BigQuery audit logs overview


Cloud Audit Logs are a collection of logs provided by Google Cloud that provide insight into operational concerns related to your use of Google Cloud services. This page provides details about BigQuery specific log information, and it demonstrates how to use BigQuery to analyze logged activity.


The audit log message system relies on structured logs, and the BigQuery service provides two distinct kinds of messages:

  • AuditData: The old version of logs, which reports API invocations.

  • BigQueryAuditMetadata: The new version of logs, which reports resource interactions such as which tables were read from and written to by a given query job and which tables expired due to having an expiration time configured.

    In general, users will want to leverage the new BigQueryAuditMetadata logs functionality.

Message Formats

AuditData format

The AuditData messages are communicated within the protoPayload.serviceData submessage within the Stackdriver Logging LogEntry message.

BigQueryAuditMetadata format

You can find BigQueryAuditMetadata details in the protoPayload.metadata submessage that is in the Stackdriver Logging LogEntry message.

In the Stackdriver Logging logs, the protoPayload.serviceData information is not set or used. In BigQueryAuditMetadata messages, there is more information:

  • resource.type is set to one of the following values:

    • bigquery_project for jobs
      • resource.labels.location contains the location of the job.
    • bigquery_dataset for storage
      • resource.labels.dataset_id contains the encapsulating dataset.
  • protoPayload.methodName is set to one of the following values:

    • InternalTableExpired
  • protoPayload.resourceName now contains the URI for the referenced resource. For example, a table created by using an insert job reports the resource URI of the table The earlier format reported the API resource (the job identifier).

  • protoPayload.authorizationInfo only includes information relevant to the specific event. With earlier AuditData messages, you could merge multiple records when source and destination tables were in the same dataset in a query job.

Mapping audit entries to log streams

Audit logs are organized into the following three streams. For more information about the streams, see the Cloud Audit Logs documentation.

  • Data access
  • System event
  • Admin activity

Data access (data_access)

The data_access stream contains entries about jobs by using the JobInsertion and JobChange events and about table data modifications by using the TableDataChange and TableDataRead events.

For example, when a load job appends data to a table, the data_access stream adds a TableDataChange event. A TableDataRead event indicates when a consumer reads a table.

Note: BigQuery does not emit data access log entries in the following scenarios:

  • Data appended to a table by using the streaming insert mechanism does not generate TableDataChange log entries.

  • Recursive dataset deletions, such as removing a dataset and its contents in a single API call, do not yield deletion entries for each resource contained in the dataset. The dataset removal is present in the activity log.

  • Partitioned tables do not generate TableDataChange entries for partition expirations.

System event (system_event)

You can set an expiration time on tables to remove them at a specified time. The system_event stream reports a TableDeletion event when the table expires and is removed.

Admin activity (activity)

The main activity stream reports all remaining activities and events such as table and dataset creation.

Visibility and access control

BigQuery audit logs can include information that users might consider sensitive, such as SQL text, schema definitions, and identifiers for resources such as table and datasets. For information about managing access to this information, see the Stackdriver Logging access control documentation.

Caller identities and resource names

All caller identities and IP addresses are redacted from the audit logs, unless at least one of the following conditions is met:

  • This isn't a read-only access.
  • The identity is a service account that belongs to the project.
  • The identity belongs to the same customer as the project.

For cross-project access, there are additional rules that apply:

The billing project must be the project that sends the request, and the data project must be the project whose resources are also accessed during the job. For example, a query job in a billing project reads some table data from the data project.

The billing project resource ID is redacted from the data project log unless the projects have the same domain associated with them or are in the same organization.

Identities and caller IP addresses are redacted from the data project log unless one of the preceding or one of the following conditions apply:

  • The billing project and the data project have the same domain associated with them or are in the same organization, and the billing project already includes the identity and caller IP address.
  • The identity has permission to run queries in the project and the action is a job.insert action.

Stackdriver Logging exports

BigQuery automatically sends audit logs to Stackdriver Logging. Stackdriver Logging lets users filter and export messages to other services, including Pub/Sub, Cloud Storage, and BigQuery.

With long term log retention and log exports to BigQuery, you can do aggregated analysis on logs data. Stackdriver Logging documents how messages are transformed when exported to BigQuery.

Filtering exports

To filter relevant BigQuery Audit messages, you can express filters as part of the export.

For example, the following advanced filter represents an export that only includes the newer BigQueryAuditMetadata format:


You can express additional filters based on the fields within the log messages. For more information about crafting advanced filters, see the advanced log filter documentation.

Defining a BigQuery log sink using gcloud

The following example command line shows how you can use the gcloud command-line tool to create a logging sink in a dataset named auditlog_dataset that only includes BigQueryAuditMetadata messages:

gcloud logging sinks create my-example-sink

Querying exported logs

AuditData examples

The following examples show how you can use AuditData messages to analyze BigQuery usage. AuditData fields are present in the protopayload_auditlog.servicedata_v1_bigquery record in the schema.

Note: Change the FROM clause, MYPROJECTID.MYDATASETID.cloudaudit_googleapis_com_data_access_YYYYMMDD, to the dataset and table date you've configured in the Stackdriver Logging export.

Example: Query cost breakdown by identity

This query shows estimated query costs by user identity. It estimates costs based on the list price for on-demand queries in the US. This pricing might not be accurate for other locations or for customers who are leveraging flat-rate billing.

  WITH data as
      protopayload_auditlog.authenticationInfo.principalEmail as principalEmail,
      protopayload_auditlog.servicedata_v1_bigquery.jobCompletedEvent AS jobCompletedEvent
    FORMAT('%9.2f',5.0 * (SUM(jobCompletedEvent.job.jobStatistics.totalBilledBytes)/POWER(2, 40))) AS Estimated_USD_Cost
    jobCompletedEvent.eventName = 'query_job_completed'
  GROUP BY principalEmail
  ORDER BY Estimated_USD_Cost DESC

Example: Hourly cost breakdown

This query shows estimated query costs by hour.

    TIMESTAMP_TRUNC(protopayload_auditlog.servicedata_v1_bigquery.jobCompletedEvent.job.jobStatistics.endTime, HOUR) AS time_window,
    FORMAT('%9.2f',5.0 * (SUM(protopayload_auditlog.servicedata_v1_bigquery.jobCompletedEvent.job.jobStatistics.totalBilledBytes)/POWER(2, 40))) AS Estimated_USD_Cost
    protopayload_auditlog.servicedata_v1_bigquery.jobCompletedEvent.eventName = 'query_job_completed'
  GROUP BY time_window
      ORDER BY time_window DESC

BigQueryAuditMetadata examples

The following examples show how you can use BigQueryAuditMetadata messages to analyze BigQuery usage. Because of the schema conversion done during the export from Stackdriver Logging into BigQuery, the message bodies are presented in semi-structured form. The protopayload_auditlog.metadataJson is a STRING field, and it contains the JSON representation of the message. You can leverage JSON functions in standard SQL to analyze this content.

Note: Change the FROM clause in each of these examples to the corresponding exported tables in your project.

Example: Report expired tables

BigQueryAuditMetadata messages log when a table is deleted because its expiration time was reached. The following sample query shows when these messages occur and includes a URI that references the table resource that was removed.

    protopayload_auditlog.resourceName AS resourceName,
    receiveTimestamp as logTime
  FROM `MYPROJECTID.MYDATASETID.cloudaudit_googleapis_com_system_event_201901*`
    protopayload_auditlog.methodName = 'InternalTableExpired'
  ORDER BY resourceName

This query shows coarse, per-dataset statistics about table reads and table modifications.

    REGEXP_EXTRACT(protopayload_auditlog.resourceName, '^projects/[^/]+/datasets/([^/]+)/tables') AS datasetRef,
    COUNT(DISTINCT REGEXP_EXTRACT(protopayload_auditlog.resourceName, '^projects/[^/]+/datasets/[^/]+/tables/(.*)$')) AS active_tables,
    COUNTIF(JSON_EXTRACT(protopayload_auditlog.metadataJson, "$.tableDataRead") IS NOT NULL) AS dataReadEvents,
    COUNTIF(JSON_EXTRACT(protopayload_auditlog.metadataJson, "$.tableDataChange") IS NOT NULL) AS dataChangeEvents
  FROM `MYPROJECTID.MYDATASETID.cloudaudit_googleapis_com_data_access_2019*`
    JSON_EXTRACT(protopayload_auditlog.metadataJson, "$.tableDataRead") IS NOT NULL
    OR JSON_EXTRACT(protopayload_auditlog.metadataJson, "$.tableDataChange") IS NOT NULL
  GROUP BY datasetRef
  ORDER BY datasetRef
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