Table: gcp_compute_disk_metric_write_ops_hourly - Query Google Cloud Compute Engine Disks using SQL

Google Cloud Compute Engine Disks are persistent, high-performance block storage for Google Cloud's Virtual Machines (VMs). They offer a range of options to accommodate varying storage capacity, performance, and cost needs. These disks can be attached to instances within the same region.

Table Usage Guide

The gcp_compute_disk_metric_write_ops_hourly table provides insights into the hourly write operations of Google Cloud Compute Engine Disks. As a system administrator or DevOps engineer, explore disk-specific details through this table, including the number of write operations, associated metadata, and timestamps. Utilize it to monitor disk usage patterns, optimize disk performance, and troubleshoot potential issues.

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

Examples

Basic info

Explore the performance of your Google Cloud Platform (GCP) compute disks by analyzing their hourly write operations. This can help you understand disk usage patterns, identify potential bottlenecks, and plan for capacity accordingly.

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

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

Intervals averaging over 100 write ops

Explore which disk operations have an average of over 10 write operations, allowing you to identify potential high-usage instances and optimize for better performance.

select
  name,
  round(minimum :: numeric, 2) as min_write_ops,
  round(maximum :: numeric, 2) as max_write_ops,
  round(average :: numeric, 2) as avg_write_ops,
  sample_count
from
  gcp_compute_disk_metric_write_ops_hourly
where
  average > 10
order by
  name;

select
  name,
  round(minimum, 2) as min_write_ops,
  round(maximum, 2) as max_write_ops,
  round(average, 2) as avg_write_ops,
  sample_count
from
  gcp_compute_disk_metric_write_ops_hourly
where
  average > 10
order by
  name;

Query examples

compute_disk_top_10_write_ops_avg

Schema for gcp_compute_disk_metric_write_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.
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.
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_compute_disk_metric_write_ops_hourly