TABLE_STORAGE view

The INFORMATION_SCHEMA.TABLE_STORAGE view provides a current snapshot of storage usage for tables and materialized views. When you query the INFORMATION_SCHEMA.TABLE_STORAGE view, the query results contain one row for each table or materialized view for the current project. The data in this table is not kept in real time, and might be delayed by a few seconds to a few minutes.

The table storage views give you a convenient way to observe your current storage consumption, and in addition provide details on whether your storage uses logical uncompressed bytes, physical compressed bytes, or time travel bytes. This information can help you with tasks like planning for future growth and understanding the update patterns for tables.

Data included in the *_BYTES columns

The *_BYTES columns in the table storage views include information about your use of storage bytes. This information is determined by looking at your storage usage for materialized views and the following types of tables:

• Permanent tables created through any of the methods described in Create and use tables.
• Temporary tables created in sessions. These tables are placed into datasets with generated names like "_c018003e063d09570001ef33ae401fad6ab92a6a".
• Temporary tables created in multi-statement queries ("scripts"). These tables are placed into datasets with generated names like "_script72280c173c88442c3a7200183a50eeeaa4073719".

Data stored in the query results cache is not billed to you and so is not included in the *_BYTES column values.

Clones and snapshots show *_BYTES column values as if they were complete tables, rather than showing the delta from the storage used by the base table, so they are an over-estimation. Your bill does account correctly for this delta in storage usage.

Forecast storage billing

In order to forecast the monthly storage billing for a dataset, you can use either the logical or physical *_BYTES columns in this view, depending on the dataset storage billing model used by the dataset. Please note that this is only a rough forecast, and the precise billing amounts are calculated based on the usage by BigQuery storage billing infrastructure and visible in Cloud Billing.

For datasets that use a logical billing model, you can forecast your monthly storage costs as follows:

((ACTIVE_LOGICAL_BYTES value / POW(1024, 3)) * active logical bytes pricing) + ((LONG_TERM_LOGICAL_BYTES value / POW(1024, 3)) * long-term logical bytes pricing)

The ACTIVE_LOGICAL_BYTES value for a table reflects the active bytes currently used by that table.

For datasets that use a physical billing model, you can forecast your storage costs as follows:

((ACTIVE_PHYSICAL_BYTES value / POW(1024, 3)) * active physical bytes pricing) + ((LONG_TERM_PHYSICAL_BYTES value / POW(1024, 3)) * long-term physical bytes pricing)

The ACTIVE_PHYSICAL_BYTES value for a table reflects the active bytes currently used by that table plus the bytes used for time travel for that table.

To see the active bytes of the table alone, subtract the TIME_TRAVEL_PHYSICAL_BYTES value from the ACTIVE_PHYSICAL_BYTES value.

For more information, see Storage pricing.

Required roles

To get the permissions that you need to query the INFORMATION_SCHEMA.TABLE_STORAGE view, ask your administrator to grant you the BigQuery Metadata Viewer (roles/bigquery.metadataViewer) IAM role on the project. For more information about granting roles, see Manage access.

This predefined role contains the permissions required to query the INFORMATION_SCHEMA.TABLE_STORAGE view. To see the exact permissions that are required, expand the Required permissions section:

Required permissions

The following permissions are required to query the INFORMATION_SCHEMA.TABLE_STORAGE view:

• bigquery.tables.get
• bigquery.tables.list

You might also be able to get these permissions with custom roles or other predefined roles.

Schema

The INFORMATION_SCHEMA.TABLE_STORAGE view has the following schema:

Column name	Data type	Value
PROJECT_ID	STRING	The project ID of the project that contains the dataset
TABLE_CATALOG	STRING	The project ID of the project that contains the dataset
PROJECT_NUMBER	INT64	The project number of the project that contains the dataset
TABLE_SCHEMA	STRING	The name of the dataset that contains the table or materialized view, also referred to as the datasetId
TABLE_NAME	STRING	The name of the table or materialized view, also referred to as the tableId
CREATION_TIME	TIMESTAMP	The table's creation time
DELETED	BOOLEAN	Indicates whether or not the table is deleted
STORAGE_LAST_MODIFIED_TIME	TIMESTAMP	The most recent time that data was written to the table.
TOTAL_ROWS	INT64	The total number of rows in the table or materialized view
TOTAL_PARTITIONS	INT64	The number of partitions present in the table or materialized view. Unpartitioned tables return 0.
TOTAL_LOGICAL_BYTES	INT64	Total number of logical (uncompressed) bytes in the table or materialized view
ACTIVE_LOGICAL_BYTES	INT64	Number of logical (uncompressed) bytes that are less than 90 days old
LONG_TERM_LOGICAL_BYTES	INT64	Number of logical (uncompressed) bytes that are more than 90 days old
TOTAL_PHYSICAL_BYTES	INT64	Total number of physical (compressed) bytes used for storage, including active, long term, time travel (deleted or changed data), and fail-safe (deleted or changed data retained after time travel window) bytes
ACTIVE_PHYSICAL_BYTES	INT64	Number of physical (compressed) bytes less than 90 days old, including time travel (deleted or changed data) bytes
LONG_TERM_PHYSICAL_BYTES	INT64	Number of physical (compressed) bytes more than 90 days old
TIME_TRAVEL_PHYSICAL_BYTES	INT64	Number of physical (compressed) bytes used by time travel storage (deleted or changed data)
FAIL_SAFE_PHYSICAL_BYTES	INT64	Number of physical (compressed) bytes used by fail-safe storage (deleted or changed data)
TABLE_TYPE	STRING	The type of table. For example, `EXTERNAL` or `BASE TABLE`

Scope and syntax

Queries against this view must include a region qualifier. The following table explains the region scope for this view:

View name	Resource scope	Region scope
[`PROJECT_ID`.]`region-REGION`.INFORMATION_SCHEMA.TABLE_STORAGE[_BY_PROJECT]	Project level	REGION

Replace the following:

• Optional: PROJECT_ID: the ID of your Google Cloud project. If not specified, the default project is used.
• REGION: any dataset region name. For example, region-us.

Note: You must specify the location to query the INFORMATION_SCHEMA views and the location of the query execution must match the region of the INFORMATION_SCHEMA view.

The following example shows how to return storage information for tables in a specified project:

SELECT * FROM `myProject`.`region-REGION`.INFORMATION_SCHEMA.TABLE_STORAGE;

The following example shows how to return storage information for tables in a specified region:

SELECT * FROM `region-REGION`.INFORMATION_SCHEMA.TABLE_STORAGE_BY_PROJECT;

Examples

Example 1:

The following example shows you the total logical bytes billed for the current project.

SELECT
  SUM(total_logical_bytes) AS total_logical_bytes
FROM
  `region-REGION`.INFORMATION_SCHEMA.TABLE_STORAGE;

The result is similar to the following:

+---------------------+
| total_logical_bytes |
+---------------------+
| 971329178274633     |
+---------------------+

Example 2:

The following example shows you how to forecast the price difference per dataset between logical and physical billing models for the next 30 days. This example assumes that future storage usage is constant over the next 30 days from the moment the query was run. Note that the forecast is limited to base tables, it excludes all other types of tables within a dataset.

The prices used in the pricing variables for this query are for the us-central1 region. If you want to run this query for a different region, update the pricing variables appropriately. See Storage pricing for pricing information.

1. Open the BigQuery page in the Google Cloud console.

   Go to the BigQuery page

2. Enter the following GoogleSQL query in the Query editor box. INFORMATION_SCHEMA requires GoogleSQL syntax. GoogleSQL is the default syntax in the Google Cloud console.

   DECLARE active_logical_gib_price FLOAT64 DEFAULT 0.02;
   DECLARE long_term_logical_gib_price FLOAT64 DEFAULT 0.01;
   DECLARE active_physical_gib_price FLOAT64 DEFAULT 0.04;
   DECLARE long_term_physical_gib_price FLOAT64 DEFAULT 0.02;
   
   WITH
    storage_sizes AS (
      SELECT
        table_schema AS dataset_name,
        -- Logical
        SUM(IF(deleted=false, active_logical_bytes, 0)) / power(1024, 3) AS active_logical_gib,
        SUM(IF(deleted=false, long_term_logical_bytes, 0)) / power(1024, 3) AS long_term_logical_gib,
        -- Physical
        SUM(active_physical_bytes) / power(1024, 3) AS active_physical_gib,
        SUM(active_physical_bytes - time_travel_physical_bytes) / power(1024, 3) AS active_no_tt_physical_gib,
        SUM(long_term_physical_bytes) / power(1024, 3) AS long_term_physical_gib,
        -- Restorable previously deleted physical
        SUM(time_travel_physical_bytes) / power(1024, 3) AS time_travel_physical_gib,
        SUM(fail_safe_physical_bytes) / power(1024, 3) AS fail_safe_physical_gib,
      FROM
        `region-REGION`.INFORMATION_SCHEMA.TABLE_STORAGE_BY_PROJECT
      WHERE total_physical_bytes > 0
        -- Base the forecast on base tables only for highest precision results
        AND table_type  = 'BASE TABLE'
        GROUP BY 1
    )
   SELECT
     dataset_name,
     -- Logical
     ROUND(active_logical_gib, 2) AS active_logical_gib,
     ROUND(long_term_logical_gib, 2) AS long_term_logical_gib,
     -- Physical
     ROUND(active_physical_gib, 2) AS active_physical_gib,
     ROUND(time_travel_physical_gib, 2) AS time_travel_physical_gib,
     ROUND(long_term_physical_gib, 2) AS long_term_physical_gib,
     -- Compression ratio
     ROUND(SAFE_DIVIDE(active_logical_gib, active_no_tt_physical_gib), 2) AS active_compression_ratio,
     ROUND(SAFE_DIVIDE(long_term_logical_gib, long_term_physical_gib), 2) AS long_term_compression_ratio,
     -- Forecast costs logical
     ROUND(active_logical_gib * active_logical_gib_price, 2) AS forecast_active_logical_cost,
     ROUND(long_term_logical_gib * long_term_logical_gib_price, 2) AS forecast_long_term_logical_cost,
     -- Forecast costs physical
     ROUND((active_no_tt_physical_gib + time_travel_physical_gib + fail_safe_physical_gib) * active_physical_gib_price, 2) AS forecast_active_physical_cost,
     ROUND(long_term_physical_gib * long_term_physical_gib_price, 2) AS forecast_long_term_physical_cost,
     -- Forecast costs total
     ROUND(((active_logical_gib * active_logical_gib_price) + (long_term_logical_gib * long_term_logical_gib_price)) -
        (((active_physical_gib + fail_safe_physical_gib) * active_physical_gib_price) + (long_term_physical_gib * long_term_physical_gib_price)), 2) AS forecast_total_cost_difference
   FROM
     storage_sizes
   ORDER BY
     (forecast_active_logical_cost + forecast_active_physical_cost) DESC;
   

   Note: INFORMATION_SCHEMA view names are case-sensitive.

3. Click Run.

The result is similar to following:

+--------------+--------------------+-----------------------+---------------------+------------------------+--------------------------+-----------------------------+------------------------------+----------------------------------+-------------------------------+----------------------------------+--------------------------------+
| dataset_name | active_logical_gib | long_term_logical_gib | active_physical_gib | long_term_physical_gib | active_compression_ratio | long_term_compression_ratio | forecast_active_logical_cost | forecaset_long_term_logical_cost | forecast_active_physical_cost | forecast_long_term_physical_cost | forecast_total_cost_difference |
+--------------+--------------------+-----------------------+---------------------+------------------------+--------------------------+-----------------------------+------------------------------+----------------------------------+-------------------------------+----------------------------------+--------------------------------+
| dataset1     |               10.0 |                  10.0 |                 1.0 |                    1.0 |                     10.0 |                        10.0 |                          0.2 |                              0.1 |                          0.04 |                             0.02 |                           0.24 |