kubernetes autoscaling yaml example

These metrics are exposed by an API service and can be readily used by our Horizontal Pod Autoscaling object. Lets create a node pool by OVH > Public Cloud > Managed Kubernetes Services > Create a Kubernetes Cluster, enable the Autoscaling at step 5 and create the cluster. Prometheus is the standard tool for monitoring deployed workloads and the Kubernetes cluster itself. You can create a Kubernetes HPA in just one line: $ kubectl autoscale deployment shell --min=2 --max=10 --cpu-percent=10 horizontalpodautoscaler.autoscaling/shell autoscaled. This is the function that run on the cluster later on. Check KEDA Operator logs again - you should see that it has reacted to the Kubernetes Volume Autoscaler (with Prometheus) This repository contains a service that automatically increases the size of a Persistent Volume Claim in Kubernetes when its nearing full. . Not compatible with Horizontal Pod Autoscaling (HPA). If you have a specific, answerable question about how to use Kubernetes, ask it on Stack Overflow.Open an issue in the GitHub repo if you want to report a problem or suggest an improvement. For example, cluster autoscaling allows you to save money on public clouds by adjusting the number of nodes in your cluster. Dont forget to update the repository. This is the executor that were using at Skillup A Kubernetes executor runs each pipeline stage in a new Pod For paint baking, drying, preheating, annealing or any other heat processing of large or numerous parts Click the cluster's Edit button, which looks like a pencil memoryOverheadFactor * spark memoryOverheadFactor * spark. Calculate the desired number of replicas. The HAProxy Kubernetes Ingress Controller exposes more than 100 different Prometheus -style metrics, which KEDA can watch and use to trigger Native Kubernetes Integration. Next, we will deploy the KEDA scaling object that monitors the lag on the specified Kafka topic and configures the Kubernetes Horizontal Pod Autoscaler (HPA) to scale your Dapr deployment in and out. Feedback. The list in a Kubernetes YAML is an order of objects, we can use many items in a list and we have to define the item with a dash (-) which is followed by the parent item. $ kubectl autoscale deployment --cpu-percent = 50 --min = 1 -- max = 10 A ReplicaSet is one of three types of Kubernetes replication. This can take several minutes. 2k members in the k8s community Then final number is 36 1(for AM) = 35 The discipline Executor, fittingly, is a mechanism that gets tasks executed Like many, we chose Kubernetes for many of its theoretical benefits, one of which is efficient resource usage The Kubernetes executor creates a new pod for every task instance To achieve HPA, you can do autoscaling in two ways. 1. Creating a YAML File First, create a Deployment using a Yaml file named ngnix.yaml like below: Deployment instructions may change from one release to the next. This page gathers resources about how to install Kubernetes on various environments like Ubuntu, Windows and CentOS. YAML is a concise, non-markup language. Using the stable/acs-engine-autoscaler Helm chart, we can install the autoscaler in our cluster. Helm is a Kubernetes package manager that helps us package, install, and manage our Kubernetes applications. Redis is a widely used ( and loved!) Reduce Kubernetes Latency with Autoscaling (this post) Protect Kubernetes APIs with Rate Limiting; (get the examples from our GitHub repo). Search: Airflow Kubernetes Executor Example. --docker # create a new function, select option 1. Installing Kubernetes There are many ways to install Kubernetes Guide and the obvious starting point is the setup section, but the installation process can sometimes be a challenge. In this article, we will learn how to create a Horizontal Pod Autoscaler (HPA) to automate the process of scaling the application. In addition, the Horizontal Pod Autoscaler (HPA), the automatic expander component of k8s, can also directly use Custom Metrics to perform user-specified scaling strategies. We will use the unmanaged nodes later in this exercise as part of a test to verify the proper functioning of the Cluster Autoscaler. Step-07: View Cluster Autoscaler logs to verify that it is monitoring your cluster load. Now, if we use kubectl get and set the output to yaml , we'll see the base64 encoded secret data. In this short tutorial we will explore how you can install and configure Cluster Autoscaler in your Amazon EKS cluster. We have dedicated one article to each type of autoscaling, and included configuration instructions and YAML file examples along the way. The Metrics Server is used to provide resource utilization to Kubernetes, and is automatically deployed in AKS clusters versions 1.10 and higher. Amazon EKS supports two autoscaling products. behavior: scaleDown: stabilizationWindowSeconds: 300 policies: - type: Percent value: 100 periodSeconds: 15 scaleUp: stabilizationWindowSeconds: 0 policies: - type: The Cluster Autoscaler is the default Kubernetes component that can scale either pods or nodes in a cluster. 2.2. It Works with major Cloud providers GCP, AWS and Azure. To know about autoscaling and its types in Kubernetes. It also tries to remove unused worker nodes from the autoscaling group (the ones with no pods running). KEDA (Kubernetes-based Event-driven Autoscaling) is an open source component developed by Microsoft and Red Hat to allow any Kubernetes workload to benefit from the event-driven architecture model. The three-way autoscaling can be done in Kubernetes. Related content: read our guide to Kubernetes autoscaling. The Kubernetes Vertical Pod Autoscaler automatically increases your pods CPU and memory reservations to help you right-size your applications. $ psql -h localhost -U postgresadmin1 --password -p 31070 postgresdb Password for user postgresadmin1: psql (10.4) Type "help" for help. Next, use eksctl to create the EKS cluster using the command shown below. It is necessary to set the min value of autoscaler to, at least, the current number of worker nodes of Fortunately with the powerful API extension mechanism, Custom Metrics has became a standard capability of k8s. The entire process here is very flexible and customizable. If the metric readings are above this value, and (currentReplicas < maxReplicas), HPA will scale up. Click the Start swarming button in the locust UI to enable the load on the rsvp app and will see These metrics are exposed by an API service and can be readily used by our Horizontal Pod Autoscaling object. The HPA can not scale your pods based on metrics from these components. It allows users to declare the desired state in the manifest (YAML) file, and the controller will change the current state to the declared state. So in order to access our environmental variable we just need to run: yq eval '.spec.template.spec.containers[0].env[0].value' example.yaml. Open the name-of-your-function.cs. Azure CLI; Azure PowerShell; Kubernetes supports horizontal pod autoscaling to adjust the number of pods in a deployment depending on CPU utilization or other select metrics. The ecosystem of static checking of Kubernetes YAML files can be grouped in the following categories: API validators: Tools in this category validate a given YAML manifest against the Kubernetes API server. Built-in checkers: Tools in this category bundle opinionated checks for security, best practices, etc. One of the key pieces which enable exposing the metrics via the Kubernetes API layer is the aggregation layer. Note: GKE Autopilot clusters use only the cos_containerd node image. The easiest way to enable the autoscaler is using the Kubernetes API, for example using kubectl. Azure CLI; Azure PowerShell; Kubernetes supports horizontal pod autoscaling to adjust the number of pods in a deployment depending on CPU utilization or other select metrics. Calculate the desired number of replicas. Lets create the namespace first: $ kubectl create namespace keda. For example, in this article, we'll pick apart the YAML definitions for creating first a Pod, and then a Deployment. Therefore Kubernetes (with the concept The Metrics Server is used to provide resource utilization to Kubernetes, and is automatically deployed in AKS clusters versions 1.10 and higher. Scale the app to the desired number of replicas. And you would also need to check the pod requested cpu resources using below cmd in order to ensure same the config with your deployment yaml. Create the TriggerAuthentication followed by the ScaledObject. Creating a new function is a simple as running a few commands: mkdir hello-keda cd hello-keda # init directory, select option 1. dotnet func init . Kubernetes supports three different types of autoscaling: Vertical Pod Autoscaler (VPA). Increases or decreases the resource limits on the pod. Horizontal Pod Autoscaler (HPA). Increases or decreases the number of pod instances. Cluster Autoscaler (CA). Increases or decreases the nodes in the node pool, based on pod scheduling. Horizontal Pod Autoscaler (HPA): adjusts the number of replicas of an application. The following example creates an AKS cluster with a single node pool backed by a virtual machine scale set. The HPA will autoscale off of the metric nginx.net.request_per_s, over the scope kube_container_name: nginx. It demonstrates how to use NGINX Ingress Controller to expose an app and then autoscale the Ingress controller pods in response to high traffic. In my previous article, I talked about the why and how of horizontal scaling in Kubernetes and I gave an insight, how to use the Horizontal Pod Autoscalar based on CPU metrics. kubectl apply -f deploy/2-trigger-auth.yaml kubectl apply -f deploy/4-kafka-scaledobject.yaml. Autoscaling the resources an application is running on is not a new idea. We then provide the scaling parameters minReplicas , maxReplicas and when to scale. Autoscaling is a function that automatically scales your resources up or down to meet changing demands. Finally, they can also configure the cluster to add more nodes once the other nodes are fully used or reserved (also known as cluster autoscaler). . This puts so much traffic on the site in question's servers that they get overloaded and crash. This sort of scaling is done on an infrastructure level. It automatically increases the size of an autoscaling group, so that pods can continue to get placed successfully. To see the version of your AKS cluster, use Tuning autoscaling parameters. Reduce Kubernetes Latency with Autoscaling (this post) Protect Kubernetes APIs with Rate Limiting; (get the examples from our GitHub repo). To autoscale an app, the Horizontal Pod Autoscaler executes an eternal control loop: The steps of this control loop are: Query the scaling metric. So, lets look at how to create and use Kubernetes deployments. $ eksctl create cluster -f eks.yaml STEP 2: Verification of the EKS cluster and AWS Auto Scaling groups Valid values are: # `ClusterFirstWithHostNet`, `ClusterFirst`, `Default` or `None`. Kubernet autoscaling is used to scale the number of pods in a Kubernetes resource such as deployment, replica set etc. named nginx. To create HPA in a single command like below Check HPA status Conclusion Here, replace with the rsvp app URL and access the locust UI from the app-port-8089 URL under the lab URL section and will see a locust UI as shown in the image below.. To generate artificial load, use the Apache Bench utility. 1. The mechanisms for building the pipeline and Kubernetes autoscaling remain the same, as we will see in detail in the next few sections. In this post, we discuss the three forms of Kubernetes capacity autoscaling. If you can verify the resources as following cmd, # kubectl top pod --namespace=. By default, Kubernetes can perform horizontal autoscaling of pods based on observed CPU utilization (average CPU load across all the pods in a deployment). This can increase cluster resource utilization while also freeing CPU and memory for other pods. Increases or decreases the number of pod instances. It increases or decreases the number of replicas running for each application according to a given number of metric thresholds, as defined by the user. This works well with HPA, but you may need to tune them. Amazon EKS supports two autoscaling products. Use Command Line Set a new deployment or use the one which is deployed in step 1 viz. In this blog post, you will learn how to use the open-source tool KEDA (Kubernetes Event-Driven Autoscaler) to monitor metrics coming out of the HAProxy Kubernetes Ingress Controller instead. Kubernetes Authentication. Its also possible to use an object called Extended Resources. Kubernetes Configuration Kubernetes Guide reads YAML files to Prometheus Adapter helps us to leverage the metrics collected by Prometheus and use them to make scaling decisions. Kubernetes Horizontal Pod Autoscaler (HPA) scales resources based on a provided metric (usually CPU or Memory utilization), however it does not have native support for event-sources. Kubernetes Deployment is the process of providing declarative updates to Pods and ReplicaSets. It also enables the cluster autoscaler on the node pool for the cluster and sets a minimum of 1 and maximum of 3 nodes: # First create a resource group az group create --name myResourceGroup --location eastus # Now create the AKS cluster and enable We will install the operator in the keda namespace. This is a major Kubernetes function that would otherwise require extensive human resources to perform manually. Defining a Kubernetes Manifest dnsPolicy: ClusterFirst. It also enables the cluster autoscaler on the node pool for the cluster and sets a minimum of 1 and maximum of 3 nodes: # First create a resource group az group create --name myResourceGroup --location eastus # Now create the AKS cluster and enable Vertical Pod Autoscaler (VPA) postgresdb=#. Scale the app to the desired number of replicas. For example, if you target a 50% CPU utilization for your pods but your pods have an 80% CPU utilization, the hpa will automatically create new pods. As such, there is a delay in scale-up of new Pods while the event source gets populated with events. Posted on but Im keeping the example simple. A kustomize patch will work well for this. When the application is running directly on EC2 instance, we can just increase or decrease the number of instances in response to a change in load. Container-Optimized OS images are backed by a team at Google that can quickly patch images for security and iterate This effectively allows you to declare resources from outside the cluster that might affect your workload scale (useful for event-driven architecture). In the most common scenarios web applications are not always under the same workload. As explained in the How nodes and node pools work guide, in your OVHcloud Managed Kubernetes cluster, nodes are grouped in node pools (groups of nodes sharing the same configuration). Enabling the autoscaling on the node pool. They are: Cluster Autoscaler (CA): adjusts the number of nodes in the cluster when pods fail to schedule or when nodes are underutilized. At the end of this process, Autoscaling is activated in our Nodes. Kubernetes only supports the creation of resource objects in YAML and JSON formats, which are used for message delivery between interfaces and are suitable for development. This effectively allows you to declare resources from outside the cluster that might affect your workload scale (useful for event-driven architecture). It is an official CNCF project and currently a part of the CNCF Sandbox.KEDA works by horizontally scaling a Kubernetes Deployment or a Removes worker nodes from a node pool when the nodes have been underutilized for an extended time, and when pods can be placed on other existing nodes. This can be done using the following command. and everyone does. The application we will be deploying as an example is a simple Ruby web application that can calculate the nth number in the Fibonacci sequence, this application uses a simple recursive algorithm, and is not very efficient (perfect for us to experiment with autoscaling). See ingress-patch.yaml.tmpl and ./kustomization.yaml.tmpl for an example. This makes sense, because CPU and memory are two of the most common metrics to use for autoscaling. The default period of the control loop is 15 seconds. Kubernetes supports three different types of autoscaling: Vertical Pod Autoscaler (VPA). The horizontal pod autoscaling controller, running within the Kubernetes control plane, periodically adjusts the desired scale of its target (for example, a Deployment) to match observed metrics such as average CPU utilization, average memory utilization, or any other custom metric you specify. Thats where KEDA comes into play. Now, we can deploy an application on the cluster and then enable the horizontal pod autoscaler. : The name or ID of the worker pool . We need to use port 31070 to connect to PostgreSQL from machine/node present in kubernetes cluster with credentials given in the configmap earlier. Step-06: Verify Image version got updated. The Job spec YAML is contained in a file called job_spec.yaml which should be placed in the same directory as the Flow and is loaded in your Environment with job_spec_file="job_spec.yaml". Lets try to use more custom metrics, for example lets scale a Gorush-server by using its own metrics, see Kubernetes: running a push-server with Gorush behind an AWS Container-Optimized OS. Step-05: Set the Cluster Autoscaler Image related to our current EKS Cluster version. The default period of the control loop is 15 seconds. It demonstrates how to use NGINX Ingress Controller to expose an app and then autoscale the Ingress controller pods in response to high traffic. Create or select a project. Kubernetes performance testing demands a place in the software development lifecycle for container-based applications. As mentioned there, CPU is not always the best choice when it comes to deciding whether the application/service should scale in or out. The Kubernetes Cluster Autoscaler and the Karpenter open source autoscaling project. The connectors relationship with Kubernetes is evolving. YAML, which stands for Yet Another Markup Language, or YAML Ain't Markup Language (depending who you ask) is a human-readable text-based format for specifying configuration-type information. Search: Airflow Kubernetes Executor Example. The easiest way to enable the autoscaler is using the Kubernetes API, for example using kubectl. min=: Specify the minimum number of worker nodes. NOTE: There are two other prominent types of autoscaling for Kubernetes: 1) The Horizontal Pod Autoscaler and 2) The Vertical Pod Autoscaler. Replication Controllers were the precursor to ReplicaSets, and Deployments are a declarative way to control ReplicaSets and pods. Let we do double check as following steps. This is a major Kubernetes function that would otherwise require extensive human resources to perform manually. When you deploy an application in GKE, you define how many replicas of the application you'd like to run. Kubernetes has a few mechanisms that allow this. ## Priorities Expander. First, get the YAML of your HorizontalPodAutoscaler in the autoscaling/v2alpha1 form: $ kubectl get hpa.autoscaling.v2alpha1 -o yaml > /tmp/hpa-v2.yaml Open the /tmp/hpa-v2.yaml file in an editor, and you should see YAML which looks like this: When you scale an application, you increase or decrease the number of replicas.. Each replica of your application represents a Kubernetes Pod that Prometheus Adapter helps us to leverage the metrics collected by Prometheus and use them to make scaling decisions. Kubernetes Configuration Kubernetes Guide reads YAML files to 3 Types of Kubernetes Replication. Figure 6:Locust UI. This gives the application developer a All metrics in the HorizontalPodAutoscaler and metrics APIs are specified using a special whole-number notation known in Kubernetes as a quantity. Most commonly Custom metrics in the Metric Server. One example is Kubernetes native capability to perform effective autoscaling of resources. Autoscaling is one of the key features in the Kubernetes cluster. Overview. This page explains how to scale a deployed application in Google Kubernetes Engine (GKE). Lets use Autoscaler on a cloud provider called OVH as an example. The goal of any type of performance test is to build highly available, scalable and stable software. For example, the quantity 10500m would be written as 10.5 in decimal notation. targetValue: threshold value for the metric. Overview. Step 5: Create security context for new user. First, locate your azuredeploy.parameters.json file generated with acs-engine from the previous step. Kubernetes has a few mechanisms that allow this. Most commonly Custom metrics in the Metric Server. # If autoscaler does not depend on cluster DNS, recommended to set this to `Default`. 2. 1. The following YAML example defines a policy that specifies all. Was this page helpful? Lets add the following Helm repo: $ helm repo add kedacore https://kedacore.github.io/charts. We have dedicated one article to each type of autoscaling, and included configuration instructions and YAML file examples along the way. References. workerpools[0]: The first worker pool to enable autoscaling. Understanding kubeconfig. Search: Airflow Kubernetes Executor Example. Example-1: Configure RBAC to define new role with modify permission. yq eval '.spec.template.spec.containers[0].name' example.yaml returns nginx. In Kubernetes there are two main areas where it provides scalability capabilities: Cluster Scaling Add and remove nodes to the cluster to provide more resources to run on. Verify that the kube-dns-autoscaler ConfigMap exists: kubectl get configmap --namespace=kube-system The output is similar to this: The maximum number of replicas created is 5 and the minimum is 1. Yes No. Note that this format corresponds to the name of the metric in Datadog. The same as before we will install KEDA on Kubernetes with Helm. Autoscaling Redis applications on Kubernetes . In your Datadog account, you should soon see the number of NGINX requests per second spiking, and eventually rising above 9, the threshold listed in your HPA manifest. When Kubernetes detects that this metric has exceeded the threshold, it should begin autoscaling your NGINX pods. And indeed, you should be able to see new NGINX pods being created: Step 1: Create User. Essentially the HPA controller get metrics from three different APIs: metrics.k8s.io, custom.metrics.k8s.io, and external.metrics.k8s.io. Step-08: Deploy simple Application. Wait for the API and related services to be enabled. Toye Idowu, Shanika Wickramasinghe. This post is part of Microservice Series - From Zero to Hero. Kubectl commands are used to interact and manage Kubernetes objects and the cluster. I can't find any examples as to where the behavior section should be specified in Kind: HorizontalPodAutoscaler.. To see the version of your AKS cluster, use The following example creates an AKS cluster with a single node pool backed by a virtual machine scale set. kubectl create -f dns-horizontal-autoscaler.yaml The output of a successful command is: deployment "kube-dns-autoscaler" created DNS horizontal autoscaling is now enabled. Weve set up Camunda BPM on Kubernetes with Prometheus metrics, logs, an ephemeral H2 database, TLS, and Ingress. As explained in the How nodes and node pools work guide, in your OVHcloud Managed Kubernetes cluster, nodes are grouped in node pools (groups of nodes sharing the same configuration). In our example, we are telling HPA to scale a Deployment named "awesome_app." The hug of death is caused when someone posts a link to a website, for example, the press, saying "Hey everyone, look at this website!" Increases or decreases the nodes in the node pool, based on pod scheduling. We can put the member of lists on the map also. Before we explore the specifics of CA, lets review the different types of autoscaling in Kubernetes. Below, well walk through an example application deployment and see that Kubernetes DNS maps the service names automatically. The deployment for this application is very simple. So the three objects mentioned above will be defined in a .yaml file. Take the following steps to enable the Kubernetes Engine API: Visit the Kubernetes Engine page in the Google Cloud console. Make sure that billing is enabled for your Cloud project. Horizontal Pod Autoscaler (HPA). Deploy KEDA autoscaler for Kafka. On creation of the cluster, we can check our cluster using the following kubectl command. Now, we can deploy an application on the cluster and then enable the horizontal pod autoscaler. This can be done using the following command. Pick a name for your new group. To autoscale an app, the Horizontal Pod Autoscaler executes an eternal control loop: The steps of this control loop are: Query the scaling metric. It is one of the key components of Kubernetes which runs on the workstation on any machine when the setup is done. Furthermore, they can configure Kubernetes to automatically replicate pods for stateless application workloads (also known as horizontal pod autoscaling). This page gathers resources about how to install Kubernetes on various environments like Ubuntu, Windows and CentOS. QueueTrigger func new. Rails from zero to kubernetes horizontal autoscaling. Great It works! (c) And its good for metrics which are already present in the cluster like the memory_usage_bytes by default collected by the cAdvisor from all containers in the cluster. The following example will execute your Flow using the custom Job specification with user provided resource requests and limits. Step 3: Create namespace (optional) Step 4: Update Kubernetes Config file with User Credentials. Configuring and enabling the Kubernetes Cluster Autoscaler is very simple and straightforward as we will see below. The Container-Optimized OS from Google node images are based on a recent version of the Linux kernel and are optimized to enhance node security. If I've defined a custom metric, my-custom AWS EKS - Elastic Kubernetes Service - Masterclass. The three dimensions of Kubernetes autoscaling Autoscaling eliminates the need for constant manual reconfiguration to match changing application workload levels. However, like most of Kubernetes, Kubernetes autoscaling is also extensible.Using the Kubernetes custom metrics API, you can create autoscalers that use custom metrics that you define (more on this soon). It has the capability to manage the nodes in the cluster. But to make things easier, we will do the creation by using the config file. Prometheus is the standard tool for monitoring deployed workloads and the Kubernetes cluster itself. Thanks for the feedback. It is a feature in which the cluster is capable of increasing the number of nodes as the demand for service response increases and decreases the number of nodes as the requirement decreases. In this chapter, we will discuss a few commands used in Kubernetes via kubectl. KEDA, Kubernetes event-driven autoscaling allows you to easily integrate a scaler into your Kubernetes cluster to monitor an external source and scale your pods accordingly. kubectl autoscale deployment hello-world --min= 1 --max= 3 --cpu-percent= 50 horizontalpodautoscaler.autoscaling/hello-world autoscaled You also need to generate some load to make sure that HPA increases the number of pods when the CPU utilization goes beyond the threshold of 50 percent. Horizontal Pod Autoscaler - HPA Vertical Pod Vertical Pod Autoscaler (VPA): adjusts the resource The three dimensions of Kubernetes autoscaling. Increases or decreases the resource limits on the pod. Here are a few of the benefits that the VPA provides: Conclusion. Its also possible to use an object called Extended Resources. Step 2: Create certificates. Before you begin. kubernetes / autoscaler Public master autoscaler/addon-resizer/deploy/example.yaml Go to file Cannot retrieve contributors at this time 145 lines (145 sloc) 2.94 KB Raw Blame # Config map Create the Kubernetes objects configuration file (in .yaml format) We are going to create three Kubernetes objects: the deployment, horizontal pod auto scaler, and service. Aggregation Layer. A Cluster Autoscaler is a Kubernetes component that automatically adjusts the size of a Kubernetes Cluster so that all pods have a place to run and there are no unneeded nodes. This blog post demonstrates how to auto-scale your Redis based applications on Kubernetes. The status field contains information about the current number of replicas and any recent autoscaling events. Installing Kubernetes There are many ways to install Kubernetes Guide and the obvious starting point is the setup section, but the installation process can sometimes be a challenge. Cluster Autoscaler (CA). Kubernetes right off the bat supports Autoscaling through the Horizontal Pod Autoscaler (HPA) which automatically scales the number of pods in a replication controller, deployment, replica set or stateful set based on observed CPU utilization (and memory with autoscaling/v2beta2as of v1.17).But at times those alone are not enough, especially if you Here are some basic examples based on the template posted above. The default group name in the example YAML files is example-group. How the Horizontal Pod Autoscaler (HPA) works The Horizontal Pod Autoscaler automatically scales the number of your pods, depending on resource utilization like CPU. Application Scaling Influence how your applications are running by changing the characteristics your pods. Enabling the autoscaling on the node pool. The Kubernetes executor creates a new pod for every task instance Configuring Fluentd to target a logging server requires a number of environment variables, including ports, hostnames, and When the application completes, the executor pods terminate and are cleaned up, but the driver pod persists logs and remains in

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