Cloud Native Catalog

Description

This YAML file defines a Kubernetes Deployment for the ack-cloudfront-controller, a component responsible for managing AWS CloudFront resources in a Kubernetes environment. The Deployment specifies that one replica of the pod should be maintained (replicas: 1). Metadata labels are provided for identification and management purposes, such as app.kubernetes.io/name, app.kubernetes.io/instance, and others, to ensure proper categorization and management by Helm. The pod template section within the Deployment spec outlines the desired state of the pods, including the container's configuration. The container, named controller, uses the ack-cloudfront-controller:latest image and will run a binary (./bin/controller) with specific arguments to configure its operation, such as AWS region, endpoint URL, logging level, and resource tags. Environment variables are defined to provide necessary configuration values to the container. The container exposes an HTTP port (8080) and includes liveness and readiness probes to monitor and manage its health, ensuring the application is running properly and is ready to serve traffic.

Caveats and Considerations

1. Environment Variables: Verify that the environment variables such as AWS_REGION, AWS_ENDPOINT_URL, and ACK_LOG_LEVEL are correctly set according to your AWS environment and logging preferences. Incorrect values could lead to improper functioning or failure of the controller. 2. Secrets Management: If AWS credentials are required, make sure the AWS_SHARED_CREDENTIALS_FILE and AWS_PROFILE environment variables are correctly configured and the referenced Kubernetes secret exists. Missing or misconfigured secrets can prevent the controller from authenticating with AWS. 3. Resource Requests and Limits: Review and adjust the resource requests and limits to match the expected workload and available cluster resources. Insufficient resources can lead to performance issues, while overly generous requests can waste cluster resources. 4. Probes Configuration: The liveness and readiness probes are configured with specific paths and ports. Ensure that these endpoints are correctly implemented in the application. Misconfigured probes can result in the pod being killed or marked as unready.

Technologies

Description

This YAML file defines a Kubernetes Deployment for the ack-cloudfront-controller, a component responsible for managing AWS CloudFront resources in a Kubernetes environment. The Deployment specifies that one replica of the pod should be maintained (replicas: 1). Metadata labels are provided for identification and management purposes, such as app.kubernetes.io/name, app.kubernetes.io/instance, and others, to ensure proper categorization and management by Helm. The pod template section within the Deployment spec outlines the desired state of the pods, including the container's configuration. The container, named controller, uses the ack-cloudfront-controller:latest image and will run a binary (./bin/controller) with specific arguments to configure its operation, such as AWS region, endpoint URL, logging level, and resource tags. Environment variables are defined to provide necessary configuration values to the container. The container exposes an HTTP port (8080) and includes liveness and readiness probes to monitor and manage its health, ensuring the application is running properly and is ready to serve traffic.

Caveats and Considerations

1. Environment Variables: Verify that the environment variables such as AWS_REGION, AWS_ENDPOINT_URL, and ACK_LOG_LEVEL are correctly set according to your AWS environment and logging preferences. Incorrect values could lead to improper functioning or failure of the controller. 2. Secrets Management: If AWS credentials are required, make sure the AWS_SHARED_CREDENTIALS_FILE and AWS_PROFILE environment variables are correctly configured and the referenced Kubernetes secret exists. Missing or misconfigured secrets can prevent the controller from authenticating with AWS. 3. Resource Requests and Limits: Review and adjust the resource requests and limits to match the expected workload and available cluster resources. Insufficient resources can lead to performance issues, while overly generous requests can waste cluster resources. 4. Probes Configuration: The liveness and readiness probes are configured with specific paths and ports. Ensure that these endpoints are correctly implemented in the application. Misconfigured probes can result in the pod being killed or marked as unready.

Technologies

Description

This YAML manifest defines a Kubernetes Deployment for the ACK RDS Controller application. It orchestrates the deployment of the application within a Kubernetes cluster, ensuring its availability and scalability. The manifest specifies various parameters such as the number of replicas, pod template configurations including container settings, environment variables, resource limits, and security context. Additionally, it includes probes for health checks, node selection preferences, tolerations, and affinity rules for optimal scheduling. The manifest encapsulates the deployment requirements necessary for the ACK RDS Controller application to run effectively in a Kubernetes environment.

Caveats and Considerations

1. Resource Allocation: Ensure that resource requests and limits are appropriately configured based on the expected workload of the application to avoid resource contention and potential performance issues. 2. Security Configuration: Review the security context settings, including privilege escalation, runAsNonRoot, and capabilities, to enforce security best practices and minimize the risk of unauthorized access or privilege escalation within the container. 3. Probe Configuration: Validate the configuration of liveness and readiness probes to ensure they accurately reflect the health and readiness of the application. Incorrect probe settings can lead to unnecessary pod restarts or deployment issues. 4. Environment Variables: Double-check the environment variables provided to the container, ensuring they are correctly set and necessary for the application's functionality. Incorrect or missing environment variables can cause runtime errors or unexpected behavior. 5. Volume Mounts: Verify the volume mounts defined in the deployment, especially if the application requires access to specific data or configuration files. Incorrect volume configurations can result in data loss or application malfunction.

Technologies

Description

Cryptographic operations are among the most compute-intensive and critical operations when it comes to secured connections. Istio uses Envoy as the “gateways/sidecar” to handle secure connections and intercept the traffic. Depending upon use cases, when an ingress gateway must handle a large number of incoming TLS and secured service-to-service connections through sidecar proxies, the load on Envoy increases. The potential performance depends on many factors, such as size of the cpuset on which Envoy is running, incoming traffic patterns, and key size. These factors can impact Envoy serving many new incoming TLS requests. To achieve performance improvements and accelerated handshakes, a new feature was introduced in Envoy 1.20 and Istio 1.14. It can be achieved with 3rd Gen Intel® Xeon® Scalable processors, the Intel® Integrated Performance Primitives (Intel® IPP) crypto library, CryptoMB Private Key Provider Method support in Envoy, and Private Key Provider configuration in Istio using ProxyConfig.

Caveats and Considerations

Ensure networking is setup properly and correct annotation are applied to each resource for custom Intel configuration

Technologies

Description

Let’s Encrypt uses the ACME protocol to verify that you control a given domain name and to issue you a certificate. To get a Let’s Encrypt certificate, you’ll need to choose a piece of ACME client software to use.

Caveats and Considerations

We recommend that most people start with the Certbot client. It can simply get a cert for you or also help you install, depending on what you prefer. It’s easy to use, works on many operating systems, and has great documentation.

Technologies

Description

This design incorporates all the key relationships, including the following: 1. Hierarchical-Parent-Inventory: This represents a parent-child relationship between components, where one component is a dependency of another. 2. Hierarchical-Parent-Wallet: In a hierarchical-parent-wallet relationship, one component (the "wallet") serves as a container or host for another component, similar to a parent-child structure. 3. Hierarchical-Sibling-MatchLabels: A Match-Labels Relationship in Meshery refers to the configuration where Kubernetes components are linked based on shared labels. 4. Edge-Binding-Mount: An Edge-Mount Relationship in Meshery represents the assignment of persistent storage to Pods via PersistentVolumeClaims (PVC). 5. Edge-Binding-Permission: The Edge-Binding-Permissions Relationship defines how components connect to establish access control and permissions in a system. In the Edge-Binding-Permissions relationship, the binding components, such as role bindings and cluster role bindings, act as essential links that establish and enforce permissions. 6. Edge-Binding-Firewall: An Edge-Firewall Relationship in Meshery models a Kubernetes Network Policy that controls ingress and egress traffic between Pods. 7. Edge-Non-Binding-Network: An Edge-Network Relationship in Meshery represents the networking configuration between Kubernetes components, typically illustrated by a dashed arrow connecting a Service to a Deployment. 8. Edge-Non-Binding-Annotation: Annotation Relationships refer to a visual representation used to indicate a relationship between two components without assigning any semantic meaning to that relationship.

Caveats and Considerations

For detailed considerations on each relationship type, refer to the corresponding individual published designs. These designs provide in-depth insights into best practices, configuration strategies, and potential impacts for each type of relationship.

Technologies

Description

This comprehensive IoT architecture harnesses the power of Amazon Web Services (AWS) to create a robust and scalable Internet of Things (IoT) ecosystem

Caveats and Considerations

It cannot be deployed because the nodes used to create the diagram are shapes and not components.

Technologies

Description

Apache Airflow (or simply Airflow) is a platform to programmatically author, schedule, and monitor workflows. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative. Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed. Airflow works best with workflows that are mostly static and slowly changing. When the DAG structure is similar from one run to the next, it clarifies the unit of work and continuity. Other similar projects include Luigi, Oozie and Azkaban. Airflow is commonly used to process data, but has the opinion that tasks should ideally be idempotent (i.e., results of the task will be the same, and will not create duplicated data in a destination system), and should not pass large quantities of data from one task to the next (though tasks can pass metadata using Airflow's XCom feature). For high-volume, data-intensive tasks, a best practice is to delegate to external services specializing in that type of work. Airflow is not a streaming solution, but it is often used to process real-time data, pulling data off streams in batches. Principles Dynamic: Airflow pipelines are configuration as code (Python), allowing for dynamic pipeline generation. This allows for writing code that instantiates pipelines dynamically. Extensible: Easily define your own operators, executors and extend the library so that it fits the level of abstraction that suits your environment. Elegant: Airflow pipelines are lean and explicit. Parameterizing your scripts is built into the core of Airflow using the powerful Jinja templating engine. Scalable: Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers.

Caveats and Considerations

Make sure to fill out your own postgres username ,password, host,port etc to see airflow working as per your database requirements. pass them as environment variables or create secrets for password and config map for ports ,host .

Technologies

Description

The ShardingSphere Kubernetes Operator automates provisioning, management, and operations of ShardingSphere Proxy clusters running on Kubernetes. Apache ShardingSphere is an ecosystem to transform any database into a distributed database system, and enhance it with sharding, elastic scaling, encryption features & more.

Caveats and Considerations

Ensure Apache ShardingSphere and Knative Service is registered as a MeshModel

Technologies

Description

Argo and Kubernetes graph app

Caveats and Considerations

No Caveats

Technologies

Description

The Argo CD server component exposes the API and UI. The operator creates a Service to expose this component and can be accessed through the various methods available in Kubernetes.

Caveats and Considerations

Dex can be used to delegate authentication to external identity providers like GitHub, SAML and others. SSO configuration of Argo CD requires updating the Argo CD CR with Dex connector settings.

Technologies

Description

This YAML configuration describes a Kubernetes Deployment for the ArgoCD Application Controller. It includes metadata defining labels for identification purposes. The spec section outlines the deployment's details, including the desired number of replicas and a pod template. Within the pod template, there's a single container named argocd-application-controller, which runs the ArgoCD Application Controller binary. This container is configured with various environment variables sourced from ConfigMaps, defining parameters such as reconciliation timeouts, repository server details, logging settings, and affinity rules. Port 8082 is specified for readiness probes, and volumes are mounted for storing TLS certificates and temporary data. Additionally, the deployment specifies a service account and defines pod affinity rules for scheduling. These settings collectively ensure the reliable operation of the ArgoCD Application Controller within Kubernetes clusters, facilitating efficient management of applications within an ArgoCD instance.

Caveats and Considerations

1. Environment Configuration: Ensure that the environment variables configured for the application controller align with your deployment requirements. Review and adjust settings such as reconciliation timeouts, logging levels, and repository server details as needed. 2. Resource Requirements: Depending on your deployment environment and workload, adjust resource requests and limits for the container to ensure optimal performance and resource utilization. 3. Security: Pay close attention to security considerations, especially when handling sensitive data such as TLS certificates. Ensure that proper encryption and access controls are in place for any secrets used in the deployment. 4. High Availability: Consider strategies for achieving high availability and fault tolerance for the ArgoCD Application Controller. This may involve running multiple replicas of the controller across different nodes or availability zones. 5. Monitoring and Alerting: Implement robust monitoring and alerting mechanisms to detect and respond to any issues or failures within the ArgoCD Application Controller deployment. Utilize tools such as Prometheus and Grafana to monitor key metrics and set up alerts for critical events.

Technologies

Description

This is design that deploys ArgoCD application that includes Nginx virtual service, Nginx server, K8s pod autoscaler, OpenEBS's Jiva volume, and a sample ArgoCD application listening on 127.0.0.4

Caveats and Considerations

Ensure networking is setup properly

Technologies

Description

This is a sample argocd design @ and mesasuring Metric@ a *$kubernetes manifes->!

Caveats and Considerations

No caveats

Technologies

Description

This YAML manifest defines a Kubernetes Deployment for the Thanos Operator, named "thanos-operator," with one replica. The deployment's pod template is labeled "app: thanos-operator" and includes security settings to run as a non-root user with specific user (1000) and group (2000) IDs. The main container, also named "thanos-operator," uses the "thanos-io/thanos:latest" image, runs with minimal privileges, and starts with the argument "--log.level=info." It listens on port 8080 for HTTP traffic and has liveness and readiness probes set to check the "/metrics" endpoint. Resource requests and limits are defined for CPU and memory. Additionally, the pod is scheduled on Linux nodes with specific node affinity rules and tolerations for certain node taints, ensuring appropriate node placement and scheduling.

Caveats and Considerations

1. Security Context: 1.1 The runAsUser: 1000 and fsGroup: 2000 settings are essential for running the container with non-root privileges. Ensure that these user IDs are correctly configured and have the necessary permissions within your environment. 1.2 Dropping all capabilities (drop: - ALL) enhances security but may limit certain functionalities. Verify that the Thanos container does not require any additional capabilities. 2. Image Tag: The image tag is set to "latest," which can introduce instability since it pulls the most recent image version that might not be thoroughly tested. Consider specifying a specific, stable version tag for better control over updates and rollbacks. 3. Resource Requests and Limits: The defined resource requests and limits (memory: "64Mi"/"128Mi", cpu: "250m"/"500m") might need adjustment based on the actual workload and performance characteristics of the Thanos Operator in your environment. Monitor resource usage and tweak these settings accordingly to prevent resource starvation or over-provisioning.

Technologies

Description

This design demonstrates how to automatically scale your Google Kubernetes Engine (GKE) workloads based on Prometheus-style metrics emitted by your application. It uses the [GKE workload metrics](https://cloud.google.com/stackdriver/docs/solutions/gke/managing-metrics#workload-metrics) pipeline to collect the metrics emitted from the example application and send them to [Cloud Monitoring](https://cloud.google.com/monitoring), and then uses the [HorizontalPodAutoscaler](https://cloud.google.com/kubernetes-engine/docs/concepts/horizontalpodautoscaler) along with the [Custom Metrics Adapter](https://github.com/GoogleCloudPlatform/k8s-stackdriver/tree/master/custom-metrics-stackdriver-adapter) to scale the application.

Caveats and Considerations

Add your own custom prometheus to GKE for better scaling of workloads

Technologies

Description

CloudWatch Agent to collect Kubernetes cluster metrics involves configuring and deploying the CloudWatch Agent within your Kubernetes environment. This agent facilitates the collection and forwarding of various system-level and application-level metrics to AWS CloudWatch, enabling comprehensive monitoring and analysis. By integrating the CloudWatch Agent, Kubernetes administrators can effortlessly monitor cluster performance metrics such as CPU utilization, memory usage, disk I/O, and network traffic. This setup enhances operational visibility, supports proactive capacity planning, and enables the creation of alarms and notifications based on customizable thresholds, ensuring robust and reliable management of Kubernetes infrastructure at scale.

Caveats and Considerations

When deploying the CloudWatch Agent to collect Kubernetes cluster metrics, there are several caveats and considerations to keep in mind: Resource Consumption: The CloudWatch Agent runs as a daemon set within Kubernetes, consuming resources (CPU, memory) on each node where it's deployed. Ensure your cluster has sufficient resources to accommodate this additional workload. Networking: Verify that nodes in your Kubernetes cluster can communicate with AWS CloudWatch endpoints over the network. This may involve configuring network policies, security groups, or VPC settings to allow outbound traffic to AWS services. Permissions: Set up IAM roles or IAM users with appropriate permissions to allow the CloudWatch Agent to publish metrics to CloudWatch. Follow the principle of least privilege to minimize security risks. Configuration: Properly configure the CloudWatch Agent to collect relevant metrics based on your application and infrastructure requirements. Incorrect configuration can lead to incomplete or inaccurate monitoring data. Version Compatibility: Ensure compatibility between the CloudWatch Agent version and your Kubernetes cluster version. Updates or changes in Kubernetes versions may require corresponding updates to the CloudWatch Agent for optimal performance and compatibility. Monitoring Costs: Regularly monitor and review the costs associated with CloudWatch metrics ingestion and storage. Depending on the volume of metrics collected, costs can vary, especially if high-resolution metrics are enabled. High Availability: Design your deployment for high availability to ensure continuous monitoring and metric collection. Consider deploying multiple instances of the CloudWatch Agent across different availability zones or regions for resilience. Security: Implement best practices for securing the CloudWatch Agent deployment, including encrypting sensitive data in transit and at rest, using secure IAM roles, and regularly updating to the latest agent version to mitigate security vulnerabilities. Integration with Monitoring Tools: Integrate CloudWatch metrics with your existing monitoring and alerting tools to streamline incident response and operational workflows. Ensure that metrics from CloudWatch can be correlated with other monitoring data for comprehensive visibility.

Technologies

Description

Azure Monitor Containers is a service that monitors and provides insights into the performance and health of Azure container apps and Kubernetes clusters: - Container insights Collects and analyzes container logs and metric data from Azure Kubernetes clusters. It supports Azure Kubernetes Service (AKS), Azure Arc-enabled Kubernetes clusters, and Windows Server 2022. - Metric data collection Regularly collects metric data from container apps to help users understand their performance and health. Users can visualize the data in the Azure portal's metrics explorer. - Hybrid Kubernetes monitoring Supports monitoring Kubernetes clusters on-premises and on Azure Stack with AKS Engine. Users can install the container agent with their AKS workloads to create alerts and gain insights into the performance of their on-premises workloads. - Log streaming Allows users to view streaming system and console logs from a container in near real-time.

Caveats and Considerations

Container insights is a feature of Azure Monitor that collects and analyzes container logs from Azure Kubernetes clusters or Azure Arc-enabled Kubernetes clusters and their components. You can analyze the collected data for the different components in your cluster with a collection of views and prebuilt workbooks.

Technologies

Description

Azure Monitor managed service for Prometheus and Container insights work together for complete monitoring of your Kubernetes environment. This article describes both features and the data they collect. Azure Monitor managed service for Prometheus is a fully managed service based on the Prometheus project from the Cloud Native Computing Foundation. It allows you to collect and analyze metrics from your Kubernetes cluster at scale and analyze them using prebuilt dashboards in Grafana. Container insights is a feature of Azure Monitor that collects and analyzes container logs from Azure Kubernetes clusters or Azure Arc-enabled Kubernetes clusters and their components. You can analyze the collected data for the different components in your cluster with a collection of views and prebuilt workbooks.

Caveats and Considerations

Container insights collects metric data from your cluster in addition to logs. This functionality has been replaced by Azure Monitor managed service for Prometheus. You can analyze that data using built-in dashboards in Managed Grafana and alert on them using prebuilt Prometheus alert rules. You can continue to have Container insights collect metric data so you can use the Container insights monitoring experience. Or you can save cost by disabling this collection and using Grafana for metric analysis. See Configure data collection in Container insights using data collection rule for configuration options. For more information checkout this doc https://learn.microsoft.com/en-us/azure/azure-monitor/containers/container-insights-overview

Technologies

Description

Bank of Anthos is a sample HTTP-based web app that simulates a bank's payment processing network, allowing users to create artificial bank accounts and complete transactions.

Caveats and Considerations

Ensure enough resources are available on the cluster.

Technologies

Description

The Bookinfo application is a collection of microservices that work together to display information about a book. The main microservice is called productpage, which fetches data from the details and reviews microservices to populate the book's page. The details microservice contains specific information about the book, such as its ISBN and number of pages. The reviews microservice contains reviews of the book and also makes use of the ratings microservice to retrieve ranking information for each review. The reviews microservice has three different versions: v1, v2, and v3. In v1, the microservice does not interact with the ratings service. In v2, it calls the ratings service and displays the rating using black stars, ranging from 1 to 5. In v3, it also calls the ratings service but displays the rating using red stars, again ranging from 1 to 5. These different versions allow for flexibility and experimentation with different ways of presenting the books ratings to users.

Caveats and Considerations

Users need to ensure that their cluster is properly configured with Istio, including the installation of the necessary components and enabling sidecar injection for the microservices. Ensure that Meshery Adapter for Istio service mesh is installed properly for easy installation/registration of Istio's MeshModels with Meshery Server. Another consideration is the resource requirements of the application. The Bookinfo application consists of multiple microservices, each running as a separate container. Users should carefully assess the resource needs of the application and ensure that their cluster has sufficient capacity to handle the workload. This includes considering factors such as CPU, memory, and network bandwidth requirements.

Technologies

Description

Use this template to structure brainstorming sessions effectively for various project types, including product launches and team collaboration sessions.

Caveats and Considerations

This template is designed as a flexible starting point for brainstorming sessions. It’s adaptable for various goals, such as ideation, planning, and team discussions. For best results, use Kanvas’s collaborative tools like sticky notes, color coding, and comments to enrich discussions and enhance readability. Adjust sections as needed based on the scope of your brainstorming session.

Technologies

Description

Chrome as a service container. Bring your own hardware or cloud. Homepage: https://www.browserless.io ## Configuration Browserless can be configured via environment variables: ```yaml env: PREBOOT_CHROME: "true" ```

Caveats and Considerations

Check out the [official documentation](https://docs.browserless.io/docs/docker.html) for the available options. ## Values | Key | Type | Default | Description | |-----|------|---------|-------------| | replicaCount | int | `1` | Number of replicas (pods) to launch. | | image.repository | string | `"browserless/chrome"` | Name of the image repository to pull the container image from. | | image.pullPolicy | string | `"IfNotPresent"` | [Image pull policy](https://kubernetes.io/docs/concepts/containers/images/#updating-images) for updating already existing images on a node. | | image.tag | string | `""` | Image tag override for the default value (chart appVersion). | | imagePullSecrets | list | `[]` | Reference to one or more secrets to be used when [pulling images](https://kubernetes.io/docs/tasks/configure-pod-container/pull-image-private-registry/#create-a-pod-that-uses-your-secret) (from private registries). | | nameOverride | string | `""` | A name in place of the chart name for `app:` labels. | | fullnameOverride | string | `""` | A name to substitute for the full names of resources. | | volumes | list | `[]` | Additional storage [volumes](https://kubernetes.io/docs/concepts/storage/volumes/). See the [API reference](https://kubernetes.io/docs/reference/kubernetes-api/workload-resources/pod-v1/#volumes-1) for details. | | volumeMounts | list | `[]` | Additional [volume mounts](https://kubernetes.io/docs/tasks/configure-pod-container/configure-volume-storage/). See the [API reference](https://kubernetes.io/docs/reference/kubernetes-api/workload-resources/pod-v1/#volumes-1) for details. | | envFrom | list | `[]` | Additional environment variables mounted from [secrets](https://kubernetes.io/docs/concepts/configuration/secret/#using-secrets-as-environment-variables) or [config maps](https://kubernetes.io/docs/tasks/configure-pod-container/configure-pod-configmap/#configure-all-key-value-pairs-in-a-configmap-as-container-environment-variables). See the [API reference](https://kubernetes.io/docs/reference/kubernetes-api/workload-resources/pod-v1/#environment-variables) for details. | | env | object | `{}` | Additional environment variables passed directly to containers. See the [API reference](https://kubernetes.io/docs/reference/kubernetes-api/workload-resources/pod-v1/#environment-variables) for details. | | serviceAccount.create | bool | `true` | Enable service account creation. | | serviceAccount.annotations | object | `{}` | Annotations to be added to the service account. | | serviceAccount.name | string | `""` | The name of the service account to use. If not set and create is true, a name is generated using the fullname template. | | podAnnotations | object | `{}` | Annotations to be added to pods. | | podSecurityContext | object | `{}` | Pod [security context](https://kubernetes.io/docs/tasks/configure-pod-container/security-context/#set-the-security-context-for-a-pod). See the [API reference](https://kubernetes.io/docs/reference/kubernetes-api/workload-resources/pod-v1/#security-context) for details. | | securityContext | object | `{}` | Container [security context](https://kubernetes.io/docs/tasks/configure-pod-container/security-context/#set-the-security-context-for-a-container). See the [API reference](https://kubernetes.io/docs/reference/kubernetes-api/workload-resources/pod-v1/#security-context-1) for details. | | service.annotations | object | `{}` | Annotations to be added to the service. | | service.type | string | `"ClusterIP"` | Kubernetes [service type](https://kubernetes.io/docs/concepts/services-networking/service/#publishing-services-service-types). | | service.loadBalancerIP | string | `nil` | Only applies when the service type is LoadBalancer. Load balancer will get created with the IP specified in this field. | | service.loadBalancerSourceRanges | list | `[]` | If specified (and supported by the cloud provider), traffic through the load balancer will be restricted to the specified client IPs. Valid values are IP CIDR blocks. | | service.port | int | `80` | Service port. | | service.nodePort | int | `nil` | Service node port (when applicable). | | service.externalTrafficPolicy | string | `nil` | Route external traffic to node-local or cluster-wide endoints. Useful for [preserving the client source IP](https://kubernetes.io/docs/tasks/access-application-cluster/create-external-load-balancer/#preserving-the-client-source-ip). | | resources | object | No requests or limits. | Container resource [requests and limits](https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/). See the [API reference](https://kubernetes.io/docs/reference/kubernetes-api/workload-resources/pod-v1/#resources) for details. | | autoscaling | object | Disabled by default. | Autoscaling configuration (see [values.yaml](values.yaml) for details). | | nodeSelector | object | `{}` | [Node selector](https://kubernetes.io/docs/concepts/scheduling-eviction/assign-pod-node/#nodeselector) configuration. | | tolerations | list | `[]` | [Tolerations](https://kubernetes.io/docs/concepts/scheduling-eviction/taint-and-toleration/) for node taints. See the [API reference](https://kubernetes.io/docs/reference/kubernetes-api/workload-resources/pod-v1/#scheduling) for details. | | affinity | object | `{}` | [Affinity](https://kubernetes.io/docs/concepts/scheduling-eviction/assign-pod-node/#affinity-and-anti-affinity) configuration. See the [API reference](https://kubernetes.io/docs/reference/kubernetes-api/workload-resources/pod-v1/#scheduling) for details. |

Technologies

Description

This design deploys simple busybox app inside Layer5-test namespace

Caveats and Considerations

None

Technologies

Description

This design deploys simple busybox app inside Layer5-test namespace

Caveats and Considerations

None

Technologies

Description

Design contains k8s resources like deployment, service

Caveats and Considerations

There is no caveats

Technologies

Description

The Pizza Store application simulates placing a Pizza Order that is going to be processed by different services. The application is composed by the Pizza Store Service which serve as the front end and backend to place the order. The order is sent to the Kitchen Service for preparation and once the order is ready to be delivered the Delivery Service takes the order to your door. As any other application, these services will need to store and read data from a persistent store such as a Database and exchange messages if a more event-driven approach is needed. This application uses PostgreSQL and Kafka, as they are well-known components among developers. As you can see in the diagram, if we want to connect to PostgreSQL from the Pizza Store Service we need to add to our applications the PostgreSQL driver that must match with the PostgreSQL instance version that we have available. A Kafka client is required in all the services that are interested in publishing or consuming messages/events. Because you have Drivers and Clients that are sensitive to the available versions on the infrastructure components, the lifecycle of the application is now bound to the lifecycle of these components. Adding Dapr to the picture not only breaks these dependencies, but also remove responsabilities from developers of choosing the right Driver/Client and how these need to be configured for the application to work correctly. Dapr provides developers building block APIs such as the StateStore and PubSub API that developer can use without know the details of which infrastructure is going to be connected under the covers.

Caveats and Considerations

The application services are written using Java + Spring Boot. These services use the Dapr Java SDK to interact with the Dapr PubSub and Statestore APIs. To run the services locally you can use the Testcontainer integration already included in the projects. For example you can start a local version of the pizza-store service by running the following command inside the pizza-store/ directory (this requires having Java and Maven installed locally): for Caveats And Consideration refer this github repo https://github.com/salaboy/pizza?tab=readme-ov-file#installation

Technologies

Description

Many applications rely on configuration which is used during either application initialization or runtime. Most times, there is a requirement to adjust values assigned to configuration parameters. ConfigMaps are a Kubernetes mechanism that let you inject configuration data into application pods. The ConfigMap concept allow you to decouple configuration artifacts from image content to keep containerized applications portable. For example, you can download and run the same container image to spin up containers for the purposes of local development, system test, or running a live end-user workload. This design provides a usage example demonstrating how to create ConfigMaps and configure Pods using data stored in ConfigMaps.

Caveats and Considerations

In essence, this configuration creates a Deployment that: Runs 3 replicas of a pod. Each pod uses the alpine:3 image. Inside each pod, a script continuously prints the current date and a message with a preferred sport fetched from a ConfigMap named "sport". The ConfigMap provides the "sport" data that's mounted into the container's file system. This example demonstrates how to use ConfigMaps to inject configuration data into your pods, allowing you to decouple configuration from your application's image.

Technologies

Description

Consul is a tool for discovering, configuring, and managing services in distributed systems. It provides features like service discovery, health checking, key-value storage, and distributed coordination. In Kubernetes, Consul can be useful in several ways: 1. Service Discovery: Kubernetes already has built-in service discovery through DNS and environment variables. However, Consul provides more advanced features such as service registration, DNS-based service discovery, and health checking. This can be particularly useful if you have services deployed both within and outside of Kubernetes, as Consul can provide a unified service discovery mechanism across your entire infrastructure. 2. Configuration Management: Consul includes a key-value store that can be used to store configuration data. This can be used to configure applications dynamically at runtime, allowing for more flexible and dynamic deployments. 3. Health Checking Consul can perform health checks on services to ensure they are functioning correctly. If a service fails its health check, Consul can automatically remove it from the pool of available instances, preventing traffic from being routed to it until it recovers. 4. Service Mesh: Consul can also be used as a service mesh in Kubernetes, providing features like traffic splitting, encryption, and observability. This can help you to manage communication between services within your Kubernetes cluster more effectively. Overall, Consul can complement Kubernetes by providing additional features and capabilities for managing services in distributed systems. It can help to simplify and streamline the management of complex microservices architectures, providing greater visibility, resilience, and flexibility.

Caveats and Considerations

customize the design according to your requirements and the image is pulled from docker hub

Technologies

Description

Depending upon use cases, when an ingress gateway must handle a large number of incoming TLS and secured service-to-service connections through sidecar proxies, the load on Envoy increases. The potential performance depends on many factors, such as size of the cpuset on which Envoy is running, incoming traffic patterns, and key size. These factors can impact Envoy serving many new incoming TLS requests. To achieve performance improvements and accelerated handshakes, a new feature was introduced in Envoy 1.20 and Istio 1.14. It can be achieved with 3rd Gen Intel® Xeon® Scalable processors, the Intel® Integrated Performance Primitives (Intel® IPP) crypto library, CryptoMB Private Key Provider Method support in Envoy, and Private Key Provider configuration in Istio using ProxyConfig.\\\\\\\\\\\\\\
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Envoy uses BoringSSL as the default TLS library. BoringSSL supports setting private key methods for offloading asynchronous private key operations, and Envoy implements a private key provider framework to allow creation of Envoy extensions which handle TLS handshakes private key operations (signing and decryption) using the BoringSSL hooks.\\\\\\\\\\\\\\
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CryptoMB private key provider is an Envoy extension which handles BoringSSL TLS RSA operations using Intel AVX-512 multi-buffer acceleration. When a new handshake happens, BoringSSL invokes the private key provider to request the cryptographic operation, and then the control returns to Envoy. The RSA requests are gathered in a buffer. When the buffer is full or the timer expires, the private key provider invokes Intel AVX-512 processing of the buffer. When processing is done, Envoy is notified that the cryptographic operation is done and that it may continue with the handshakes.

Caveats and Considerations

None

Technologies

Description

Envoy uses BoringSSL as the default TLS library. BoringSSL supports setting private key methods for offloading asynchronous private key operations, and Envoy implements a private key provider framework to allow creation of Envoy extensions which handle TLS handshakes private key operations (signing and decryption) using the BoringSSL hooks.\\
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CryptoMB private key provider is an Envoy extension which handles BoringSSL TLS RSA operations using Intel AVX-512 multi-buffer acceleration. When a new handshake happens, BoringSSL invokes the private key provider to request the cryptographic operation, and then the control returns to Envoy. The RSA requests are gathered in a buffer. When the buffer is full or the timer expires, the private key provider invokes Intel AVX-512 processing of the buffer. When processing is done, Envoy is notified that the cryptographic operation is done and that it may continue with the handshakes.\\
Envoy uses BoringSSL as the default TLS library. BoringSSL supports setting private key methods for offloading asynchronous private key operations, and Envoy implements a private key provider framework to allow creation of Envoy extensions which handle TLS handshakes private key operations (signing and decryption) using the BoringSSL hooks.\\
\\
CryptoMB private key provider is an Envoy extension which handles BoringSSL TLS RSA operations using Intel AVX-512 multi-buffer acceleration. When a new handshake happens, BoringSSL invokes the private key provider to request the cryptographic operation, and then the control returns to Envoy. The RSA requests are gathered in a buffer. When the buffer is full or the timer expires, the private key provider invokes Intel AVX-512 processing of the buffer. When processing is done, Envoy is notified that the cryptographic operation is done and that it may continue with the handshakes.\\
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Caveats and Considerations

None

Technologies

Description

Cryptographic operations are among the most compute-intensive and critical operations when it comes to secured connections. Istio uses Envoy as the “gateways/sidecar” to handle secure connections and intercept the traffic. Depending upon use cases, when an ingress gateway must handle a large number of incoming TLS and secured service-to-service connections through sidecar proxies, the load on Envoy increases. The potential performance depends on many factors, such as size of the cpuset on which Envoy is running, incoming traffic patterns, and key size. These factors can impact Envoy serving many new incoming TLS requests. To achieve performance improvements and accelerated handshakes, a new feature was introduced in Envoy 1.20 and Istio 1.14. It can be achieved with 3rd Gen Intel® Xeon® Scalable processors, the Intel® Integrated Performance Primitives (Intel® IPP) crypto library, CryptoMB Private Key Provider Method support in Envoy, and Private Key Provider configuration in Istio using ProxyConfig.

Caveats and Considerations

Ensure networking is setup properly and correct annotation are applied to each resource for custom Intel configuration

Technologies

Description

Cryptographic operations are among the most compute-intensive and critical operations when it comes to secured connections. Istio uses Envoy as the “gateways/sidecar” to handle secure connections and intercept the traffic. Depending upon use cases, when an ingress gateway must handle a large number of incoming TLS and secured service-to-service connections through sidecar proxies, the load on Envoy increases. The potential performance depends on many factors, such as size of the cpuset on which Envoy is running, incoming traffic patterns, and key size. These factors can impact Envoy serving many new incoming TLS requests. To achieve performance improvements and accelerated handshakes, a new feature was introduced in Envoy 1.20 and Istio 1.14. It can be achieved with 3rd Gen Intel® Xeon® Scalable processors, the Intel® Integrated Performance Primitives (Intel® IPP) crypto library, CryptoMB Private Key Provider Method support in Envoy, and Private Key Provider configuration in Istio using ProxyConfig.

Caveats and Considerations

Ensure networking is setup properly and correct annotation are applied to each resource for custom Intel configuration

Technologies

Description

A standard Dapr control plane design.

Caveats and Considerations

none

Technologies

Description

This design walks you through the steps of setting up the OAuth middleware to enable a service to interact with external services requiring authentication. This design seperates the authentication/authorization concerns from the application. checkout this https://github.com/dapr/samples/tree/master/middleware-oauth-microsoftazure for more inoformation and try out in your own environment.

Caveats and Considerations

Certainly! Here's how you would replace the placeholders with actual values and apply the configuration to your Kubernetes cluster: 1. Replace `"YOUR_APPLICATION_ID"`, `"YOUR_CLIENT_SECRET"`, and `"YOUR_TENANT_ID"` with your actual values in the `msgraphsp` component metadata: ```yaml metadata: # OAuth2 ClientID, for Microsoft Identity Platform it is the AAD Application ID - name: clientId value: "your_actual_application_id" # OAuth2 Client Secret - name: clientSecret value: "your_actual_client_secret" # Application Scope for Microsoft Graph API (vs. User Scope) - name: scopes value: "https://graph.microsoft.com/.default" # Token URL for Microsoft Identity Platform, TenantID is the Tenant (also sometimes called Directory) ID of the AAD - name: tokenURL value: "https://login.microsoftonline.com/your_actual_tenant_id/oauth2/v2.0/token" ``` 2. Apply the modified YAML configuration to your Kubernetes cluster using `kubectl apply -f your_file.yaml`. Ensure you've replaced `"your_actual_application_id"`, `"your_actual_client_secret"`, and `"your_actual_tenant_id"` with the appropriate values corresponding to your Microsoft Graph application and Azure Active Directory configuration before applying the configuration to your cluster.

Technologies

Description

This design will show an example of running Dapr with a Kubernetes events input binding. You'll be deploying the Node application and will require a component definition with a Kubernetes event binding component. checkout this https://github.com/dapr/samples/tree/master/read-kubernetes-events#read-kubernetes-events for more info .

Caveats and Considerations

make sure to replace some things like docker images ,credentials to try out on your local cluster .

Technologies

Description

A simple example

Caveats and Considerations

An example the delay action

Technologies

Description

Simple deployment of web application

Caveats and Considerations

There is no caveats

Technologies

Description

Design with proper validation

Caveats and Considerations

Nothing specific

Technologies

Description

The Distributed Database with ShardingSphere design outlines a robust architecture for deploying and managing a distributed database system. ShardingSphere facilitates horizontal partitioning of data across multiple nodes, known as shards, to enhance scalability, performance, and fault tolerance. This design details the configuration of ShardingSphere to manage shard key distribution, data sharding strategies, and query routing mechanisms efficiently. It emphasizes optimizing data access and synchronization while ensuring high availability and data integrity. Key considerations include defining shard key strategies for balanced data distribution, implementing robust monitoring and alerting systems, and integrating backup and recovery solutions for comprehensive data management. This design is ideal for applications requiring scalable and resilient database solutions in distributed computing environments.

Caveats and Considerations

While deploying the Distributed Database with ShardingSphere design, several caveats should be considered to ensure optimal performance and reliability. First, configuring and maintaining the sharding strategy requires careful planning to avoid uneven data distribution among shards, which can lead to performance bottlenecks. Additionally, managing shard synchronization and ensuring data consistency across distributed nodes can be complex and requires robust monitoring and management tools. Security concerns such as data encryption, access control, and securing inter-node communication must also be addressed to prevent unauthorized access and data breaches. Furthermore, the operational overhead of maintaining multiple database instances and coordinating schema changes across shards can be significant and should be managed effectively to minimize downtime and operational risks.

Technologies

Description

This design provides everything needed to set up the ACK (AWS Controllers for Kubernetes) EC2 controller in your Kubernetes cluster, including CRDs, permissions, and pod configuration for managing EC2 resources directly from your cluster.

Caveats and Considerations

1. Kubernetes Cluster Connection: Ensure you have your cluster connected to Meshery. 2. Set up a Secret: Base64 encode your AWS access key and secret access key, and store them in the Kubernetes Secret. 3. Environment Variables: The design is pre-configured to use the access keys from the Secret as environment variables. Simply provide your encoded keys in the secret. 4. AWS Region: Specify the correct AWS region in the controller pod configuration. This design uses us-east-1. 5. Namespace Deployment: Deploy all resources within a dedicated namespace. This design uses the ack-system namespace.

Technologies

Description

This design has two EC2 instances, a Bastion host that will be deployed in a public Subnet and a private Instance that will be deployed in a private subnet.

Caveats and Considerations

1. Before deploying this design, ensure that the EC2 Controller design is deployed first, followed by the VPC Workflow design. 2; You have to Configure the Security Group ID and Subnet ID for both components.

Technologies

Description

ELK stack in kubernetes deployed with simple python app using logstash ,kibana , filebeat ,elastic search.

Caveats and Considerations

here technologies included are kubernetes , elastic search ,log stash ,log stash ,kibana ,python etc

Technologies

Description

A relationship that binds permission between components. Eg: ClusterRole defines a set of permissions, ClusterRoleBinding binds those permissions to subjects like service accounts.

Caveats and Considerations

NA

Technologies

Description

Annotation Relationships refer to a visual representation used to indicate a relationship between two components without assigning any semantic meaning to that relationship. In this context, the relationship is depicted simply using an arrow to connect the components, signifying that they are related in some way, but not specifying the nature or significance of that relationship.

Caveats and Considerations

1. Ensure that the use of annotations clearly conveys the intended relationships between components. While annotations don’t have semantic meaning, their placement and direction should help viewers understand the context of the connection. 2. Consider providing accompanying documentation or a legend that explains the purpose of the annotations. This helps viewers understand why certain components are annotated and what the relationships represent, even if they lack specific semantics.

Technologies

Description

The Edge-Binding-Permissions Relationship defines how components connect to establish access control and permissions in a system. In the Edge-Binding-Permissions relationship, the binding components, such as role bindings and cluster role bindings, act as essential links that establish and enforce permissions. They connect service accounts to roles or cluster roles, determining what actions the service accounts are allowed to perform.

Caveats and Considerations

1. Clearly define the roles and their associated permissions before creating bindings. Understand what actions the service accounts will need to perform and ensure that roles are designed to grant only the necessary permissions to follow the principle of least privilege. 2. Plan for how role bindings and cluster role bindings will be managed over time. Consider the implications of adding or removing bindings, especially in dynamic environments where service accounts may change frequently. Ensure that you have processes in place for reviewing and updating permissions as needed.

Technologies

Description

An Edge Firewall Relationship in Meshery models a Kubernetes Network Policy that controls ingress and egress traffic between Pods. This relationship defines rules for Pod-to-Pod communication, specifying allowed and blocked traffic paths. By enforcing network policies, it secures inter-Pod communication and enhances overall cluster security.

Caveats and Considerations

1. Ensure Pods are properly labeled, as Kubernetes Network Policies rely on labels to apply rules. Inconsistent or missing labels can cause the policy to behave unpredictably or fail to enforce the intended traffic rules. 2. Ensure that the correct ports and protocols are specified in the network policy. Misconfigured ports or protocols can result in unintended traffic blocking, preventing necessary communication between Pods or services. 3. Be mindful of how multiple network policies are configured within the same namespace. Overlapping or conflicting rules across policies may cause unexpected traffic behavior, so ensure that policies are clearly defined and ordered to avoid conflicts.

Technologies

Description

An Edge-Network Relationship in Meshery represents the networking configuration between Kubernetes components, typically illustrated by a dashed arrow connecting a Service to a Deployment. This dashed arrow signifies that the Service is linked to the Pods in the Deployment, exposing network access to them through a specified port. The accompanying port/network protocol notation indicates the port exposed by the Service and the corresponding protocol, such as TCP.

Caveats and Considerations

1. Use consistent and accurate labels in both the Service and Deployment configurations. This consistency is crucial for the Service to correctly route traffic to the intended Pods, ensuring proper communication within the edge-network relationship. 2. Clearly specify the network protocol (e.g., TCP or UDP) in the Service configuration. Ensure that the chosen protocol aligns with the requirements of the application and the behavior of the underlying Pods. Mismatched protocols can lead to unexpected communication failures. 3. Carefully manage the exposed ports for Services to avoid conflicts and ensure proper communication. Be sure to match the target port in the Service configuration to the container port in the Pods to ensure that traffic is routed correctly. This alignment is essential for the application to function properly, as mismatched ports can lead to connectivity issues.

Technologies

Description

An Edge-Mount Relationship in Meshery represents the assignment of persistent storage to Pods via PersistentVolumeClaims (PVC). This relationship models how Pods claim storage from PersistentVolumes (PV) for data persistence, ensuring that workloads have access to the required storage resources.

Caveats and Considerations

1. Ensure the correct StorageClass is configured for your PersistentVolume. 2. Verify that the requested storage size in the PersistentVolumeClaim matches the actual storage needs of your application.

Technologies

Description

Kubernetes makes it trivial for anyone to easily build and scale Elasticsearch clusters. Here, you'll find how to do so. Current Elasticsearch version is 5.6.2.

Caveats and Considerations

Elasticsearch for Kubernetes: Current pod descriptors use an emptyDir for storing data in each data node container. This is meant to be for the sake of simplicity and should be adapted according to your storage needs.

Technologies

Description

Envoy uses BoringSSL as the default TLS library. BoringSSL supports setting private key methods for offloading asynchronous private key operations, and Envoy implements a private key provider framework to allow creation of Envoy extensions which handle TLS handshakes private key operations (signing and decryption) using the BoringSSL hooks.

CryptoMB private key provider is an Envoy extension which handles BoringSSL TLS RSA operations using Intel AVX-512 multi-buffer acceleration. When a new handshake happens, BoringSSL invokes the private key provider to request the cryptographic operation, and then the control returns to Envoy. The RSA requests are gathered in a buffer. When the buffer is full or the timer expires, the private key provider invokes Intel AVX-512 processing of the buffer. When processing is done, Envoy is notified that the cryptographic operation is done and that it may continue with the handshakes.
Envoy uses BoringSSL as the default TLS library. BoringSSL supports setting private key methods for offloading asynchronous private key operations, and Envoy implements a private key provider framework to allow creation of Envoy extensions which handle TLS handshakes private key operations (signing and decryption) using the BoringSSL hooks.

CryptoMB private key provider is an Envoy extension which handles BoringSSL TLS RSA operations using Intel AVX-512 multi-buffer acceleration. When a new handshake happens, BoringSSL invokes the private key provider to request the cryptographic operation, and then the control returns to Envoy. The RSA requests are gathered in a buffer. When the buffer is full or the timer expires, the private key provider invokes Intel AVX-512 processing of the buffer. When processing is done, Envoy is notified that the cryptographic operation is done and that it may continue with the handshakes.

Caveats and Considerations

test

Technologies

Description

A relationship that act as a firewall for ingress and egress traffic from Pods.

Caveats and Considerations

NA

Technologies

Description

The design showcases the operational dynamics of the Edge-Network Relationship. There are two ways you can use this design in your architecture design. 1. Cloning this design by clicking the clone button. 2. Start from scratch by creating an edge-network relationship on your own. How to create an Edge-Network relationship on your own? 1. Navigate to MeshMap. 2. Click on the Kubernetes icon inside the dock it will open a Kubernetes drawer from where you can select any component that Kubernetes supports. 3. Search for the Ingress and Service component from the search bar provided in the drawer. 4. Drag-n-drop both the components on the canvas. 5. Hover over the Ingress component, Some handlebars will show up on four sides of the component. 6. Move the cursor close to either of the handlebars, an arrow will show up, click on that arrow. This will open up two options: 1. Question mark: Opens the Help Center 2. Arrow (Edge handle): This edge handle is used for creating the edge relationship 7. Click on the Edge handle and move your cursor close to the Service component. An edge will appear going from the Ingress to Service component which represents the edge relationship between the two components. 8. Congratulations! You just created a relationship between Ingress and Service.

Caveats and Considerations

No Caveats or Considerations

Technologies

Description

The design showcases the operational dynamics of the Edge-Permission relationship. To engage with its functionality, adhere to the sequential steps below: 1. Duplicate this design by cloning it. 2. Modify the name of the service account. Upon completion, you'll notice that the connection visually represented by the edge vanishes, and the ClusterRoleBinding (CRB) is disassociated from both the ClusterRole (CR) and Service Account (SA). To restore this relationship, you can either, 1. Drag the CRB from the CR to the SA, then release the mouse click. This action triggers the recreation of the relationship, as the relationship constraints get satisfied. 2. Or, revert the name of the SA. This automatically recreates the relationship, as the relationship constraints get satisfied. These are a few of the ways to experience this relationship.

Caveats and Considerations

NA

Technologies

Description

This design contains example of how label and annotation can be created and organised

Caveats and Considerations

No caveats

Technologies

Description

This design maps to the "Exploring Kubernetes Pods with Meshery" tutorial and is the end result of the design. It can be used to quickly deploy an nginx pod exposed through a service.

Caveats and Considerations

Service type is NodePort.

Technologies

Description

ExternalDNS synchronizes exposed Kubernetes Services and Ingresses with DNS providers. Kubernetes' cluster-internal DNS server, ExternalDNS makes Kubernetes resources discoverable via public DNS servers. Like KubeDNS, it retrieves a list of resources (Services, Ingresses, etc.) from the Kubernetes API to determine a desired list of DNS records. Unlike KubeDNS, however, it's not a DNS server itself, but merely configures other DNS providers accordingly—e.g. AWS Route 53 or Google Cloud DNS. In a broader sense, ExternalDNS allows you to control DNS records dynamically via Kubernetes resources in a DNS provider-agnostic way.

Caveats and Considerations

For more information and considerations checkout this repo https://github.com/kubernetes-sigs/external-dns/?tab=readme-ov-file

Technologies

Description

A batch workload is a process typically designed to have a start and a completion point. You should consider batch workloads on GKE if your architecture involves ingesting, processing, and outputting data instead of using raw data. Areas like machine learning, artificial intelligence, and high performance computing (HPC) feature different kinds of batch workloads, such as offline model training, batched prediction, data analytics, simulation of physical systems, and video processing. By designing containerized batch workloads, you can leverage the following GKE benefits: An open standard, broad community, and managed service. Cost efficiency from effective workload and infrastructure orchestration and specialized compute resources. Isolation and portability of containerization, allowing the use of cloud as overflow capacity while maintaining data security. Availability of burst capacity, followed by rapid scale down of GKE clusters.

Caveats and Considerations

Ensure proper networking of components for efficient functioning

Technologies

Description

This infrastructure design defines a service and a deployment for a component called Fortio-server **Service: fortio-server-service**- Type: Kubernetes Service - Namespace: Default - Port: Exposes port 8080 - Selector: Routes traffic to pods with the label app: fortio-server - Session Affinity: None - Service Type: ClusterIP - MeshMap Metadata: Describes its relationship with Kubernetes and its category as Scheduling & Orchestration. - Position: Positioned within a graphical representation of infrastructure. **Deployment: fortio-server-deployment** - Type: Kubernetes Deployment - Namespace: Default - Replicas: 1 - Selector: Matches pods with the label app: fortio-server - Pod Template: Specifies a container image for Fortio-server, its resource requests, and a service account. - Container Image: Uses the fortio/fortio:1.32.1 image - MeshMap Metadata: Specifies its parent-child relationship with the fortio-server-service and provides styling information. - Position: Positioned relative to the service within the infrastructure diagram. This configuration sets up a service and a corresponding deployment for Fortio-server in a Kubernetes environment. The service exposes port 8080, while the deployment runs a container with the Fortio-server image. These components are visualized using MeshMap for tracking and visualization purposes.

Caveats and Considerations

Ensure networking is setup properly and enuough resources are available

Technologies

Description

*Implementing Data Mesh on Google Cloud Platform:* Leverage Google Cloud's comprehensive suite of data services, including BigQuery, Dataflow, and Dataproc, to build a scalable and flexible Data Mesh platform. Utilize tools like Dataplex to create a unified data catalog and metadata management system, facilitating data discovery and access. Implement robust data governance policies and access controls using tools like Cloud IAM and DLP to ensure data security and compliance.

Caveats and Considerations

This design is a reference architecture only, and as such is not immediately deployable.

Technologies

Description

This YAML configuration defines a Kubernetes Deployment named "gerrit-operator-deployment" for managing a containerized application called "gerrit-operator". It specifies that one replica of the application should be deployed. The Deployment ensures that the application is always running by managing pod replicas based on the provided selector labels. The template section describes the pod specification, including labels, service account, security context, and container configuration. The container named "gerrit-operator-container" is configured with an image from a container registry, with resource limits and requests defined for CPU and memory. Environment variables are set for various parameters like the namespace, pod name, and platform type. Additionally, specific intervals for syncing Gerrit projects and group members are defined. Further configuration options can be added as needed, such as volumes and initContainers.

Caveats and Considerations

1. Resource Requirements: Ensure that the resource requests and limits specified for CPU and memory are appropriate for the workload and the cluster's capacity to prevent performance issues or resource contention. 2. Image Pull Policy: The imagePullPolicy set to "Always" ensures that the latest image version is always pulled from the container registry. This may increase deployment time and consume more network bandwidth, so consider the trade-offs based on your deployment requirements. 3. Security Configuration: The security context settings, such as runAsNonRoot and allowPrivilegeEscalation: false, enhance pod security by enforcing non-root user execution and preventing privilege escalation. Verify that these settings align with your organization's security policies. 4. Environment Variables: Review the environment variables set for WATCH_NAMESPACE, POD_NAME, PLATFORM_TYPE, GERRIT_PROJECT_SYNC_INTERVAL, and GERRIT_GROUP_MEMBER_SYNC_INTERVAL to ensure they are correctly configured for your deployment environment and application requirements.

Technologies

Description

GlusterFS is implemented as a distributed storage backend that spans multiple nodes, ensuring high availability and data redundancy. This design typically includes components such as GlusterFS servers deployed across Kubernetes nodes, which collectively form a distributed storage pool accessible to applications running in the cluster. The design leverages Kubernetes' persistent volume framework to dynamically provision and manage storage volumes backed by GlusterFS, enabling applications to store and retrieve data seamlessly.

Caveats and Considerations

While GlusterFS offers scalability, performance can vary depending on the workload and network conditions. Latency issues may arise

Technologies

Description

The GuestBook App is a cloud-native application designed using Kubernetes as the underlying orchestration and management system. It consists of various services and components deployed within Kubernetes namespaces. The default namespace represents the main environment where the application operates. The frontend-cyrdx service is responsible for handling frontend traffic and is deployed as a Kubernetes service with a selector for the guestbook application and frontend tier. The frontend-fsfct deployment runs multiple replicas of the frontend component, which utilizes the gb-frontend image and exposes port 80. The guestbook namespace serves as a logical grouping for components related to the GuestBook App. The redis-follower-armov service handles follower Redis instances for the backend, while the redis-follower-nwlew deployment manages multiple replicas of the follower Redis container. The redis-leader-fhxla deployment represents the leader Redis container, and the redis-leader-vjtmi service exposes it as a Kubernetes service. These components work together to create a distributed and scalable architecture for the GuestBook App, leveraging Kubernetes for container orchestration and management.

Caveats and Considerations

Networking should be properly configured to enable communication between the frontend and backend components of the app.

Technologies

Description

The GuestBook App is a cloud-native application designed using Kubernetes as the underlying orchestration and management system. It consists of various services and components deployed within Kubernetes namespaces. The default namespace represents the main environment where the application operates. The frontend-cyrdx service is responsible for handling frontend traffic and is deployed as a Kubernetes service with a selector for the guestbook application and frontend tier. The frontend-fsfct deployment runs multiple replicas of the frontend component, which utilizes the gb-frontend image and exposes port 80. The guestbook namespace serves as a logical grouping for components related to the GuestBook App. The redis-follower-armov service handles follower Redis instances for the backend, while the redis-follower-nwlew deployment manages multiple replicas of the follower Redis container. The redis-leader-fhxla deployment represents the leader Redis container, and the redis-leader-vjtmi service exposes it as a Kubernetes service. These components work together to create a distributed and scalable architecture for the GuestBook App, leveraging Kubernetes for container orchestration and management.

Caveats and Considerations

Networking should be properly configured to enable communication between the frontend and backend components of the app.

Technologies

Description

The GuestBook App is a cloud-native application designed using Kubernetes as the underlying orchestration and management system. It consists of various services and components deployed within Kubernetes namespaces. The default namespace represents the main environment where the application operates. The frontend-cyrdx service is responsible for handling frontend traffic and is deployed as a Kubernetes service with a selector for the guestbook application and frontend tier. The frontend-fsfct deployment runs multiple replicas of the frontend component, which utilizes the gb-frontend image and exposes port 80. The guestbook namespace serves as a logical grouping for components related to the GuestBook App. The redis-follower-armov service handles follower Redis instances for the backend, while the redis-follower-nwlew deployment manages multiple replicas of the follower Redis container. The redis-leader-fhxla deployment represents the leader Redis container, and the redis-leader-vjtmi service exposes it as a Kubernetes service. These components work together to create a distributed and scalable architecture for the GuestBook App, leveraging Kubernetes for container orchestration and management.

Caveats and Considerations

Networking should be properly configured to enable communication between the frontend and backend components of the app.

Technologies

Description

This is a sample guestbook app to demonstrate distributed systems

Caveats and Considerations

1. Ensure networking is setup properly. 2. Ensure enough disk space is available

Technologies

Description

HAProxy Ingress is a Kubernetes ingress controller: it configures a HAProxy instance to route incoming requests from an external network to the in-cluster applications. The routing configurations are built reading specs from the Kubernetes cluster. Updates made to the cluster are applied on the fly to the HAProxy instance.

Caveats and Considerations

Make sure that paths in ingress are configured correctly and for more Caveats And Considerations checkout this docs https://haproxy-ingress.github.io/docs/

Technologies

Description

The Vault server cluster can run directly on Kubernetes. This can be used by applications running within Kubernetes as well as external to Kubernetes, as long as they can communicate to the server via the network. Accessing and Storing Secrets: Applications using the Vault service running in Kubernetes can access and store secrets from Vault using a number of different secret engines and authentication methods. Running a Highly Available Vault Service: By using pod affinities, highly available backend storage (such as Consul) and auto-unseal, Vault can become a highly available service in Kubernetes. Encryption as a Service: Applications using the Vault service running in Kubernetes can leverage the Transit secret engine as "encryption as a service". This allows applications to offload encryption needs to Vault before storing data at rest. Audit Logs for Vault: Operators can choose to attach a persistent volume to the Vault cluster which can be used to store audit logs. And more! Vault can run directly on Kubernetes, so in addition to the native integrations provided by Vault itself, any other tool built for Kubernetes can choose to leverage Vault.

Caveats and Considerations

refer for Caveats And Considerations from this docs https://developer.hashicorp.com/vault/docs/platform/k8s

Technologies

Description

This tutorial will get you up and running with Dapr in a Kubernetes cluster. You will be deploying the same applications from Hello World. To recap, the Python App generates messages and the Node app consumes and persists them.

Caveats and Considerations

make sure to deploy dapr helm chart into meshery playground before deplying this application including crd's , so that native dapr objects can come into consideration

Technologies

Description

Sample WASM implementation with service mesh

Caveats and Considerations

No caveats

Technologies

Description

A relationship that defines whether a component can be a parent of other components. Eg: Namespace is Parent and Role, ConfigMap are children.

Caveats and Considerations

""

Technologies

Description

A hierarchical inventory relationship in which the configuration of (parent) component is patched with the configuration of child component. Eg: The configuration of the Deployment (parent) component is patched with the configuration as received from ConfigMap (child) component.

Caveats and Considerations

NA

Technologies

Description

This represents a parent-child relationship between components, where one component is a dependency of another. Parent-child relationships show clear lineage, similar to a family tree. In this design, the namespace serves as a parent to the config map and role.

Caveats and Considerations

Dependency Awareness: Users should understand that the child components (e.g., config maps, roles) depend on the parent (e.g., namespace). If the parent component is deleted or modified, it will affect the child components.

Technologies

Description

This represents a parent-child relationship between components, where one component is a dependency of another. Parent-child relationships show clear lineage, similar to a family tree. In this design, the namespace serves as a parent to the config map and role.

Caveats and Considerations

Dependency Awareness: Users should understand that the child components (e.g., config maps, roles) depend on the parent (e.g., namespace). If the parent component is deleted or modified, it will affect the child components.

Technologies

Description

In a hierarchical-parent-wallet relationship, one component (the "wallet") serves as a container or host for another component, similar to a parent-child structure. In this example, the WASM plugin acts as the parent (or wallet) that "contains" the WASM filter, representing the idea that the filter operates within the scope and capabilities provided by the plugin. For instance, in a service mesh like Istio, a WASM plugin is deployed into an Envoy proxy and serves as the runtime environment for a WASM filter. A custom WASM filter may be designed to modify HTTP requests (e.g., by adding a security header), but it relies on the plugin to intercept network traffic and integrate with the proxy’s pipeline. The plugin manages the lifecycle of the filter, ensuring it is executed whenever relevant traffic is processed. Without the plugin, the filter would not be able to apply its logic, emphasizing how the plugin enables the filter’s operation. This wallet nature highlights the idea that the plugin acts as a container that securely "holds" the filter, providing it with the necessary infrastructure and environment to function. Just as a wallet holds its contents, the plugin ensures the filter operates properly within the boundaries and resources it provides, without which the filter would not function independently.

Caveats and Considerations

Understand that the child component (e.g., the filter) is dependent on the parent component (e.g., the plugin). If the parent (wallet) is not correctly configured or functional, the child component will not work as expected. Ensure the parent component is properly configured.

Technologies

Description

A HorizontalPodAutoscaler (HPA for short) automatically updates a workload resource (such as a Deployment or StatefulSet), with the aim of automatically scaling the workload to match demand Horizontal scaling means that the response to increased load is to deploy more Pods. This is different from vertical scaling, which for Kubernetes would mean assigning more resources (for example: memory or CPU) to the Pods that are already running for the workload. If the load decreases, and the number of Pods is above the configured minimum, the HorizontalPodAutoscaler instructs the workload resource (the Deployment, StatefulSet, or other similar resource) to scale back down.

Caveats and Considerations

Modify deployments and names according to requirement

Technologies

Description

Connect Kubernetes clusters to iSCSI devices for scalable storage solutions, supporting direct or multipath connections with CHAP authentication.

Caveats and Considerations

Ensure compatibility of Kubernetes and iSCSI versions, configure network settings appropriately, and monitor performance and scalability of both storage and network infrastructure.

Technologies

Description

This design creates a ServiceAccount, DaemonSet, Service, ClusterRole, and ClusterRoleBinding resources for Traefik. The DaemonSet ensures that a single Traefik instance is deployed on each node in the cluster, facilitating load balancing and routing of incoming traffic. The Service allows external traffic to reach Traefik, while the ClusterRole and ClusterRoleBinding provide the necessary permissions for Traefik to interact with Kubernetes resources such as services, endpoints, and ingresses. Overall, this setup enables Traefik to efficiently manage ingress traffic within the Kubernetes environment, providing features like routing, load balancing, and SSL termination.

Caveats and Considerations

-Resource Utilization: Ensure monitoring and scalability to manage resource consumption across nodes, especially in large clusters. -Security Measures: Implement strict access controls and firewall rules to protect Traefik's admin port (8080) from unauthorized access. -Configuration Complexity: Understand Traefik's configuration intricacies for routing rules and SSL termination to avoid misconfigurations. -Compatibility Testing: Regularly test Traefik's compatibility with Kubernetes and other cluster components before upgrading versions. -High Availability Setup: Employ strategies like pod anti-affinity rules to ensure Traefik's availability and uptime. -Performance Optimization: Conduct performance tests to minimize latency and overhead introduced by Traefik in the data path.

Technologies

Description

This design includes an Istio control plane, which will deploy to the istio-system namespace by default.

Caveats and Considerations

No namespaces are annotated for sidecar provisioning in this design.

Technologies

Description

This is a test design

Caveats and Considerations

NA

Technologies

Description

This YAML defines a Kubernetes Deployment for the Istio Operator within the istio-operator namespace. The deployment ensures a single replica of the Istio Operator pod is always running, which is managed by a service account named istio-operator. The deployment's metadata includes the namespace and the deployment name. The pod selector matches pods with the label name: istio-operator, ensuring the correct pods are managed. The pod template specifies metadata and details for the containers, including the container name istio-operator and the image gcr.io/istio-testing/operator:1.5-dev, which runs the istio-operator command with the server argument.

Caveats and Considerations

1. Namespace Configuration: Ensure that the istio-operator namespace exists before applying this deployment. If the namespace is not present, the deployment will fail. 2. Image Version: The image specified (gcr.io/istio-testing/operator:1.5-dev) is a development version. It is crucial to verify the stability and compatibility of this version for production environments. Using a stable release version is generally recommended. 3. Resource Allocation: The resource limits and requests are set to specific values (200m CPU, 256Mi memory for limits; 50m CPU, 128Mi memory for requests). These values should be reviewed and adjusted based on the actual resource availability and requirements of your Kubernetes cluster to prevent resource contention or overallocation. 4. Leader Election: The environment variables include LEADER_ELECTION_NAMESPACE which is derived from the pod's namespace. Ensure that the leader election mechanism is properly configured and that only one instance of the operator becomes the leader to avoid conflicts. 5. Security Context: The deployment does not specify a security context for the container. It is advisable to review and define appropriate security contexts to enhance the security posture of the deployment, such as running the container as a non-root user.

Technologies

Description

JAX is a rapidly growing Python library for high-performance numerical computing and machine learning (ML) research. With applications in large language models, drug discovery, physics ML, reinforcement learning, and neural graphics, JAX has seen incredible adoption in the past few years. JAX offers numerous benefits for developers and researchers, including an easy-to-use NumPy API, auto differentiation and optimization. JAX also includes support for distributed processing across multi-node and multi-GPU systems in a few lines of code, with accelerated performance through XLA-optimized kernels on NVIDIA GPUs. We show how to run JAX multi-GPU-multi-node applications on GKE (Google Kubernetes Engine) using the A2 ultra machine series, powered by NVIDIA A100 80GB Tensor Core GPUs. It runs a simple Hello World application on 4 nodes with 8 processes and 8 GPUs each.

Caveats and Considerations

Ensure networking is setup properly and correct annotation are applied to each resource

Technologies

Description

This YAML configuration defines a Kubernetes Deployment for the Jaeger Operator. This Deployment, named "jaeger-operator," specifies that a container will be created using the jaegertracing/jaeger-operator:master image. The container runs with the argument "start," which initiates the operator's main process. Additionally, the container is configured with an environment variable, LOG-LEVEL, set to "debug," enabling detailed logging for troubleshooting and monitoring purposes. This setup allows the Jaeger Operator to manage Jaeger tracing instances within the Kubernetes cluster, ensuring efficient deployment, scaling, and maintenance of distributed tracing components.

Caveats and Considerations

1. Image Tag: The image tag master indicates that the latest, potentially unstable version of the Jaeger Operator is being used. For production environments, it's safer to use a specific, stable version to avoid unexpected issues. 2. Resource Limits and Requests: The deployment does not specify resource requests and limits for the container. It's crucial to define these to ensure that the Jaeger Operator has enough CPU and memory to function correctly, while also preventing it from consuming excessive resources on the cluster. 3. Replica Count: The spec section does not specify the number of replicas for the deployment. By default, Kubernetes will create one replica, which might not provide high availability. Consider increasing the replica count for redundancy. 4. Namespace: The deployment does not specify a namespace. Ensure that the deployment is applied to the appropriate namespace, particularly if you have a multi-tenant cluster. 5. Security Context: There is no security context defined. Adding a security context can enhance the security posture of the container by restricting permissions and enforcing best practices like running as a non-root user.

Technologies

Description

This YAML configuration defines a Kubernetes Deployment for the Jenkins Operator, ensuring the deployment of a single instance within the cluster. It specifies metadata including labels and annotations for identification and description purposes. The deployment is set to run one replica of the Jenkins Operator container, configured with security settings to run as a non-root user and disallow privilege escalation. Environment variables are provided for dynamic configuration within the container, such as the namespace and Pod name. Resource requests and limits are also defined to manage CPU and memory allocation effectively. Overall, this Deployment aims to ensure the smooth and secure operation of the Jenkins Operator within the Kubernetes environment.

Caveats and Considerations

1. Resource Allocation: The CPU and memory requests and limits defined in the configuration should be carefully adjusted based on the workload and available resources in the Kubernetes cluster to avoid resource contention and potential performance issues. 2. Image Repository Access: Ensure that the container image specified in the configuration (myregistry/jenkins-operator:latest) is accessible from the Kubernetes cluster. Proper image pull policies and authentication mechanisms should be configured to allow the Kubernetes nodes to pull the image from the specified registry. 3. Security Context: The security settings configured in the security context of the container (runAsNonRoot, allowPrivilegeEscalation) are essential for maintaining the security posture of the Kubernetes cluster. Ensure that these settings align with your organization's security policies and best practices. 4. Environment Variables: The environment variables defined in the configuration, such as WATCH_NAMESPACE, POD_NAME, OPERATOR_NAME, and PLATFORM_TYPE, are used to dynamically configure the Jenkins Operator container. Ensure that these variables are correctly set to provide the necessary context and functionality to the operator.

Technologies

Description

This design provide a buffer for cluster autoscaling to allow overprovisioning of cluster nodes. This is desired when you have work loads that need to scale up quickly without waiting for the new cluster nodes to be created and join the cluster. It works by creating a deployment that creates pods of a lower than default PriorityClass. These pods request resources from the cluster but don't actually consume any resources. These pods are then evicted allowing other normal pods to be created while also triggering a scale-up by the .

Caveats and Considerations

get info from this https://github.com/kubernetes/autoscaler/blob/master/cluster-autoscaler/FAQ.md#how-can-i-configure-overprovisioning-with-cluster-autoscaler

Technologies

Description

This design makes use of the external add-on, KEDA HTTP, for event-based autoscaling of HTTP workloads on Kubernetes. See https://artifacthub.io/packages/keda-scaler/keda-official-external-scalers/keda-add-ons-http for details on this specific scaler. The KEDA HTTP Add-on allows Kubernetes users to automatically scale their HTTP servers up and down (including to/from zero) based on incoming HTTP traffic.

Caveats and Considerations

KEDA scalers can both detect if a deployment should be activated or deactivated, and feed custom metrics for a specific event source.

Technologies

Description

This design makes use of the external add-on, KEDA HTTP, for event-based autoscaling of HTTP workloads on Kubernetes. See https://artifacthub.io/packages/keda-scaler/keda-official-external-scalers/keda-add-ons-http for details on this specific scaler. The KEDA HTTP Add-on allows Kubernetes users to automatically scale their HTTP servers up and down (including to/from zero) based on incoming HTTP traffic. In order to do so, KEDA HTTP add-on, deploys a proxy service and requires all traffic to be routed via this proxy service. The proxy service is deployed automatically by the KEDA add-on operator, the name for the deployed service follows the following scheme "keda-add-ons-http-interceptor-proxy".

Caveats and Considerations

1. The dependent design "KEDA Setup", needs to be deployed first for the overall design to function properly. 2. After deploying the design, initiate the performance test on the exposed endpoint for the service named "keda-add-ons-http-interceptor-proxy", ensuring to include "httpbin.com" as the host header if utilizing a different host.

Technologies

Description

KEDA is a Kubernetes-based Event Driven Autoscaling component. It provides event driven scale for any container running in Kubernetes. The design deploys essential KEDA components (including CRDs) to ensure your cluster is properly setup for autoscaling using KEDA.

Caveats and Considerations

For this design to function properly, Kubernetes version v1.23 or above is required.

Technologies

Description

KEDA is a Kubernetes-based Event Driven Autoscaling component. It provides event driven scale for any container running in Kubernetes. The design deploys essential KEDA components (including CRDs) to ensure your cluster is properly setup for autoscaling using KEDA.

Caveats and Considerations

For this design to function properly, Kubernetes version v1.23 or above is required.

Technologies

Description

This YAML snippet describes a Kubernetes Deployment for a Keycloak operator, ensuring a single replica. It specifies labels and annotations for metadata, including a service account. The pod template defines a container running the Keycloak operator image, with environment variables set for namespace and pod name retrieval. Security context settings prevent privilege escalation. Probes are configured for liveness and readiness checks on port 8081, with resource requests and limits ensuring proper resource allocation for the container.

Caveats and Considerations

1. Single Replica: The configuration specifies only one replica, which means there's no built-in redundancy or high availability. Consider adjusting the replica count based on your availability requirements. 2. Resource Allocation: Resource requests and limits are set for CPU and memory. Ensure these values are appropriate for your workload and cluster capacity to avoid performance issues or resource contention. 3. Security Context: The security context is configured to run the container as a non-root user and disallow privilege escalation. Ensure these settings align with your security policies and container requirements. 4. Probes Configuration: Liveness and readiness probes are set up to check the health of the container on port 8081. Ensure that the specified endpoints (/healthz and /readyz) are correctly implemented in the application code. 5. Namespace Configuration: The WATCH_NAMESPACE environment variable is set to an empty string, potentially causing the operator to watch all namespaces. Ensure this behavior aligns with your intended scope of operation and namespace isolation requirements.

Technologies

Description

This design sets up a Kubernetes Deployment deploying two NGINX containers. Each container utilizes an Azure File storage volume for shared data. The NGINX instances serve web content while accessing an Azure File share, enabling scalable and shared storage for the web servers.

Caveats and Considerations

1. Azure Configuration: Ensure that your Azure configuration, including secrets, is correctly set up to access the Azure File share.

2. Data Sharing: Multiple NGINX containers share the same storage. Be cautious when handling write operations to avoid conflicts or data corruption.

3. Scalability: Consider the scalability of both NGINX and Azure File storage to meet your application's demands.

4. Security: Safeguard the secrets used to access Azure resources and limit access to only authorized entities.

5. Pod Recovery: Ensure that the pod recovery strategy is well-defined to handle disruptions or node failures.

6. Azure Costs: Monitor and manage costs associated with Azure File storage, as it may incur charges based on usage.

7. Maintenance: Plan for regular maintenance and updates of both NGINX and Azure configurations to address security and performance improvements.

8. Monitoring: Implement monitoring and alerts for both the NGINX containers and Azure File storage to proactively detect and address issues.

9. Backup and Disaster Recovery: Establish a backup and disaster recovery plan to safeguard data stored in Azure File storage.

Technologies

Description

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Caveats and Considerations

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Technologies

Description

K8GB ,commonly referred to as GSLB (Global Server Load Balancing) solutions, has been typically the domain of proprietary network software and hardware vendors and installed and managed by siloed network teams. k8gb is a completely open source, cloud native, global load balancing solution for Kubernetes. k8gb focuses on load balancing traffic across geographically dispersed Kubernetes clusters using multiple load balancing strategies to meet requirements such as region failover for high availability. Global load balancing for any Kubernetes Service can now be enabled and managed by any operations or development teams in the same Kubernetes native way as any other custom resource.

Caveats and Considerations

Key Differentiators Load balancing is based on timeproof DNS protocol which is perfect for global scope and extremely reliable. No dedicated management cluster and no single point of failure. Kubernetes native application health checks utilizing status of Liveness and Readiness probes for load balancing decisions Configuration with a single Kubernetes CRD of Gslb kind refer this repo for more info https://github.com/k8gb-io/k8gb

Technologies

Description

This design configures the Kubernetes Metrics Server for monitoring cluster-wide resource metrics. It defines a Kubernetes Deployment, Role-Based Access Control (RBAC) rules, and other resources for the Metrics Server's deployment and operation.

Caveats and Considerations

This design configures the Kubernetes Metrics Server for resource monitoring. Ensure that RBAC and ServiceAccount configurations are secure to prevent unauthorized access. Adjust Metrics Server settings for specific metrics and monitor resource usage regularly to prevent resource overuse. Implement probes for reliability and maintain correct API service settings. Plan for scalability and choose the appropriate namespace. Set up monitoring for issue detection and establish data backup and recovery plans. Regularly update components for improved security and performance.

Technologies

Description

This design installs a namespace, a deployment and a service. Both deployment and service are deployed in my-bookinfo namespace. Service is exposed at port 9081.

Caveats and Considerations

Ensure sufficient resources are available in the cluster and networking is exopsed properly.

Technologies

Description

This design contains a single Kubernetes Cronjob.

Caveats and Considerations

This design is for learning purposes and may be freely copied and distributed.

Technologies

Description

Kubernetes policies for resource allocation and usage limits within Pods and containers. It provides guidelines on setting constraints such as CPU and memory limits, ensuring efficient resource management across clusters. This design supports enforcing resource quotas at the namespace level, promoting fair resource distribution and preventing resource contention. It emphasizes Kubernetes' flexibility in defining and enforcing resource limits based on application requirements and cluster capacity.

Caveats and Considerations

Careful consideration is required when setting resource limits to avoid underprovisioning or overprovisioning resources, which can affect application performance and cluster efficiency.

Technologies

Description

This YAML file defines a Kubernetes Deployment for the Litmus Chaos Operator. It creates a single replica of the chaos-operator pod within the litmus namespace. The deployment is labeled for organization and management purposes, specifying details like the version and component. The container runs the litmuschaos/chaos-operator:ci image with a command to enable leader election and sets various environment variables for operation. Additionally, it uses the litmus service account to manage permissions, ensuring the operator runs with the necessary access rights within the Kubernetes cluster.

Caveats and Considerations

1. Namespace Watch: The WATCH_NAMESPACE environment variable is set to an empty string, which means the operator will watch all namespaces. This can have security implications and might require broader permissions. Consider restricting it to specific namespaces if not required. 2. Image Tag: The image is set to litmuschaos/chaos-operator:ci, which uses the latest code from the continuous integration pipeline. This might include unstable or untested features. For production environments, it's recommended to use a stable and tagged version of the image. 3. Leader Election: The -leader-elect=true argument ensures high availability by allowing only one active instance of the operator at a time. Ensure that this behavior aligns with your high-availability requirements. 4. Resource Limits and Requests: There are no resource requests or limits defined for the chaos-operator container. It's good practice to specify these to ensure the container has the necessary resources and to prevent it from consuming excessive resources.

Technologies

Description

This design illustrates a robust and scalable architecture for deploying applications on Amazon Web Services (AWS) with load balancing capabilities. This design leverages AWS Elastic Load Balancers (ELB) to distribute incoming traffic across multiple instances of your application, ensuring high availability and reliability. The architecture typically includes Auto Scaling groups to automatically adjust the number of running instances based on traffic demand, further enhancing the system’s ability to handle varying loads.

Caveats and Considerations

1. AWS services can accumulate costs quickly, especially with high traffic volumes and large-scale deployments. It's essential to monitor and manage usage to avoid unexpected expenses. 2. Network latency can be introduced by load balancers and cross-region data transfers. It's important to design your architecture to minimize latency, particularly for latency-sensitive applications.

Technologies

Description

A Match Labels Relationship in Meshery refers to the configuration where Kubernetes components are linked based on shared labels. This relationship is essential for identifying and managing groups of Pods, Services, or other resources that share common characteristics. By using matching labels, Kubernetes can efficiently route traffic, apply policies, and manage workloads across components that are part of the same logical grouping.

Caveats and Considerations

1. Ensure that labels are consistently applied across related Kubernetes components. Inconsistent or misspelled labels can lead to unexpected behavior and make it difficult to manage or identify related resources. 2. Use clear and descriptive labels that convey the purpose or role of the component. This helps with understanding the architecture and functionality of your application, making it easier to manage and troubleshoot. 3. Consider how label relationships will affect scaling and updating strategies. Changes to labels or the addition of new components may require adjustments to selectors and configurations to maintain proper functionality and resource management across your Kubernetes environment.

Technologies

Description

The cluster-installation service is based on the Mattermost Operator model and operates at version 0.3.3. It is responsible for managing the installation and configuration of the Mattermost operator in default namespace

Caveats and Considerations

Ensure sufficient resources are available in the cluster

Technologies

Description

A self-service engineering platform, Meshery, is the open source, cloud native manager that enables the design and management of all Kubernetes-based infrastructure and applications. Among other features, As an extensible platform, Meshery offers visual and collaborative GitOps, freeing you from the chains of YAML while managing Kubernetes multi-cluster deployments.