Understanding QUALIFY in BigQuery
As a seasoned Data Scientist, I’ve often found myself working with large datasets and complex queries. One powerful feature in BigQuery that has streamlined my workflow is the QUALIFY clause. This blog post aims to demystify QUALIFY, demonstrating how it can be used to filter results based on window functions. By the end, you’ll see how this clause can simplify and enhance your data querying process.
What is QUALIFY?
QUALIFY is a clause in BigQuery used to filter the results of a query based on the output of window functions. It operates similarly to WHERE and HAVING but specifically applies to the results after window functions are computed.
Why Use QUALIFY?
In scenarios where you need to filter rows based on the results of window functions, QUALIFY can make your queries more readable and concise. Instead of nesting subqueries or using complex joins, you can directly filter the results of window functions.
Example 1: Finding Top Sales per Employee
Let’s start with a simple example. Suppose we have a sales table containing the following columns: sale_id, employee_id, and sale_amount. We want to find the top sale made by each employee.
Sample Data
SELECT 1 AS sale_id, 101 AS employee_id, 100 AS sale_amount UNION ALL
SELECT 2, 101, 150 UNION ALL
SELECT 3, 102, 200 UNION ALL
SELECT 4, 102, 50 UNION ALL
SELECT 5, 103, 300
)
Query
Without QUALIFY, you might write:
FROM (
SELECT
sale_id,
employee_id,
sale_amount,
ROW_NUMBER() OVER (PARTITION BY employee_id ORDER BY sale_amount DESC) AS row_num
FROM sales
)
WHERE row_num = 1;
With QUALIFY, the query becomes more concise:
SELECT 1 AS sale_id, 101 AS employee_id, 100 AS sale_amount UNION ALL
SELECT 2, 101, 150 UNION ALL
SELECT 3, 102, 200 UNION ALL
SELECT 4, 102, 50 UNION ALL
SELECT 5, 103, 300
)
SELECT
sale_id,
employee_id,
sale_amount,
ROW_NUMBER() OVER (PARTITION BY employee_id ORDER BY sale_amount DESC) AS row_num
FROM sales
QUALIFY row_num = 1;
Example 2: Ranking Products by Sales
Now, let’s consider a products table where we want to rank products based on their sales and filter the top 3 products for each category.
Sample Data
SELECT 1 AS product_id, ‘A’ AS category, 500 AS sales UNION ALL
SELECT 2, ‘A’, 600 UNION ALL
SELECT 3, ‘A’, 200 UNION ALL
SELECT 4, ‘A’, 700 UNION ALL
SELECT 5, ‘B’, 300 UNION ALL
SELECT 6, ‘B’, 400 UNION ALL
SELECT 7, ‘B’, 100 UNION ALL
SELECT 8, ‘B’, 200
)
Query
SELECT 1 AS product_id, ‘A’ AS category, 500 AS sales UNION ALL
SELECT 2, ‘A’, 600 UNION ALL
SELECT 3, ‘A’, 200 UNION ALL
SELECT 4, ‘A’, 700 UNION ALL
SELECT 5, ‘B’, 300 UNION ALL
SELECT 6, ‘B’, 400 UNION ALL
SELECT 7, ‘B’, 100 UNION ALL
SELECT 8, ‘B’, 200
)
SELECT
product_id,
category,
sales,
RANK() OVER (PARTITION BY category ORDER BY sales DESC) AS sales_rank
FROM products
QUALIFY sales_rank <= 3;
Example 3: Filtering Employees by Tenure and Performance
In an employees table, we want to identify employees who rank in the top 2 by performance score within each department and have been with the company for over 5 years.
Sample Data
SELECT 1 AS employee_id, ‘HR’ AS department, 90 AS performance_score, 6 AS years_with_company UNION ALL
SELECT 2, ‘HR’, 85, 4 UNION ALL
SELECT 3, ‘HR’, 95, 7 UNION ALL
SELECT 4, ‘IT’, 88, 10 UNION ALL
SELECT 5, ‘IT’, 75, 3 UNION ALL
SELECT 6, ‘IT’, 92, 6
)
Query
SELECT 1 AS employee_id, ‘HR’ AS department, 90 AS performance_score, 6 AS years_with_company UNION ALL
SELECT 2, ‘HR’, 85, 4 UNION ALL
SELECT 3, ‘HR’, 95, 7 UNION ALL
SELECT 4, ‘IT’, 88, 10 UNION ALL
SELECT 5, ‘IT’, 75, 3 UNION ALL
SELECT 6, ‘IT’, 92, 6
)
SELECT
employee_id,
department,
performance_score,
years_with_company,
RANK() OVER (PARTITION BY department ORDER BY performance_score DESC) AS perf_rank
FROM employees
QUALIFY perf_rank <= 2 AND years_with_company > 5;
Conclusion
The QUALIFY clause in BigQuery is a powerful tool for filtering results based on window functions. It simplifies queries, making them more readable and maintainable. Whether you’re ranking sales, identifying top-performing products, or filtering employees based on performance and tenure, QUALIFY can enhance your data querying capabilities.
By incorporating QUALIFY into your BigQuery toolkit, you can write cleaner, more efficient SQL queries, ultimately saving time and improving the accuracy of your data analysis.
Resources for Learning About QUALIFY in BigQuery:
Google Cloud BigQuery Documentation
Overview: The official Google Cloud BigQuery documentation is the most authoritative source for understanding all features of BigQuery, including the QUALIFY clause. It provides detailed explanations, syntax, and examples.
Why It’s Useful: This resource is maintained by Google, ensuring that it is up-to-date with the latest features and best practices. It also includes links to related concepts and advanced usage scenarios.
Link: BigQuery Documentation – QUALIFY Clause
Stack Overflow
Overview: Stack Overflow is a community-driven Q&A platform where you can find real-world problems and solutions related to BigQuery and the QUALIFY clause. Experienced data scientists and SQL experts often share their insights and solutions here.
Why It’s Useful: This resource provides a diverse range of examples and solutions to specific issues that other users have encountered. You can also ask your own questions and get responses from the community.
Link: Stack Overflow – Questions Tagged with BigQuery
DataCamp – BigQuery SQL for Data Analysis
Overview: DataCamp offers an interactive course specifically focused on SQL for data analysis using BigQuery. This course covers various SQL functions, including the use of window functions and the QUALIFY clause.
Why It’s Useful: DataCamp’s hands-on approach allows you to practice writing and executing queries in a simulated BigQuery environment. This practical experience can help solidify your understanding of the QUALIFY clause and other advanced SQL concepts.
Link: DataCamp – BigQuery SQL for Data Analysis
Best,
Kemal Cholovich
