Match Row Value in a Column with Other Column’s Name in BIGQUERY

As a developer working with large datasets, we often encounter scenarios where we need to perform complex matching operations between columns. In the context of BigQuery, Standard SQL offers various ways to achieve this goal. In this article, we will explore three different approaches to match row values in a column with other column names.

Introduction
Option 1: Using CASE Statement
Option 2: Creating a User-Defined Function (UDF)
Option 3: Using Regular Expressions

Introduction

BigQuery is a powerful data analytics engine that allows us to process and analyze large datasets efficiently. However, when dealing with complex matching operations between columns, we may need more advanced techniques. In this article, we will explore three different approaches to match row values in a column with other column names.

Option 1: Using `CASE` Statement

One straightforward approach to achieve the desired result is by using the CASE statement in BigQuery Standard SQL. This method involves creating two separate cases for each matching pair of columns.

#standardSQL
SELECT col1, col2, foo, bar, 
  CASE col2 
    WHEN 'BAR' THEN bar
    WHEN 'FOO' THEN foo
  END L
FROM `project.dataset.table` t

This approach has the advantage of being simple and easy to understand. However, it may not be suitable for larger datasets or more complex matching operations.

Option 2: Creating a User-Defined Function (UDF)

Another approach is by creating a user-defined function (UDF) in BigQuery Standard SQL. This method allows us to encapsulate our matching logic into a reusable function that can be called from anywhere in the query.

#standardSQL
CREATE TEMP FUNCTION L(val STRING, t ANY TYPE) AS (
  CASE val 
    WHEN 'BAR' THEN t.bar
    WHEN 'FOO' THEN t.foo
  END  
);
WITH `project.dataset.table` AS (
  SELECT 'identif11' col1, 'BAR' col2, 'VALUE1' foo, 'VALUE3' bar UNION ALL
  SELECT 'identif22', 'FOO', 'VALUE2', 'VALUE4' UNION ALL
  SELECT 'identif22', 'AAA', 'VALUE2', 'VALUE4'
)
SELECT col1, col2, foo, bar, L(col2, t) L
FROM `project.dataset.table` t

This approach has the advantage of being flexible and reusable. However, it may require more setup and maintenance compared to other approaches.

Option 3: Using Regular Expressions

A third approach is by using regular expressions in BigQuery Standard SQL. This method allows us to match row values in a column with other column names without explicitly referencing their names.

#standardSQL
CREATE TEMP FUNCTION L(val STRING, t ANY TYPE) AS (
  REGEXP_EXTRACT(
    TO_JSON_STRING(t),
    CONCAT(r'(?i)"', val, '":"?([^"}]+)"?')
  ) 
);
SELECT col1, col2, foo, bar, L(col2, t) L
FROM `project.dataset.table` t

This approach has the advantage of being concise and flexible. However, it may have some edge cases or performance implications compared to other approaches.

Conclusion

In this article, we explored three different approaches to match row values in a column with other column names in BigQuery Standard SQL. Each approach has its strengths and weaknesses, and the choice of which one to use depends on the specific requirements of our project. Whether we need simplicity, flexibility, or performance, there is an approach that can help us achieve our goals.

Recommendations

Use the CASE statement when:
- The matching logic is simple and easy to understand.
- Performance is not a top priority.
Create a user-defined function (UDF) when:
- We need flexibility and reusability in our matching logic.
- We want to encapsulate our matching logic into a reusable function.
Use regular expressions when:
- The matching logic is concise and flexible.
- We are comfortable working with regular expressions.

Example Use Cases

Matched data: Suppose we have a table orders that contains the following columns: customer_id, order_date, and product_name. We want to extract the product name from the order details. Using Option 1, we can create a query like this:

#standardSQL
SELECT customer_id, order_date, 
       CASE WHEN product_name = 'Product A' THEN 'A' ELSE NULL END AS product_type,
       CASE WHEN product_name = 'Product B' THEN 'B' ELSE NULL END AS product_type_2
FROM `orders` t;

Reused matching logic: Suppose we have a table customer_data that contains customer information, including the customer_id and product_info. We want to extract the product name from the product information. Using Option 2, we can create a UDF like this:

#standardSQL
CREATE TEMP FUNCTION get_product_name(val STRING) AS (
  CASE WHEN val = 'Product A' THEN 'A' ELSE NULL END  
);
WITH `customer_data` AS (
  SELECT customer_id, product_info FROM ... 
)
SELECT customer_id, product_info, 
       get_product_name(product_info) AS product_name
FROM `customer_data`;

Regular expression-based matching: Suppose we have a table log_entries that contains log data in the following format: {timestamp: 1643723400, message: "Product A"}. We want to extract the product name from the log messages. Using Option 3, we can create a regular expression like this:

#standardSQL
CREATE TEMP FUNCTION get_product_name(val STRING) AS (
  REGEXP_EXTRACT(
    TO_JSON_STRING({'message': val}),
    r'(?i)"message":?([^"}]+)')
);
SELECT timestamp, message, 
       get_product_name(message) AS product_name
FROM `log_entries`;

These examples demonstrate how to use each approach in a real-world scenario. The choice of which one to use depends on the specific requirements of our project.

Last modified on 2023-07-26