List Tables in a BigQuery Dataset using Python

In this tutorial, we will learn how to retrieve a list of all tables within a specific BigQuery dataset using Python. This is a common operation for managing datasets and auditing resources.

Python Code to List Tables

We will use the list_tables method from the google-cloud-bigquery library.

Python

# Import the packages
from dotenv import load_dotenv
import os
from google.oauth2 import service_account
from google.cloud import bigquery

def main():
    # Loads environment variables from a .env file
    load_dotenv()

    # Use environment variables from .env file
    credentials_path = os.getenv("GOOGLE_APPLICATION_CREDENTIALS")
    project_name = os.getenv("project_id")

    # Get the service account credentials
    credentials = service_account.Credentials.from_service_account_file(credentials_path)

    # Create the BigQuery client
    client = bigquery.Client(credentials=credentials, project=project_name)

    # dataset_id = 'your-project.your_dataset'
    dataset_id = 'ashishcoder.ds_from_tf'

    # List the tables in the dataset
    tables = client.list_tables(dataset_id)

    if not tables:
        print(f"No tables found in dataset {dataset_id}.")
    else:
        print(f"Tables in dataset {dataset_id}:")
        for table in tables:
            print(f"Table Name: {table.table_id}")

if __name__ == "__main__":
    main()

Key Steps Explained

client.list_tables(dataset_id): Returns an iterator over the tables in the specified dataset.
table.table_id: Access the identifier of each table object.

Note: Listing tables requires the bigquery.tables.list permission on the dataset.

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