What is an efficient strategy for inserting a large number of small rows into a BigQuery table?

What is an efficient strategy for inserting a large number of small rows into a BigQuery table?

• A. Include multiple rows with each request
• B. Perform the inserts in parallel by creating multiple threads
• C. Write each row to a Cloud Storage object, then load into BigQuery
• D. Write each row to a Cloud Storage object in parallel, then load into BigQuery

Answer: (A)

An efficient strategy for inserting a large number of small rows into a BigQuery table is to include multiple rows with each request. This method takes advantage of BigQuery’s ability to handle batch processing, allowing users to group several inserts into a single API request. By minimizing the number of requests made to BigQuery, this approach reduces latency and overhead associated with network communication, resulting in better performance and lower costs. When sending multiple rows in a single request, BigQuery can optimize the ingestion process since it processes the data in larger batches. This can lead to more efficient use of resources and faster overall data loading, especially when dealing with many small rows that could slow down performance if inserted individually. Using this method aligns well with best practices for data loading into cloud databases, as it streamlines the insertion process and efficiently utilizes the system's capabilities.

An efficient strategy for inserting a large number of small rows into a BigQuery table is to include multiple rows with each request. This method takes advantage of BigQuery’s ability to handle batch processing, allowing users to group several inserts into a single API request. By minimizing the number of requests made to BigQuery, this approach reduces latency and overhead associated with network communication, resulting in better performance and lower costs.

When sending multiple rows in a single request, BigQuery can optimize the ingestion process since it processes the data in larger batches. This can lead to more efficient use of resources and faster overall data loading, especially when dealing with many small rows that could slow down performance if inserted individually.

Using this method aligns well with best practices for data loading into cloud databases, as it streamlines the insertion process and efficiently utilizes the system's capabilities.