Configure log buckets

This document describes how to create and manage Cloud Logging buckets using the Google Cloud console, the Google Cloud CLI, and the Logging API. It also provides instructions for creating and managing log buckets at the Google Cloud project level. You can't create log buckets at the folder or organization level; however, Cloud Logging automatically creates _Default and _Required buckets at the folder and organization level for you.

You can upgrade log buckets to use Log Analytics. Log Analytics lets you run SQL queries on your log data, helping you troubleshoot application, security, and networking issues. You can also use BigQuery to view the data stored in a log bucket when the log bucket is upgraded to use Log Analytics and when a linked BigQuery dataset exists. Creating a linked dataset lets you join your log data with other data stored in BigQuery, and access data from other tools like Looker Studio and Looker.

For a conceptual overview of buckets, see Routing and storage overview: Log buckets.

Before you begin

To get started with buckets, do the following:

  • Ensure that you've enabled billing for your Google Cloud project.

  • Ensure that your Identity and Access Management (IAM) role grants you the permissions necessary to create, upgrade, and link buckets.

    The Logs Configuration Writer (roles/logging.configWriter) role is the minimal predefined role that grants the permissions required to manage buckets. For the full list of permissions and roles, see Access control with IAM.

  • Understand the LogBucket formatting requirements, including the supported regions in which you can store your logs.

  • Consider setting a default resource location to apply a default storage region for the _Required and _Default buckets in your organization.

  • To use BigQuery to view the data stored in a log bucket, do the following:

  • For information about how to create a log bucket that uses a customer-managed encryption key (CMEK), see Configure CMEK for logs storage.

Create a bucket

You can create a maximum of 100 buckets per Google Cloud project.

To create a user-defined log bucket for your Google Cloud project, do the following:

Google Cloud console

To create a log bucket in your Google Cloud project, do the following:

  1. In the navigation panel of the Google Cloud console, select Logging, and then select Logs Storage:

    Go to Logs Storage

  2. Click Create log bucket.

  3. Enter a Name and Description for your bucket.

  4. Optional: Upgrade your bucket to use Log Analytics.

    1. Select Upgrade to use Log Analytics.

      When you upgrade a bucket to use Log Analytics, you can query your logs in the Log Analytics page by using SQL queries. You can also continue to view your logs by using the Logs Explorer.

      Not all regions are supported for Log Analytics. For more information, see Supported regions.

    2. Optional: To view your logs in BigQuery, select Create a new BigQuery dataset that links to this bucket and enter a unique dataset name.

      When you select this option, BigQuery can read the data stored in your log bucket. You can now query in the BigQuery interface where you can join your log data, and also access data from other tools like Looker Studio and Looker.

  5. Optional: To select the storage region for your logs, click the Select log bucket region menu and select a region. If you don't select a region, then the global region is used, which means that the logs could be physically located in any of the regions.

  6. Optional: To set a custom retention period for the logs in the bucket, click Next.

    In the Retention period field, enter the number of days, between 1 day and 3650 days, that you want Cloud Logging to retain your logs. If you don't customize the retention period, the default is 30 days.

    You can also update your bucket to apply custom retention after you create it.

  7. Click Create bucket.

    After the log bucket is created, Logging upgrades the bucket and creates the dataset link, if these options were selected.

    It might take a moment for these steps to complete.

gcloud

To only create a log bucket, run the gcloud logging buckets create command. If you want to upgrade the log bucket to use Log Analytics, then include the --enable-analytics and --async flags, and ensure that you set the variable LOCATION to a region supported for Log Analytics:

gcloud logging buckets create BUCKET_ID --location=LOCATION --enable-analytics --async OPTIONAL_FLAGS

The flag --async forces the command to be asynchronous. The return of an asynchronous method is an Operation object, it contains information about the progress of the method. When the method completes, the Operation object contains the status. For more information, see Asynchronous API methods.

If you don't want to upgrade the log bucket to use Log Analytics, then omit the --enable-analytics and --async flags. You can set the the variable LOCATION to any supported region.

For example, if you want to create a bucket with the BUCKET_ID my-bucket in the asia-east2 region, your command would look like the following:

gcloud logging buckets create my-bucket --location asia-east2 --description "My first bucket"

For example, to create a bucket with the BUCKET_ID my-upgraded-bucket in the us location, and then upgrade the log bucket to use Log Analytics, your command would look like the following:

gcloud logging buckets create my-upgraded-bucket --location us \
      --description "My first upgraded bucket" \
      --enable-analytics --retention-days=45

API

To create a bucket, use the projects.locations.buckets.create or the projects.locations.buckets.createAsync method. Prepare the arguments to the method as follows:

  1. Set the parent parameter to be the resource in which to create the bucket: projects/PROJECT_ID/locations/LOCATION

    The variable LOCATION refers to the region in which you want your logs to be stored. Not all regions are supported for Log Analytics. For more information, see Supported regions.

    For example, if you want to create a bucket for project my-project in the in the asia-east2 region, your parent parameter would look like this: projects/my-project/locations/asia-east2

  2. Set the bucketId parameter; for example, my-bucket.

  3. Do one of the following:

    • To create a log bucket and then upgrade the log bucket to use Log Analytics:

      1. Set the LogBucket.analyticsEnabled boolean to true.

      2. Call the asynchronous method projects.locations.buckets.createAsync to create the bucket.

        The response to the asynchronous methods is an Operation object. This object contains information about the progress of the method. When the method completes, the Operation object contains the status. For more information, see Asynchronous API methods.

        The createAsync method takes several minutes to complete. This method method doesn't generate an error message or fail when the analyticsEnabled boolean is set to true and the region isn't supported for upgraded buckets. For example, if you set the location to asia-east2, then the log bucket is created but the bucket isn't upgraded to use Log Analytics.

    • Otherwise, call the synchronous method projects.locations.buckets.create to create the bucket.

After creating a bucket, create a sink to route log entries to your bucket and configure log views to control who can access the logs in your new bucket and which logs are accessible to them. You can also update the bucket to configure custom retention and restricted fields.

Track volume of logs stored in log buckets

The Logs Storage page in the Google Cloud console tracks the volume of logs data stored in log buckets:

In the navigation panel of the Google Cloud console, select Logging, and then select Logs Storage:

Go to Logs Storage

The Logs Storage page displays a summary of statistics for your Google Cloud project:

The summary statistics report the amount of log data stored in log buckets
for the selected project.

The following statistics are reported:

  • Current month ingestion: The amount of logs data that your Google Cloud project has stored in log buckets since the first day of the current calendar month.

  • Previous month ingestion: The amount of logs data that your Google Cloud project stored in log buckets in the last calendar month.

  • Projected ingestion by EOM: The estimated amount of logs data that your Google Cloud project will store in log buckets by the end of the current calendar month, based on current usage.

  • Current month billable storage: The amount of logs data that has been retained for over 30 days that is billed.

The previous statistics don't include logs in the _Required bucket. The logs in that bucket can't be excluded or disabled.

The Log Router page in the Google Cloud console gives you tools that you can use to minimize any charges for storing logs in log buckets or for storage that exceeds your monthly allotment. You can do the following:

  • Disable logs from being stored at the bucket level.
  • Exclude certain log entries from being stored in log buckets.

For more information, see Manage sinks.

Manage buckets

This section describes how to manage your log buckets using the Google Cloud CLI or the Google Cloud console.

Update a bucket

To update the properties of your bucket, such as the description or retention period, do the following:

Google Cloud console

To update your bucket's properties, do the following:

  1. In the navigation panel of the Google Cloud console, select Logging, and then select Logs Storage:

    Go to Logs Storage

  2. For the bucket you want to update, click More.

  3. Select Edit bucket.

  4. Edit your bucket as needed.

  5. Click Update bucket.

gcloud

To update your bucket's properties, run the gcloud logging buckets update command:

gcloud logging buckets update BUCKET_ID --location=LOCATION UPDATED_ATTRIBUTES

For example:

gcloud logging buckets update my-bucket --location=global --description "Updated description"

API

To update your bucket's properties, use projects.locations.buckets.patch in the Logging API.

Upgrade a bucket to use Log Analytics

To upgrade an existing bucket to use Log Analytics, the following restrictions apply:

  • The log bucket was created at the Google Cloud project level.
  • The log bucket is unlocked unless it is the _Required bucket.
  • There aren't pending updates to the bucket.
  • Not all regions are supported for Log Analytics. For more information, see Supported regions.

Google Cloud console

To upgrade an existing bucket to use Log Analytics, do the following:

  1. In the navigation panel of the Google Cloud console, select Logging, and then select Logs Storage:

    Go to Logs Storage

  2. Locate the bucket that you want to upgrade.

  3. When the Log Analytics available column displays Upgrade, you can upgrade the log bucket to use Log Analytics. Click Upgrade.

    A dialog opens. Click Confirm.

gcloud

To upgrade your log bucket to use Log Analytics, run the gcloud logging buckets update command. You must set the --enable-analytics flag, and we recommend that you also include the --async flag:

gcloud logging buckets update BUCKET_ID --location=LOCATION --enable-analytics --async

The flag --async forces the command to be asynchronous. The return of an asynchronous method is an Operation object, and it contains information about the progress of the method. When the method completes, the Operation object contains the status. For more information, see Asynchronous API methods.

API

To upgrade a log bucket to use Log Analytics, use the projects.locations.buckets.updateAsync method of the Cloud Logging API.

Prepare the arguments to the method as follows:

  1. Set the LogBucket.analyticsEnabled boolean to true.
  2. For the query parameter of the command, use updateMask=analyticsEnabled.

The response to the asynchronous methods is an Operation object. This object contains information about the progress of the method. When the method completes, the Operation object contains the status. For more information, see Asynchronous API methods.

The updateAsync might take several minutes to complete.

When you want to use the capabilities of BigQuery to analyze your log data, upgrade a log bucket to use Log Analytics, and then create a linked dataset. With this configuration, you can use BigQuery to read the logs stored in the log bucket.

Google Cloud console

To create a link to a BigQuery dataset for an existing log bucket, do the following:

  1. Review the Before you begin section of this document.

  2. In the navigation panel of the Google Cloud console, select Logging, and then select Logs Storage:

    Go to Logs Storage

  3. Locate the log bucket and verify that the Log Analytics available column displays Open.

    If this column displays Upgrade, then the log bucket hasn't been upgraded to use Log Analytics. Configure Log Analytics:

    1. Click Upgrade.
    2. Click Confirm in the dialog.

    After the upgrade completes, proceed to the next step.

  4. On the log bucket, click More, and then click Edit bucket.

    The Edit log bucket dialog opens.

  5. Select Create a new BigQuery dataset that links to this bucket and enter the name for the new dataset.

    The dataset name must be unique for each Google Cloud project. If you enter the name of an existing dataset, then you receive the following error: Dataset name must be unique in the selected region.

  6. Click Done and then click Update bucket.

    After Logging displays the linked dataset name on the Logs Storage page, it might take several minutes before BigQuery recognizes the dataset.

gcloud

To create a linked dataset for a log bucket that is upgraded to use Log Analytics, run the gcloud logging links create command:

gcloud logging links create LINK_ID --bucket=BUCKET_ID --location=LOCATION

The LINK_ID field must be unique for your Google Cloud project.

The links create command is asynchronous. The return of an asynchronous method is an Operation object, and it contains information about the progress of the method. When the method completes, the Operation object contains the status. For more information, see Asynchronous API methods.

The links create command takes several minutes to complete.

For example, the following command creates a linked dataset for the bucket with the name my-bucket:

gcloud logging links create mylink --bucket=my-bucket --location=global

The dataset name must be unique for each Google Cloud project. If you attempt to create a dataset with the same name as an existing dataset, then you receive the following error:

BigQuery dataset with name "my-bucket" already exists.

If you attempt to create a linked dataset for a log bucket that isn't upgraded to use Log Analytics, then the following error is reported:

A link can only be created for an analytics-enabled bucket.

API

To create a linked a BigQuery dataset for an existing log bucket that is upgraded use Log Analytics, call the asynchronous projects.locations.buckets.links.create method of the Cloud Logging API.

Prepare the arguments to the method as follows:

  1. Construct the request body for the create command. The request body is formatted as a Link object.
  2. For the query parameter of the command, use linkId=LINK_ID. The LINK_ID that you provide is used as the name of the dataset, therefore LINK_ID shares the same value as the output-only DATASET_ID.

The response to the asynchronous methods is an Operation object. This object contains information about the progress of the method. When the method completes, the Operation object contains the status. For more information, see Asynchronous API methods.

The links.create method takes several minutes to complete.

The dataset name must be unique for each Google Cloud project. If you attempt to create a dataset with the same name as an existing dataset, then you receive the following error:

BigQuery dataset with name "my-bucket" already exists.

If you attempt to create a linked dataset for a log bucket that isn't upgraded to use Log Analytics, then the following error is reported:

A link can only be created for an analytics-enabled bucket.

Lock a bucket

When you lock a bucket against updates, you also lock the bucket's retention policy. After a retention policy is locked, you can't delete the bucket until every log entry in the bucket has fulfilled the bucket's retention period.

To prevent anyone from updating or deleting a log bucket, lock the bucket. To lock the bucket, do the following:

Google Cloud console

The Google Cloud console doesn't support locking a log bucket.

gcloud

To lock your bucket, run the gcloud logging buckets update command with the --locked flag:

gcloud logging buckets update BUCKET_ID --location=LOCATION --locked

For example:

gcloud logging buckets update my-bucket --location=global --locked

API

To lock your bucket's attributes, use projects.locations.buckets.patch in the Logging API. Set the locked parameter to true.

List buckets

To list the log buckets associated with a Google Cloud project, and to see details such as retention settings, do the following:

Google Cloud console

In the navigation panel of the Google Cloud console, select Logging, and then select Logs Storage:

Go to Logs Storage

A table named Log buckets lists the buckets associated with the current Google Cloud project.

The table lists the following attributes for each log bucket:

  • Name: The name of the log bucket.
  • Description: The description of the bucket.
  • Retention period: The number of days that the bucket's data will be stored by Cloud Logging.
  • Region: The geographic location in which the bucket's data is stored.
  • Status: Whether the bucket is locked or unlocked.

If a bucket is pending deletion by Cloud Logging, then its table entry is annotated with a warning symbol.

gcloud

Run the gcloud logging buckets list command:

gcloud logging buckets list

You see the following attributes for the log buckets:

  • LOCATION: The region in which the bucket's data is stored.
  • BUCKET_ID: The name of the log bucket.
  • RETENTION_DAYS: The number of days that the bucket's data will be stored by Cloud Logging.
  • LIFECYCLE_STATE: Indicates whether the bucket is pending deletion by Cloud Logging.
  • LOCKED: Whether the bucket is locked or unlocked.
  • CREATE_TIME: A timestamp that indicates when the bucket was created.
  • UPDATE_TIME: A timestamp that indicates when the bucket was last modified.

You can also view the attributes for just one bucket. For example, to view the details for the _Default log bucket in the global region, run the gcloud logging buckets describe command:

gcloud logging buckets describe _Default --location=global

API

To list the log buckets associated with a Google Cloud project, use projects.locations.buckets.list in the Logging API.

View a bucket's details

To view the details of a single log bucket, do the following:

Google Cloud console

In the navigation panel of the Google Cloud console, select Logging, and then select Logs Storage:

Go to Logs Storage

On the log bucket, click More and then select View bucket details.

The dialog lists the following attributes for the log bucket:

  • Name: The name of the log bucket.
  • Description: The description of the log bucket.
  • Retention period: The number of days that the bucket's data will be stored by Cloud Logging.
  • Region: The geographic location in which the bucket's data is stored.
  • Log analytics: Indicates whether your bucket is upgraded to use Log Analytics.
  • BigQuery analysis: Indicates whether a BigQuery dataset is linked to your bucket.
  • BigQuery dataset: Provides a link to your BigQuery dataset, which opens in the BigQuery Studio page. The date that BigQuery linking was enabled is also shown.

gcloud

Run the gcloud logging buckets describe command.

For example, the following command reports the details of the _Default bucket:

gcloud logging buckets describe _Default --location=global

You see the following attributes for the log bucket:

  • createTime: A timestamp that indicates when the bucket was created.
  • description: The description of the log bucket.
  • lifecycleState: Indicates whether the bucket is pending deletion by Cloud Logging.
  • name: The name of the log bucket.
  • retentionDays: The number of days that the bucket's data will be stored by Cloud Logging.
  • updateTime: A timestamp that indicates when the bucket was last modified.

API

To view the details of a single log bucket, use projects.locations.buckets.get in the Logging API.

Delete a bucket

To delete a log bucket, do the following:

Google Cloud console

To delete a log bucket, do the following:

  1. In the navigation panel of the Google Cloud console, select Logging, and then select Logs Storage:

    Go to Logs Storage

  2. Locate the bucket that you want to delete, and click More.

  3. If the Linked dataset in BigQuery column displays a link, then delete the linked BigQuery dataset:

    1. Click Edit bucket.

    2. Clear Create a new BigQuery dataset that links to this bucket, click Done, and then click Update bucket.

      After you return to the Logs Storage page, click More for the bucket you want to delete, then proceed to the next steps.

  4. Select Delete bucket.

  5. On the confirmation panel, click Delete.

  6. On the Logs Storage page, your bucket has an indicator that it's pending deletion. The bucket, including all the logs in it, is deleted after 7 days.

gcloud

To delete a log bucket, run the gcloud logging buckets delete command:

gcloud logging buckets delete BUCKET_ID --location=LOCATION

You can't delete a log bucket when that bucket has a linked BigQuery dataset:

API

To delete a bucket, use projects.locations.buckets.delete in the Logging API.

It is an error to delete a log bucket if that bucket has a linked BigQuery dataset. You must delete the linked dataset before deleting the log bucket:

A deleted bucket stays in this pending state for 7 days, and Logging continues to route logs to the bucket during that time. To stop routing logs to a deleted bucket, you can delete the log sinks that have that bucket as a destination, or you can modify the filter for the sinks to stop routing logs to the deleted bucket.

You can't create a new log bucket that uses the same name as a deleted bucket that is in a pending state.

Restore a deleted bucket

You can restore, or undelete, a log bucket that's in the pending deletion state. To restore a log bucket, do the following:

Google Cloud console

To restore a log bucket that is pending deletion, do the following:

  1. In the navigation panel of the Google Cloud console, select Logging, and then select Logs Storage:

    Go to Logs Storage

  2. For the bucket you want to restore, click More, and then select Restore deleted bucket.

  3. On the confirmation panel, click Restore.

  4. On the Logs Storage page, the pending-deletion indicator is removed from your log bucket.

gcloud

To restore a log bucket that is pending deletion, run the gcloud logging buckets undelete command:

gcloud logging buckets undelete BUCKET_ID --location=LOCATION

API

To restore a bucket that is pending deletion, use projects.locations.buckets.undelete in the Logging API.

Alert on monthly log bytes stored in log buckets

To create an alerting policy, on the Logs Storage page in the Google Cloud console, click Create usage alert. This button opens the Create alerting policy page in Monitoring, and populates the metric type field with logging.googleapis.com/billing/bytes_ingested.

To create an alerting policy that triggers when the number of log bytes written to your log buckets exceeds your user-defined limit for Cloud Logging, use the following settings.

New condition
Field

Value
Resource and Metric In the Resources menu, select Global.
In the Metric categories menu, select Logs-based metric.
In the Metrics menu, select Monthly log bytes ingested.
Filter None.
Across time series
Time series aggregation
sum
Rolling window 60 m
Rolling window function max
Configure alert trigger
Field

Value
Condition type Threshold
Alert trigger Any time series violates
Threshold position Above threshold
Threshold value You determine the acceptable value.
Retest window Minimum acceptable value is 30 minutes.

For more information about alerting policies, see Alerting overview.

Write to a bucket

You don't directly write logs to a log bucket. Rather, you write logs to Google Cloud resource: a Google Cloud project, folder, or organization. The sinks in the parent resource then route the logs to destinations, including log buckets. A sink routes logs to a log bucket destination when the logs match the sink's filter and the sink has permission to route the logs to the log bucket.

Read from a bucket

Each log bucket has a set of log views. To read logs from a log bucket, you need access to a log view on the log bucket. Log views let you grant a user access to only a subset of the logs stored in a log bucket. For information about how to configure log views, and how to grant access to specific log views, see Configure log views on a log bucket.

To read logs from a log bucket, do the following:

Google Cloud console

  1. In the navigation panel of the Google Cloud console, select Logging, and then select Logs Explorer:

    Go to Logs Explorer

  2. To customize which logs are displayed in the Logs Explorer, click Refine scope, and then select an option:

    • To view all logs that are generated in, or routed directly to the current project, select Scope by current project.

    • To view certain logs stored in log buckets in the current project, select Scope by storage, and then select one or more log views.

    • To view certain logs stored in log buckets that are in different projects, select Scope by storage, click Import project in the project and log view selector, and then select one or more log views.

  3. Click Apply. The Query results pane reloads with logs that match the option you selected.

For more information about customizing which logs are displayed in the Logs Explorer, see Logs Explorer overview: Refine scope.

gcloud

To read logs from a log bucket, use the gcloud logging read command and add a LOG_FILTER to select data:

gcloud logging read LOG_FILTER --bucket=BUCKET_ID --location=LOCATION --view=VIEW_ID

API

To read logs from a log bucket, use the entries.list method. Set resourceNames to specify the appropriate bucket and log view, and set filter to select data.

For detailed information about the filtering syntax, see Logging query language.

Configure custom retention

When you create a log bucket, you have the option to customize the period for how long Cloud Logging stores the bucket's logs. You can configure the retention period for any user-defined log bucket and also for the _Default log bucket.

If you shorten the bucket's retention, then there is a 7-day grace period in which expired logs aren't deleted. You can't query or view those expired logs but, in those 7 days, you can restore full access by extending the bucket's retention. Logs stored during the grace period count towards your retention costs.

To update the retention period for a log bucket, do the following:

Google Cloud console

To update a log bucket's retention period, do the following:

  1. In the navigation panel of the Google Cloud console, select Logging, and then select Logs Storage:

    Go to Logs Storage

  2. For the bucket you want to update, click More, and then select Edit bucket.

  3. In the Retention field, enter the number of days, between 1 day and 3650 days, that you want Cloud Logging to retain your logs.

  4. Click Update bucket. Your new retention duration appears in the Logs bucket list.

gcloud

To update the retention period for a log bucket, run the gcloud logging buckets update command, after setting a value for RETENTION_DAYS:

gcloud logging buckets update BUCKET_ID  --location=LOCATION --retention-days=RETENTION_DAYS

For example, to retain the logs in the _Default bucket in the global location for a year, your command would look like the following:

gcloud logging buckets update _Default --location=global --retention-days=365

If you extend a bucket's retention period, then the retention rules apply going forward and not retroactively. Logs can't be recovered after the applicable retention period ends.

Asynchronous API methods

The response of an asynchronous method like projects.locations.buckets.createAsync is an Operation object.

Applications that call an asynchronous API method should poll the operation.get endpoint until the value of the Operation.done field is true:

  • When done is false, the operation is in progress.

    To refresh the status information, send a GET request to the operation.get endpoint.

  • When done is true, the operation is complete and either the error or response field is set:

    • error: When set, the asynchronous operation failed. The value of this field is a Status object that contains a gRPC error code and an error message.
    • response: When set, the asynchronous operation completed successfully, and the value reflects the result.

To poll an asynchronous command by using the Google Cloud CLI, run the following command:

gcloud logging operations describe OPERATION_ID --location=LOCATION --project=PROJECT_ID

For more information, see gcloud logging operations describe.

Troubleshoot common issues

If you encounter problems when using log buckets, refer to the following troubleshooting steps and answers to common questions.

Why can't I delete this bucket?

If you're trying to delete a bucket, do the following:

  • Ensure that you have the correct permissions to delete the bucket. For the list of the permissions that you need, see Access control with IAM.

  • Determine whether the bucket is locked by listing the bucket's attributes. If the bucket is locked, check the bucket's retention period. You can't delete a locked bucket until all of the logs in the bucket have fulfilled the bucket's retention period.

  • Verify that the log bucket doesn't have a linked BigQuery dataset. You can't delete a log bucket with a linked dataset.

    The following error is shown in response to a delete command on a log bucket that has a linked dataset:

    FAILED_PRECONDITION: This bucket is used for advanced analytics and has an active link. The link must be deleted first before deleting the bucket
    

    To list the links associated with a log bucket, run the gcloud logging links list command or run the projects.locations.buckets.links.list API method.

Which service accounts are routing logs to my bucket?

To determine if any service accounts have IAM permissions to route logs to your bucket, do the following:

  1. In the navigation panel of the Google Cloud console, select IAM:

    Go to IAM

  2. From the Permissions tab, view by Roles. You see a table with all the IAM roles and principals associated with your Google Cloud project.

  3. In the table's Filter text box, enter Logs Bucket Writer.

    You see any principals with the Logs Bucket Writer role. If a principal is a service account, its ID contains the string gserviceaccount.com.

  4. Optional: If you want to remove a service account from being able to route logs to your Google Cloud project, select the check box for the service account and click Remove.

Why do I see logs for a Google Cloud project even though I excluded them from my _Default sink?

You might be viewing logs in a log bucket in a centralized Google Cloud project, which aggregates logs from across your organization.

If you're using the Logs Explorer to access these logs and see logs that you excluded from the _Default sink, then your view might be scoped to the Google Cloud project level.

To fix this issue, select Scope by storage in the Refine scope panel and then select the _Default bucket in your Google Cloud project. You shouldn't see the excluded logs anymore.

What's next

For information on the log bucket API methods, refer to the LogBucket reference documentation.

For information on addressing common use cases with log buckets, see the following topics: