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# ask-ai
p

Paul Herzog

03/05/2024, 3:29 PM
@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()
s

Scout

03/05/2024, 3:29 PM
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:
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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.
p

Paul Herzog

03/05/2024, 5:09 PM
@Scout can i delete the rows before loading the new rows?
s

Scout

03/05/2024, 5:09 PM
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:
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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.
p

Paul Herzog

03/05/2024, 5:19 PM
@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()
s

Scout

03/05/2024, 5:19 PM
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:
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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.
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