Table: aws_rds_db_instance_metric_cpu_utilization_hourly - Query AWS RDS DB Instance Metrics using SQL

The AWS RDS DB Instance Metrics is a feature of Amazon Relational Database Service (RDS) that allows you to monitor the performance of your databases. It provides CPU utilization metrics, which indicate the percentage of CPU utilization for an Amazon RDS instance. This can be used to assess the load on your database and optimize performance as necessary.

Table Usage Guide

The aws_rds_db_instance_metric_cpu_utilization_hourly table in Steampipe provides you with information about the CPU utilization metrics of AWS RDS DB instances on an hourly basis. This table enables you, as a DevOps engineer, to query specific details about CPU usage, including maximum, minimum, and average utilization, as well as the sum of all utilization within the specified time frame. You can utilize this table to monitor and analyze the CPU consumption of your RDS DB instances, which can assist you in optimizing resource usage and identifying potential performance issues. The schema outlines the various attributes of the CPU utilization metric for you, including the DB instance identifier, timestamp, and various statistics related to CPU utilization.

The aws_rds_db_instance_metric_cpu_utilization_hourly table provides you with metric statistics at 1 hour intervals for the most recent 60 days.

Examples

Basic info

Analyze the CPU utilization of AWS RDS database instances over time to understand performance trends and identify potential bottlenecks or periods of high demand.

select
  db_instance_identifier,
  timestamp,
  minimum,
  maximum,
  average,
  sample_count
from
  aws_rds_db_instance_metric_cpu_utilization_hourly
order by
  db_instance_identifier,
  timestamp;

select
  db_instance_identifier,
  timestamp,
  minimum,
  maximum,
  average,
  sample_count
from
  aws_rds_db_instance_metric_cpu_utilization_hourly
order by
  db_instance_identifier,
  timestamp;

CPU Over 80% average

Discover the instances where the average CPU utilization of your AWS RDS database instances exceeds 80%. This could be used to identify potential performance issues and manage resources more effectively.

select
  db_instance_identifier,
  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
  aws_rds_db_instance_metric_cpu_utilization_hourly
where
  average > 80
order by
  db_instance_identifier,
  timestamp;

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

CPU hourly average < 2%

Explore which AWS RDS database instances have an average CPU utilization of less than 2% on an hourly basis. This can be useful to identify potentially under-utilized resources and optimize infrastructure costs.

select
  db_instance_identifier,
  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
  aws_rds_db_instance_metric_cpu_utilization_hourly
where
  average < 2
order by
  db_instance_identifier,
  timestamp;

select
  db_instance_identifier,
  timestamp,
  round(minimum, 2) as min_cpu,
  round(maximum, 2) as max_cpu,
  round(average, 2) as avg_cpu,
  sample_count
from
  aws_rds_db_instance_metric_cpu_utilization_hourly
where
  average < 2
order by
  db_instance_identifier,
  timestamp;

Schema for aws_rds_db_instance_metric_cpu_utilization_hourly

Name	Type	Operators	Description
_ctx	jsonb		Steampipe context in JSON form.
account_id	text	=, !=, ~~, ~~, !~~, !~~	The AWS Account ID in which the resource is located.
average	double precision		The average of the metric values that correspond to the data point.
db_instance_identifier	text		The friendly name to identify the DB Instance.
maximum	double precision		The maximum metric value for the data point.
metric_name	text		The name of the metric.
minimum	double precision		The minimum metric value for the data point.
namespace	text		The metric namespace.
partition	text		The AWS partition in which the resource is located (aws, aws-cn, or aws-us-gov).
region	text		The AWS Region in which the resource is located.
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 standard unit for the data point.

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)" -- aws

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

steampipe_export_aws --config '<your_config>' aws_rds_db_instance_metric_cpu_utilization_hourly