Passing table content as parameter for BigQuery UDF

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Summary

The question revolves around passing table content as a parameter to a BigQuery User-Defined Function (UDF) for easier unit testing. The goal is to create a procedure that can accept a table as an input parameter, similar to CREATE OR REPLACE PROCEDURE dataset.function1(tab TABLE).

Root Cause

The root cause of the issue is the limitation of BigQuery UDFs in accepting tables as input parameters directly. BigQuery UDFs are designed to operate on scalar values, not on tables. The current implementation does not support passing tables as parameters to UDFs.

Why This Happens in Real Systems

This limitation occurs in real systems due to the following reasons:

  • Scalability constraints: BigQuery is designed to handle large-scale data processing, and passing tables as parameters could lead to performance issues.
  • Data processing paradigm: BigQuery follows a columnar storage and processing paradigm, which is optimized for batch processing rather than row-by-row processing.

Real-World Impact

The impact of this limitation is significant, especially for unit testing and data validation:

  • Inconvenient testing: Without the ability to pass tables as parameters, unit testing becomes more complicated, requiring workarounds such as creating temporary tables or using alternative testing methods.
  • Limited flexibility: The limitation restricts the flexibility of BigQuery UDFs, making it more challenging to implement complex data processing logic.

Example or Code (if necessary and relevant)

CREATE OR REPLACE TABLE dataset.example_table
AS
SELECT * FROM dataset.source_table;

CREATE OR REPLACE FUNCTION dataset.example_udf(input INT64)
RETURNS INT64
AS (input * 2);

-- Workaround: using a temporary table or a view
CREATE OR REPLACE TEMP TABLE temp_table
AS
SELECT * FROM dataset.example_table;

SELECT dataset.example_udf(column1) FROM temp_table;

How Senior Engineers Fix It

Senior engineers address this limitation by:

  • Using workarounds: Implementing temporary tables, views, or alternative data processing approaches to mimic the desired behavior.
  • Leveraging BigQuery’s built-in functions: Utilizing BigQuery’s built-in functions and features, such as array and struct data types, to process data in a more flexible manner.
  • Optimizing data processing pipelines: Designing data processing pipelines that minimize the need for passing tables as parameters to UDFs.

Why Juniors Miss It

Junior engineers may overlook this limitation due to:

  • Lack of experience: Limited exposure to BigQuery’s capabilities and limitations.
  • Insufficient understanding: Not fully grasping the scalability constraints and data processing paradigm of BigQuery.
  • Inadequate testing: Failing to thoroughly test and validate their data processing pipelines, leading to unexpected issues and workarounds.