Table: azure_compute_disk_metric_read_ops_hourly - Query Azure Compute Disk Metrics using SQL

Azure Compute Disks are data storage units available in Microsoft Azure. They are used to store data for Azure Virtual Machines and other services. Azure Compute Disks provide high-performance, durable storage for I/O-intensive workloads.

Table Usage Guide

The azure_compute_disk_metric_read_ops_hourly table provides insights into read operations of Azure Compute Disks on an hourly basis. As a system administrator or a DevOps engineer, explore disk-specific details through this table, including the number of read operations, the time of operations, and associated metadata. Utilize it to monitor disk performance, identify usage patterns, and detect potential performance issues.

Examples

Basic info

Assess the elements within the Azure compute disk's read operations on an hourly basis. This can help in identifying patterns, understanding usage trends, and planning for capacity or performance optimization.

select
  name,
  timestamp,
  minimum,
  maximum,
  average,
  sample_count
from
  azure_compute_disk_metric_read_ops_hourly
order by
  name,
  timestamp;

select
  name,
  timestamp,
  minimum,
  maximum,
  average,
  sample_count
from
  azure_compute_disk_metric_read_ops_hourly
order by
  name,
  timestamp;

Operations Over 10 Bytes average

This query is used to monitor disk read operations on Azure, specifically focusing on instances where the average read operation exceeds 10 bytes. This is useful for identifying potential performance issues or bottlenecks in the system, allowing for proactive management and optimization of resources.

select
  name,
  timestamp,
  round(minimum :: numeric, 2) as min_read_ops,
  round(maximum :: numeric, 2) as max_read_ops,
  round(average :: numeric, 2) as avg_read_ops,
  sample_count
from
  azure_compute_disk_metric_read_ops_hourly
where
  average > 10
order by
  name,
  timestamp;

select
  name,
  timestamp,
  round(minimum, 2) as min_read_ops,
  round(maximum, 2) as max_read_ops,
  round(average, 2) as avg_read_ops,
  sample_count
from
  azure_compute_disk_metric_read_ops_hourly
where
  average > 10
order by
  name,
  timestamp;

Schema for azure_compute_disk_metric_read_ops_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.
cloud_environment	text		The Azure Cloud Environment.
maximum	double precision		The maximum metric value for the data point.
minimum	double precision		The minimum metric value for the data point.
name	text		The name of the disk.
resource_group	text		The resource group which holds this 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.
subscription_id	text	=, !=, ~~, ~~, !~~, !~~	The Azure Subscription ID in which the resource is located.
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 units in which the metric value is reported.

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)" -- azure

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

steampipe_export_azure --config '<your_config>' azure_compute_disk_metric_read_ops_hourly