Note:

Monitor disk utilization using Oracle Cloud Infrastructure custom metrics

Introduction

Oracle Observability and Management platform services enable customers to monitor, analyze, and manage multicloud applications and infrastructure environments. It uses metrics to monitor resources and alarms to notify you when these metrics meet alarm-specified triggers. Metrics are emitted to the Monitoring service as raw data points, or timestamp-value pairs, along with dimensions and metadata.

Metrics come from a variety of sources:

  1. Resource metrics automatically posted by Oracle Cloud Infrastructure (OCI) resources. For example, CpuUtilization.
  2. Custom metrics published using the Monitoring API.

Objective

Monitor disk utilization using OCI custom metrics.

Prerequisites

Task 1: Create the Python file

  1. Create disk_usage.py in compute instance where disk utilization metric needs to be collected.

    Note: Use your preferred text editor based on the operating system.

  2. Copy the below sample script to disk_usage.py.

    # This is a sample python script to post disk utilization custom metric to oci monitoring.
# Command: python disk_usage.py
   
import oci,psutil,datetime
from pytz import timezone
   
# initialize service client with OCI python SDK
signer = oci.auth.signers.InstancePrincipalsSecurityTokenSigner()
monitoring_client = oci.monitoring.MonitoringClient(config={}, signer=signer, service_endpoint="https://telemetry-ingestion.ap-mumbai-1.oraclecloud.com")
   
# get disk usage with psutil
disk = psutil.disk_usage('/')
disk_usage=disk.percent
print(disk_usage)
   
times_stamp = datetime.datetime.now(timezone('UTC'))
   
# post custom metric to oci monitoring
# replace "compartment_ocid“ with your compartmet ocid and srv01 with your compute instance
post_metric_data_response = monitoring_client.post_metric_data(
   post_metric_data_details=oci.monitoring.models.PostMetricDataDetails(
      metric_data=[
            oci.monitoring.models.MetricDataDetails(
               namespace="custom_metrics",
               compartment_id="your_compartment_ocid",
               name="disk_usage",
               dimensions={'resourceDisplayName': 'srv01'},
               datapoints=[
                  oci.monitoring.models.Datapoint(
                        timestamp=datetime.datetime.strftime(
                           times_stamp,"%Y-%m-%dT%H:%M:%S.%fZ"),
                        value=disk_usage)]
               )]
   )
)
   
# Get the data from response
print(post_metric_data_response.data)

    Note: Refer psutil commands to extract more custom metrics.

  3. Add execute permission to the script using the following command.

    chmod +x disk_usage.py

  4. Update telemetry ingestion endpoint as per your region.

    Note: Endpoints vary by operation. For posting metrics, use the telemetry-ingestion endpoints.

  5. Update the namespace as per your requirement.

    Note: For the metric namespace, don’t use a reserved prefix (oci_ or oracle_).

  6. Update compartment ocid with your compartment ocid and srv01 with your compute instance.

  7. Add more metrics in script, if required to collect for same compute instance. Below is example to collect disk free space in GB.

    # get metric details using psutil
disk_free=round(disk.free/1024/1024/1024,2)
print(disk_free)
   
# Add more metric to post if required
post_metric_data_response = monitoring_client.post_metric_data(
      post_metric_data_details=oci.monitoring.models.PostMetricDataDetails(
         metric_data=[
               oci.monitoring.models.MetricDataDetails(
                  namespace="custom_metrics",
                  compartment_id="your_compartment_ocid",
                  name="disk_free",
                  dimensions={'resourceDisplayName': 'srv01'},
                  datapoints=[
                     oci.monitoring.models.Datapoint(
                           timestamp=datetime.datetime.strftime(
                              times_stamp,"%Y-%m-%dT%H:%M:%S.%fZ"),
                           value=disk_free)]
                  )]
      )
   )

Task 2: Post custom metric data

  1. Execute the script manually from CLI to validate the success.

    python disk_usage.py

    Output:
27.1
{
"failed_metrics": [],
"failed_metrics_count": 0
}

  2. Schedule the script through cron job or scheduling task to post data frequently to OCI monitoring service.

    • Add the script details in the crontab using crontab -e on non Windows compute instance.

      Note: custom metrics can be posted as frequently as every second and minimum aggregation interval is one minute. Best practice is to post custom metric every 1 minute or higher interval.

      # Cron job example with every 1 min execution.  
*/1 * * * * /usr/bin/python3 /home/opc/disk_usage.py

    • Check the output in cron log using sudo cat /var/log/cron | grep disk.

Task 3: View disk utilization metric using OCI metric explorer

  1. Open the navigation menu and click Observability & Management.

  2. Under Monitoring, click Metrics Explorer.

  3. Choose the compartment that contains the custom metric that you want to view, and then click the name of the metric namespace. For example, custom_metrics.

  4. Under Resources, click Metrics. Select metric name, interval and dimension name and dimension value.

    metrics explorer

  5. Click Update Chart to view custom metric in Metrics Explorer.

Acknowledgments

Author - Dipesh Kumar Rathod (Master Principal Cloud Architect, Infrastructure)

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