Table: gcp_sql_database_instance_metric_cpu_utilization_hourly - Query Google Cloud SQL Database Instances using SQL

Google Cloud SQL is a fully-managed database service that makes it easy to set up, maintain, manage, and administer your relational databases on Google Cloud Platform. The service offers seamless scalability, secure connections, and the flexibility to support various SQL workloads. Google Cloud SQL Database Instances are the building blocks of this service where the databases are hosted.

Table Usage Guide

The gcp_sql_database_instance_metric_cpu_utilization_hourly table provides insights into the CPU utilization of Google Cloud SQL Database Instances on an hourly basis. As a database administrator or DevOps engineer, you can use this table to monitor and analyze the CPU usage of your instances, helping you to identify resource-intensive operations, potential performance issues, and opportunities for optimization. This information is crucial for maintaining the efficiency and reliability of your database operations.

GCP Monitoring metrics provide data about the performance of your systems. The gcp_sql_database_instance_metric_cpu_utilization_hourly table provides metric statistics at 1 hour intervals for the most recent 60 days.

Examples

Basic info

Explore the CPU utilization of your Google Cloud SQL database instances over the past hour. This can help you understand their performance and identify any instances that may be under or over-utilized.

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

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

Intervals averaging over 80%

Identify instances where the CPU utilization of your Google Cloud SQL database averages over 80% to help manage resources and optimize performance.

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_hourly
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_hourly
where
  average > 80
order by
  instance_id;

Intervals averaging < 1%

Analyze the CPU utilization of your Google Cloud SQL database instances to identify those with an average CPU utilization of less than 1%. This can be particularly useful in optimizing resource allocation and reducing costs by pinpointing underutilized instances.

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_hourly
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_hourly
where
  average < 1
order by
  instance_id;

Schema for gcp_sql_database_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.
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_hourly