Docs » Monitor services and hosts in Splunk Infrastructure Monitoring » Monitor Google Cloud Platform

Monitor Google Cloud Platform 🔗

Note

To start monitoring Google Cloud Platform resources, you must first connect to GCP, and log in with your administrator credentials. See Connect to Google Cloud Platform for details.

Monitor Google Cloud Platform (GCP) service metrics with Splunk Observability Cloud. Observability Cloud provides infrastructure monitoring features using Google Cloud Operations. See the Google Cloud documentation for more information.

You can also export and monitor data from these sources running in your GCP environment, as described in the following table.

Get data in

Monitor

Description

Collect Kubernetes data

Monitor Kubernetes

Collect metrics and logs from Kubernetes clusters running in Google Compute Engine (GCE) or Google Kubernetes Engine (GKE) instances.

Monitor hosts

Collect metrics and logs from Linux and Windows hosts running in GCE instances.

Instrument back-end applications to send spans to Splunk APM

Introduction to Splunk APM

Collect application metrics and spans running in hosts or Kubernetes clusters.

Monitor GCP services and identify problems 🔗

View the health of GCP services at a glance from the Infrastructure page. This page provides a key metric for each service. You can also drill down into specific instances of a GCP service. For example, view key metrics for the GCE service, and filter for a specific ID to analyze a particular GCE instance.

Follow these steps to analyze problems with GCP services from the Infrastructure page:

  1. Select Navigation menu > Infrastructure and view the Google Cloud Platform category.

  2. Select the specific service you want to analyze. For example, select Cloud Storage to view metrics for a specific bucket. If you see “No Data Found,” you need to first configure an integration.

  3. Compare instances of the service along the following metrics with the Color by drop-down list. In the heat map, colors represent the health of instances based on the metrics you select. For example, a heat map that shows green and red, uses green to denote healthy and red to denote unhealthy instances. If your heat map has multiple colors, then the lighter gradient represents less activity, and the darker gradient represents more activity. To apply visually accessible color palettes on custom dashboards and charts and throughout Infrastructure Monitoring, select Account Settings > Color Accessibility.

    You can color by metrics like CPU utilization and filter by dimensions like geographic region.

  4. Group instances based on metadata about each instance with the Group by drop-down list.

    You can group instances according to the region or resource group they are running in or the environment tag. Use this to see correlations between different parts of your infrastructure and its performance.

  5. Find outliers for your metrics with the Find Outliers setting. Specify the Scope and Strategy.

    Set the Scope to analyze outliers from across the entire visible population of instances, or only within groups defined by the dimension or property you grouped instances by.

    You can select one of two Strategies to find outliers, as described in the following table.

    Strategy

    Description

    Deviation from Mean

    Instances appear as red that exceed the mean value of the metric by at least three standard deviations. Use this setting to find the most extreme outliers.

    Deviation from Median

    Instances appear as red that exceed the median absolute deviation value by at least three absolute deviations. This setting does not weigh extreme outliers as heavily as the standard deviation.

  6. Select a specific instance you want to investigate further to view all the metadata and key metrics for the instance. For every instance, Observability Cloud provides a default dashboard.

    Analyze all the available metadata about the cloud service the instance is running in, the instance itself, and any custom tags associated with the instance. The default dashboard provides metric time series (MTS) for key metrics.

Use default dashboards to monitor GCP services 🔗

Splunk Observability Cloud provides default dashboards for supported GCP services. Default dashboards are available in dashboard groups based on the GCP service that a dashboard represents data for.

To find default dashboards for GCP services, select Navigation menu > Dashboards and search for the GCP service you want to view dashboards for.

Explore built-in content 🔗

Observability Cloud collects data from many cloud services. To see all of the navigators provided for data collected in your organization, go to the Infrastructure page. To see all the pre-built dashboards for data collected in your organization, select Dashboards > Built-in.

Note

GCP Compute Engine instances are powered by their respective public cloud service as well as the Splunk Distribution of OpenTelemetry Collector. You need both for all the charts to display data in the built-in dashboards.

  • If you have only the public cloud service and the Smart Agent configured, some charts in the built-in dashboards for GCP Compute Engine instances display no data.

  • If you have only the public cloud service configured, you can see all the cards representing the services where data come from, but some charts in the built-in dashboards for GCP Compute Engine instances display no data.

  • If you have only Smart Agent configured, GCP Compute Engine instance navigator isn’t available.

Uniquely identifying Google Cloud Platform resources 🔗

All of the metrics that the StackDriver integration sends contain a dimension called gcp_id. The value of this dimension starts with the project ID that contains the resource followed by _ (underscore) and then other properties specific to that resource. If you install collectd on a Compute Engine instance using the standard install script this dimension is automatically added.

The simplest way to manually send metrics with this dimension to discover the unique ID value is to find a time series that contains this dimension using the Metadata Catalog. The time series should contain other dimensions that give a more friendly identification to the underlying Google Cloud Platform resource.

Dimensions 🔗

The metric time series associated with Google Cloud Platform metrics have the following generic dimensions that are common to all services.

Dimension name

Description

gcp_id

unique identifier for GCP objects

project_id

project ID of the monitored resource

monitored_resource

name of the monitored resource

service

service to which the metric belongs

Apart from the above dimensions, each service also has a dimension that identifies the resource to which the metric belongs. For example, Compute instances have an instance_id dimension to identify an instance, and Storage buckets have a bucket_name dimension to identify a bucket.

Resource metadata 🔗

The Google Cloud Platform integration also queries the GCP API for metadata about the resources it is monitoring, so you can filter and group metrics by this metadata in charts and in the Infrastructure Navigator.

  • Metadata that are common to all services within a project (project-level metadata) are put on properties of project_id dimension.

  • Metadata that are service-specific (service-level metadata) are put on properties of the gcp_id dimension.

Project-level metadata 🔗

Here is the metadata that is currently synced at a project level:

GCP name

Custom property

Description

creationTimestamp

gcp_project_creation_time

time project was created (e.g. Thu Oct 19 18:16:25 UTC 2017)

Labels *

gcp_project_label_<name-of-label> (if user has labels)

all project-wide labels except for signalfx-id

name

gcp_project_name

human readable project name

project_number

gcp_project_number

project_number given by GCP

status

gcp_project_status

project status (e.g. ACTIVE, DELETE_IN_PROGRESS, DELETE_REQUESTED)

* This property is a list of key value pairs in GCP. For example, if GCP has [key1:label01, key2:label02] as the labels property, we will have two properties: gcp_project_label_key1 and gcp_project_label_key2.)

Service-level metadata 🔗

Here is the metadata that is synced at a service level for the services listed below.

Compute Engine instance 🔗

For Google Cloud Platform Compute Engine instances, Infrastructure Monitoring gets a subset of metadata about the instance, as well as custom metadata specified by the user at an instance level.

Note

The Compute Engine instance metadata table includes two custom properties that are now deprecated, as well as information about which properties replace the deprecated properties.

Compute Engine instance metadata

GCP name

Custom property

Description

scheduling.automaticRestart

gcp_auto_restart

Whether the instance should be automatically restarted if it is terminated by Compute Engine (not terminated by a user)

scheduling.onHostMaintenance

gcp_behavior_on_maintenance

Maintenance behavior for the instance

scheduling.preemptible

gcp_preemptibility

True if the instance is preemptible; otherwise false

cpuPlatform

gcp_cpu_platform

CPU platform used by this instance

CPU

gcp_cpus

Number of virtual CPUs that are available to the instance

creationTimestamp

gcp_creation_time

Time when the instance was created, (e.g. Thu Oct 19 18:16:25 UTC 2017)

description

gcp_description

Description of this instance

disks[].licenses[] *

gcp_image_license

License corresponding to the disks used by the instance

canIpForward

gcp_ip_forward

Whether to allow this instance to send and receive packets with non-matching destination or source IPs

machineType

gcp_machine_type

Type of gcp machine to which this instance corresponds

memory

gcp_memory

Amount of physical memory available to the instance, defined in MB

metadata **

gcp_metadata_<metadata-key>

Custom metadata key for the instance (generated based on includelisted properties specified when completing the integration in Splunk Infrastructure Monitoringx)

status

gcp_status

String containing instance status and status code, for example Code=2, Status=RUNNING. This property is now deprecated, and won’t contain new statuses introduced by GCP such as REPAIRING or SUSPENDING. Use gcp_instance_status instead.

status

gcp_instance_status

Status of the instance, for example RUNNING or STAGING.

self_link

gcp_self_link

Instance self link as reported by GCP

standard_id

gcp_standard_id

Instance ID in a format enforced by Splunk Observability Cloud, for example https://compute.googleapis.com/compute/v1/projects/testProject/zones/us-central1-a/instances/testInstance. This property is now deprecated. Use gcp_self_link instead.

* There is not a one-to-one mapping between the gcp_image_license property to one in GCP because the property value is derived from the licenses of the disks associated with the compute instance.

** This property is a list of key value pairs in GCP. For example, if GCP has [key1:val1, key2:val2] as the metadata property, we will have two properties: gcp_metadata_key1 and gcp_metadata_key2.)

For detailed information on properties, see Google Cloud documentation.

Cloud Spanner instance 🔗

Spanner instances currently sync the following properties:

GCP name

Custom property

Description

state

gcp_state

state of the spanner instance (e.g. CREATING, READY)

Labels *

gcp_label_<name-of-label> (if user has labels)

user‑specified labels

* This property is a list of key value pairs in GCP. For example, if GCP has [key1:label01, key2:label02] as the labels property, we will have two properties: gcp_label_key1 and gcp_label_key2.)

Cloud Storage Bucket 🔗

Storage buckets currently sync the following properties:

GCP name

Custom property

Description

creationTimestamp

gcp_creation_time

time at which the bucket was created, (e.g. Thu Oct 19 18:16:25 UTC 2017)

Labels *

gcp_label_<name-of-label> (if user has labels)

user‑specified labels

Storage class

gcp_storage_class

bucket’s storage class, such as coldline

* This property is a list of key value pairs in GCP. For example, if GCP has [key1:label01, key2:label02] as the labels property, we will have two properties: gcp_label_key1 and gcp_label_key2.)

Supported GCP services 🔗

You can monitor the following GCP services in Observability Cloud.