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Example queries for Cloud Billing data export

This document provides some examples of how to query the Cloud Billing data exported to and stored in BigQuery.

For more information about exporting your billing data to BigQuery, see the overview and limitations.

To quickly write a SQL query that returns the equivalent results in BigQuery as the results in a Billing Report, you can use the Generate query feature in the Billing Reports, available in the Google Cloud console.

Get started with billing data exports using this interactive tutorial. In the tutorial, you'll enable Cloud Billing data to export to a BigQuery dataset. Next, you'll run some sample queries through the BigQuery web interface to examine billing data.

In these examples, to query your exported Cloud Billing data in BigQuery, you need to specify the full path to the table name in the FROM clause. The table name is determined using three values separated by dots:

project-ID.dataset_name.BQ_table_name

• project-ID is the ID of the Google Cloud project you set up that contains your BigQuery dataset.
• dataset_name is the name of the BigQuery dataset you set up to contain the BigQuery tables with your exported Cloud Billing data.

• BQ_table_name is the name of the BigQuery table that contains the exported Cloud Billing data that you want to query.

  The BigQuery tables that contain the exported Cloud Billing data are automatically assigned a name, based on the type of export you enable:

  • Standard usage cost table: In your BigQuery dataset, this table is named gcp_billing_export_v1_<BILLING-ACCOUNT-ID>.

  • Detailed usage cost table: In your BigQuery dataset, this table is named gcp_billing_export_resource_v1_<BILLING-ACCOUNT-ID>.

  • Pricing table: In your BigQuery dataset, this table is named cloud_pricing_export.

For example, assume you have a Cloud Billing account that's configured to export detailed usage cost data and the components of the billing export configuration are as follows:

• project-ID: my-billing-project
• dataset_name: my_billing_dataset
• BQ_table_name: gcp_billing_export_resource_v1_<BILLING-ACCOUNT-ID>
• BILLING-ACCOUNT-ID: 010101-F0FFF0-10X10X – this is the ID of the Cloud Billing account that contains the billing data that you're exporting. Learn how to find your billing account ID.

Based on the example values, you'd write the FROM clause as follows:

my-billing-project.my_billing_dataset.gcp_billing_export_resource_v1_010101-F0FFF0-10X10X

This section provides query examples for your standard usage cost data, detailed usage cost data, and pricing data.

If you opted to export detailed usage cost data then you can use the Standard usage cost query examples in addition to the Detailed usage cost query examples.

This section provides examples of how to query the Cloud Billing standard usage cost data exported to BigQuery.

These query examples also work with the detailed usage cost data exported to BigQuery, although they aren't written to retrieve any of the resource-level information that's provided with the detailed usage cost export option.

The query examples in this section use the following value for Table name: project-ID.dataset.gcp_billing_export_v1_XXXXXX-XXXXXX-XXXXXX

The following queries demonstrate two ways of viewing cost and credit values using exported billing data.

• The total field directly sums the floating point cost and credit values, which can result in floating point rounding errors.
• The total_exact field converts costs and credit values to micros before summing, then converts back to dollars after summing, avoiding the floating point rounding error.

This query shows the invoice total for each month, as a sum of regular costs, taxes, adjustments, and rounding errors.

Standard SQL

SELECT
  invoice.month,
  SUM(cost)
    + SUM(IFNULL((SELECT SUM(c.amount)
                  FROM UNNEST(credits) c), 0))
    AS total,
  (SUM(CAST(cost AS NUMERIC))
    + SUM(IFNULL((SELECT SUM(CAST(c.amount AS NUMERIC))
                  FROM UNNEST(credits) AS c), 0)))
    AS total_exact
FROM `project-ID.dataset.gcp_billing_export_v1_XXXXXX-XXXXXX-XXXXXX`
GROUP BY 1
ORDER BY 1 ASC
;

For example, the result of the preceding query might be:

This query shows the totals for each cost_type for each month. Cost types include regular costs, taxes, adjustments, and rounding errors.

Standard SQL

SELECT
  invoice.month,
  cost_type,
  SUM(cost)
    + SUM(IFNULL((SELECT SUM(c.amount)
                  FROM   UNNEST(credits) c), 0))
    AS total,
  (SUM(CAST(cost AS NUMERIC))
    + SUM(IFNULL((SELECT SUM(CAST(c.amount AS NUMERIC))
                  FROM UNNEST(credits) AS c), 0)))
    AS total_exact
FROM `project-ID.dataset.gcp_billing_export_v1_XXXXXX-XXXXXX-XXXXXX`
GROUP BY 1, 2
ORDER BY 1 ASC, 2 ASC
;

For example, the result of the preceding query might be:

The following examples illustrate other ways to query your data with labels.

For the examples in this section, assume the following:

• You have two apps (grapefruit-squeezer and chocolate-masher).
• For each app, you have two environments (dev and prod).
• The dev environment has one small instance per app.
• The prod environment has one small instance in Americas and one small instance in Asia.
• Each instance is labeled with the app and environment.
• You have one instance with no labels that you use for experimentation.

Your total bill is $24 with the following breakdown:

The most granular view of these costs would be to query every row without grouping. Assume all fields, except labels and sku description, are the same (project, service, and so on).

Standard SQL

SELECT
  sku.description,
  TO_JSON_STRING(labels) as labels,
 cost as cost
FROM `project-ID.dataset.gcp_billing_export_v1_XXXXXX-XXXXXX-XXXXXX`;

Legacy SQL

TO_JSON_STRING not supported.

This is a basic way to break down cost by each label combination.

Standard SQL

SELECT
  TO_JSON_STRING(labels) as labels,
  sum(cost) as cost
FROM `project-ID.dataset.gcp_billing_export_v1_XXXXXX-XXXXXX-XXXXXX`
GROUP BY labels;

Legacy SQL

TO_JSON_STRING not supported.

Breaking down costs for values of a specific label key is a common use case. By using a LEFT JOIN and putting the key filter in the JOIN condition (rather than WHERE), you include cost that doesn't contain this key, and so receive a complete view of your cost.

Standard SQL

SELECT
  labels.value as environment,
  SUM(cost) as cost
FROM `project-ID.dataset.gcp_billing_export_v1_XXXXXX-XXXXXX-XXXXXX`
LEFT JOIN UNNEST(labels) as labels
  ON labels.key = "environment"
GROUP BY environment;

Legacy SQL

SELECT
  labels.value as environment,
  SUM(cost) as cost
FROM [project-ID:dataset.gcp_billing_export_v1_XXXXXX-XXXXXX-XXXXXX]
WHERE labels.key = "environment" OR labels.key IS NULL
GROUP BY environment;

Be careful when interpreting or exporting these results. An individual row here shows a valid sum without any double counting, but shouldn't be combined with other rows (except possibly if the key is the same, or if you're certain the keys are never set on the same resource).

Standard SQL

SELECT
  labels.key as key,
  labels.value as value,
  SUM(cost) as cost
FROM `project-ID.dataset.gcp_billing_export_v1_XXXXXX-XXXXXX-XXXXXX`
LEFT JOIN UNNEST(labels) as labels
GROUP BY key, value;

Legacy SQL

SELECT
  labels.key as key,
  labels.value as value,
  SUM(cost)
FROM [project-ID:dataset.gcp_billing_export_v1_XXXXXX-XXXXXX-XXXXXX]
GROUP BY key, value;

Note that the total sum is greater than your bill.

The following queries demonstrate ways of viewing the fees and credits associated with committed use discounts in exported billing data. To understand how your commitment fees and credits are attributed to your Cloud Billing account and projects, see Attribution of committed use discounts.

To view the commitment fees for your committed use discounts in your billing data export, use the following sample query.

Standard SQL

SELECT
    invoice.month AS invoice_month,
    SUM(cost) as commitment_fees
FROM `project-ID.dataset.gcp_billing_export_v1_XXXXXX-XXXXXX-XXXXXX`
WHERE LOWER(sku.description) LIKE "commitment%"
GROUP BY 1

To view your committed use discount credits in your billing data export, use the following sample query.

Standard SQL

SELECT
    invoice.month AS invoice_month,
    SUM(credits.amount) as CUD_credits
FROM `project-ID.dataset.gcp_billing_export_v1_XXXXXX-XXXXXX-XXXXXX`
LEFT JOIN UNNEST(credits) AS credits
WHERE credits.type = "COMMITTED_USAGE_DISCOUNT"
GROUP BY 1

You can use resource hierarchy filters to aggregate costs by hierarchy elements such as projects, folders, and organizations. These query examples show methods for summing costs filtered by resource hierarchy elements and displaying project ancestries.

This example demonstrates queries that group costs by project ancestry and filter for only costs generated under a specified hierarchy element, identified by the relative resource name.

SELECT
    invoice.month AS invoice_month,
    TO_JSON_STRING(project.ancestors) as ancestors,
    SUM(cost)
        + SUM(IFNULL((SELECT SUM(c.amount) FROM UNNEST(credits) c), 0))
        AS net_cost
  FROM `project-ID.dataset.gcp_billing_export_v1_XXXXXX-XXXXXX-XXXXXX` as bq
  WHERE TO_JSON_STRING(project.ancestors) like "%resource_name\":\"folders/1234"
  GROUP BY invoice_month, ancestors
  ORDER BY invoice_month, ancestors

SELECT
    invoice.month AS invoice_month,
    TO_JSON_STRING(project.ancestors) as ancestors,
    SUM(cost)
        + SUM(IFNULL((SELECT SUM(c.amount) FROM UNNEST(credits) c), 0))
        AS net_cost
  FROM `project-ID.dataset.gcp_billing_export_v1_XXXXXX-XXXXXX-XXXXXX` as bq, UNNEST(project.ancestors) as ancestor
  WHERE ancestor.resource_name = "folders/1234"
  GROUP BY invoice_month, ancestors
  ORDER BY invoice_month, ancestors

This example demonstrates queries that group costs by project ancestry and filter for only costs generated under a specified hierarchy element, identified by the user-provided display name.

SELECT
    invoice.month AS invoice_month,
    TO_JSON_STRING(project.ancestors) as ancestors,
    SUM(cost)
        + SUM(IFNULL((SELECT SUM(c.amount) FROM UNNEST(credits) c), 0))
        AS net_cost
  FROM `project-ID.dataset.gcp_billing_export_v1_XXXXXX-XXXXXX-XXXXXX` as bq
  WHERE TO_JSON_STRING(project.ancestors) like "%display_name\":\"MyFolderName%"
  GROUP BY invoice_month, ancestors
  ORDER BY invoice_month, ancestors

SELECT
    invoice.month AS invoice_month,
    TO_JSON_STRING(project.ancestors) as ancestors,
    SUM(cost)
        + SUM(IFNULL((SELECT SUM(c.amount) FROM UNNEST(credits) c), 0))
        AS net_cost
  FROM `project-ID.dataset.gcp_billing_export_v1_XXXXXX-XXXXXX-XXXXXX` as bq, UNNEST(project.ancestors) as ancestor
  WHERE ancestor.display_name = "MyFolderName"
  GROUP BY invoice_month, ancestors
  ORDER BY invoice_month, ancestors

The following examples illustrate ways to query your data with tags.

The following query demonstrates how you can use return costs by invoice month for the cost_center tag.

SELECT
  invoice.month AS invoice_month,
  tag.value AS cost_center,
  ROUND((SUM(CAST(cost AS NUMERIC))
    + SUM(IFNULL((SELECT SUM (CAST(c.amount AS NUMERIC))
                  FROM UNNEST(credits) AS c), 0))), 2)
    AS net_cost
FROM `project-ID.dataset.gcp_billing_export_resource_v1_XXXXXX-XXXXXX-XXXXXX`, UNNEST(tags) AS tag
WHERE tag.key = "cost_center" AND tag.namespace = "821092389413"
GROUP BY invoice.month, tag.value
ORDER BY invoice.month, tag.value;

For example, the result of the preceding query might be:

This query shows the invoice total for untagged resources, grouped by invoice month.

SELECT
 invoice.month AS invoice_month,
 ROUND((SUM(CAST(cost AS NUMERIC))
   + SUM(IFNULL((SELECT SUM(CAST(c.amount AS NUMERIC))
                 FROM UNNEST(credits) AS c), 0))), 2)
   AS net_cost
FROM
 `project-ID.dataset.gcp_billing_export_v1_XXXXXX-XXXXXX-XXXXXX`
WHERE "color" NOT IN (SELECT key FROM UNNEST(tags))
GROUP BY invoice_month
ORDER BY invoice_month;

For example, the result of the preceding query might be:

By providing a specific invoice month of June 2020 (in the format YYYYMM), this query returns a view of the costs and credits grouped by project along with showing project labels.

Standard SQL

SELECT
  project.name,
  TO_JSON_STRING(project.labels) as project_labels,
  sum(cost) as total_cost,
  SUM(IFNULL((SELECT SUM(c.amount) FROM UNNEST(credits) c), 0)) as total_credits
FROM `project-ID.dataset.gcp_billing_export_v1_XXXXXX-XXXXXX-XXXXXX`
WHERE invoice.month = "202006"
GROUP BY 1, 2
ORDER BY 1;

By providing a specific invoice month and filtering by date where the usage date occurred before the invoice month, this simplified query returns cost totals for corrections or late-monetized usage (charges that should have been on a previous invoice).

Standard SQL

SELECT
  SUM(cost)
    + SUM(IFNULL((SELECT SUM(c.amount)
      FROM   UNNEST(credits) c), 0))
    AS total
FROM `project-ID.dataset.gcp_billing_export_v1_XXXXXX-XXXXXX-XXXXXX`
WHERE
  invoice.month = '202403' AND
    DATE(TIMESTAMP_TRUNC(usage_start_time, Day, 'US/Pacific')) < '2024-03-01';

For example, the result of the preceding query might be:

This example demonstrates a query that returns a cost breakdown by service, for invoice charges, where the usage date occurred before the invoice month. This query returns cost and credit totals by service for corrections or late-monetized usage (charges that should have been on a previous invoice).

In this query example, the WHERE clause filters for all charges with an invoice month of March 2024 (in the format YYYYMM), then further refines the results to return only those invoice month charges that have a usage date before March 1, 2024.

SELECT
  DATE(TIMESTAMP_TRUNC(usage_start_time, Day, 'US/Pacific')) AS `Day`,
  service.description AS `Service Description`,
  SUM(CAST(cost_at_list AS NUMERIC)) AS `List cost`,
  SUM(CAST(cost AS NUMERIC)) - SUM(CAST(cost_at_list AS NUMERIC)) AS `Negotiated savings`,
  SUM(IFNULL((SELECT SUM(CAST(c.amount AS numeric)) FROM UNNEST(credits) c WHERE c.type IN ('SUSTAINED_USAGE_DISCOUNT', 'DISCOUNT', 'SPENDING_BASED_DISCOUNT', 'COMMITTED_USAGE_DISCOUNT', 'FREE_TIER', 'COMMITTED_USAGE_DISCOUNT_DOLLAR_BASE', 'SUBSCRIPTION_BENEFIT', 'RESELLER_MARGIN')), 0)) AS `Discounts`,
  SUM(IFNULL((SELECT SUM(CAST(c.amount AS numeric)) FROM UNNEST(credits) c WHERE c.type IN ('CREDIT_TYPE_UNSPECIFIED', 'PROMOTION')), 0)) AS `Promotions and others`,
  SUM(CAST(cost_at_list AS NUMERIC)) + SUM(IFNULL((SELECT SUM(CAST(c.amount AS numeric)) FROM UNNEST(credits) c WHERE c.type IN ('SUSTAINED_USAGE_DISCOUNT', 'DISCOUNT', 'SPENDING_BASED_DISCOUNT', 'COMMITTED_USAGE_DISCOUNT', 'FREE_TIER', 'COMMITTED_USAGE_DISCOUNT_DOLLAR_BASE', 'SUBSCRIPTION_BENEFIT', 'RESELLER_MARGIN')), 0)) + SUM(CAST(cost AS NUMERIC)) - SUM(CAST(cost_at_list AS NUMERIC))+ SUM(IFNULL((SELECT SUM(CAST(c.amount AS numeric)) FROM UNNEST(credits) c WHERE c.type IN ('CREDIT_TYPE_UNSPECIFIED', 'PROMOTION')), 0)) AS `Subtotal`
FROM
  `project-ID.dataset.gcp_billing_export_v1_XXXXXX-XXXXXX-XXXXXX`
WHERE
  invoice.month = '202403' AND
  DATE(TIMESTAMP_TRUNC(usage_start_time, Day, 'US/Pacific')) < '2024-03-01'
GROUP BY
  Day,
  service.description
ORDER BY
  Day DESC,
  Subtotal DESC;

For example, the result of the preceding query might be:

This section provides examples of how to query the Cloud Billing detailed usage cost data exported to BigQuery.

Because the detailed usage cost schema includes all of the fields from the standard usage cost schema, the query examples provided for the standard data exported to BigQuery also work with the detailed data that's exported. The standard query examples aren't written to retrieve any of the resource-level information that's provided with the detailed usage cost export option. When creating queries for the detailed data, you can use a standard query example as a template, update the Table name, and add any of the fields that are available in the detailed usage cost schema.

For further requirements and limitations to the detailed data exported, see Schema of the detailed usage cost data.

The query examples in this section use the following value for Table name: project-ID.dataset.gcp_billing_export_resource_v1_XXXXXX-XXXXXX-XXXXXX

The following queries demonstrate two ways of viewing resource-level cost and credit values on an invoice using exported billing data.

• The total field directly sums the floating point cost and credit values, which can result in floating point rounding errors.
• The total_exact field converts costs and credit values to micros before summing, then converts back to dollars after summing, avoiding the floating point rounding error.

This query shows the invoice total for each resource.name per month, as a sum of regular costs, taxes, adjustments, and rounding errors. Any costs not associated with a resource-level item are aggregated under the name null for the month.

Standard SQL

SELECT
  invoice.month,
  resource.name,
  SUM(cost)
    + SUM(IFNULL((SELECT SUM(c.amount)
                  FROM UNNEST(credits) c), 0))
    AS total,
  (SUM(CAST(cost AS NUMERIC))
    + SUM(IFNULL((SELECT SUM(CAST(c.amount AS NUMERIC))
                  FROM UNNEST(credits) AS c), 0)))
    AS total_exact
FROM `project-ID.dataset.gcp_billing_export_resource_v1_XXXXXX-XXXXXX-XXXXXX`
GROUP BY 1, 2
ORDER BY 1 ASC, 2 ASC
;

For example, the result of the preceding query might be:

This query shows the totals for each cost_type for each resource.name per month. Cost types include regular costs, taxes, adjustments, and rounding errors. Any costs not associated with a resource-level item are aggregated under the name null for the month.

Standard SQL

SELECT
  invoice.month,
  cost_type,
  resource.name,
  SUM(cost)
    + SUM(IFNULL((SELECT SUM(c.amount)
                  FROM   UNNEST(credits) c), 0))
    AS total,
  (SUM(CAST(cost AS NUMERIC))
    + SUM(IFNULL((SELECT SUM(CAST(c.amount AS NUMERIC))
                  FROM UNNEST(credits) AS c), 0)))
    AS total_exact
FROM `project-ID.dataset.gcp_billing_export_resource_v1_XXXXXX-XXXXXX-XXXXXX`
GROUP BY 1, 2, 3
ORDER BY 1 ASC, 2 ASC, 3 ASC
;

For example, the result of the preceding query might be:

This section provides examples of filtering GKE cluster costs in your BigQuery export reports. To learn more about GKE cluster costs, visit View breakdown of cluster costs.

The following example queries show you how to filter and group your GKE costs for supported resource types by cluster name, namespace, and label.

GKE cluster costs before credits

SELECT
  SUM(cost) AS cost_before_credits,
  labels.value AS cluster_name
FROM  `project-ID.dataset.gcp_billing_export_resource_v1_XXXXXX-XXXXXX-XXXXXX`
LEFT JOIN UNNEST(labels) as labels
  ON labels.key = "goog-k8s-cluster-name"
GROUP BY labels.value
;

GKE costs after credits by namespace

SELECT
  labels.value as namespace,
  SUM(cost) + SUM(IFNULL((SELECT SUM(c.amount) FROM UNNEST(credits) c), 0)) AS cost_after_credits,
FROM `project-ID.dataset.gcp_billing_export_resource_v1_XXXXXX-XXXXXX-XXXXXX`
LEFT JOIN UNNEST(labels) as labels
  ON labels.key = "k8s-namespace"
GROUP BY namespace
;

GKE costs by SKU

SELECT
  project.id AS project_id,
  labels.value AS cluster_name,
  sku.id AS sku_id,
  sku.description AS sku_description,
  SUM(cost) AS cost
FROM `project-ID.dataset.gcp_billing_export_resource_v1_XXXXXX-XXXXXX-XXXXXX`
JOIN UNNEST(labels) AS labels
  ON labels.key = "goog-k8s-cluster-name"
GROUP BY
  cluster_name, project_id, sku_description, sku_id
;

This section provides different examples of how to query the Cloud Billing pricing data exported to BigQuery.

The query examples in this section use the following values:

• Table name: project-ID.dataset.cloud_pricing_export
• SKU ID: 2DA5-55D3-E679 (Cloud Run - Requests)

This example demonstrates a basic query that returns the list_price for each pricing tier for a specified SKU.

Standard SQL

SELECT sku.id,  sku.description, list_price.*
FROM `project-ID.dataset.cloud_pricing_export`
WHERE DATE(_PARTITIONTIME) = "2020-07-20"
      AND sku.id = "2DA5-55D3-E679"
;

_PARTITIONTIME is a field auto-generated by BigQuery and represents the date that the data belongs to. Instead of _PARTITIONTIME, you can use a field that Cloud Billing export explicitly generates, such as pricing_as_of_time.

Here's the same query configured to use the pricing_as_of_time field:

SELECT sku.id,  sku.description, list_price.*
FROM `project-ID.dataset.cloud_pricing_export`
WHERE DATE(pricing_as_of_time) = "2020-07-20"
      AND sku.id = "2DA5-55D3-E679"
;

The two examples in this section demonstrate queries that return the list_price for each pricing tier for a specified SKU, and includes the SKU description and the service description.

• Example 1 returns one SKU per row, with the pricing tiers displayed as nested data.
• Example 2 demonstrates unnesting the data to return one row per SKU per pricing tier.

This example queries a single SKU to return the list_price data. This SKU has multiple pricing tiers. The list price field values display in individual rows that are nested under the SKU ID row.

Standard SQL

SELECT sku.id AS sku_id,
       sku.description AS sku_description,
       service.id AS service_id,
       service.description as service_description,
       list_price.*
FROM project-ID.dataset.cloud_pricing_export
WHERE DATE(_PARTITIONTIME) = "2020-07-20"
      AND sku.id = "2DA5-55D3-E679"
;

This example queries a single SKU to return the list price. The SKU has multiple pricing tiers. The query demonstrates using the UNNEST operator to flatten the tiered_rates array and join the fields with the same table, resulting in one row per pricing tier.

Standard SQL

SELECT sku.id AS sku_id,
       sku.description AS sku_description,
       service.id AS service_id,
       service.description as service_description,
       tier.*
FROM `project-ID.dataset.cloud_pricing_export` as sku_pricing, UNNEST (sku_pricing.list_price.tiered_rates) as tier
WHERE DATE(_PARTITIONTIME) = "2020-07-20"
      AND sku.id = "2DA5-55D3-E679"
;

Beta

This feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms. Pre-GA features are available "as is" and might have limited support. For more information, see the launch stage descriptions.

• Product taxonomy is a list of product categories that apply to the SKU, such as Serverless, Cloud Run, or VMs On Demand.
• Geo taxonomy is the geographic metadata that applies to a SKU, consisting of type and region values.

This example demonstrates a query that returns the product_taxonomy list for a specified SKU, where the SKU ID = 2DA5-55D3-E679 (Cloud Run - Requests).

Standard SQL

SELECT sku.id AS sku_id,
       sku.description AS sku_description,
       service.id AS service_id,
       service.description as service_description,
       product_taxonomy
FROM `project-ID.dataset.cloud_pricing_export`
WHERE DATE(_PARTITIONTIME) = "2020-07-20"
      AND sku.id = "2DA5-55D3-E679"
;

This example demonstrates a query that returns all SKUs that match a specified product_taxonomy. In this query, set Serverless as the product taxonomy value.

Standard SQL

SELECT sku.id AS sku_id,
       sku.description AS sku_description,
       service.id AS service_id,
       service.description as service_description,
       product_taxonomy
FROM `project-ID.dataset.cloud_pricing_export`
WHERE DATE(_PARTITIONTIME) = "2020-07-20"
     AND "Serverless" in UNNEST(product_taxonomy)
LIMIT 10
;

This example demonstrates a query that returns all SKUs that match a specified geo_taxonomy region and a specified product_taxonomy, where region = us-east4 and product_taxonomy = VMs On Demand.

Standard SQL

SELECT sku.id AS sku_id,
       sku.description AS sku_description,
       service.id AS service_id,
       service.description as service_description,
       geo_taxonomy,
       product_taxonomy
FROM `project-ID.dataset.cloud_pricing_export`
WHERE DATE(_PARTITIONTIME) = "2020-07-20"
      AND "VMs On Demand" in UNNEST(product_taxonomy)
      AND geo_taxonomy.type = "REGIONAL"
      AND "us-east4" in UNNEST (geo_taxonomy.regions)
;

This query shows how to join Price and Cost Data exports, to see detailed pricing information in line with your costs. You can configure this query to pull exported data from your detailed usage cost data (as Exports), and join your usage cost data with your exported pricing data (as Prices).

Use your detailed usage cost table name to pull the Exports data: gcp_billing_export_resource_v1_<BILLING_ACCOUNT_ID>

Use your pricing table name for the Prices data: project-ID.dataset.cloud_pricing_export

WITH
  Exports AS (
    SELECT *
    FROM `project-ID.dataset.gcp_billing_export_resource_v1_XXXXXX-XXXXXX-XXXXXX`
  ),
  Prices AS (
    SELECT *
    FROM `project-ID.dataset.cloud_pricing_export`
  )
SELECT
  Exports.sku.description AS sku_description,
  Exports.cost,
  Exports.usage,
  FlattenedPrices.pricing_unit_description,
  FlattenedPrices.account_currency_amount,
  FlattenedPrices.account_currency_code,
FROM Exports
JOIN (SELECT * FROM Prices CROSS JOIN UNNEST(Prices.list_price.tiered_rates)) AS FlattenedPrices
  ON
    Exports.sku.id = FlattenedPrices.sku.id
    AND Exports.price.tier_start_amount = FlattenedPrices.start_usage_amount
WHERE
  DATE(Exports.export_time) = '2023-06-30'
  AND DATE(FlattenedPrices.export_time) = '2023-06-30'
  AND cost > 0
LIMIT 1000

For example, the result of the preceding query might be:

• Set up Cloud Billing data export to BigQuery
• Understanding the Cloud Billing data tables in BigQuery
• Visualize spend over time with Looker Studio

• View your Cloud Billing reports and cost trends
• View and download the cost details of your invoice or statement
• View and download prices for Google's cloud services
• Understand your savings with cost breakdown reports
• Analyze the effectiveness of your committed use discounts
• View your cost and payment history

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