Table: gcp_sql_database_instance_metric_cpu_utilization - Query Google Cloud Platform SQL Database Instances using SQL

Google Cloud SQL is a fully-managed relational database service that makes it easy to set up, manage, and administer relational databases on Google Cloud Platform. It provides a way to run standard SQL queries on your data with ease. This service supports MySQL, PostgreSQL, and SQL Server.

Table Usage Guide

The gcp_sql_database_instance_metric_cpu_utilization table provides insights into the CPU utilization of Google Cloud SQL Database Instances. As a database administrator or a cloud engineer, you can use this table to monitor CPU usage patterns and identify potential performance issues. This can be particularly useful in capacity planning and performance optimization tasks.

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

Examples

Basic info

Determine the performance of your Google Cloud SQL database instances by analyzing CPU utilization over time. This query helps in identifying instances with irregular CPU usage patterns, which could indicate potential issues or areas for optimization.

select
  instance_id,
  minimum,
  maximum,
  average,
  sample_count,
  timestamp
from
  gcp_sql_database_instance_metric_cpu_utilization
order by
  instance_id;

select
  instance_id,
  minimum,
  maximum,
  average,
  sample_count,
  timestamp
from
  gcp_sql_database_instance_metric_cpu_utilization
order by
  instance_id;

Intervals averaging over 80%

Explore which instances in your Google Cloud SQL database have an average CPU utilization over 80%. This can help determine areas of potential overload or inefficiency, allowing you to better manage your resources.

select
  instance_id,
  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_sql_database_instance_metric_cpu_utilization
where
  average > 80
order by
  instance_id;

select
  instance_id,
  round(minimum, 2) as min_cpu,
  round(maximum, 2) as max_cpu,
  round(average, 2) as avg_cpu,
  sample_count
from
  gcp_sql_database_instance_metric_cpu_utilization
where
  average > 80
order by
  instance_id;

Intervals averaging < 1%

Explore instances of Google Cloud SQL databases where the average CPU utilization is less than 1%. This can help identify under-utilized resources, potentially leading to cost savings.

select
  instance_id,
  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_sql_database_instance_metric_cpu_utilization
where
  average < 1
order by
  instance_id;

select
  instance_id,
  round(minimum, 2) as min_cpu,
  round(maximum, 2) as max_cpu,
  round(average, 2) as avg_cpu,
  sample_count
from
  gcp_sql_database_instance_metric_cpu_utilization
where
  average < 1
order by
  instance_id;

Query examples

sql_database_instance_cpu_utilization

Schema for gcp_sql_database_instance_metric_cpu_utilization

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.
instance_id	text		The ID of the instance.
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.
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_sql_database_instance_metric_cpu_utilization