Table: gcp_compute_disk_metric_read_ops - Query Google Cloud Compute Engine Disk Read Operations using SQL

Google Cloud's Compute Engine is an Infrastructure as a Service that allows you to run your large-scale computing workloads on virtual machines hosted on Google's infrastructure. Disk Read Operations represents the count of read operations completed by the Compute Engine. This count can be used to analyze disk usage and performance.

Table Usage Guide

The gcp_compute_disk_metric_read_ops table provides insights into Disk Read Operations within Google Cloud's Compute Engine. As a System Administrator or a DevOps engineer, explore disk-specific details through this table, including the number of read operations. Utilize it to uncover information about disk usage, such as frequent read operations, which can help in identifying potential performance issues and optimizing disk usage.

GCP Monitoring metrics provide data about the performance of your systems. The gcp_compute_disk_metric_read_ops table provides metric statistics at 5 minute intervals for the most recent 5 days.

Examples

Basic info

Explore which Google Cloud Platform Compute Disk has the highest and lowest read operations by assessing the minimum, maximum, and average values. This can help optimize disk usage and improve system performance.

select
  name,
  minimum,
  maximum,
  average,
  sample_count
from
  gcp_compute_disk_metric_read_ops
order by
  name;

select
  name,
  minimum,
  maximum,
  average,
  sample_count
from
  gcp_compute_disk_metric_read_ops
order by
  name;

Intervals averaging over 100 read ops

Explore which disk operations have an average read operation count over 10. This can help in identifying potential areas of high disk usage and performance bottlenecks.

select
  name,
  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
  gcp_compute_disk_metric_read_ops
where
  average > 10
order by
  name;

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

Intervals averaging fewer than 10 read ops

Analyze disk performance by identifying those with an average of less than 10 read operations. This can assist in pinpointing underutilized resources and optimizing system performance.

select
  name,
  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
  gcp_compute_disk_metric_read_ops
where
  average < 10
order by
  name;

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

Schema for gcp_compute_disk_metric_read_ops

Name	Type	Operators	Description
_ctx	jsonb		Steampipe context in JSON form, e.g. connection_name.
average	double precision		The average of the metric values that correspond to the data point.
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.
name	text	=	The name of the disk.
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.
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_compute_disk_metric_read_ops