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

The AWS RDS DB Instance Metrics is a service that allows you to monitor database instances. It provides key metrics related to connections, which are computed on an hourly basis, helping you understand the usage and performance of your databases. These metrics can be queried using SQL, enabling easy integration with existing monitoring systems or custom analysis.

Table Usage Guide

The aws_rds_db_instance_metric_connections_hourly table in Steampipe provides you with information about the connection metrics for each DB instance in your Amazon RDS environment, aggregated on an hourly basis. This table allows you, as a DevOps engineer, database administrator, or other technical professional, to query connection-specific details, including the number of connections, the time of the connections, and associated metadata. You can utilize this table to gather insights on connection patterns, such as peak connection times, connection durations, and more. The schema outlines the various attributes of the DB instance connection metrics for you, including the timestamp, maximum and minimum number of connections, and the sample count.

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

Examples

Basic info

Explore the performance of your AWS RDS instances by examining the minimum, maximum, and average hourly connection metrics. This allows you to identify patterns and potential issues, ensuring optimal performance and resource allocation.

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

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

Intervals averaging over 100 connections

This example helps you identify instances in your AWS RDS database where the average number of connections exceeds 100 in an hour. It's useful for monitoring heavy traffic periods and potential performance issues in your database.

select
  db_instance_identifier,
  timestamp,
  minimum,
  maximum,
  average,
  sample_count
from
  aws_rds_db_instance_metric_connections_hourly
where
  average > 100
order by
  db_instance_identifier,
  timestamp;

select
  db_instance_identifier,
  timestamp,
  minimum,
  maximum,
  average,
  sample_count
from
  aws_rds_db_instance_metric_connections_hourly
where
  average > 100
order by
  db_instance_identifier,
  timestamp;

Schema for aws_rds_db_instance_metric_connections_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_connections_hourly