Billable usage system table reference

This article provides an overview of the billable usage system table, including the schema and example queries. With system tables, your account’s billable usage data is centralized and routed to all regions, so you can view your account’s global usage from whichever region your workspace is in.

For information on using this table to monitor job costs, see Monitor job costs with system tables.

For strategies on analyzing serverless usage, see Monitor the cost of serverless compute.

Billable usage table schema

The billable usage system table is located at system.billing.usage and uses the following schema:

Analyze usage metadata

The values in usage_metadata tell you about the resources involved in the usage record.

Analyze identity metadata

The identity_metadata column can help you identify who is responsible for a serverless billing record. The column includes a run_as value that attributes the usage to an identity. The identity recorded in identity_metadata.run_as depends on the product associated with the usage.

Reference the following table for the identity_metadata.run_as behavior:

Analyze correction records

The billing.usage table supports corrections. Corrections occur when any field of the usage record is incorrect and must be fixed.

When a correction happens, Azure Databricks adds two new records to the table. A retraction record negates the original incorrect record, then a restatement record includes the corrected information. Correction records are identified using the record_type field:

• RETRACTION: Used to negate the original incorrect usage. All fields are identical to the ORIGINAL record except usage_quantity, which is a negative value that cancels out the original usage quantity. For example, if the original record’s usage quantity was 259.4356, then the retraction record would have a usage quantity of -259.4356.
• RESTATEMENT: The record that includes the correct fields and usage quantity.

For example, the following query returns the correct hourly usage quantity related to a job_id, even if corrections have been made. By aggregating the usage quantity, the retraction record negates the original record and only the restatement’s values are returned.

SELECT
  usage_metadata.job_id, usage_start_time, usage_end_time,
  SUM(usage_quantity) as usage_quantity
FROM system.billing.usage
GROUP BY ALL
HAVING usage_quantity != 0

View information about the product associated with the usage

Some Databricks products are billed under the same shared SKU. To help you differentiate usage, the billing_origin_product and product_features columns provide more insight into the specific product and features associated with the usage.

The billing_origin_product column shows the Databricks product associated with the usage record. The values include:

• JOBS
• DLT
• SQL
• ALL_PURPOSE
• MODEL_SERVING
• INTERACTIVE
• MANAGED_STORAGE
• VECTOR_SEARCH
• LAKEHOUSE_MONITORING
• PREDICTIVE_OPTIMIZATION
• ONLINE_TABLES
• FOUNDATION_MODEL_TRAINING

The product_features column is an object containing information about the specific product features used and includes the following key/value pairs:

• jobs_tier: values include LIGHT, CLASSIC, or null
• sql_tier: values include CLASSIC, PRO, or null
• dlt_tier: values include CORE, PRO, ADVANCED, or null
• is_serverless: values include true or false, or null
• is_photon: values include true or false, or null
• serving_type: values include MODEL, GPU_MODEL, FOUNDATION_MODEL, FEATURE, or null

Sample queries

You can use the following sample queries to answer common questions about billable usage:

• What is the daily trend in DBU consumption?
• How many DBUs of each SKU have been used throughout this month?
• How much of each SKU did a workspace use on June 1?
• Which jobs consumed the most DBUs?
• How much usage can be attributed to resources with a certain tag?
• Show me the SKUs where usage is growing
• What is the usage trend of All Purpose Compute (Photon)?
• What is the DBU consumption of a materialized view or streaming table?
• What is the DBU consumption of a serverless DLT pipeline?

What is the daily trend in DBU consumption?

SELECT
  usage_date as `Date`, sum(usage_quantity) as `DBUs Consumed`
FROM
  system.billing.usage
WHERE
  sku_name = "STANDARD_ALL_PURPOSE_COMPUTE"
GROUP BY
  usage_date
ORDER BY
  usage_date ASC

How many DBUs of each SKU have been used throughout this month?

SELECT
  sku_name, usage_date, sum(usage_quantity) as `DBUs`
FROM
  system.billing.usage
WHERE
  month(usage_date) = month(NOW())
AND
  year(usage_date) = year(NOW())
GROUP BY
  sku_name, usage_date

How much of each SKU did a workspace use on June 1?

Be sure to replace workspace_id with your actual workspace ID.

SELECT
  sku_name, sum(usage_quantity) as `DBUs consumed`
FROM
  system.billing.usage
WHERE
  workspace_id = 1234567890123456
AND
  usage_date = "2023-06-01"
GROUP BY
  sku_name

Which jobs consumed the most DBUs?

SELECT
  usage_metadata.job_id as `Job ID`, sum(usage_quantity) as `DBUs`
FROM
  system.billing.usage
WHERE
  usage_metadata.job_id IS NOT NULL
GROUP BY
  `Job ID`
ORDER BY
  `DBUs` DESC

How much usage can be attributed to resources with a specific tag?

You can break down costs in various ways. This example shows you how to break down costs by a custom tag. Be sure to replace the custom tag’s key and value in the query.

SELECT
  sku_name, usage_unit, SUM(usage_quantity) as `DBUs consumed`
FROM
  system.billing.usage
WHERE
  custom_tags.{{key}} = "{{value}}"
GROUP BY 1, 2

Show me the SKUs where usage is growing

SELECT
  after.sku_name, before_dbus, after_dbus, ((after_dbus - before_dbus)/before_dbus * 100) AS growth_rate
FROM
  (SELECT
     sku_name, sum(usage_quantity) as before_dbus
   FROM
     system.billing.usage
   WHERE
     usage_date BETWEEN "2023-04-01" and "2023-04-30"
   GROUP BY
     sku_name
  ) as before
JOIN
  (SELECT
     sku_name, sum(usage_quantity) as after_dbus
   FROM
     system.billing.usage
   WHERE
     usage_date
   BETWEEN
     "2023-05-01" and "2023-05-30"
   GROUP BY
     sku_name
  ) as after
WHERE
  before.sku_name = after.sku_name
SORT BY
  growth_rate DESC

What is the usage trend of All Purpose Compute (Photon)?

SELECT
  sku_name,
  usage_date,
  sum(usage_quantity) as `DBUs consumed`
FROM
  system.billing.usage
WHERE
  year(usage_date) = year(CURRENT_DATE)
AND
  sku_name = "ENTERPRISE_ALL_PURPOSE_COMPUTE_(PHOTON)"
AND
  usage_date > "2023-04-15"
GROUP BY
  sku_name, usage_date

What is the DBU consumption of a materialized view or streaming table?

To get the DBU usage and SKU for a specific materialized view or streaming table, submit a query to the billable usage system table for records where usage_metadata.dlt_pipeline_id is set to the ID of the pipeline associated with the materialized view or streaming table. You can find the pipeline ID in the Details tab in Catalog Explorer when viewing the materialized view or streaming table. To optionally limit the consumption by date, specify a start date, end date, or a date range. The following query retrieves the DBU usage for the pipeline with ID 00732f83-cd59-4c76-ac0d-57958532ab5b and a usage start date of 2023-05-30:

SELECT
  sku_name,
  usage_date,
  SUM(usage_quantity) AS `DBUs`
FROM
  system.billing.usage
WHERE
  usage_metadata.dlt_pipeline_id = "00732f83-cd59-4c76-ac0d-57958532ab5b"
AND
  usage_start_time > "2023-05-30"
GROUP BY
  ALL

What is the DBU consumption of a serverless DLT pipeline?

To get the DBU usage and SKU for a serverless DLT pipeline, submit a query to the billable usage system table for records where usage_metadata.dlt_pipeline_id is set to the ID of the pipeline. You can find the pipeline ID on the Pipeline Details tab when viewing a pipeline in the Delta Live Tables UI. To optionally limit the consumption by date, specify a start date, end date, or a date range. The following query retrieves the DBU usage from December 2023 for the pipeline with ID 00732f83-cd59-4c76-ac0d-57958532ab5b.

SELECT
  sku_name,
  usage_date,
  SUM(usage_quantity) AS `DBUs`
FROM
  system.billing.usage
WHERE
  usage_metadata.dlt_pipeline_id = "00732f83-cd59-4c76-ac0d-57958532ab5b"
AND
  usage_start_time >= "2023-12-01"
AND
  usage_end_time < "2024-01-01"
GROUP BY
  ALL