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# ask-ai
a

Axel Mendoza

03/12/2024, 3:13 PM
@Scout With this asset I retrieve reporting_provider_id, reporting_site_id
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@asset(group_name=REC_TEST_GROUP_NAME, deps=[reconciliation_labels])
def reconciliation_labels_latest(
    context: AssetExecutionContext,
    bigquery: BigQueryResource,
) -> pd.DataFrame:
    """

    Args:
        context (AssetExecutionContext): Dagster context.
        bigquery (BigQueryResource): The BigQuery resource used for querying.
    """
    table_id = get_bq_table_id(
        project_id=bigquery.project,
        dataset_name=env_vars.feature_store_bq_dataset,
        table_name="final_reconciliation",
    )
    query = f"""
    SELECT entity_id, date, reporting_provider_id, reporting_site_id
    FROM {table_id}
    WHERE date >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 30 DAY)
    """  # nosec

    df_reconciliation = run_query(context, bigquery, query, to_dataframe=True)

    return df_reconciliation
What is the most efficient way to join the ids I've retrieve from this dataframe with other existing tables in BQ?
s

Scout

03/12/2024, 3:13 PM
When working with BigQuery and Dagster, you have several options for joining data from a DataFrame with other tables in BigQuery. The most efficient way often depends on the size of the data, the complexity of the join, and the specific requirements of your use case. Here are some general strategies you can consider: 1. Perform the join directly in BigQuery: Instead of pulling the data into a DataFrame and then joining it with other tables, you can write a SQL query that performs the join within BigQuery itself. This leverages BigQuery's distributed computing power and can be more efficient, especially for large datasets. 2. Use BigQuery's external data sources feature: If you have a relatively small DataFrame that you want to join with a BigQuery table, you can save the DataFrame to a location accessible by BigQuery (like Google Cloud Storage) and then use it as an external data source in a SQL query. 3. Materialize the DataFrame as a temporary table in BigQuery: You can write the DataFrame to a temporary table in BigQuery and then perform the join using SQL. This can be a good option if you need to perform complex transformations in Python before joining, but still want to leverage BigQuery's performance for the join operation. 4. Use the BigQuery Storage API: If you need to perform the join in Python for some reason (e.g., you're using a Python library for a specific type of join that isn't supported in SQL), you can use the BigQuery Storage API to efficiently download the data you need to join with your DataFrame. Here's an example of how you might perform the join directly in BigQuery using SQL, which is generally the most efficient approach:
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python
@asset(group_name=REC_TEST_GROUP_NAME, deps=[reconciliation_labels])
def reconciliation_labels_latest(
    context: AssetExecutionContext,
    bigquery: BigQueryResource,
) -> pd.DataFrame:
    table_id = get_bq_table_id(
        project_id=bigquery.project,
        dataset_name=env_vars.feature_store_bq_dataset,
        table_name="final_reconciliation",
    )
    other_table_id = "your_project.your_dataset.your_other_table"  # Replace with your table's details

    # Write a SQL query that performs the join directly in BigQuery
    query = f"""
    SELECT r.entity_id, r.date, r.reporting_provider_id, r.reporting_site_id, o.other_column
    FROM {table_id} AS r
    INNER JOIN {other_table_id} AS o ON r.reporting_provider_id = o.provider_id
    WHERE r.date >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 30 DAY)
    """  # nosec

    df_reconciliation = run_query(context, bigquery, query, to_dataframe=True)

    return df_reconciliation
In this example,
other_table_id
should be replaced with the identifier of the other table you want to join with, and
o.other_column
s
hould be replaced with the columns you want to select from the other table. Adjust the
ON
clause to match the join condition relevant to your data. Remember to always consider the size of the datasets and the costs associated with the operations in BigQuery. If you're dealing with very large datasets, it's often more cost-effective to perform operations within BigQuery rather than transferring large amounts of data into a local environment for processing.