Billable usage log schema (legacy)

Important

This documentation has been retired and might not be updated. The products, services, or technologies mentioned in this content are no longer supported. To view current admin documentation, see Manage your Databricks account.

Note

This article includes details about the legacy usage logs, which do not record usage for all products. Databricks recommends using the billable usage system table to access and query complete usage data.

This article explains how to read and analyze the usage log data downloaded from the account console.

You can view and download billable usage directly in the account console, or by using the Account API.

CSV file schema

Column

Type

Description

Example

workspaceId

string

ID of the workspace.

1234567890123456

timestamp

datetime

End of the hour for the provided usage.

2019-02-22T09:59:59.999Z

clusterId

string

ID of the cluster (for a cluster) or of the warehouse (for a SQL warehouse)

Cluster example: 0406-020048-brawl507

SQL warehouse example: 8e00f0c8b392983e

clusterName

string

User-provided name for the cluster/warehouse.

Shared Autoscaling

clusterNodeType

string

Instance type of the cluster/warehouse.

Cluster example: m4.16xlarge

SQL warehouse example: db.xlarge

clusterOwnerUserId

string

ID of the user who created the cluster/warehouse.

12345678901234

clusterCustomTags

string (“-escaped json)

Custom tags associated with the cluster/warehouse during this hour.

"{""dept"":""mktg"",""op_phase"":""dev""}"

sku

string

Billing SKU. See the Billing SKUs table for a list of values.

STANDARD_ALL_PURPOSE_COMPUTE

dbus

double

Number of DBUs used by the user during this hour.

1.2345

machineHours

double

Total number of machine hours used by all containers in the cluster/warehouse.

12.345

clusterOwnerUserName

string

Username (email) of the user who created the cluster/warehouse.

user@yourcompany.com

tags

string (“-escaped json)

Default and custom cluster/warehouse tags, and default and custom instance pool tags (if applicable) associated with the cluster during this hour. See Cluster tags, Warehouse tags, and Pool tags. This is a superset of the clusterCustomTags column.

"{""dept"":""mktg"",""op_phase"":""dev"", ""Vendor"":""Databricks"", ""ClusterId"":""0405-020048-brawl507"", ""Creator"":""user@yourcompany.com""}"

Billing SKUs

  • AWS_ENHANCED_SECURITY_AND_COMPLIANCE

  • ENTERPRISE_ALL_PURPOSE_COMPUTE

  • ENTERPRISE_ALL_PURPOSE_COMPUTE_(PHOTON)

  • ENTERPRISE_DLT_CORE_COMPUTE

  • ENTERPRISE_DLT_CORE_COMPUTE_(PHOTON)

  • ENTERPRISE_DLT_PRO_COMPUTE

  • ENTERPRISE_DLT_PRO_COMPUTE_(PHOTON)

  • ENTERPRISE_DLT_ADVANCED_COMPUTE

  • ENTERPRISE_DLT_ADVANCED_COMPUTE_(PHOTON)

  • ENTERPRISE_JOBS_COMPUTE

  • ENTERPRISE_JOBS_COMPUTE_(PHOTON)

  • ENTERPRISE_JOBS_LIGHT_COMPUTE

  • ENTERPRISE_SERVERLESS_REAL_TIME_INFERENCE_US_EAST_N_VIRGINIA

  • ENTERPRISE_SERVERLESS_REAL_TIME_INFERENCE_US_EAST_OHIO

  • ENTERPRISE_SERVERLESS_REAL_TIME_INFERENCE_US_WEST_OREGON

  • ENTERPRISE_SERVERLESS_REAL_TIME_INFERENCE_CANADA

  • ENTERPRISE_SERVERLESS_REAL_TIME_INFERENCE_EUROPE_IRELAND

  • ENTERPRISE_SERVERLESS_REAL_TIME_INFERENCE_EUROPE_FRANKFURT

  • ENTERPRISE_SERVERLESS_REAL_TIME_INFERENCE_AP_SINGAPORE

  • ENTERPRISE_SERVERLESS_REAL_TIME_INFERENCE_AP_SYDNEY

  • ENTERPRISE_SERVERLESS_REAL_TIME_INFERENCE_LAUNCH_US_EAST_N_VIRGINIA

  • ENTERPRISE_SERVERLESS_REAL_TIME_INFERENCE_LAUNCH_US_EAST_OHIO

  • ENTERPRISE_SERVERLESS_REAL_TIME_INFERENCE_LAUNCH_US_WEST_OREGON

  • ENTERPRISE_SERVERLESS_REAL_TIME_INFERENCE_LAUNCH_CANADA

  • ENTERPRISE_SERVERLESS_REAL_TIME_INFERENCE_LAUNCH_EUROPE_IRELAND

  • ENTERPRISE_SERVERLESS_REAL_TIME_INFERENCE_LAUNCH_EUROPE_FRANKFURT

  • ENTERPRISE_SERVERLESS_REAL_TIME_INFERENCE_LAUNCH_AP_SINGAPORE

  • ENTERPRISE_SERVERLESS_REAL_TIME_INFERENCE_LAUNCH_AP_SYDNEY

  • ENTERPRISE_SERVERLESS_SQL_COMPUTE_US_EAST_N_VIRGINIA

  • ENTERPRISE_SERVERLESS_SQL_COMPUTE_US_WEST_OREGON

  • ENTERPRISE_SERVERLESS_SQL_COMPUTE_EUROPE_IRELAND

  • ENTERPRISE_SERVERLESS_SQL_COMPUTE_AP_SYDNEY

  • ENTERPRISE_SQL_COMPUTE

  • ENTERPRISE_SQL_PRO_COMPUTE_US_EAST_N_VIRGINIA

  • ENTERPRISE_SQL_PRO_COMPUTE_US_EAST_OHIO

  • ENTERPRISE_SQL_PRO_COMPUTE_US_WEST_OREGON

  • ENTERPRISE_SQL_PRO_COMPUTE_US_WEST_CALIFORNIA

  • ENTERPRISE_SQL_PRO_COMPUTE_CANADA

  • ENTERPRISE_SQL_PRO_COMPUTE_SA_BRAZIL

  • ENTERPRISE_SQL_PRO_COMPUTE_EUROPE_IRELAND

  • ENTERPRISE_SQL_PRO_COMPUTE_EUROPE_FRANKFURT

  • ENTERPRISE_SQL_PRO_COMPUTE_EUROPE_LONDON

  • ENTERPRISE_SQL_PRO_COMPUTE_EUROPE_FRANCE

  • ENTERPRISE_SQL_PRO_COMPUTE_AP_SYDNEY

  • ENTERPRISE_SQL_PRO_COMPUTE_AP_MUMBAI

  • ENTERPRISE_SQL_PRO_COMPUTE_AP_SINGAPORE

  • ENTERPRISE_SQL_PRO_COMPUTE_AP_TOKYO

  • ENTERPRISE_SQL_PRO_COMPUTE_AP_SEOUL

  • PREMIUM_ALL_PURPOSE_COMPUTE

  • PREMIUM_ALL_PURPOSE_COMPUTE_(PHOTON)

  • PREMIUM_DLT_CORE_COMPUTE

  • PREMIUM_DLT_CORE_COMPUTE_(PHOTON)

  • PREMIUM_DLT_PRO_COMPUTE

  • PREMIUM_DLT_PRO_COMPUTE_(PHOTON)

  • PREMIUM_DLT_ADVANCED_COMPUTE

  • PREMIUM_DLT_ADVANCED_COMPUTE_(PHOTON)

  • PREMIUM_JOBS_COMPUTE

  • PREMIUM_JOBS_COMPUTE_(PHOTON)

  • PREMIUM_JOBS_LIGHT_COMPUTE

  • PREMIUM_SERVERLESS_REAL_TIME_INFERENCE_US_EAST_N_VIRGINIA

  • PREMIUM_SERVERLESS_REAL_TIME_INFERENCE_US_EAST_OHIO

  • PREMIUM_SERVERLESS_REAL_TIME_INFERENCE_US_WEST_OREGON

  • PREMIUM_SERVERLESS_REAL_TIME_INFERENCE_CANADA

  • PREMIUM_SERVERLESS_REAL_TIME_INFERENCE_EUROPE_IRELAND

  • PREMIUM_SERVERLESS_REAL_TIME_INFERENCE_EUROPE_FRANKFURT

  • PREMIUM_SERVERLESS_REAL_TIME_INFERENCE_AP_SINGAPORE

  • PREMIUM_SERVERLESS_REAL_TIME_INFERENCE_AP_SYDNEY

  • PREMIUM_SERVERLESS_REAL_TIME_INFERENCE_LAUNCH_US_EAST_N_VIRGINIA

  • PREMIUM_SERVERLESS_REAL_TIME_INFERENCE_LAUNCH_US_EAST_OHIO

  • PREMIUM_SERVERLESS_REAL_TIME_INFERENCE_LAUNCH_US_WEST_OREGON

  • PREMIUM_SERVERLESS_REAL_TIME_INFERENCE_LAUNCH_CANADA

  • PREMIUM_SERVERLESS_REAL_TIME_INFERENCE_LAUNCH_EUROPE_IRELAND

  • PREMIUM_SERVERLESS_REAL_TIME_INFERENCE_LAUNCH_EUROPE_FRANKFURT

  • PREMIUM_SERVERLESS_REAL_TIME_INFERENCE_LAUNCH_AP_SINGAPORE

  • PREMIUM_SERVERLESS_REAL_TIME_INFERENCE_LAUNCH_AP_SYDNEY

  • PREMIUM_SERVERLESS_SQL_COMPUTE_US_EAST_N_VIRGINIA

  • PREMIUM_SERVERLESS_SQL_COMPUTE_US_WEST_OREGON

  • PREMIUM_SERVERLESS_SQL_COMPUTE_EUROPE_FRANKFURT

  • PREMIUM_SERVERLESS_SQL_COMPUTE_EUROPE_IRELAND

  • PREMIUM_SERVERLESS_SQL_COMPUTE_AP_SYDNEY

  • PREMIUM_SQL_COMPUTE

  • PREMIUM_SQL_PRO_COMPUTE_US_EAST_N_VIRGINIA

  • PREMIUM_SQL_PRO_COMPUTE_US_EAST_OHIO

  • PREMIUM_SQL_PRO_COMPUTE_US_WEST_OREGON

  • PREMIUM_SQL_PRO_COMPUTE_US_WEST_CALIFORNIA

  • PREMIUM_SQL_PRO_COMPUTE_CANADA

  • PREMIUM_SQL_PRO_COMPUTE_SA_BRAZIL

  • PREMIUM_SQL_PRO_COMPUTE_EUROPE_IRELAND

  • PREMIUM_SQL_PRO_COMPUTE_EUROPE_FRANKFURT

  • PREMIUM_SQL_PRO_COMPUTE_EUROPE_LONDON

  • PREMIUM_SQL_PRO_COMPUTE_EUROPE_FRANCE

  • PREMIUM_SQL_PRO_COMPUTE_AP_SYDNEY

  • PREMIUM_SQL_PRO_COMPUTE_AP_MUMBAI

  • PREMIUM_SQL_PRO_COMPUTE_AP_SINGAPORE

  • PREMIUM_SQL_PRO_COMPUTE_AP_TOKYO

  • PREMIUM_SQL_PRO_COMPUTE_AP_SEOUL

  • STANDARD_ALL_PURPOSE_COMPUTE

  • STANDARD_ALL_PURPOSE_COMPUTE_(PHOTON)

  • STANDARD_DLT_CORE_COMPUTE

  • STANDARD_DLT_CORE_COMPUTE_(PHOTON)

  • STANDARD_DLT_PRO_COMPUTE

  • STANDARD_DLT_PRO_COMPUTE_(PHOTON)

  • STANDARD_DLT_ADVANCED_COMPUTE

  • STANDARD_DLT_ADVANCED_COMPUTE_(PHOTON)

  • STANDARD_JOBS_COMPUTE

  • STANDARD_JOBS_COMPUTE_(PHOTON)

  • STANDARD_JOBS_LIGHT_COMPUTE

Deprecated SKUs

The following SKUs have been deprecated:

Deprecated SKU Name

Deprecation Date

Replacement SKUs

LIGHT_AUTOMATED_NON_OPSEC LIGHT_AUTOMATED_OPSEC

March 2020

STANDARD_JOBS_LIGHT_COMPUTE PREMIUM_JOBS_LIGHT_COMPUTE ENTERPRISE_JOBS_LIGHT_COMPUTE

STANDARD_AUTOMATED_NON_OPSEC STANDARD_AUTOMATED_OPSEC

March 2020

STANDARD_JOBS_COMPUTE PREMIUM_JOBS_COMPUTE ENTERPRISE_JOBS_COMPUTE

STANDARD_INTERACTIVE_NON_OPSEC STANDARD_INTERACTIVE_OPSEC

March 2020

STANDARD_ALL_PURPOSE_COMPUTE PREMIUM_ALL_PURPOSE_COMPUTE ENTERPRISE_ALL_PURPOSE_COMPUTE

ENTERPRISE_ALL_PURPOSE_COMPUTE_(DLT) PREMIUM_ALL_PURPOSE_COMPUTE_(DLT) STANDARD_ALL_PURPOSE_COMPUTE_(DLT)

April 2022

ENTERPRISE_DLT_CORE_COMPUTE PREMIUM_DLT_CORE_COMPUTE STANDARD_DLT_CORE_COMPUTE

ENTERPRISE_SERVERLESS_SQL_COMPUTE PREMIUM_SERVERLESS_SQL_COMPUTE STANDARD_SERVERLESS_SQL_COMPUTE

June 2022

ENTERPRISE_SERVERLESS_SQL_COMPUTE_US_EAST_N_VIRGINIA ENTERPRISE_SERVERLESS_SQL_COMPUTE_US_WEST_OREGON ENTERPRISE_SERVERLESS_SQL_COMPUTE_EUROPE_IRELAND ENTERPRISE_SERVERLESS_SQL_COMPUTE_AP_SYDNEY PREMIUM_SERVERLESS_SQL_COMPUTE_US_EAST_N_VIRGINIA PREMIUM_SERVERLESS_SQL_COMPUTE_US_WEST_OREGON PREMIUM_SERVERLESS_SQL_COMPUTE_EUROPE_IRELAND PREMIUM_SERVERLESS_SQL_COMPUTE_AP_SYDNEY

Analyze usage data in Databricks

This section describes how to make the data in the billable usage CSV file available to Databricks for analysis.

The CSV file uses a format that is standard for commercial spreadsheet applications but requires a modification to be read by Apache Spark. You must use option("escape", "\"") when you create the usage table in Databricks.

Total DBUs are the sum of the dbus column.

Import the log using the Create Table UI

You can use the Upload files to Databricks to import the CSV file into Databricks for analysis.

Create a Spark DataFrame

You can also use the following code to create the usage table from a path to the CSV file:

df = (spark.
      read.
      option("header", "true").
      option("inferSchema", "true").
      option("escape", "\"").
      csv("/FileStore/tables/usage_data.csv"))

df.createOrReplaceTempView("usage")

If the file is stored in an S3 bucket, for example when it is used with log delivery, the code will look like the following. You can specify a file path or a directory. If you pass a directory, all files are imported. The following example specifies a file.

df = (spark.
      read.
      option("header", "true").
      option("inferSchema", "true").
      option("escape", "\"").
      load("s3://<bucketname>/<pathprefix>/billable-usage/csv/workspaceId=<workspace-id>-usageMonth=<month>.csv"))

df.createOrReplaceTempView("usage")

The following example imports a directory of billable usage files:

df = (spark.
      read.
      option("header", "true").
      option("inferSchema", "true").
      option("escape", "\"").
      load("s3://<bucketname>/<pathprefix>/billable-usage/csv/"))

df.createOrReplaceTempView("usage")

Create a Delta table

To create a Delta table from the DataFrame (df) in the previous example, use the following code:

(df.write
    .format("delta")
    .mode("overwrite")
    .saveAsTable("database_name.table_name")
)

Warning

The saved Delta table is not updated automatically when you add or replace new CSV files. If you need the latest data, re-run these commands before you use the Delta table.