Introduction to audit logs in BigQuery

Logs are text records that are generated in response to particular events or actions. For instance, BigQuery creates log entries for actions such as creating or deleting a table, purchasing slots, or running a load job.

Google Cloud also writes logs, including audit logs that provide insight into operational concerns related to your use of Google Cloud services. For more information about how Google Cloud handles logging, see the Cloud Logging documentation and Cloud Audit Logs overview.

Audit logs versus INFORMATION_SCHEMA views

Your Google Cloud projects contain audit logs only for the resources that are directly within the Google Cloud project. Other Google Cloud resources, such as folders, organizations, and billing accounts, contain their own audit logs.

Audit logs help you answer the question "Who did what, where, and when?" within your Google Cloud resources. Audit logs are the definitive source of information for system activity by user and access patterns and should be your primary source for audit or security questions.

INFORMATION_SCHEMA views in BigQuery are another source of insights that you can use along with metrics and logs. These views contain metadata about jobs, datasets, tables, and other BigQuery entities. For example, you can get real-time metadata about which BigQuery jobs ran during a specified time. Then, you can group or filter the results by project, user, tables referenced, and other dimensions.

INFORMATION_SCHEMA views provide you information to perform a more detailed analysis about your BigQuery workloads, such as the following:

  • What is the average slot utilization for all queries over the past seven days for a given project?
  • What streaming errors occurred in the past 30 minutes, grouped by error code?

BigQuery audit logs contain log entries for API calls, but they don't describe the impact of the API calls. A subset of API calls creates jobs (such as query and load) whose information is captured by INFORMATION_SCHEMA views. For example, you can find information about the time and slots that are utilized by a specific query in INFORMATION_SCHEMA views but not in the audit logs.

To get insights into the performance of your BigQuery workloads in particular, see jobs metadata, streaming metadata, and reservations metadata.

For more information about the types of audit logs that Google Cloud services write, see Types of audit logs.

Audit log format

Google Cloud services write audit logs in a structured JSON format. The base data type for Google Cloud log entries is the LogEntry structure. This structure contains the name of the log, the resource that generated the log entry, the timestamp (UTC), and other basic information.

Logs include details of the logged event in a subfield that's called the payload field. For audit logs, the payload field is named protoPayload. This field's type (protoPayload.@type) is set to, which indicates that the field uses the AuditLog log structure.

For operations on datasets, tables, and jobs, BigQuery writes audit logs in two different formats, although both formats share the AuditLog base type.

The older format includes the following fields and values:

  • The value for the resource.type field is bigquery_resource.
  • BigQuery writes the details about an operation in the protoPayload.serviceData field. The value of this field uses the AuditData log structure.

The newer format includes the following fields and values:

  • The value for the resource.type field is either bigquery_project or bigquery_dataset. The bigquery_project resource has log entries about jobs, while the bigquery_dataset resource has log entries about storage.
  • BigQuery writes the details about an operation in the protoPayload.metadata field. The value of this field uses the BigQueryAuditMetadata structure.

We recommend consuming logs in the newer format. For more information, see Audit logs migration guide.

The following is an abbreviated example of a log entry that shows a failed operation:

  "protoPayload": {
    "@type": "",
    "status": {
      "code": 5,
      "message": "Not found: Dataset myproject:mydataset was not found in location US"
    "authenticationInfo": { ... },
    "requestMetadata":  { ... },
    "serviceName": "",
    "methodName": "",
    "metadata": {
  "resource": {
    "type": "bigquery_project",
    "labels": { .. },
  "severity": "ERROR",
  "logName": "projects/myproject/logs/",

For operations on BigQuery reservations, the protoPayload field uses the AuditLog structure, and the protoPayload.request and protoPayload.response fields contain more information. You can find the field definitions in BigQuery Reservation API. For more information, see Monitoring BigQuery reservations.

For a deeper understanding of the audit log format, see Understand audit logs.


Log messages have a size limit of 100,000 bytes. For more information, see Truncated log entry.

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 tables and datasets. For information about managing access to this information, see the Cloud Logging access control documentation.

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