Incremental load from Big Query to Cloud MySQL

[busuu]

I am currently running a Cloud Function that exports an entire table in BigQuery  as csv files into Cloud Storage. From Cloud Storage I load the csv files into Cloud MySQL via the Cloud SQL Admin API. I run a truncate table statement for the table in MySQL before importing the csv files so the table in MySQL is rewritten each time the pipeline is run basically. 

This is working fine for now but as the data size increases, exporting and importing the entire table from BigQuery to Cloud MySQL might not be viable. I want to change the pipeline to only importing the new data from BigQuery into MySQL. I'm thinking of setting up a staging table in BigQuery, identify the MAX(Date) in the MySQL table A, insert into the staging table where table B.Date > MAX(table A.Date). Finally, export the staging table as csv files and import them into MySQL via the Cloud SQL Admin API. 

How does this sound? Does anyone know if there a better way to do this?

[ms4446 · Accepted solution]

Yes, your approach to optimizing the data pipeline from BigQuery to Cloud MySQL by focusing on incremental updates is generally correct and should indeed lead to a more efficient process. Below is a refined breakdown of the logic, along with potential improvements for further optimization:

• Identify Recent Data: Utilizing a MAX(Date) query to determine the most recent data point in your MySQL table is an efficient strategy to isolate the new data required from BigQuery. This step is crucial for ensuring that only new or updated records are processed.

• Staging Table: The idea of creating a staging table in BigQuery to hold only the new data is a prudent optimization. It minimizes the volume of data that needs to be exported and subsequently imported, streamlining the process.

• Export and Import: Following the established method of exporting the staging table as CSV files and importing them into Cloud SQL via the Cloud SQL Admin API is correct. This method aligns with Google Cloud's recommended practices for data movement.

Potential Improvements

• Incremental Load Pattern: Shifting towards an incremental load pattern, where new data is directly appended to your MySQL table, could be a significant enhancement. This method reduces the need for complete table rewrites, potentially leading to more efficient and faster updates.

• Data Type Considerations: It's crucial to ensure that the schema of your staging table in BigQuery matches exactly with your target MySQL table. Paying close attention to data types and date/time formats is essential to avoid any discrepancies or errors during the CSV import process.

• BigQuery to MySQL Connector: Investigating the use of data integration services, such as Cloud Data Fusion, which might offer direct connectors for replicating incremental data from BigQuery to Cloud SQL, could simplify your pipeline. While this introduces a more managed solution, it's important to weigh its benefits against potential costs and complexity.

Specific Considerations

• Error Handling: Implementing robust error handling mechanisms is vital to catch and address potential issues during the export, import, or query execution stages, enhancing the pipeline's reliability.

• Scheduling: Properly scheduling the execution of your updated Cloud Function pipeline is important. Consider the frequency of updates in your BigQuery table and the necessity for timely reflection of this new data in MySQL to determine the optimal schedule.

Example

While the initial SQL example provided a good starting point, integrating direct MySQL queries into BigQuery operations requires intermediary steps, typically handled by an orchestration tool or a Cloud Function. Here's a conceptual overview of how the process might look, with adjustments for practical implementation:

1. Extract MAX(Date) from MySQL: Use a Cloud Function or similar tool to query the MAX(Date) from your MySQL table and store this value.

2. Populate Staging Table in BigQuery:

   -- Assuming 'last_updated_date' is obtained from MySQL
   CREATE OR REPLACE TABLE staging_table AS
   SELECT *
   FROM main_data
   WHERE Date > last_updated_date;

3. Export Staging Table to Cloud Storage:

   • Utilize BigQuery's export functionality to save the staging table data as CSV files in a Cloud Storage bucket.

4. Import into MySQL:

   • Import the CSV files into MySQL, considering the use of append logic if avoiding a full table truncate and reload. This step might involve Cloud SQL Admin API calls or custom scripts tailored to your specific requirements.