resource (in `analysis-success-rate.yaml`) to ensure there are no 4xx-5xx HTTP errors present in the NGINX upstream metrics produced for this canary service. This analysis template is configured to run every minute. This rollout has a rule set to pause indefinitely at 30% traffic. How do we complete the rollout? + +1. Switch to your original shell and run the following to continue (or, "promote") the rollout: + + ```shell + kubectl argo rollouts promote rollouts-demo + ``` + + > Note: In the shell window that the kubectl rollouts plugin is running, you will see the rollout progress over the next several minutes - gradually shifting traffic over to the canary service represented in the demo app. Once complete, the canary service will become the new stable release. + +1. Once the rollout completes, you should see that the ui of the demo application has shifted entirely over to using the yellow service. + + ![canary rollout yellow complete ui](images/canary-rollout-yellow-complete-ui.png) + +1. Switch to the kubectl rollouts plugin window. You will notice that there are 2 replicasets with 5 pods each - one representing the new stable release, and another representing the previous release. Since all the traffic is being routed to the new stable release, why do we need to keep the previous pods around? If it were determined that there is an undetected issue with the stable release, you can switch back to the previous revision of the application without having to wait for new pods to initialize first. + + ![rollout complete](images/canary-rollout-yellow-complete.png) + +### Simulate a Failed Rollout + +We have just seen what an ideal rollout looks like. What about a rollout where failures are detected by the AnalysisTemplate based on Prometheus metrics? We will deploy a new Canary version of this app by selecting an image that intentionally throws HTTP errors. + +1. In the original shell window you opened, use the CLI to start a Canary rollout of the `rollouts-demo:bad-red` image: + + ```shell + kubectl argo rollouts set image rollouts-demo "*=argoproj/rollouts-demo:bad-red" + ``` + +1. Switch back to your browser and you should see something similar to this + + ![canary rollout ui red](images/canary-rollout-red-ui.png) + +1. Wait at least a minute, and you should see something like this: + + ![canary rollout ui red rollback](images/canary-rollout-red-rollback-ui.png) + + > Note: You will see a portion of red service responses for about a minute, then reverts back to 100% yellow. Why? Look at the kubectl argo rollouts plugin to see what is going on. You may observe that the AnalysisRun has failed at least one time, triggering Argo Rollouts to perform an automatic rollback to the last successful rollout version. Cool, right? + + ![failing canary rollout red](images/canary-rollout-red-rollback.png) + +## Conclusion + +This was only a taste of what can be accomplished with Argo Rollouts and NGINX Gateway Fabric. However, I hope you have witnessed the benefits of adopting progressive delivery patters using tools such as these. I would encourage you to further explore what you are able to accomplish in your own environments. diff --git a/examples/nginx-gateway-fabric/README.md b/examples/nginx-gateway-fabric/README.md new file mode 100644 index 000000000..2db43b45e --- /dev/null +++ b/examples/nginx-gateway-fabric/README.md @@ -0,0 +1,3 @@ +# Argo Rollouts Example for NGINX Gateway Fabric + +This directory contains a [walkthrough article](ARTICLE.md) and example manifests to perform a Canary rollout (with Analysis) of the demo application using NGINX Gateway Fabric. diff --git a/examples/nginx-gateway-fabric/analysis-success-rate.yaml b/examples/nginx-gateway-fabric/analysis-success-rate.yaml new file mode 100644 index 000000000..2bcd28ea1 --- /dev/null +++ b/examples/nginx-gateway-fabric/analysis-success-rate.yaml @@ -0,0 +1,29 @@ +apiVersion: argoproj.io/v1alpha1 +kind: AnalysisTemplate +metadata: + name: success-rate +spec: + args: + - name: namespace + - name: service-name + - name: service-port + metrics: + - name: success-rate + interval: 1m + successCondition: len(result) == 0 || result[0] >= 0.99 + failureLimit: 0 + provider: + prometheus: + address: http://prometheus-server.prometheus + query: >+ + sum(rate( + nginx_gateway_fabric_upstream_server_responses{upstream="{{args.namespace}}_{{args.service-name}}_{{args.service-port}}", + code!~"[4-5].*"}[1m]) + ) by (upstream) + / + sum(rate( + nginx_gateway_fabric_upstream_server_responses{upstream="{{args.namespace}}_{{args.service-name}}_{{args.service-port}}"}[1m]) + ) by (upstream) + unless sum(rate( + nginx_gateway_fabric_upstream_server_responses{upstream="{{args.namespace}}_{{args.service-name}}_{{args.service-port}}"}[1m]) + ) by (upstream) == 0 diff --git a/examples/nginx-gateway-fabric/canary-service.yaml b/examples/nginx-gateway-fabric/canary-service.yaml new file mode 100644 index 000000000..776404ad1 --- /dev/null +++ b/examples/nginx-gateway-fabric/canary-service.yaml @@ -0,0 +1,12 @@ +apiVersion: v1 +kind: Service +metadata: + name: rollouts-demo-canary-service +spec: + ports: + - port: 8080 + targetPort: 8080 + protocol: TCP + name: http + selector: + app: rollouts-demo diff --git a/examples/nginx-gateway-fabric/gateway.yaml b/examples/nginx-gateway-fabric/gateway.yaml new file mode 100644 index 000000000..082472ded --- /dev/null +++ b/examples/nginx-gateway-fabric/gateway.yaml @@ -0,0 +1,10 @@ +apiVersion: gateway.networking.k8s.io/v1beta1 +kind: Gateway +metadata: + name: rollouts-demo-gateway +spec: + gatewayClassName: nginx + listeners: + - protocol: HTTP + name: web + port: 80 diff --git a/examples/nginx-gateway-fabric/httproute.yaml b/examples/nginx-gateway-fabric/httproute.yaml new file mode 100644 index 000000000..f24941eb8 --- /dev/null +++ b/examples/nginx-gateway-fabric/httproute.yaml @@ -0,0 +1,18 @@ +--- +apiVersion: gateway.networking.k8s.io/v1 +kind: HTTPRoute +metadata: + name: rollouts-demo-httproute +spec: + parentRefs: + - name: rollouts-demo-gateway + rules: + - matches: + - path: + type: PathPrefix + value: / + backendRefs: + - name: rollouts-demo-stable-service + port: 8080 + - name: rollouts-demo-canary-service + port: 8080 diff --git a/examples/nginx-gateway-fabric/images/canary-deployments.png b/examples/nginx-gateway-fabric/images/canary-deployments.png new file mode 100644 index 0000000000000000000000000000000000000000..c934c26654a5b6eaf0da4dae18a179e537ff18fe GIT binary patch literal 218943 zcmZsDWmH^Evo7xL?(XgyY>+@81lM38xVsZvhQS?zOK^9G!2<+$cY-@y-t(RJOU_#N z$E-cmyRE9Ls;ld%p70OKa;Qi|NDvSZsPX`5H3$eOCkO~=LIk+C6072xkhceCHNgSQ{jW?J&*@83f(y?sZ3fDE;SfcdA&+lT1w^VT#65(4_|3HeW14%EMIp`3D{ z|NRb4_)o<&PdFL~2nh&zX-Rc=$YURPBmMrw8;?)zib-aQNy`r1^M!=dR{kH<0F*Q| zK#4^aJ{$gYYghhPTfY1F<3fpdQ28wR5>NpW`=r)UHr87)US~C?F6|YskGmZnEuKlH z;A?P3b@pky)86>#^XX{jS(e~lR1vZy4+B($KUEIG|2|OUVcMi>W_2uOBrrd~;6VQG zBgh{lXoK*7DjLHijG}IY7JbtEQ%yz!oYC-aoBwGV!U3hQ8}hq%x|I3vaZHt${u-14 zN{RrI)=BDa!}1sI-_5y-Z2q1`0A-XvsG0KVcirLNQ(;J*=KC)JZ*+8F$Vz~hF?`*1 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5 + revisionHistoryLimit: 1 + selector: + matchLabels: + app: rollouts-demo + strategy: + canary: + analysis: + templates: + - templateName: success-rate + args: + - name: namespace + value: default + - name: service-name + value: rollouts-demo-canary-service + - name: service-port + value: "8080" + canaryService: rollouts-demo-canary-service # our created canary service + stableService: rollouts-demo-stable-service # our created stable service + trafficRouting: + plugins: + argoproj-labs/gatewayAPI: + httpRoute: rollouts-demo-httproute # our created httproute + namespace: default # namespace where this rollout resides + steps: + - setWeight: 30 + - pause: {} + - setWeight: 40 + - pause: + duration: 1m + - setWeight: 60 + - pause: + duration: 1m + - setWeight: 80 + - pause: + duration: 1m + template: + metadata: + labels: + app: rollouts-demo + spec: + containers: + - name: rollouts-demo + image: argoproj/rollouts-demo:blue + ports: + - name: http + containerPort: 8080 + protocol: TCP + resources: + requests: + memory: 32Mi + cpu: 5m diff --git a/examples/nginx-gateway-fabric/stable-service.yaml b/examples/nginx-gateway-fabric/stable-service.yaml new file mode 100644 index 000000000..b2a9489cb --- /dev/null +++ b/examples/nginx-gateway-fabric/stable-service.yaml @@ -0,0 +1,12 @@ +apiVersion: v1 +kind: Service +metadata: + name: rollouts-demo-stable-service +spec: + ports: + - port: 8080 + targetPort: 8080 + protocol: TCP + name: http + selector: + app: rollouts-demo

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