Table: gcp_compute_instance_metric_cpu_utilization_hourly - Query GCP Compute Engine Instance Metrics using SQL

The Google Compute Engine is a service within Google Cloud Platform that provides highly customizable virtual machines with best-in-class features. It offers predefined virtual machines with specific amounts of CPU, memory, and storage to accommodate the needs of different workloads. Compute Engine also allows users to create custom machine types optimized for specific needs.

Table Usage Guide

The gcp_compute_instance_metric_cpu_utilization_hourly table provides insights into the CPU utilization of Compute Engine instances on an hourly basis. As a system administrator or DevOps engineer, you can use this table to monitor and analyze the performance of your GCP virtual machines, helping you to optimize resource allocation and troubleshoot performance issues. It offers detailed information about CPU usage, allowing you to identify patterns and potential bottlenecks in your compute resources.

GCP Monitoring Metrics provide data about the performance of your systems. The gcp_compute_instance_metric_cpu_utilization_hourly table provides metric statistics at 60 minute intervals for the most recent 5 days.

Examples

Basic info

Explore the CPU utilization metrics of your Google Cloud Compute instances on an hourly basis. This can help you identify instances where resources might be under or over-utilized, enabling efficient resource allocation and cost optimization.

select
  name,
  timestamp,
  minimum,
  maximum,
  average,
  sample_count
from
  gcp_compute_instance_metric_cpu_utilization_hourly
order by
  name,
  timestamp;

select
  name,
  timestamp,
  minimum,
  maximum,
  average,
  sample_count
from
  gcp_compute_instance_metric_cpu_utilization_hourly
order by
  name,
  timestamp;

CPU Over 80% average

Identify instances where the average CPU utilization exceeds 80% on your Google Cloud Platform compute instances. This can assist in pinpointing potential performance issues or areas where resource allocation may need to be adjusted.

select
  name,
  timestamp,
  round(minimum :: numeric, 2) as min_cpu,
  round(maximum :: numeric, 2) as max_cpu,
  round(average :: numeric, 2) as avg_cpu,
  sample_count
from
  gcp_compute_instance_metric_cpu_utilization_hourly
where
  average > 0.80
order by
  name,
  timestamp;

select
  name,
  timestamp,
  round(minimum, 2) as min_cpu,
  round(maximum, 2) as max_cpu,
  round(average, 2) as avg_cpu,
  sample_count
from
  gcp_compute_instance_metric_cpu_utilization_hourly
where
  average > 0.80
order by
  name,
  timestamp;

Query examples

compute_top10_cpu_past_week

Schema for gcp_compute_instance_metric_cpu_utilization_hourly

Name	Type	Operators	Description
_ctx	jsonb		Steampipe context in JSON form.
average	double precision		The average of the metric values that correspond to the data point.
location	text		The GCP multi-region, region, or zone in which the resource is located.
maximum	double precision		The maximum metric value for the data point.
metadata	jsonb		The associated monitored resource metadata.
metric_kind	text		The metric type.
metric_labels	jsonb		The set of label values that uniquely identify this metric.
metric_type	text		The associated metric. A fully-specified metric used to identify the time series.
minimum	double precision		The minimum metric value for the data point.
name	text	=	The name of the instance.
project	text	=, !=, ~~, ~~, !~~, !~~	The GCP Project in which the resource is located.
resource	jsonb		The associated monitored resource.
sample_count	double precision		The number of metric values that contributed to the aggregate value of this data point.
sp_connection_name	text	=, !=, ~~, ~~, !~~, !~~	Steampipe connection name.
sp_ctx	jsonb		Steampipe context in JSON form.
sum	double precision		The sum of the metric values for the data point.
timestamp	timestamp with time zone		The time stamp used for the data point.
unit	text		The data points of this time series. When listing time series, points are returned in reverse time order.When creating a time series, this field must contain exactly one point and the point's type must be the same as the value type of the associated metric. If the associated metric's descriptor must be auto-created, then the value type of the descriptor is determined by the point's type, which must be BOOL, INT64, DOUBLE, or DISTRIBUTION.

Export

This table is available as a standalone Exporter CLI. Steampipe exporters are stand-alone binaries that allow you to extract data using Steampipe plugins without a database.

You can download the tarball for your platform from the Releases page, but it is simplest to install them with the steampipe_export_installer.sh script:

/bin/sh -c "$(curl -fsSL https://steampipe.io/install/export.sh)" -- gcp

You can pass the configuration to the command with the --config argument:

steampipe_export_gcp --config '<your_config>' gcp_compute_instance_metric_cpu_utilization_hourly