Transfer BigQuery Table to Composer Bucket

[ayushmaheshwari]

Hi @ms4446 

Here is my problem description:

1) I am running an Airflow DAG and I am using the Cloud Composer. I have generated a table in BigQuery. I now want to transfer this table as a csv to Composer bucket.

2) This is because I want to send the csv as an email to the final customer

3) I will be using EmailOperator and the EmailOperator will expect the csv to live in Composer Bucket

4) Can I use BigQuerytoGCSOperator to tarnsfer the table to Composer bucket (like we normally transfer to a GCS bucket from BigQuery)?

5) My requirement is to send a csv generated through a BQ table as email attachment and this what I figured out

Any tips?

Questions

6) How to transfer csv from BQ table to Composer Bucket?

7) Do I have to sue Composer API: https://cloud.google.com/composer/docs/how-to/using/managing-dags#python

😎 Can I use BigQuerytoGCSOperator?

9)IS EmailOperator the best solution for this use case?

https://cloud.google.com/composer/docs/how-to/using/managing-dags#python

[ms4446]

Yes, BigQueryToGCSOperator is the most suitable way to handle this. It's designed specifically for moving data between BigQuery and Google Cloud Storage (GCS). Since your Composer environment has its own associated GCS bucket, you can seamlessly use this operator.

• Make sure you specify the correct destination bucket path when configuring the BigQueryToGCSOperator. It should point to your Composer's GCS bucket.

Workflow Outline

1. BigQuery Table Generation: Your DAG's initial tasks generate the desired BigQuery table.

2. BigQueryToGCSOperator: Use this operator to export the BigQuery table as a CSV file to your Composer's GCS bucket.

3. EmailOperator: Configure this operator to send the email, attaching the CSV file from the Composer bucket.

Answers to Your Specific Questions:

6. How to transfer the CSV from the BQ table to Composer Bucket? Use the BigQueryToGCSOperator.

7. Do I have to use the Composer API? No, you shouldn't need to directly interact with the Composer API for this task. The operators provided by Airflow should be sufficient.

8. Can I use BigQueryToGCSOperator? Yes, absolutely! This is the recommended way to move data from BigQuery to GCS.

9. Is EmailOperator the best solution for this use case? Yes, it's well-suited for sending emails with attachments within Airflow DAGs.

Important Tips

• File Naming: Consider using a dynamic filename for the CSV (e.g., include the execution date) to avoid overwriting files in your Composer bucket.

• Error Handling: Implement appropriate error handling and retries in your DAG to ensure robustness.

• Security: If your data is sensitive, consider encrypting the CSV file before storing it in the Composer bucket and potentially including password protection in the email attachment.

[ayushmaheshwari]

Hi @ms4446 

Thank you for the reply. One more question:

1) I was able to transfer the file from BigQuery to Composer bucket using BigQuerytoGCS Operator

2) I was able to email using Email operator

3) Now after emailing I want to archive the file into a normal GCS bucket (rather than keeping in Composer bucket). How do I do that? What is the best way to do?

Is the below a good approach?

Ayush

storage_client = storage.Client()
source_bucket = storage_client.bucket(gcs_bucket)
source_blob = source_bucket.blob(xxx)
archive_path = yyy
source_bucket.rename_blob(source_blob, archive_path)

[ms4446]

To archive the file from the Composer bucket to another GCS bucket, your proposed approach is on the right track, but there's a more straightforward method to achieve this using the Google Cloud Storage Python client library. Specifically, the copy_blob and delete_blob methods would be more appropriate, as they give you control over both copying and removing the file from the Composer bucket.

from google.cloud import storage

def archive_file(gcs_source_bucket, source_blob_name, gcs_destination_bucket, archive_blob_name):
    storage_client = storage.Client()

    # Get the source and destination buckets
    source_bucket = storage_client.bucket(gcs_source_bucket)
    destination_bucket = storage_client.bucket(gcs_destination_bucket)

    # Get the source blob
    source_blob = source_bucket.blob(source_blob_name)

    # Copy the blob to the destination bucket
    source_bucket.copy_blob(source_blob, destination_bucket, archive_blob_name)

    # Delete the blob from the source bucket
    source_blob.delete()

# Example usage:
archive_file(
    gcs_source_bucket='your-composer-bucket',
    source_blob_name='path/to/file.csv',
    gcs_destination_bucket='your-archive-bucket',
    archive_blob_name='archived/path/to/file.csv'
)

• Copy the Blob: The copy_blob method copies the file from the Composer bucket to the archive bucket.
• Delete the Blob: After copying, the delete method removes the original file from the Composer bucket.

[ayushmaheshwari]

Thanks @ms4446 , yes, I have used copy_blob and delete_blob but I thought rename_blob is better because it requires one action that would simply move the file to another location, isn't it?

[ms4446 · Accepted solution]

You're correct that rename_blob is a more direct way to move a file from one location to another within the same bucket. However, the key limitation of rename_blob is that it only works within the same bucket. It essentially performs a copy followed by a delete operation internally, but it does so within the same GCS bucket.