Table: oci_core_boot_volume_metric_read_ops_daily - Query OCI Core Boot Volume Metrics using SQL
The Oracle Cloud Infrastructure (OCI) Core Boot Volume is a persistent and durable block storage volume in OCI. It provides high performance, reliability, and scalability for your applications. Boot volumes are used to boot up an instance and contain the image of the operating system running on your instance.
Table Usage Guide
The oci_core_boot_volume_metric_read_ops_daily
table provides insights into the daily read operations metrics for OCI Core Boot Volumes. As a Database Administrator or a DevOps engineer, you can use this table to monitor the performance of your boot volumes, which can help in analyzing the workload and making data-driven decisions for optimizing resource allocation. Utilize it to uncover information about the read operations, such as the volume of data read from your boot volumes, and the time taken for these operations, which can be crucial for performance tuning and troubleshooting.
Examples
Basic info
Analyze the daily read operations of boot volumes in Oracle Cloud Infrastructure (OCI) to understand the range, average, total count, and specific instances of these operations. This could help in monitoring the performance and usage patterns of the boot volumes.
select id, timestamp, minimum, maximum, average, sum, sample_countfrom oci_core_boot_volume_metric_read_ops_dailyorder by id, timestamp;
select id, timestamp, minimum, maximum, average, sum, sample_countfrom oci_core_boot_volume_metric_read_ops_dailyorder by id, timestamp;
Intervals where volumes exceed 1000 average read ops
Analyze the intervals where the average read operations on boot volumes exceed a thousand. This analysis can help identify periods of high demand or potential performance issues.
select id, timestamp, minimum, maximum, average, sum, sample_countfrom oci_core_boot_volume_metric_read_ops_dailywhere average > 1000order by id, timestamp;
select id, timestamp, minimum, maximum, average, sum, sample_countfrom oci_core_boot_volume_metric_read_ops_dailywhere average > 1000order by id, timestamp;
Intervals where volumes exceed 8000 max read ops
Determine the instances where the maximum read operations on a boot volume surpass 8000, allowing you to identify potential bottlenecks or high-demand periods in your system. This can be crucial for capacity planning and optimizing system performance.
select id, timestamp, minimum, maximum, average, sum, sample_countfrom oci_core_boot_volume_metric_read_ops_dailywhere maximum > 8000order by id, timestamp;
select id, timestamp, minimum, maximum, average, sum, sample_countfrom oci_core_boot_volume_metric_read_ops_dailywhere maximum > 8000order by id, timestamp;
Read, Write, and Total IOPS
Determine the areas in which the input/output operations per second (IOPS) for a boot volume are at their maximum, minimum, and average. This analysis can help optimize the performance of your system by identifying potential bottlenecks or areas for improvement.
select r.id, r.timestamp, round(r.average) + round(w.average) as iops_avg, round(r.average) as read_ops_avg, round(w.average) as write_ops_avg, round(r.maximum) + round(w.maximum) as iops_max, round(r.maximum) as read_ops_max, round(w.maximum) as write_ops_max, round(r.minimum) + round(w.minimum) as iops_min, round(r.minimum) as read_ops_min, round(w.minimum) as write_ops_minfrom oci_core_boot_volume_metric_read_ops_daily as r, oci_core_boot_volume_metric_write_ops_daily as wwhere r.id = w.id and r.timestamp = w.timestamporder by r.id, r.timestamp;
select r.id, r.timestamp, round(r.average) + round(w.average) as iops_avg, round(r.average) as read_ops_avg, round(w.average) as write_ops_avg, round(r.maximum) + round(w.maximum) as iops_max, round(r.maximum) as read_ops_max, round(w.maximum) as write_ops_max, round(r.minimum) + round(w.minimum) as iops_min, round(r.minimum) as read_ops_min, round(w.minimum) as write_ops_minfrom oci_core_boot_volume_metric_read_ops_daily as r, oci_core_boot_volume_metric_write_ops_daily as wwhere r.id = w.id and r.timestamp = w.timestamporder by r.id, r.timestamp;
Query examples
Schema for oci_core_boot_volume_metric_read_ops_daily
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. | |
compartment_id | text | The ID of the compartment. | |
id | text | The OCID of the boot volume. | |
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. | |
region | text | The OCI 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. | |
tenant_id | text | =, !=, ~~, ~~*, !~~, !~~* | The OCID of the Tenant in which the resource is located. |
tenant_name | text | The name of the Tenant in which the resource is located. | |
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)" -- oci
You can pass the configuration to the command with the --config
argument:
steampipe_export_oci --config '<your_config>' oci_core_boot_volume_metric_read_ops_daily