Monitoring Logging for K8

How to setup monitoring and logging in K8 cluster?

Setting up monitoring and logging in a Kubernetes (K8s) cluster is essential for maintaining visibility into the health, performance, and activities of the cluster and its workloads. Here's a general guide on how to set up monitoring and logging in a Kubernetes cluster:

Monitoring:

1. Prometheus and Grafana:

• Prometheus: Deploy Prometheus for metrics collection. You can use Helm charts or manifests to deploy Prometheus components like prometheus-server, alertmanager, and node-exporter.
• Grafana: Deploy Grafana for visualization. Use Helm charts or manifests to deploy Grafana, and configure it to connect to Prometheus as a data source.

bash        

kubectl apply -f https://raw.githubusercontent.com/prometheus-operator/prometheus-operator/release-0.49/bundle.yaml

2. Node-Level Metrics:

• For node-level metrics, deploy node-exporter on each node. Prometheus can scrape these metrics.

bash        

kubectl apply -f https://raw.githubusercontent.com/prometheus/node_exporter/master/deploy/manifests/node-exporter-daemonset.yaml

3. Kube-State-Metrics:

• Deploy kube-state-metrics for Kubernetes-specific metrics.

bash        

kubectl apply -f https://github.com/kubernetes/kube-state-metrics/releases/download/v2.2.0/kube-state-metrics-2.2.0-0-ga47645b-linux-amd64.yml

4. Alerting (Optional):

• Set up alerting rules in Prometheus and configure alertmanager for alert notifications.

Logging:

1. Elasticsearch, Fluentd, and Kibana (EFK Stack):

• Deploy an EFK stack for log management.
• Elasticsearch: Stores logs.
• Fluentd: Collects and forwards logs to Elasticsearch.
• Kibana: Provides a web interface for log search and visualization.

bashCopy code        

kubectl apply -f https://raw.githubusercontent.com/kubernetes/kubernetes/master/cluster/addons/fluentd-elasticsearch/fluentd-es-ds.yaml

2. Prometheus Alertmanager Integration (Optional):

• Integrate Prometheus alertmanager with your logging solution. For example, you can forward Prometheus alerts to Elasticsearch via Fluentd.

3. Centralized Logging (Optional):

• Consider using centralized logging solutions like Loki or Splunk for more advanced features and scalability.

Observability and Tracing (Optional):

1. Jaeger or Zipkin:

• Deploy Jaeger or Zipkin for distributed tracing.

bash        

kubectl apply -f https://raw.githubusercontent.com/jaegertracing/jaeger-kubernetes/master/all-in-one/jaeger-all-in-one-template.yml

2. Instrument Applications:

• Modify your applications to emit traces. Use OpenTelemetry, Jaeger Tracer, or other instrumentation libraries.

Important Considerations:

• Resource Requirements: Ensure that the resources allocated to monitoring and logging components are appropriate for your cluster size.
• Security: Secure your monitoring and logging components. Restrict access to dashboards and APIs to prevent unauthorized access.
• Backups: Regularly back up your monitoring and logging configurations to facilitate recovery in case of failures.
• Documentation: Document your setup, including configuration details and any customizations made to the monitoring and logging components.

Remember, the specific steps may vary based on the tools chosen and the configurations needed for your environment. Always refer to the official documentation of the tools you're using for the most accurate and up-to-date information.

How to setup monitoring and logging in K8 cluster in OpenShift on prem?

Setting up monitoring and logging in an on-premises OpenShift cluster involves deploying specific tools and components to collect and visualize metrics and logs. In OpenShift, the monitoring stack is typically based on the Prometheus and Grafana combination for metrics, and the EFK (Elasticsearch, Fluentd, Kibana) stack for logs. Here's a general guide:

Monitoring:

1. Prometheus and Grafana:

1.1 Deploy Prometheus Operator:

• Use the Prometheus Operator to manage Prometheus and related components.

bash        

oc create -f https://raw.githubusercontent.com/coreos/prometheus-operator/release-0.49/bundle.yaml

1.2 Custom Resource (CR):

• Create a Prometheus custom resource defining your monitoring configuration.

yamlCopy code        

apiVersion: monitoring.coreos.com/v1 kind: Prometheus metadata: name: my-prometheus spec: serviceMonitorSelector: matchLabels: team: frontend resources: requests: memory: 400Mi alerting: alertmanagers: - name: alertmanager-main namespace: openshift-monitoring port: web

1.3 Deploy Grafana:

• Deploy Grafana for visualization.

bash        

oc create -f https://raw.githubusercontent.com/coreos/prometheus-operator/release-0.49/example/prometheus-operator-crd/monitoring.coreos.com_servicemonitors.yaml oc create -f https://raw.githubusercontent.com/coreos/prometheus-operator/release-0.49/example/prometheus-operator-crd/monitoring.coreos.com_podmonitors.yaml oc create -f https://raw.githubusercontent.com/coreos/prometheus-operator/release-0.49/example/prometheus-operator-crd/monitoring.coreos.com_probes.yaml oc create -f https://raw.githubusercontent.com/coreos/prometheus-operator/release-0.49/example/prometheus-operator-crd/monitoring.coreos.com_prometheuses.yaml oc create -f https://raw.githubusercontent.com/coreos/prometheus-operator/release-0.49/example/prometheus-operator-crd/monitoring.coreos.com_alertmanagers.yaml oc create -f https://raw.githubusercontent.com/coreos/prometheus-operator/release-0.49/example/prometheus-operator-crd/monitoring.coreos.com_thanosrulers.yaml

1.4 Access Grafana:

• Access Grafana through its service, and configure it with Prometheus as a data source.

2. Node-Level Metrics:

• Deploy node-exporter to collect node-level metrics.

bash        

oc create -f https://raw.githubusercontent.com/coreos/kube-prometheus/release-0.8.0/manifests/node-exporter/node-exporter-daemonset.yaml

3. Kube-State-Metrics:

• Deploy kube-state-metrics for Kubernetes-specific metrics.

bash        

oc create -f https://raw.githubusercontent.com/coreos/kube-prometheus/release-0.8.0/manifests/kube-state-metrics

4. Alerting (Optional):

• Set up alerting rules in Prometheus and configure alertmanager for alert notifications.

Logging:

1. Elasticsearch, Fluentd, and Kibana (EFK Stack):

• Deploy an EFK stack for log management.

bash        

oc create -f https://raw.githubusercontent.com/openshift/origin-aggregated-logging/4.6/deployer/deployer.yaml

2. Cluster Logging Operator:

• Deploy the Cluster Logging Operator to manage the logging components.

bashCopy code        

oc create -f https://raw.githubusercontent.com/openshift/cluster-logging-operator/4.6/manifests/00-clusterlogging-operator.v0.1.14.clusterserviceversion.yaml

3. Custom Resource (CR):

• Create a custom resource for the Cluster Logging Operator to define your logging configuration.

yaml        

apiVersion: "logging.openshift.io/v1" kind: "ClusterLogging" metadata: name: "instance" spec: managementState: "Managed" logStore: type: "elasticsearch" elasticsearch: nodeCount: 3 storage: storageClassName: "gp2" size: "100G"

4. Access Kibana:

• Access Kibana through its route, and configure it with Elasticsearch as the backend.

Observability and Tracing (Optional):

1. Jaeger or Zipkin:

• Deploy Jaeger or Zipkin for distributed tracing.

bash        

oc create -f https://raw.githubusercontent.com/jaegertracing/jaeger-operator/main/deploy/crds/jaegertracing_v1_jaeger_crd.yaml oc create -f https://raw.githubusercontent.com/jaegertracing/jaeger-operator/main/deploy/service_account.yaml oc create -f https://raw.githubusercontent.com/jaegertracing/jaeger-operator/main/deploy/role.yaml oc create -f https://raw.githubusercontent.com/jaegertracing/jaeger-operator/main/deploy/role_binding.yaml oc create -f https://raw.githubusercontent.com/jaegertracing/jaeger-operator/main/deploy/operator.yaml

2. Instrument Applications:

• Modify your applications to emit traces using OpenTelemetry, Jaeger Tracer, or other instrumentation libraries.

Important Considerations:

• Resource Requirements: Ensure that the resources allocated to monitoring and logging components are appropriate for your cluster size.
• Security: Secure your monitoring and logging components. Restrict access to dashboards and APIs to prevent unauthorized access.
• Backups: Regularly back up your monitoring and logging configurations to facilitate recovery in case of failures.
• Documentation: Document your setup, including configuration details and any customizations made to the monitoring and logging components.

This is a general guide, and specifics might differ based on your OpenShift version and other configurations. Always refer to the official OpenShift and related tool documentation for accurate and up-to-date information.

How to setup monitoring and logging in K8 cluster in AWS EKSor EC2?

Setting up monitoring and logging in a Kubernetes (K8s) cluster on AWS can be done using various tools and services. Here, I'll provide a guide for setting up monitoring and logging in an AWS EKS (Elastic Kubernetes Service) cluster, as well as on a self-managed Kubernetes cluster on EC2 instances:

AWS EKS (Elastic Kubernetes Service):

1. CloudWatch Container Insights for Metrics:

1.1 Enable Container Insights:

• Enable CloudWatch Container Insights for your EKS cluster through the AWS Management Console or CLI.

1.2 Deploy Metrics Server:

• Deploy the Kubernetes Metrics Server to collect resource metrics.

bash        

kubectl apply -f https://github.com/kubernetes-sigs/metrics-server/releases/latest/download/components.yaml

1.3 Configure EKS Cluster:

• When creating your EKS cluster, ensure that it's configured to send metrics to CloudWatch Container Insights.

2. Prometheus and Grafana:

2.1 Deploy Prometheus Operator:

• Use Helm to deploy the Prometheus Operator.

bash        

helm repo add prometheus-community https://prometheus-community.github.io/helm-charts helm repo update helm install prometheus prometheus-community/kube-prometheus-stack

2.2 Access Grafana:

• Access Grafana through the service or set up an Ingress resource. Configure it with Prometheus as a data source.

3. Elasticsearch, Fluentd, and Kibana (EFK Stack):

3.1 Deploy EFK Components:

• Deploy Elasticsearch, Fluentd, and Kibana components.

bash        

kubectl apply -f https://raw.githubusercontent.com/kubernetes/kubernetes/master/cluster/addons/fluentd-elasticsearch/fluentd-es-ds.yaml

3.2 Access Kibana:

• Access Kibana through the service or set up an Ingress resource. Configure it with Elasticsearch as the backend.

Self-Managed Kubernetes on EC2 Instances:

1. CloudWatch Container Insights for Metrics:

1.1 Enable Container Insights:

• Enable CloudWatch Container Insights for your self-managed Kubernetes cluster by configuring your container runtime (e.g., Docker or containerd) to send metrics to CloudWatch.

1.2 Deploy Metrics Server:

• Deploy the Kubernetes Metrics Server to collect resource metrics.

bashCopy code        

kubectl apply -f https://github.com/kubernetes-sigs/metrics-server/releases/latest/download/components.yaml

2. Prometheus and Grafana:

2.1 Deploy Prometheus Operator:

• Use Helm to deploy the Prometheus Operator.

bash        

helm repo add prometheus-community https://prometheus-community.github.io/helm-charts helm repo update helm install prometheus prometheus-community/kube-prometheus-stack

2.2 Access Grafana:

• Access Grafana through the service or set up an Ingress resource. Configure it with Prometheus as a data source.

3. Elasticsearch, Fluentd, and Kibana (EFK Stack):

3.1 Deploy EFK Components:

• Deploy Elasticsearch, Fluentd, and Kibana components.

bash        

kubectl apply -f https://raw.githubusercontent.com/kubernetes/kubernetes/master/cluster/addons/fluentd-elasticsearch/fluentd-es-ds.yaml

3.2 Access Kibana:

• Access Kibana through the service or set up an Ingress resource. Configure it with Elasticsearch as the backend.

Observability and Tracing (Optional):

1. Jaeger or Zipkin:

1.1 Deploy Jaeger or Zipkin:

• Deploy Jaeger or Zipkin for distributed tracing.

bash        

kubectl apply -f https://raw.githubusercontent.com/jaegertracing/jaeger-operator/main/deploy/crds/jaegertracing_v1_jaeger_crd.yaml kubectl apply -f https://raw.githubusercontent.com/jaegertracing/jaeger-operator/main/deploy/service_account.yaml kubectl apply -f https://raw.githubusercontent.com/jaegertracing/jaeger-operator/main/deploy/role.yaml kubectl apply -f https://raw.githubusercontent.com/jaegertracing/jaeger-operator/main/deploy/role_binding.yaml kubectl apply -f https://raw.githubusercontent.com/jaegertracing/jaeger-operator/main/deploy/operator.yaml

2. Instrument Applications:

2.1 Instrument with OpenTelemetry:

• Modify your applications to emit traces using OpenTelemetry, Jaeger Tracer, or other instrumentation libraries.

Important Considerations:

• AWS IAM Roles: Ensure that the nodes in your cluster have the necessary IAM roles to publish metrics and logs to CloudWatch.
• Node IAM Role Permissions: If you're using EKS, ensure that the worker node IAM role has the necessary permissions for EKS and CloudWatch.
• Resource Requirements: Adjust the resources allocated to monitoring and logging components based on your cluster size and workload.
• Security: Secure your monitoring and logging components. Restrict access to dashboards and APIs to prevent unauthorized access.
• Backups: Regularly back up your monitoring and logging configurations to facilitate recovery in case of failures.

Always refer to the official documentation of the tools you're using for accurate and up-to-date information.

How to setup monitoring and logging in K8 cluster in GCP GKE?

Setting up monitoring and logging in a Kubernetes (K8s) cluster in Google Cloud Platform (GCP) using Google Kubernetes Engine (GKE) involves using GCP's native tools for observability. Here's a guide on how to set up monitoring and logging in a GKE cluster:

Monitoring with Stackdriver:

1. Enable Monitoring API:

1.1 Enable the Stackdriver Monitoring API:

• In the GCP Console, navigate to the "APIs & Services" > "Dashboard" > "Enable APIs and Services."
• Search for "Stackdriver Monitoring API" and enable it.

2. Enable Logging:

2.1 Enable the Stackdriver Logging API:

• In the GCP Console, navigate to the "APIs & Services" > "Dashboard" > "Enable APIs and Services."
• Search for "Stackdriver Logging API" and enable it.

3. Create GKE Cluster:

3.1 Create a GKE Cluster with Monitoring and Logging Enabled:

• When creating a GKE cluster, ensure that "Stackdriver Logging" and "Stackdriver Monitoring" are enabled.

bash        

gcloud container clusters create <cluster-name> --enable-stackdriver-kubernetes

4. Access Monitoring Dashboard:

4.1 Access the Stackdriver Monitoring Dashboard:

• In the GCP Console, navigate to "Monitoring" > "Dashboards."
• View pre-built dashboards for Kubernetes, nodes, workloads, and more.

5. Configure Alerting (Optional):

5.1 Set Up Stackdriver Monitoring Alerts:

• Create alerting policies to get notified about issues or anomalies in your GKE cluster.

Logging with Stackdriver:

1. Configure Logging in GKE Cluster:

1.1 Create GKE Cluster with Logging Enabled:

• When creating a GKE cluster, ensure that "Stackdriver Logging" is enabled.

bash        

gcloud container clusters create <cluster-name> --enable-stackdriver-kubernetes

1.2 Configure Logging for Individual Pods (Optional):

• For more granular logging, you can configure individual pods to send logs to Stackdriver Logging.

2. Access Logging Viewer:

2.1 Access the Stackdriver Logging Viewer:

• In the GCP Console, navigate to "Logging" > "Logs Explorer."
• View logs from your GKE cluster.

3. Create Custom Log Metrics (Optional):

3.1 Create Custom Metrics for Logs:

• Define custom log-based metrics to extract specific information from logs.

4. Create Log-Based Metrics Alerts (Optional):

4.1 Set Up Stackdriver Logging Alerts:

• Create alerts based on log entries to receive notifications for specific log events.

Observability and Tracing (Optional):

1. Cloud Trace Integration:

1.1 Enable Cloud Trace API:

• In the GCP Console, navigate to "APIs & Services" > "Dashboard" > "Enable APIs and Services."
• Search for "Cloud Trace API" and enable it.

2. Instrument Applications:

2.1 Instrument Applications with OpenTelemetry:

• Modify your applications to emit traces using OpenTelemetry for distributed tracing.

Important Considerations:

• GKE Cluster Version: Ensure that your GKE cluster is running a version that supports Stackdriver integration.
• IAM Permissions: Ensure that the Google Cloud service account associated with your GKE nodes has the necessary permissions to interact with Stackdriver Monitoring and Logging.
• Resource Requirements: Adjust the resources allocated to monitoring and logging components based on your cluster size and workload.
• Security: Secure your monitoring and logging components. Restrict access to dashboards and APIs to prevent unauthorized access.
• Backups: Regularly back up your monitoring and logging configurations to facilitate recovery in case of failures.

Always refer to the official GCP and GKE documentation for accurate and up-to-date information: Google Cloud Monitoring Documentation and Google Kubernetes Engine Documentation.

How to setup monitoring and logging in K8 cluster in Azure AKS?

Setting up monitoring and logging in a Kubernetes (K8s) cluster in Azure AKS (Azure Kubernetes Service) involves using Azure Monitor for monitoring and Azure Monitor Logs for logging. Here's a guide on how to set up monitoring and logging in an AKS cluster:

Monitoring with Azure Monitor:

1. Enable Monitoring for AKS Cluster:

1.1 Enable Monitoring during AKS Cluster Creation:

• When creating an AKS cluster, enable the "Monitoring" feature to integrate with Azure Monitor.

bash        

az aks create --resource-group <resource-group> --name <aks-cluster-name> --enable-addons monitoring --workspace-resource-id <log-analytics-workspace-id>

1.2 Enable Monitoring for Existing AKS Cluster:

• If your AKS cluster is already created, you can enable monitoring later.

bash        

az aks enable-addons --resource-group <resource-group> --name <aks-cluster-name> --addons monitoring --workspace-resource-id <log-analytics-workspace-id>

2. Access Azure Monitor Metrics:

2.1 Access Azure Monitor Metrics Explorer:

• In the Azure Portal, navigate to your AKS resource.
• Under "Monitoring," access "Metrics Explorer" to view and analyze metrics.

3. Set Up Azure Monitor Alerts (Optional):

3.1 Create Alerts in Azure Monitor:

• Set up alerts based on metric thresholds to receive notifications for specific conditions.

Logging with Azure Monitor Logs (formerly Log Analytics):

1. Enable Azure Monitor Logs Integration:

1.1 Enable Azure Monitor Logs during AKS Cluster Creation:

• When creating an AKS cluster, enable the "Azure Monitor Logs" feature to integrate with Log Analytics.

bash        

az aks create --resource-group <resource-group> --name <aks-cluster-name> --enable-addons monitoring --workspace-resource-id <log-analytics-workspace-id>

1.2 Enable Azure Monitor Logs for Existing AKS Cluster:

• If your AKS cluster is already created, you can enable Azure Monitor Logs later.

bash        

az aks enable-addons --resource-group <resource-group> --name <aks-cluster-name> --addons monitoring --workspace-resource-id <log-analytics-workspace-id>

2. Access Log Analytics Workspace:

2.1 Access Log Analytics Workspace in Azure Portal:

• In the Azure Portal, navigate to your Log Analytics workspace.
• Explore logs using the "Logs" feature.

3. Create Log Queries and Alerts:

3.1 Write Log Queries:

• Write log queries using the Kusto Query Language (KQL) to filter and analyze logs.

3.2 Create Log Alerts:

• Create alerts based on log entries to receive notifications for specific log events.

Observability and Tracing (Optional):

1. Azure Application Insights Integration:

1.1 Enable Application Insights Integration (Optional):

• Integrate Azure Application Insights with your applications for additional monitoring and tracing capabilities.

2. Instrument Applications:

2.1 Instrument Applications with OpenTelemetry (Optional):

• Modify your applications to emit traces using OpenTelemetry for distributed tracing.

Important Considerations:

• Log Analytics Workspace: Ensure you have a Log Analytics workspace in Azure and provide its resource ID during AKS cluster creation or enabling.
• Resource Requirements: Adjust the resources allocated to monitoring and logging components based on your cluster size and workload.
• Security: Secure your monitoring and logging components. Restrict access to dashboards and APIs to prevent unauthorized access.
• Backups: Regularly back up your monitoring and logging configurations to facilitate recovery in case of failures.

Always refer to the official Azure documentation for accurate and up-to-date information: Azure Monitor Documentation and Azure Kubernetes Service Documentation

How to setup monitoring and logging in K8 cluster in OCI OKE?

ChatGP

Setting up monitoring and logging in a Kubernetes (K8s) cluster in Oracle Cloud Infrastructure (OCI) using Oracle Kubernetes Engine (OKE) involves using OCI's native tools for observability. Here's a guide on how to set up monitoring and logging in an OKE cluster:

Monitoring with OCI Monitoring:

1. Enable Monitoring for OKE Cluster:

1.1 Enable Monitoring during OKE Cluster Creation:

• When creating an OKE cluster, enable the "Monitoring" feature to integrate with OCI Monitoring.

bash        

oci ce cluster create --compartment-id <compartment-id> --name <cluster-name> --kubernetes-version <version> --vcn-id <vcn-id> --enable-monitoring

1.2 Enable Monitoring for Existing OKE Cluster:

• If your OKE cluster is already created, you can enable monitoring later.

bash        

oci ce cluster update --cluster-id <cluster-id> --enable-monitoring true

2. Access OCI Monitoring Metrics:

2.1 Access OCI Monitoring Metrics Explorer:

• In the OCI Console, navigate to the "Monitoring" service.
• View and analyze metrics using the "Metrics Explorer."

3. Set Up OCI Monitoring Alarms (Optional):

3.1 Create Alarms in OCI Monitoring:

• Set up alarms based on metric thresholds to receive notifications for specific conditions.

Logging with OCI Logging:

1. Enable OCI Logging Integration:

1.1 Enable OCI Logging during OKE Cluster Creation:

• When creating an OKE cluster, enable the "Logging" feature to integrate with OCI Logging.

bash        

oci ce cluster create --compartment-id <compartment-id> --name <cluster-name> --kubernetes-version <version> --vcn-id <vcn-id> --enable-logging

1.2 Enable OCI Logging for Existing OKE Cluster:

• If your OKE cluster is already created, you can enable logging later.

bash        

oci ce cluster update --cluster-id <cluster-id> --enable-logging true

2. Access OCI Logging Explorer:

2.1 Access OCI Logging Explorer in OCI Console:

• In the OCI Console, navigate to the "Logging" service.
• Explore logs using the "Log Explorer" feature.

3. Create Log Searches and Alerts:

3.1 Write Log Searches:

• Write log searches using the Query Language to filter and analyze logs.

3.2 Create Log Alerts:

• Create alerts based on log entries to receive notifications for specific log events.

Observability and Tracing (Optional):

1. OCI APM Integration:

1.1 Enable APM (Application Performance Monitoring) Integration (Optional):

• Integrate OCI APM with your applications for additional monitoring and tracing capabilities.

2. Instrument Applications:

2.1 Instrument Applications with OpenTelemetry (Optional):

• Modify your applications to emit traces using OpenTelemetry for distributed tracing.

Important Considerations:

• OCI Compartment: Ensure that your OKE cluster is created within the appropriate OCI compartment.
• Resource Requirements: Adjust the resources allocated to monitoring and logging components based on your cluster size and workload.
• Security: Secure your monitoring and logging components. Restrict access to dashboards and APIs to prevent unauthorized access.
• Backups: Regularly back up your monitoring and logging configurations to facilitate recovery in case of failures.

Always refer to the official OCI documentation for accurate and up-to-date information: OCI Monitoring Documentation and OCI Logging Documentation.

How to setup monitoring and logging in K8 cluster in IBM Cloud IKS?

Setting up monitoring and logging in a Kubernetes cluster on IBM Cloud Kubernetes Service (IKS) involves using various tools and services to collect and analyze data from your cluster. Here's a general guide to help you get started:

Monitoring:

1. IBM Cloud Monitoring with Sysdig:

IBM Cloud Kubernetes Service integrates with Sysdig for monitoring. Sysdig provides detailed insights into your containerized applications.

• Enable Monitoring: When creating your Kubernetes cluster, you can enable monitoring by selecting the checkbox for "Monitoring" during the cluster creation process in the IBM Cloud console.

2. Prometheus and Grafana:

If you prefer an open-source solution, you can set up Prometheus and Grafana for monitoring.

• Install Prometheus: You can deploy Prometheus using Helm charts. Helm is a package manager for Kubernetes.bashhelm install prometheus stable/prometheus
• Install Grafana: Similarly, use Helm to deploy Grafana.bashhelm install grafana stable/grafana
• Configure Grafana to Use Prometheus: Once both Prometheus and Grafana are installed, you need to configure Grafana to use Prometheus as a data source.

Logging:

1. IBM Cloud Log Analysis with LogDNA:

IBM Cloud Kubernetes Service integrates with LogDNA for log analysis.

• Enable LogDNA: During the cluster creation process, you can enable LogDNA by selecting the checkbox for "Logging" in the IBM Cloud console.

2. Elasticsearch and Kibana:

Alternatively, you can use the ELK (Elasticsearch, Logstash, and Kibana) stack for logging.

• Install Elasticsearch and Kibana: Use Helm charts to deploy Elasticsearch and Kibana.bashhelm install elastic-search elastic/elasticsearch helm install kibana elastic/kibana
• Configure Log Shipping: Configure your application to ship logs to Elasticsearch. You might use tools like Fluentd or Filebeat for log shipping.

Additional Tips:

• Service Mesh: Consider using a service mesh like Istio, which can provide additional observability features, including tracing and monitoring.
• Custom Metrics: Depending on your application, you may want to set up custom metrics using tools like Prometheus or StatsD.
• Alerting: Configure alerting rules to receive notifications when certain conditions are met. Both Sysdig and Prometheus support alerting.
• Documentation: Always refer to the official documentation for the tools you are using for the most up-to-date and detailed instructions.

Remember to adjust these instructions based on your specific requirements and preferences.

How to setup monitoring and logging in K8 cluster in Alibaba Cloud ACK?

Setting up monitoring and logging in a Kubernetes cluster on Alibaba Cloud Container Service for Kubernetes (ACK) involves using various tools and services. Alibaba Cloud provides built-in solutions for monitoring and logging, making it relatively straightforward. Here's a general guide to help you get started:

Monitoring:

1. Alibaba Cloud Container Service Monitoring (CMS):

Alibaba Cloud provides a built-in monitoring solution called Container Service Monitoring (CMS). It allows you to monitor the performance of your containers, nodes, and clusters.

• Enable CMS: CMS can be enabled during the creation of the ACK cluster or can be added to an existing cluster. When creating a cluster, look for the "Enable Container Monitoring" option in the Alibaba Cloud Container Service console.

2. Prometheus and Grafana:

If you prefer to use open-source solutions like Prometheus and Grafana, you can deploy them on your ACK cluster.

• Install Prometheus and Grafana: Use Helm charts to deploy Prometheus and Grafana on your cluster.bashhelm install prometheus stable/prometheus helm install grafana stable/grafana
• Configure Grafana to Use Prometheus: Once both Prometheus and Grafana are installed, configure Grafana to use Prometheus as a data source.

Logging:

1. Alibaba Cloud Log Service (SLS):

Alibaba Cloud Log Service (SLS) is a fully-managed service for log data. It can be integrated with your Kubernetes cluster to collect and analyze logs.

• Enable SLS: During the creation of the ACK cluster, you can enable Log Service in the Alibaba Cloud Container Service console.

2. Fluentd or Logstash:

To ship logs from your Kubernetes pods to Alibaba Cloud Log Service, you can use log shippers like Fluentd or Logstash.

• Deploy Fluentd: You can deploy Fluentd as a DaemonSet on your Kubernetes cluster. Fluentd can be configured to send logs to Alibaba Cloud Log Service.

Additional Tips:

• Service Mesh: Consider using a service mesh like Istio for additional observability features, including tracing and monitoring.
• Custom Metrics: Depending on your application, you may want to set up custom metrics using tools like Prometheus or custom exporters.
• Alerting: Configure alerting rules to receive notifications when certain conditions are met. Both CMS and Prometheus support alerting.
• Documentation: Always refer to the official Alibaba Cloud documentation for the most up-to-date and detailed instructions on setting up monitoring and logging in ACK.