Table: datadog_logs_metric - Query Datadog Logs Metrics using SQL

Datadog Logs Metrics is a feature within the Datadog Log Management service that allows you to generate metrics from logs and analyze data in the form of counts, distributions, or gauges over a given period. This feature provides a way to measure high volumes of log data, and track application performance, system behavior, and key business metrics. It is a useful tool for monitoring, alerting, and conducting historical analysis.

Table Usage Guide

The datadog_logs_metric table provides insights into log-based metrics within Datadog Log Management. As a DevOps engineer or system administrator, explore detailed information about these metrics, including their types, query definitions, and associated metadata. Utilize it to monitor and analyze system performance, application behavior, and business metrics, and to create alerts based on these metrics.

Examples

Basic info

Explore the configuration of your log-based metrics in Datadog to understand the aggregation types and paths. This can help in identifying any issues or potential improvements in your log management strategy.

select
  id,
  compute_aggregation_type,
  compute_path,
  filter_query,
  jsonb_pretty(group_by) as group_by
from
  datadog_logs_metric;

select
  id,
  compute_aggregation_type,
  compute_path,
  filter_query,
  group_by
from
  datadog_logs_metric;

Get count of metrics by compute_aggregation_type

Explore the distribution of metrics based on their aggregation types to gain insights into the different computation methods used in your Datadog logs.

select
  count(*),
  compute_aggregation_type
from
  datadog_logs_metric
group by
  compute_aggregation_type;

select
  count(*),
  compute_aggregation_type
from
  datadog_logs_metric
group by
  compute_aggregation_type;

Get details of filter_query and group_by clause for a specific log metric

Explore the specific log metrics to understand the grouping and filtering details. This can be beneficial in analyzing how data is categorized and segmented for a particular metric.

select
  filter_query,
  jsonb_pretty(group_by) as group_by
from
  datadog_logs_metric
where
  id = 's3_bucket_by_region';

select
  filter_query,
  group_by
from
  datadog_logs_metric
where
  id = 's3_bucket_by_region';

Schema for datadog_logs_metric

Name	Type	Operators	Description
_ctx	jsonb		Steampipe context in JSON form.
compute_aggregation_type	text		The type of aggregation to used for computing metric. Can be one of "count", "distribution".
compute_path	text		The path to the value the log-based metric will aggregate on (only used if the aggregation type is a "distribution").
filter_query	text		The search query - following the log search syntax to filter logs.
group_by	jsonb		List of rules for the group by.
id	text	=	The name of the log-based metric.
sp_connection_name	text	=, !=, ~~, ~~, !~~, !~~	Steampipe connection name.
sp_ctx	jsonb		Steampipe context in JSON form.

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)" -- datadog

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

steampipe_export_datadog --config '<your_config>' datadog_logs_metric