New Catalog publish request
no-r...@layer5.io
New publish request received from Bhuminjay Soni for catalog content of type design named nginx ingress . 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
- nginx-ingress
Description
Creates a Kubernetes deployment with two replicas running NGINX containers and a service to expose these pods internally within the Kubernetes cluster. The NGINX containers are configured to listen on port 80, and the service routes traffic to these containers.
Caveats and Consideration
ImagePullPolicy: In the Deployment spec, the imagePullPolicy is set to Never. This means that Kubernetes will never attempt to pull the NGINX image from a container registry, assuming it's already present on the node where the pod is scheduled. This can be problematic if the image is not present or if you need to update to a newer version. Consider setting the imagePullPolicy to Always or IfNotPresent depending on your deployment requirements. Resource Allocation: The provided manifest doesn't specify resource requests and limits for the NGINX container. Without resource limits, the container can consume excessive resources, impacting other workloads on the same node. It's recommended to define resource requests and limits based on the expected workload characteristics to ensure stability and resource efficiency.
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no-r...@layer5.io
New publish request received from Bhuminjay Soni for catalog content of type design named postgreSQL cluster. 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
- postgres-operator
Description
This YAML configuration defines a PostgreSQL cluster deployment tailored for Google Kubernetes Engine (GKE) utilizing the Cloud Native PostgreSQL (CNPG) operator. The cluster, named "gke-pg-cluster," is designed to offer a standard PostgreSQL environment, featuring three instances for redundancy and high availability. Each instance is provisioned with 2Gi of premium storage, ensuring robust data persistence. Resource allocations are specified, with each instance requesting 1Gi of memory and 1000m (milliCPU) of CPU, and limits set to the same values. Additionally, the cluster is configured with pod anti-affinity, promoting distribution across nodes for fault tolerance. Host-based authentication is enabled for security, permitting access from IP range 10.48.0.0/20 using the "md5" method. Monitoring capabilities are integrated, facilitated by enabling pod monitoring. The configuration also includes tolerations and additional pod affinity rules, enhancing scheduling flexibility and optimizing resource utilization within the Kubernetes environment. This deployment exemplifies a robust and scalable PostgreSQL infrastructure optimized for cloud-native environments, aligning with best practices for reliability, performance, and security.
Caveats and Consideration
1. Resource Requirements: The specified resource requests and limits (memory and CPU) should be carefully evaluated to ensure they align with the expected workload demands. Adjustments may be necessary based on actual usage patterns and performance requirements. 2. Storage Class: The choice of storage class ("premium-rwo" in this case) should be reviewed to ensure it meets performance, availability, and cost requirements. Depending on the workload characteristics, other storage classes may be more suitable. 3. Networking Configuration: The configured host-based authentication rules may need adjustment based on the network environment and security policies in place. Ensure that only authorized entities have access to the PostgreSQL cluster.
no-r...@layer5.io
New publish request received from Bhuminjay Soni for catalog content of type design named minIO Deployment. 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
- minio-operator
Description
This configuration sets up a single MinIO instance with specific environment variables, health checks, and life cycle actions, utilising a PersistentVolumeClaim for data storage within a Kubernetes cluster. It ensures that MinIO is deployed and managed according to the specified parameters.
Caveats and Consideration
1. Replication and High Availability: The configuration specifies only one replica (replicas: For production environments requiring high availability and fault tolerance, consider increasing the number of replicas and configuring MinIO for distributed mode to ensure data redundancy and availability. 2. Security Considerations: The provided configuration includes hard-coded access and secret keys (MINIO_ACCESS_KEY and MINIO_SECRET_KEY) within the YAML file. It is crucial to follow best practices for secret management in Kubernetes, such as using Kubernetes Secrets or external secret management solutions, to securely manage sensitive information. 3. Resource Requirements: Resource requests and limits for CPU, memory, and storage are not defined in the configuration. Assess and adjust these resource specifications according to the expected workload and performance requirements to ensure optimal resource utilisation and avoid resource contention. 4. Storage Provisioning: The configuration relies on a PersistentVolumeClaim (PVC) named minio to provide storage for MinIO. Ensure that the underlying storage provisioner and PersistentVolume (PV) configuration meet the performance, capacity, and durability requirements of the MinIO workload.
parti...@meshery.dev
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no-r...@layer5.io
New publish request received from Bhuminjay Soni for catalog content of type design named fluentd deployment. Head over to Meshery Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
observability
Technology
- fluentd-operator
Description
This configuration sets up Fluentd-ES to collect and forward logs from Kubernetes pods to Elasticsearch for storage and analysis. Ensure that Elasticsearch is properly configured and accessible by Fluentd-ES for successful log aggregation and visualization. Additionally, adjust resource requests and limits according to your cluster's capacity and requirements.
Caveats and Consideration
1. Resource Utilisation: Fluentd can consume significant CPU and memory resources, especially in environments with high log volumes. Monitor resource usage closely and adjust resource requests and limits according to your cluster's capacity and workload requirements. 2. Configuration Complexity: Fluentd's configuration can be complex, particularly when configuring input, filtering, and output plugins. Thoroughly test and validate the Fluentd configuration to ensure it meets your logging requirements and effectively captures relevant log data. 3. Security Considerations: Secure the Fluentd deployment by following best practices for managing secrets and access control. Ensure that sensitive information, such as credentials and configuration details, are properly encrypted and protected.
no-r...@layer5.io
New publish request received from Bhuminjay Soni for catalog content of type design named gitlab runner deployment. 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
- gitlab-runner-operator
Description
This configuration ensures that a single instance of the GitLab Runner is deployed within the gitlab-runner namespace. The GitLab Runner is configured with a specific ServiceAccount, CPU resource requests and limits, and is provided with a ConfigMap containing the configuration file config.toml. The deployment is designed to continuously restart the pod (restartPolicy: Always) to ensure the GitLab Runner remains available for executing jobs.
Caveats and Consideration
1. Resource Allocation: Ensure that the CPU resource requests and limits specified in the configuration are appropriate for the workload of the GitLab Runner. Monitor resource usage and adjust these values as necessary to prevent resource contention and ensure optimal performance. 2. Image Pull Policy: The configuration specifies imagePullPolicy: Always, which causes Kubernetes to pull the Docker image (gitlab/gitlab-runner:latest) every time the pod is started. While this ensures that the latest image is always used, it may increase deployment time and consume additional network bandwidth. Consider whether this policy aligns with your deployment requirements and constraints. 3. Security: Review the permissions granted to the gitlab-admin ServiceAccount to ensure that it has appropriate access rights within the Kubernetes cluster. Limit the permissions to the minimum required for the GitLab Runner to perform its tasks to reduce the risk of unauthorized access or privilege escalation. 4. ConfigMap Management: Ensure that the gitlab-runner-config ConfigMap referenced in the configuration contains the correct configuration settings for the GitLab Runner. Monitor and manage changes to the ConfigMap to ensure that the GitLab Runner's configuration remains up-to-date and consistent across deployments.
no-r...@layer5.io
New publish request received from Bhuminjay Soni for catalog content of type design named grafana deployment. 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
- grafana-ui-server
Description
The provided YAML configuration defines a Kubernetes Deployment named "grafana" within the "monitoring" namespace. This Deployment ensures the availability of one instance of Grafana, a monitoring and visualization tool. It specifies resource requirements, including memory and CPU limits, and mounts volumes for persistent storage and configuration. The container runs the latest version of the Grafana image, exposing port 3000 for access. The configuration also includes a Pod template with labels for Pod identification and a selector to match labels for managing Pods.
Caveats and Consideration
1. Container Image Version: While the configuration uses grafana/grafana:latest for the container image, it's important to note that relying on the latest tag can introduce instability if Grafana publishes a new version that includes breaking changes or bugs. Consider specifying a specific version tag for more predictable behavior. 2. Resource Limits: Resource limits (memory and cpu) are specified for the container. Ensure that these limits are appropriate for your deployment environment and the expected workload of Grafana. Adjust these limits based on performance testing and monitoring. 3. Storage: The configuration uses an emptyDir volume for Grafana's storage. This volume is ephemeral and will be deleted if the Pod restarts or is rescheduled to a different node. Consider using a persistent volume (e.g., PersistentVolumeClaim) for storing Grafana data to ensure data persistence across Pod restarts. 4. Configurations: Configuration for Grafana's data sources is mounted using a ConfigMap. Ensure that the ConfigMap (grafana-datasources) is properly configured with the required data source configurations. Verify that changes to the ConfigMap are propagated to the Grafana Pod without downtime.
no-r...@layer5.io
New publish request received from Sangram Rath for catalog content of type design named Exploring Kubernetes Pods With Meshery. 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
- kubernetes
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 Consideration
Service type is NodePort.
no-r...@layer5.io
New publish request received from Deepak Reddy for catalog content of type design named Consul on kubernetes. Head over to Meshery Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
workloads
Technology
- kubernetes
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 Consideration
customize the design according to your requirements and the image is pulled from docker hub
no-r...@layer5.io
New publish request received from Deepak Reddy for catalog content of type design named Datadog agent on k8's. Head over to Meshery Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
observability
Technology
- kubernetes
Description
The Datadog Agent is a lightweight software component deployed within Kubernetes clusters to collect metrics, traces, and logs. It automatically monitors Kubernetes resources, including pods and nodes, providing visibility into system performance and application behavior. With features like autodiscovery, tracing, log collection, and extensive integrations, the Datadog Agent helps teams efficiently monitor, troubleshoot, and optimize their Kubernetes-based applications and infrastructure.
Caveats and Consideration
This is an basic example to deploy datadog agent on kubernetes for more please refer offical docs https://docs.datadoghq.com/containers/kubernetes/installation/?tab=operator
Lee Calcote
On Apr 10, 2024, at 11:10 AM, 'no-reply via Meshery Maintainers' via employees <empl...@layer5.io> wrote:
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Visit and engage with the Meshery community in the forum at http://discuss.meshery.io or in Slack at https://slack.meshery.io.
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no-r...@layer5.io
New publish request received from Deepak Reddy for catalog content of type design named Apache Airflow. Head over to Meshery Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
workloads
Technology
- kubernetes
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 Consideration
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 .
no-r...@layer5.io
New publish request received from Deepak Reddy for catalog content of type design named Autoscaling based on Metrics in GKE. Head over to Meshery Cloud to approve or deny user's request. User will be notified if their request is approved or denied.
Type
scaling
Technology
- gcp
- kubernetes
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 Consideration
Add your own custom prometheus to GKE for better scaling of workloads
no-r...@layer5.io
New publish request received from Bhuminjay Soni for catalog content of type design named Jenkins 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
- jenkins-operator
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 Consideration
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.
no-r...@layer5.io
New publish request received from Bhuminjay Soni for catalog content of type design named Gerrit 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
- gerrit-operator
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 Consideration
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.
no-r...@layer5.io
New publish request received from Bhuminjay Soni for catalog content of type design named Key cloak 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
- keycloak-operator
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 Consideration
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.
no-r...@layer5.io
New publish request received from Sangram Rath for catalog content of type design named [TUTORIAL] Simple MySQL Pod. 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
- kubernetes
Description
This design is used as a starting point for the 'Kubernetes ConfigMaps and Secrets with Meshery' tutorial.
Caveats and Consideration
This is a simple pod that is not managed through a deployment. It does not use persistent storage, service or any other production properties. This should be used for tutorial, demonstration or experimental purposes only.
Lee Calcote
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Visit and engage with the Meshery community in the forum at http://discuss.meshery.io or in Slack at https://slack.meshery.io.
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no-r...@layer5.io
New publish request received from Sangram Rath for catalog content of type design named [Tutorial] Simple MySQL Pod. 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
- kubernetes
Description
This design is used as a starting point for the 'Kubernetes ConfigMaps and Secrets with Meshery' tutorial.
Caveats and Consideration
This is a simple pod that is not managed through a deployment. It does not use persistent storage, service or any other production properties. This should be used for tutorial, demonstration or experimental purposes only.
Sangram Rath
Sangram K Rath
no-r...@layer5.io
New publish request received from Priyank Upadhyay for catalog content of type design named Simple MySQL Pod. 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
Testing patterns
Caveats and Consideration
None