Table: gcp_sql_database_instance_metric_cpu_utilization_daily - Query GCP SQL Database Instance Metrics using SQL

A Google Cloud SQL Database Instance is a fully-managed relational database service that makes it easy to set up, manage, and administer relational databases on Google Cloud. It provides a cost-effective and scalable way to operate MySQL, PostgreSQL, and SQL Server instances in the cloud. This service is designed to handle the demanding, heavy-duty workloads of high-performance applications.

Table Usage Guide

The gcp_sql_database_instance_metric_cpu_utilization_daily table provides insights into the CPU utilization of SQL Database Instances within Google Cloud Platform (GCP). As a Database Administrator or DevOps Engineer, explore instance-specific details through this table, including daily CPU usage patterns. Utilize it to uncover information about instances, such as those with high resource usage, helping you to optimize performance and manage costs effectively.

GCP Monitoring metrics provide data about the performance of your systems. The gcp_sql_database_instance_metric_cpu_utilization_daily table provides metric statistics at 24 hour intervals for the past year.

Examples

Basic info

Explore the daily CPU utilization metrics of your Google Cloud SQL database instances to understand their performance. This can help you identify any instances that may be under or over-utilized, allowing you to optimize resource allocation and cost.

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

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

Intervals averaging over 100%

Explore which instances have an average connection that exceeds 100%, allowing you to identify potential areas of overutilization for further investigation.

select
  instance_id,
  round(minimum :: numeric, 2) as min_connection,
  round(maximum :: numeric, 2) as max_connection,
  round(average :: numeric, 2) as avg_connection,
  sample_count
from
  gcp_sql_database_instance_metric_cpu_utilization_daily
where
  average > 100
order by
  instance_id;

select
  instance_id,
  round(minimum, 2) as min_connection,
  round(maximum, 2) as max_connection,
  round(average, 2) as avg_connection,
  sample_count
from
  gcp_sql_database_instance_metric_cpu_utilization_daily
where
  average > 100
order by
  instance_id;

Intervals averaging < 1%

Determine the areas in which Google Cloud SQL database instances have an average CPU utilization of less than 1% per day. This can help optimize resource allocation by identifying under-utilized instances.

select
  instance_id,
  round(minimum :: numeric, 2) as min_connection,
  round(maximum :: numeric, 2) as max_connection,
  round(average :: numeric, 2) as avg_connection,
  sample_count
from
  gcp_sql_database_instance_metric_cpu_utilization_daily
where
  average < 1
order by
  instance_id;

select
  instance_id,
  round(minimum, 2) as min_connection,
  round(maximum, 2) as max_connection,
  round(average, 2) as avg_connection,
  sample_count
from
  gcp_sql_database_instance_metric_cpu_utilization_daily
where
  average < 1
order by
  instance_id;

Schema for gcp_sql_database_instance_metric_cpu_utilization_daily

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_daily