@Scout i have an asset which loads data to google bigquery. i want the table to be replaced instead of append. how to? with bigquery.get_client() as client: table_ref = f"adroit-medium-379911.{bq_data_set}.{bq_table_name}" load_job = client.load_table_from_dataframe( dataframe=tt_api_get_location_data, destination=table_ref, location="europe-west3" ) load_job.result()

To replace a table in Google BigQuery instead of appending to it when using the

load_table_from_dataframe

method, you need to specify the desired write disposition. In the BigQuery API, the

WriteDisposition

enum provides options for this behavior, such as

WRITE_TRUNCATE

to replace the table,

WRITE_APPEND

to append to the table, or

WRITE_EMPTY

to only write if the table is empty. However, in the context of Dagster, you would typically configure this behavior through the I/O manager associated with the asset. The documentation provides an example of how to configure the BigQuery I/O manager with custom job configurations, which can include settings like the write disposition. Here's an example based on the documentation's guidance:

python from dagster import asset from dagster_gcp.bigquery import bigquery_io_manager  @asset(io_manager_key="io_manager") def my_bigquery_asset():     # Your asset logic here     ...  # In your repository definition from dagster import repository, define_asset_job, IOManagerDefinition  @repository def my_repo():     bigquery_io_manager_with_write_truncate = IOManagerDefinition(         resource_fn=lambda _: bigquery_io_manager.configured({             "project": "my-gcp-project",             "gcp_credentials": {"env": "GOOGLE_APPLICATION_CREDENTIALS"},             "job_config": {                 "write_disposition": "WRITE_TRUNCATE",  # This will replace the table             }         })     )      return [         my_bigquery_asset,         define_asset_job("my_asset_job", selection="*"),         bigquery_io_manager_with_write_truncate,     ]

In this example, the

bigquery_io_manager

is configured with a

job_config

that includes the

write_disposition

set to

WRITE_TRUNCATE

. This configuration will ensure that when the asset is materialized, the table in BigQuery will be replaced. Please note that the example above is illustrative and based on the documentation's guidance. You may need to adjust the configuration to match your specific environment and credentials setup. If you have a more complex scenario or need to pass additional configurations to the BigQuery job, you might need to customize the I/O manager further or use the

LoadJobConfig

class from the

google.cloud.bigquery

library to specify additional settings.

@Scout can i delete the rows before loading the new rows?

Yes, you can delete the rows from a BigQuery table before loading new rows. This can be done by executing a

DELETE

statement in BigQuery to remove all existing rows from the table, or by using the

WRITE_TRUNCATE

write disposition when loading the new data, which effectively replaces the entire table. If you want to explicitly delete rows before loading new data, you could execute a

DELETE

statement using the BigQuery client. Here's a conceptual example of how you might do this:

python from google.cloud import bigquery  # Initialize a BigQuery client client = bigquery.Client()  # Specify your table table_ref = f"adroit-medium-379911.{bq_data_set}.{bq_table_name}"  # Construct a BigQuery DELETE statement to remove all rows from the table delete_query = f"DELETE FROM `{table_ref}` WHERE TRUE"  # Execute the DELETE statement client.query(delete_query).result()  # Now you can proceed to load new data into the table

Please note that this operation is not idempotent and will permanently delete the data from the table. It's important to ensure that this is the desired behavior before executing such a statement. In the context of Dagster, you would typically handle such operations within an asset or an op, ensuring that the deletion and subsequent load are part of a single transactional workflow. This helps maintain data integrity and allows for better observability and error handling within your data pipeline. Remember to test your deletion logic thoroughly to prevent accidental data loss and consider implementing safeguards such as backing up data before performing deletions.

@Scout how to update this for the delete sql statement with bigquery.get_client() as client: table_ref = f"adroit-medium-379911.{bq_data_set}.{bq_table_name}" load_job = client.load_table_from_dataframe( dataframe=merged_df, destination=table_ref, location="europe-west3" ) load_job.result()

To update your code to include a delete SQL statement before loading the new data into BigQuery, you can use the

query

method of the BigQuery client to execute a

DELETE

statement. Here's how you can modify your existing code:

python from google.cloud import bigquery  # Initialize a BigQuery client client = bigquery.Client()  # Specify your table table_ref = f"adroit-medium-379911.{bq_data_set}.{bq_table_name}"  # Construct a BigQuery DELETE statement to remove all rows from the table delete_query = f"DELETE FROM `{table_ref}` WHERE TRUE"  # Execute the DELETE statement client.query(delete_query).result()  # Now you can proceed to load new data into the table load_job = client.load_table_from_dataframe(     dataframe=merged_df,     destination=table_ref,     location="europe-west3" ) load_job.result()

This code will first delete all existing rows in the specified BigQuery table and then load the new data from the

merged_df

DataFrame into the same table. Please ensure that you have the necessary permissions to perform delete operations on the BigQuery table and that you handle any potential exceptions that might occur during the execution of the delete or load operations. Also, be aware that the

DELETE

statement is a DML operation that can incur costs in BigQuery, and it's important to consider this if you're working with large tables or executing deletes frequently.