gcp_bigquery_dataset_info – Gather info for GCP Dataset — Ansible Documentation

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# gcp_bigquery_dataset_info – Gather info for GCP Dataset

New in version 2.8.

- Synopsis
- Requirements
- Parameters
- Notes
- Examples
- Return Values
- Status

## Synopsis

- Gather info for GCP Dataset
- This module was called `gcp_bigquery_dataset_facts` before Ansible 2.9. The usage has not changed.

Aliases: gcp_bigquery_dataset_facts

## Requirements

The below requirements are needed on the host that executes this module.

- python >= 2.6
- requests >= 2.18.4
- google-auth >= 1.3.0

## Parameters

Parameter | Choices/Defaults | Comments
--- | --- | ---
auth_kind string / required | Choices: application machineaccount serviceaccount | The type of credential used.
env_type string |  | Specifies which Ansible environment you're running this module within. This should not be set unless you know what you're doing. This only alters the User Agent string for any API requests.
project string |  | The Google Cloud Platform project to use.
scopes list |  | Array of scopes to be used.
service_account_contents jsonarg |  | The contents of a Service Account JSON file, either in a dictionary or as a JSON string that represents it.
service_account_email string |  | An optional service account email address if machineaccount is selected and the user does not wish to use the default email.
service_account_file path |  | The path of a Service Account JSON file if serviceaccount is selected as type.

## Notes

Note

- for authentication, you can set service_account_file using the c(gcp_service_account_file) env variable.
- for authentication, you can set service_account_contents using the c(GCP_SERVICE_ACCOUNT_CONTENTS) env variable.
- For authentication, you can set service_account_email using the `GCP_SERVICE_ACCOUNT_EMAIL` env variable.
- For authentication, you can set auth_kind using the `GCP_AUTH_KIND` env variable.
- For authentication, you can set scopes using the `GCP_SCOPES` env variable.
- Environment variables values will only be used if the playbook values are not set.
- The service_account_email and service_account_file options are mutually exclusive.

## Examples

    - name: get info on a dataset
      gcp_bigquery_dataset_info:
        project: test_project
        auth_kind: serviceaccount
        service_account_file: "/tmp/auth.pem"

## Return Values

Common return values are documented here, the following are the fields unique to this module:

Key | Returned | Description
--- | --- | ---
resources complex | always | List of resources
| access complex | success | An array of objects that define dataset access for one or more entities.
|  | domain string | success | A domain to grant access to. Any users signed in with the domain specified will be granted the specified access .
|  | groupByEmail string | success | An email address of a Google Group to grant access to.
|  | role string | success | Describes the rights granted to the user specified by the other member of the access object. Primitive, Predefined and custom roles are supported. Predefined roles that have equivalent primitive roles are swapped by the API to their Primitive counterparts, and will show a diff post-create. See [official docs](https://cloud.google.com/bigquery/docs/access-control).
|  | specialGroup string | success | A special group to grant access to. Possible values include: * `projectOwners`: Owners of the enclosing project. * `projectReaders`: Readers of the enclosing project. * `projectWriters`: Writers of the enclosing project. * `allAuthenticatedUsers`: All authenticated BigQuery users. .
|  | userByEmail string | success | An email address of a user to grant access to. For example: fred@example.com .
|  | view complex | success | A view from a different dataset to grant access to. Queries executed against that view will have read access to tables in this dataset. The role field is not required when this field is set. If that view is updated by any user, access to the view needs to be granted again via an update operation.
|  |  | datasetId string | success | The ID of the dataset containing this table.
|  |  | projectId string | success | The ID of the project containing this table.
|  |  | tableId string | success | The ID of the table. The ID must contain only letters (a-z, A-Z), numbers (0-9), or underscores. The maximum length is 1,024 characters.
| creationTime integer | success | The time when this dataset was created, in milliseconds since the epoch.
| datasetReference complex | success | A reference that identifies the dataset.
|  | datasetId string | success | A unique ID for this dataset, without the project name. The ID must contain only letters (a-z, A-Z), numbers (0-9), or underscores. The maximum length is 1,024 characters.
|  | projectId string | success | The ID of the project containing this dataset.
| defaultPartitionExpirationMs integer | success | The default partition expiration for all partitioned tables in the dataset, in milliseconds. Once this property is set, all newly-created partitioned tables in the dataset will have an `expirationMs` property in the `timePartitioning` settings set to this value, and changing the value will only affect new tables, not existing ones. The storage in a partition will have an expiration time of its partition time plus this value. Setting this property overrides the use of `defaultTableExpirationMs` for partitioned tables: only one of `defaultTableExpirationMs` and `defaultPartitionExpirationMs` will be used for any new partitioned table. If you provide an explicit `timePartitioning.expirationMs` when creating or updating a partitioned table, that value takes precedence over the default partition expiration time indicated by this property.
| defaultTableExpirationMs integer | success | The default lifetime of all tables in the dataset, in milliseconds. The minimum value is 3600000 milliseconds (one hour). Once this property is set, all newly-created tables in the dataset will have an `expirationTime` property set to the creation time plus the value in this property, and changing the value will only affect new tables, not existing ones. When the `expirationTime` for a given table is reached, that table will be deleted automatically. If a table's `expirationTime` is modified or removed before the table expires, or if you provide an explicit `expirationTime` when creating a table, that value takes precedence over the default expiration time indicated by this property.
| description string | success | A user-friendly description of the dataset.
| etag string | success | A hash of the resource.
| friendlyName string | success | A descriptive name for the dataset.
| id string | success | The fully-qualified unique name of the dataset in the format projectId:datasetId. The dataset name without the project name is given in the datasetId field .
| labels dictionary | success | The labels associated with this dataset. You can use these to organize and group your datasets .
| lastModifiedTime integer | success | The date when this dataset or any of its tables was last modified, in milliseconds since the epoch.
| location string | success | The geographic location where the dataset should reside. See [official docs](https://cloud.google.com/bigquery/docs/dataset-locations). There are two types of locations, regional or multi-regional. A regional location is a specific geographic place, such as Tokyo, and a multi-regional location is a large geographic area, such as the United States, that contains at least two geographic places. Possible regional values include: `asia-east1`, `asia-northeast1`, `asia-southeast1`, `australia-southeast1`, `europe-north1`, `europe-west2` and `us-east4`. Possible multi-regional values: `EU` and `US`. The default value is multi-regional location `US`. Changing this forces a new resource to be created.
| name string | success | Dataset name.

## Status

- This module is not guaranteed to have a backwards compatible interface. [preview]
- This module is maintained by the Ansible Community. [community]

### Authors

- Google Inc. (@googlecloudplatform)

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