New Catalog publish request
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New publish request received from Bhuminjay Soni for catalog content of type design named Jaeger operator. Head over to Meshery Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- jaeger-operator
Description
The provided 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 likely 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 Consideration
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.
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Copyright 2023, Layer5, Inc. All right reserved.
no-r...@layer5.io
New publish request received from Bhuminjay Soni for catalog content of type design named Thanos Operator. Head over to Meshery Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- thanos-operator
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 Consideration
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.
no-r...@layer5.io
New publish request received from Bhuminjay Soni for catalog content of type design named Jaeger operator. Head over to Meshery Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- jaeger-operator
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 Consideration
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.
no-r...@layer5.io
New publish request received from Bhuminjay Soni for catalog content of type design named Litmus Chaos Operator. Head over to Meshery Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- litmus-core
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 Consideration
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.
no-r...@layer5.io
New publish request received from Bhuminjay Soni for catalog content of type design named AWS cloudfront controller. Head over to Meshery Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- aws-cloudfront-controller
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 Consideration
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.
no-r...@layer5.io
New publish request received from Bhuminjay Soni for catalog content of type design named AWS rds controller. Head over to Meshery Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- aws-rds-controller
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 Consideration
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.
no-r...@layer5.io
New publish request received from Bhuminjay Soni for catalog content of type design named ArgoCD application controller. Head over to Meshery Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- argo-cd
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 Consideration
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.
no-r...@layer5.io
New publish request received from Bhuminjay Soni for catalog content of type design named Istio Operator. Head over to Meshery Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- istio-operator
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 Consideration
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.
no-r...@layer5.io
New publish request received from Bhuminjay Soni for catalog content of type design named Prometheus adapter. Head over to Meshery Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- kube-prometheus-stack
Description
This YAML configuration defines a Kubernetes Deployment for the prometheus-adapter, a component of the kube-prometheus stack within the monitoring namespace. The deployment manages two replicas of the prometheus-adapter pod to ensure high availability. Each pod runs a container using the prometheus-adapter image from the Kubernetes registry, configured with various command-line arguments to specify settings like the configuration file path, metrics re-list interval, and Prometheus URL.
Caveats and Consideration
1. Namespace: Ensure that the monitoring namespace exists before deploying this configuration. 2. ConfigMap: Verify that the adapter-config ConfigMap is created and contains the correct configuration data required by the prometheus-adapter. 3. TLS Configuration: The deployment includes TLS settings with specific cipher suites; ensure these align with your security policies and requirements. 4. Resource Allocation: The specified CPU and memory limits and requests should be reviewed to match the expected load and cluster capacity. 5. Service Account: Ensure that the prometheus-adapter service account has the necessary permissions to operate correctly within the cluster
no-r...@layer5.io
New publish request received from Bhuminjay Soni for catalog content of type design named Vault operator. Head over to Meshery Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- vault-operator
Description
This YAML configuration defines a Kubernetes Deployment for the vault-operator using the apps/v1 API version. It specifies that a single replica of the vault-operator pod should be maintained by Kubernetes. The deployment's metadata sets the name of the deployment to vault-operator. The pod template within the deployment includes metadata labels that tag the pod with name: vault-operator, which helps in identifying and managing the pod. The pod specification details a single container named vault-operator that uses the image quay.io/coreos/vault-operator:latest. This container is configured with two environment variables: MY_POD_NAMESPACE and MY_POD_NAME, which derive their values from the pod's namespace and name respectively using the Kubernetes downward API. This setup ensures that the vault-operator container is aware of its deployment context within the Kubernetes cluster.
Caveats and Consideration
1. Single Replica: The deployment is configured with a single replica. This might be a single point of failure. Consider increasing the number of replicas for high availability and fault tolerance. 2. Image Tagging: The container image is specified as latest, which can lead to unpredictable deployments because latest may change over time. It's recommended to use a specific version tag to ensure consistency and repeatability in deployments. 3. Environment Variables: The deployment uses environment variables (MY_POD_NAMESPACE and MY_POD_NAME) obtained from the downward API. Ensure these variables are correctly referenced and required by your application. 4. Resource Requests and Limits: The deployment does not specify resource requests and limits for CPU and memory. This could lead to resource contention or overcommitment issues. It’s good practice to define these to ensure predictable performance and resource usage.
no-r...@layer5.io
New publish request received from Bhuminjay Soni for catalog content of type design named mysql operator. Head over to Meshery Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- mysql-operator
Description
This YAML file defines a Kubernetes Deployment for the mysql-operator in the mysql-operator namespace. The deployment specifies a single replica of the operator to manage MySQL instances within the cluster. The operator container uses the image container-registry.oracle.com/mysql/community-operator:8.4.0-2.1.3 and runs the mysqlsh command with specific arguments for the MySQL operator.
Caveats and Consideration
1. Single Replica: Running a single replica of the operator can be a single point of failure. Consider increasing the number of replicas for high availability if supported. 2. Image Version: The image version 8.4.0-2.1.3 is specified, ensuring consistent deployments. Be mindful of updating this version in accordance with operator updates and testing compatibility. 3. Security Context: The security context is configured to run as a non-root user (runAsUser: 2), with no privilege escalation (allowPrivilegeEscalation: false), and a read-only root filesystem (readOnlyRootFilesystem: true). This enhances the security posture of the deployment. 4. Environment Variables: Sensitive information should be handled securely. Environment variables such as credentials should be managed using Kubernetes Secrets if necessary. 5. Readiness Probe: The readiness probe uses a file-based check, which is simple but ensure that the mechanism creating the /tmp/mysql-operator-ready file is reliable.
no-r...@layer5.io
New publish request received from Bhuminjay Soni for catalog content of type design named mattermost operator. Head over to Meshery Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- mattermost-operator
Description
This YAML file defines a Kubernetes Deployment for the mattermost-operator in the mattermost-operator namespace. The deployment is configured to run a single replica of the Mattermost operator, which manages Mattermost instances within the Kubernetes cluster. The pod template specifies the container details for the operator. The container, named mattermost-operator, uses the image mattermost/mattermost-operator:latest and is set to pull the image if it is not already present (IfNotPresent). The container runs the /mattermost-operator command with arguments to enable leader election and set the metrics address to 0.0.0.0:8383. Several environment variables are defined to configure the operator's behaviour, such as MAX_RECONCILING_INSTALLATIONS (set to 20), REQUEUE_ON_LIMIT_DELAY (set to 20 seconds), and MAX_RECONCILE_CONCURRENCY (set to 10). These settings control how the operator handles the reconciliation process for Mattermost installations. The container also exposes a port (8383) for metrics, allowing monitoring and observation of the operator's performance. The deployment specifies that the pods should use the mattermost-operator service account, ensuring they have the appropriate permissions to interact with the Kubernetes API and manage Mattermost resources.
Caveats and Consideration
1. Resource Allocation: The deployment specifies no resource limits or requests for the mattermost-operator container. It is crucial to define these to ensure the operator has sufficient CPU and memory to function correctly without affecting other workloads in the cluster. 2. Image Tag: The latest tag is used for the Mattermost operator image. This practice can lead to unpredictability in deployments, as the latest tag may change and introduce unexpected changes or issues. It is recommended to use a specific version tag to ensure consistency. 3. Security Context: The deployment does not specify a detailed security context for the container. Adding constraints such as runAsNonRoot, readOnlyRootFilesystem, and dropCapabilities can enhance security by limiting the container’s privileges. 4. Environment Variables: The environment variables like MAX_RECONCILING_INSTALLATIONS, REQUEUE_ON_LIMIT_DELAY, and MAX_RECONCILE_CONCURRENCY are set directly in the deployment. If these values need to be adjusted frequently, consider using a ConfigMap to manage them externally. 5. Metrics and Monitoring: The metrics address is exposed on port 8383. Ensure that appropriate monitoring tools are in place to capture and analyse these metrics for performance tuning and troubleshooting.
no-r...@layer5.io
New publish request received from Shail Pujan for catalog content of type design named knative-service. Head over to Meshery Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- knative-serving
Description
This YAML configuration defines a Kubernetes Deployment for a Knative service. This Deployment, named "knative-service," specifies that a container will be created using a specified container image, which should be replaced with the actual image name. The container is configured to listen on port 8080. The Deployment ensures that a single replica of the container is maintained within the "knative-serving" namespace. The Deployment uses labels to identify the pods it manages. Additionally, a Kubernetes Service is defined to expose the Deployment. This Service, named "knative-service," is also created within the "knative-serving" namespace. It uses a selector to match the pods labeled with "app: knative-service" and maps the Service port 80 to the container port 8080, facilitating external access to the deployed application. Furthermore, a Knative Service resource is configured to manage the Knative service. This Knative Service, also named "knative-service" and located in the "knative-serving" namespace, is configured with the same container image and port settings. The Knative Service template includes metadata labels and container specifications, ensuring consistent deployment and management within the Knative environment. This setup allows the Knative service to handle HTTP requests efficiently and leverage Knative's autoscaling capabilities.
Caveats and Consideration
Image Pull Policy:Ensure the image pull policy is appropriately set, especially if using a custom or private container image. You may need to configure Kubernetes to access private image repositories by setting up image pull secrets. Resource Requests and Limits: Define resource requests and limits for CPU and memory to ensure that the Knative service runs efficiently without exhausting cluster resources. This helps in resource allocation and autoscaling. Namespace Management: Deploying to the knative-serving namespace is typical for Knative components, but for user applications, consider using a separate namespace for better organization and access control. Autoscaling Configuration: Knative supports autoscaling based on metrics like concurrency or CPU usage. Configure autoscaling settings to match your application's load characteristics. Networking and Ingress: Ensure your Knative service is properly exposed via an ingress or gateway if external access is required. Configure DNS settings and TLS for secure access. Monitoring and Logging: Implement monitoring and logging to track the performance and health of your Knative service. Use tools like Prometheus, Grafana, and Elasticsearch for this purpose.
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Copyright 2024, Layer5, Inc. All right reserved.
no-r...@layer5.io
New publish request received from Deepak Reddy for catalog content of type design named prometheus-postgres-exporter. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
observability
Technology
- pg-db
- postgres-with-operator
Description
A PostgreSQL metric exporter for Prometheus This exporter supports the multi-target pattern. This allows running a single instance of this exporter for multiple postgres targets. Using the multi-target functionality of this exporter is optional and meant for cases where it is impossible to install the exporter as a sidecar, for example SaaS-managed services. To use the multi-target functionality, send an http request to the endpoint /probe?target=foo:5432 where target is set to the DSN of the postgres instance to scrape metrics from. To avoid putting sensitive information like username and password in the URL, preconfigured auth modules are supported via the auth_modules section of the config file. auth_modules for DSNs can be used with the /probe endpoint by specifying the ?auth_module=foo http parameter. for more infomation checkout this repo https://github.com/prometheus-community/postgres_exporter
Caveats and Consideration
make sure u have your own postgress database running and fill in the confiiguration details according to it .
no-r...@layer5.io
New publish request received from Deepak Reddy for catalog content of type design named gcp-quota-exporter. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
observability
Technology
- k8s-config-connector
Description
Google Cloud Platform Quota Exporter - Obtains the resource quotas from a GCP project and allows to export them to Prometheus As we want to get the resource quotas of our GCP projects and check when we are exhausting the limits we need to get the data from GCP and export them somewhere, in this case this exports the data to Prometheus.
Caveats and Consideration
Make sure to use prometheus to export the data and for more detailed information checkout this repo https://github.com/softonic/gcp-quota-exporter-helm-chart
no-r...@layer5.io
New publish request received from Deepak Reddy for catalog content of type design named External-Dns for Kubernetes. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
traffic-management
Technology
- kubernetes
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 Consideration
For more information and considerations checkout this repo https://github.com/kubernetes-sigs/external-dns/?tab=readme-ov-file
no-r...@layer5.io
New publish request received from Deepak Reddy for catalog content of type design named node-feature-discovery. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
workloads
Technology
- kubernetes
Description
Node Feature Discovery (NFD) is a Kubernetes add-on for detecting hardware features and system configuration. Detected features are advertised as node labels. NFD provides flexible configuration and extension points for a wide range of vendor and application specific node labeling needs.
Caveats and Consideration
Checkout this docs for Caveats And Considerations https://kubernetes-sigs.github.io/node-feature-discovery/v0.16/get-started/introduction.html
no-r...@layer5.io
New publish request received from Deepak Reddy for catalog content of type design named rabbitmq-cluster-operator. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
resiliency
Technology
- rabbitmq-cluster-operator
- rabbitmq-operator
Description
The RabbitMQ Cluster Kubernetes Operator automates provisioning, management, and operations of RabbitMQ clusters running on Kubernetes. Kubernetes operator to deploy and manage RabbitMQ clusters. This repository contains a custom controller and custom resource definition (CRD) designed for the lifecycle (creation, upgrade, graceful shutdown) of a RabbitMQ cluster.
Caveats and Consideration
For Caveats And Considerations for this design checkout this repo https://github.com/rabbitmq/cluster-operator?tab=readme-ov-file and docs of https://www.rabbitmq.com/kubernetes/operator/quickstart-operator
no-r...@layer5.io
New publish request received from Deepak Reddy for catalog content of type design named HAProxy_Ingress_Controller. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
traffic-management
Technology
- kubernetes-ingress
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 Consideration
Make sure that paths in ingress are configured correctly and for more Caveats And Considerations checkout this docs https://haproxy-ingress.github.io/docs/
no-r...@layer5.io
New publish request received from Deepak Reddy for catalog content of type design named Azure-monitor-containers. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
observability
Technology
- aks-appgw-fe
- kubernetes
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 Consideration
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
no-r...@layer5.io
New publish request received from Deepak Reddy for catalog content of type design named aws-otel-collector. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
observability
Technology
- aws-ecs-controller
- aws-eks-controller
- opentelemetry-operator
Description
AWS Distro for OpenTelemetry Collector (ADOT Collector) is an AWS supported version of the upstream OpenTelemetry Collector and is distributed by Amazon. It supports the selected components from the OpenTelemetry community. It is fully compatible with AWS computing platforms including EC2, ECS, and EKS. It enables users to send telemetry data to AWS CloudWatch Metrics, Traces, and Logs backends as well as the other supported backends. See the AWS Distro for OpenTelemetry documentation for more information. Additionally, the ADOT Collector is now generally available for metrics.
Caveats and Consideration
To build the ADOT Collector locally, you will need to have Golang installed. You can download and install Golang . ADOT Collector Configuration- The ADOT Collector is built with a default configuration. The ADOT Collector configuration uses the same configuration syntax/design from OpenTelemetry Collector. For more information regarding OpenTelemetry Collector configuration please refer to the upstream documentation. so you can customize or port your OpenTelemetry Collector configuration files when running ADOT Collector. Please refer to the Try out the ADOT Collector section on configuring ADOT Collector. For more information about otel collector checkout this repo https://github.com/aws-observability/aws-otel-collector
no-r...@layer5.io
New publish request received from Deepak Reddy for catalog content of type design named marblerun. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- kubernetes
Description
MarbleRun -The control plane for confidential computing. MarbleRun is a framework for deploying distributed confidential computing applications. MarbleRun acts as a confidential operator for your deployment. Think of a trusted party in the control plane. Build your confidential microservices with EGo, Gramine, or similar runtimes, orchestrate them with Kubernetes on an SGX-enabled cluster, and let MarbleRun take care of the rest. Deploy end-to-end secure and verifiable AI pipelines or crunch on sensitive big data in the cloud. Confidential computing at scale has never been easier. MarbleRun simplifies the process by handling much of the groundwork. It ensures that your app's topology adheres to your specified manifest. It verifies the identity and integrity of all your services, bootstraps them, and establishes secure, encrypted communication channels. As your app needs to scale, MarbleRun manages the addition of new instances, ensuring their secure verification.
Caveats and Consideration
A working SGX DCAP environment is required for MarbleRun. For ease of exploring and testing, we provide a simulation mode with --simulation that runs without SGX hardware. Depending on your setup, you may follow the quickstart for SGX-enabled clusters. Alternatively, if your setup doesn't support SGX, you can follow the quickstart in simulation mode by selecting the respective tabs. For getting more context on consideration and caveats ,get into this docs of https://docs.edgeless.systems/marblerun/getting-started/quickstart
no-r...@layer5.io
New publish request received from Deepak Reddy for catalog content of type design named prometheus-opencost-exporter. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
observability
Technology
- kubernetes
Description
Prometheus exporter for OpenCost Kubernetes cost monitoring data. This design bootstraps a Prometheus OpenCost Exporter deployment on a Kuberentes cluster using the meshery playground . OpenCost is a vendor-neutral open source project for measuring and allocating cloud infrastructure and container costs in real time. Built by Kubernetes experts and supported by Kubernetes practitioners, OpenCost shines a light into the black box of Kubernetes spend.
Caveats and Consideration
Set the PROMETHEUS_SERVER_ENDPOINT environment variable to the address of your Prometheus server. Add the scrapeConfig to it, using the preferred means for your Prometheus install (ie. -f https://raw.githubusercontent.com/opencost/opencost/develop/kubernetes/prometheus/extraScrapeConfigs.yaml). Consider using the OpenCost Helm Chart for additional Prometheus configuration options. for more information refer this docs https://www.opencost.io/docs/installation/prometheus
no-r...@layer5.io
New publish request received from Deepak Reddy for catalog content of type design named fluentd-kubernetes-aws. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
observability
Technology
- fluentd-operator
- kubernetes
- elasticsearch-operator
Description
Collect Kubernetes logs with Fluentd and forward to AWS-hosted Elasticsearch. meshery design to run fluentd on kubernetes and connect to an AWS Elasticsearch domain protected by IAM. This specialized chart covers the case where: your Kubernetes cluster has RBAC enabled you are using kiam to assign IAM roles to pods you have an AWS Elastic search . you have created an IAM role that has access to Elasticsearch you want Fluentd to collect logs from your Kubernetes cluster and forward them to Elasticsearch.
Caveats and Consideration
This specialized design covers the case where: your Kubernetes cluster has RBAC enabled you are using kiam to assign IAM roles to pods you have an AWS Elastic search . you have created an IAM role that has access to Elasticsearch you want Fluentd to collect logs from your Kubernetes cluster and forward them to Elasticsearch. This meshery design is based on https://github.com/fluent/fluentd-kubernetes-daemonset/tree/8c76f51696bdeea7643ec0e0696afebf336c45a9 check this for more information .
no-r...@layer5.io
New publish request received from Rudraksh Tyagi for catalog content of type design named Autogenerated. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- amd-gpu
Description
hello
Caveats and Consideration
Just a test. Boo!
no-r...@layer5.io
New publish request received from Deepak Reddy for catalog content of type design named bug_test. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- aws-apigatewayv2-controller
Description
bug testing ..........
Caveats and Consideration
cievets and consideration for bug,ov,ov
no-r...@layer5.io
New publish request received from Deepak Reddy for catalog content of type design named Untitled Design. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- amd-gpu
Description
gmkgmm,k vv
Caveats and Consideration
g m bkggb vbb
no-r...@layer5.io
New publish request received from Deepak Reddy for catalog content of type design named aws-iam-authenticator. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
security
Technology
- aws-iam-controller
- aws-kms-controller
- aws
Description
Runs the AWS Iam Authenticator as a daemonset on master nodes in order to authenticate your users with AWS IAM. This requires additional setup on your cluster to work. f you are an administrator running a Kubernetes cluster on AWS, you already need to manage AWS IAM credentials to provision and update the cluster. By using AWS IAM Authenticator for Kubernetes, you avoid having to manage a separate credential for Kubernetes access. AWS IAM also provides a number of nice properties such as an out of band audit trail (via CloudTrail) and 2FA/MFA enforcement. If you are building a Kubernetes installer on AWS, AWS IAM Authenticator for Kubernetes can simplify your bootstrap process. You won't need to somehow smuggle your initial admin credential securely out of your newly installed cluster. Instead, you can create a dedicated KubernetesAdmin role at cluster provisioning time and set up Authenticator to allow cluster administrator logins.
Caveats and Consideration
Assuming you have a cluster running in AWS and you want to add AWS IAM Authenticator for Kubernetes support, you need to Create an IAM role you'll use to identify users. Run the Authenticator server as a DaemonSet. Configure your API server to talk to Authenticator. Set up kubectl to use Authenticator tokens. you can consider to refer this github repo for more information https://github.com/kubernetes-sigs/aws-iam-authenticator
no-r...@layer5.io
New publish request received from Deepak Reddy for catalog content of type design named aws-autoscaling-exporter. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
observability
Technology
- aws-applicationautoscaling-controller
- aws-prometheusservice-controller
- aws-secretsmanager-controller
Description
This design is a Prometheus exporter for AWS auto scaling groups, part of the Hollowtrees project. Provides auto scaling group level metrics similar to CloudWatch metrics and instance level metrics for spot instances in the auto scaling group. For group level metrics the exporter is polling the AWS APIs for auto scaling groups. For instance level metrics it queries the Banzai Cloud spot instance recommender API to report cost and stability related metrics for spot instances.
Caveats and Consideration
make to fill in aws access id's in secret ,so that metrics are smoothly exported . For instance level metrics it queries the Banzai Cloud spot instance [recommender API](https://github.com/banzaicloud/telescopes) to report cost and stability related metrics for spot instances.
no-r...@layer5.io
New publish request received from Deepak Reddy for catalog content of type design named aws-secrets-synchronizer. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
security
Technology
- aws-secretsmanager-controller
- kubernetes-secret-generator
Description
Kubernetes operator that synchronizes secrets from AWS Secrets Manager with kube secrets The Kubernetes Operator for AWS Secrets Manager is a specialized tool that automates the synchronization of secrets between AWS Secrets Manager and Kubernetes secrets. It continuously monitors and updates Kubernetes secrets with any changes from AWS Secrets Manager, ensuring consistency and security. The operator supports custom configurations, secure access controls, and real-time updates through AWS event integration. It is designed for easy deployment and management, making it ideal for maintaining secure and consistent secret management across multiple environments.
Caveats and Consideration
Requirements Minimal IAM rights for service account used by operator. make sure to add your own custom secret manager access id's .
no-r...@layer5.io
New publish request received from Deepak Reddy for catalog content of type design named crossplane. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
workloads
Technology
- crossplane-provider-openstack
Description
Crossplane connects your Kubernetes cluster to external, non-Kubernetes resources, and allows platform teams to build custom Kubernetes APIs to consume those resources. Crossplane creates Kubernetes Custom Resource Definitions (CRDs) to represent the external resources as native Kubernetes objects. As native Kubernetes objects, you can use standard commands like kubectl create and kubectl describe. The full Kubernetes API is available for every Crossplane resource. Crossplane also acts as a Kubernetes Controller to watch the state of the external resources and provide state enforcement. If something modifies or deletes a resource outside of Kubernetes, Crossplane reverses the change or recreates the deleted resource. With Crossplane installed in a Kubernetes cluster, users only communicate with Kubernetes. Crossplane manages the communication to external resources like AWS, Azure or Google Cloud. Crossplane also allows the creation of custom Kubernetes APIs. Platform teams can combine external resources and simplify or customize the APIs presented to the platform consumers.
Caveats and Consideration
Pre-requisites , Kuberentes Cluster minimum version v1.16.0+ , Helm minimum version v3.0.0+. for more information u can checkout this docs https://artifacthub.io/packages/helm/crossplane/crossplane
no-r...@layer5.io
New publish request received from Deepak Reddy for catalog content of type design named Dapr-commercetools-graphql-sample. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
workloads
Technology
- dapr
Description
In this quickstart design sample, you'll create a microservice with an output binding. You'll bind to commercetools, but note that there are a myriad of components that Dapr can bind to (see Dapr components). This quickstart includes one microservice: Python microservice that utilizes an output binding The binding connects to commercetools, allowing you to query or manipulate a commercetools projects using a provided GraphlQL query without having to know where the instance is hosted. Instead, connect through the sidecar using the Dapr API.
Caveats and Consideration
for Caveats And Considerations consider looking into this repo https://github.com/dapr/samples/tree/master/commercetools-graphql-sample
no-r...@layer5.io
New publish request received from Deepak Reddy for catalog content of type design named instana-agent-for-Kubernetes. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
observability
Technology
- kubernetes
Description
instana agent is built for microservices that enables IT Ops to build applications faster and deliver higher quality services by automating monitoring, tracing and root cause analysis. It provides automated observability with AI and the ability to democratize observability, making it accessible to anyone across DevOps, SRE, platform engineering, ITOps and development. Instana gives you 1-second granularity, which helps you quickly detect problems or transactions Additionally, you get 100% traces that allow you to fix issues easily Instana contextualizes data from all sources, including OpenTelemetry, to provide the insights needed to keep up with the pace of change
Caveats and Consideration
for Caveats And Considerations consider checking this docs https://www.ibm.com/products/instana
no-r...@layer5.io
New publish request received from Deepak Reddy for catalog content of type design named mlflow. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- kubernetes
Description
MLflow tracking, a tool to track Machine Learning experiments. MLflow Tracking is one of the primary service components of MLflow. In these guides, you will gain an understanding of what MLflow Tracking can do to enhance your MLOps related activities while building ML models.
Caveats and Consideration
By default, MLflow deployment uses Flask, a widely used WSGI web application framework for Python, to serve the inference endpoint. However, Flask is mainly designed for a lightweight application and might not be suitable for production use cases at scale. To address this gap, MLflow integrates with MLServer as an alternative deployment option, which is used as a core Python inference server in Kubernetes-native frameworks like Seldon Core and KServe (formerly known as KFServing). Using MLServer, you can take advantage of the scalability and reliability of Kubernetes to serve your model at scale. See Serving Framework for the detailed comparison between Flask and MLServer, and why MLServer is a better choice for ML production use cases. for more information refer this docs https://www.mlflow.org/docs/latest/deployment/deploy-model-to-kubernetes/index.html
no-r...@layer5.io
New publish request received from Aviral Asthana for catalog content of type design named Autogenerated. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- appmesh-controller
Description
New Design
Caveats and Consideration
Anything
no-r...@layer5.io
New publish request received from Aviral Asthana for catalog content of type design named Autogenerated (Copy). Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- appmesh-controller
Description
asdawdaqwdaqwdqwdascac
Caveats and Consideration
adqwdqwcaxcqwfcvbswdv
no-r...@layer5.io
New publish request received from Uzair Shaikh for catalog content of type design named Catalog. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- apisix-ingress-controller
Description
Tetew
Caveats and Consideration
rqwerewrewr
no-r...@layer5.io
New publish request received from Uzair Shaikh for catalog content of type design named Catalog. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- apisix-ingress-controller
no-r...@layer5.io
New publish request received from we qw for catalog content of type design named test design. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- aws-api-gateway-operator
Description
My test design
Caveats and Consideration
NA
no-r...@layer5.io
New publish request received from Deepak Reddy for catalog content of type design named Hello Kubernetes Tutorial. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- dapr
- kubernetes
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 Consideration
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
no-r...@layer5.io
New publish request received from Shabana Shaikh for catalog content of type design named Kinesis. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- aws-kinesis-controller
Description
NA
Caveats and Consideration
NA
no-r...@layer5.io
New publish request received from Akshansh Modi for catalog content of type design named Autogenerated (Copy). Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
troubleshooting
Technology
- appmesh-controller
Description
just trying out meshery for doing my open source contributions
Caveats and Consideration
just trying out meshery for doing my open source contributions
no-r...@layer5.io
New publish request received from meshmap for catalog content of type design named nginx. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- amd-gpu
Description
something
Caveats and Consideration
something
no-r...@layer5.io
New publish request received from meshmap for catalog content of type design named nginx-service.yml. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- aws-apigatewayv2-controller
Description
Something
Caveats and Consideration
something
no-r...@layer5.io
New publish request received from meshmap for catalog content of type design named test-abhi-meshery-helm. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- amd-gpu
- apisix-ingress-controller
Description
Something
Caveats and Consideration
Something
no-r...@layer5.io
New publish request received from meshmap for catalog content of type design named robot-shop-1.1.0. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- appmesh-controller
- aws-applicationautoscaling-controller
Description
Something
Caveats and Consideration
Something
no-r...@layer5.io
New publish request received from meshmap for catalog content of type design named meshery-v0.6.72. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- aws-apigatewayv2-controller
- appmesh-controller
Description
Something
Caveats and Consideration
Something
no-r...@layer5.io
New publish request received from meshmap for catalog content of type design named nginx-service.yml. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- amd-gpu
Description
Something
Caveats and Consideration
Something
no-r...@layer5.io
New publish request received from meshmap for catalog content of type design named meshery-v0.6.72. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- amd-gpu
- apisix-ingress-controller
Description
Something
Caveats and Consideration
Something
no-r...@layer5.io
New publish request received from meshmap for catalog content of type design named Test Namespace. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- amd-gpu
- aws-apigatewayv2-controller
Description
Something
Caveats and Consideration
Something
no-r...@layer5.io
New publish request received from meshmap for catalog content of type design named robot-shop-1.1.0. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- appmesh-controller
- aws-applicationautoscaling-controller
Description
Something
Caveats and Consideration
this
no-r...@layer5.io
New publish request received from meshmap for catalog content of type design named nginx. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- gcp
- grafana-agent
Description
soine
Caveats and Consideration
Asda
no-r...@layer5.io
New publish request received from meshmap for catalog content of type design named Test. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- aws-apigatewayv2-controller
Description
add
Caveats and Consideration
add
no-r...@layer5.io
New publish request received from meshmap for catalog content of type design named Test. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- aws-api-gateway-operator
- aws-apigatewayv2-controller
Description
ada
Caveats and Consideration
adad
no-r...@layer5.io
New publish request received from meshmap for catalog content of type design named nginx. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- apisix-ingress-controller
Description
wd
Caveats and Consideration
adad
no-r...@layer5.io
New publish request received from meshmap for catalog content of type design named nginx-service.yml. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- apisix-ingress-controller
Description
adad
Caveats and Consideration
adad
no-r...@layer5.io
New publish request received from meshmap for catalog content of type design named meshery-v0.6.72. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- aws-cloudfront-controller
- aws-cloudtrail-controller
Description
addd
Caveats and Consideration
adadad
no-r...@layer5.io
New publish request received from meshmap for catalog content of type design named test-abhi-meshery-helm. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- aws-api-gateway-operator
Description
adad
Caveats and Consideration
adad
no-r...@layer5.io
New publish request received from meshmap for catalog content of type design named Test Namespace. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- appmesh-controller
- aws-cloudfront-controller
Description
ada
Caveats and Consideration
add
no-r...@layer5.io
New publish request received from meshmap for catalog content of type design named robot-shop-1.1.0. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- apisix-ingress-controller
- aws-api-gateway-operator
Description
dad
Caveats and Consideration
added
no-r...@layer5.io
New publish request received from Deepak Reddy for catalog content of type design named Cloud native pizza store. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
deployment
Technology
- dapr
- pg-db
- postgres-with-operator
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 Consideration
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
no-r...@layer5.io
New publish request received from meshmap for catalog content of type design named . Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
observability
Technology
- amd-gpu
- apisix-ingress-controller
- aws-api-gateway-operator
- aws-apigatewayv2-controller
Description
Na
Caveats and Consideration
Na
no-r...@layer5.io
New publish request received from Deepak Reddy for catalog content of type design named Keycloak. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
security
Technology
- keycloak-operator
Description
Add authentication to applications and secure services with minimum effort. No need to deal with storing users or authenticating users. Keycloak provides user federation, strong authentication, user management, fine-grained authorization, and more.
Caveats and Consideration
for Caveats And Considerations refer this docs https://www.keycloak.org/documentation
no-r...@layer5.io
New publish request received from Deepak Reddy for catalog content of type design named mongoDB-Sample-app. Head over to Layer5 Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
workloads
Technology
- kubernetes
Description
This design contains a very simple application that you can use to test your MongoDB Deployment. This application requires a MongoDB resource deployed with one of the MongoDB Operators.
Caveats and Consideration
make sure to use mongodb operator deployed with this sample app and make sure to use own custom secrets to connect mongodb