I develop the same thing, in Python, for exporting to BQ. Searching in BigQuery is easier than in a file. The code is very similar for GCS storage. Here my working code with BQ
import os
from google.cloud import asset_v1
from google.cloud.asset_v1.proto import asset_service_pb2
from google.cloud.asset_v1 import enums
def GCF_ASSET_TO_BQ(request):
client = asset_v1.AssetServiceClient()
parent = 'organizations/{}'.format(os.getenv('ORGANIZATION_ID'))
output_config = asset_service_pb2.OutputConfig()
output_config.bigquery_destination.dataset = 'projects/{}/datasets/{}'.format(os.getenv('PROJECT_ID'),os.getenv('DATASET'))
content_type = enums.ContentType.RESOURCE
output_config.bigquery_destination.table = 'asset_export'
output_config.bigquery_destination.force = True
response = client.export_assets(parent, output_config, content_type=content_type)
# For waiting the finish
# response.result()
# Do stuff after export
return "done", 200
if __name__ == "__main__":
GCF_ASSET_TO_BQ('')
As you can see, there is some values in Env Var (OrganizationID, projectId and Dataset). For exporting to Cloud Storage, you have to change the definition of the output_config
like this
output_config = asset_service_pb2.OutputConfig()
output_config.gcs_destination.uri = 'gs://path/to/file'
You have example in other languages here