A Comprehensive Guide to Using Window Functions in BigQuery

BigQuery window functions are essential for data analysts, business intelligence professionals, SQL developers, and data engineers. They provide powerful tools for performing advanced calculations and analysis of data. This guide will help you understand the importance of window functions in SQL querying for analytical tasks.

Learn how to perform calculations over a set of rows related to the current row, enhancing your ability to analyze data efficiently.

Window functions allow for detailed insights without collapsing rows into a single output. They simplify complex analyses that would otherwise require subqueries, making your data analysis more efficient and insightful.

Window functions are powerful tools that allow you to perform calculations across table rows related to the current row.

Unlike aggregate functions, window functions do not collapse rows into a single output row, making them incredibly useful for analytical tasks.

They enable running totals, moving averages, and ranking without losing the row-level detail.

They are called window functions because they perform calculations across a "window" of table rows defined by the user.

This "window" can include a specific range of rows around the current row, creating a dynamic view for each calculation. The window can slide over the dataset, allowing for flexible and detailed analysis.

BigQuery offers a variety of window functions that allow you to perform complex calculations across rows of data, maintaining the individual row details. These functions can be categorized into three main types: aggregation functions, navigation functions, and numbering functions.

Navigation functions in BigQuery allow you to access data from other rows relative to the current row within the same window frame.

These functions enable you to perform tasks like retrieving values from previous or subsequent rows and computing specific percentile values.

Numbering functions in BigQuery assign unique identifiers to rows within a specified window.

They are essential for tasks such as ranking rows, distributing rows into buckets, and assigning sequential numbers, facilitating detailed data analysis and reporting.

Aggregation functions in BigQuery perform calculations on a set of values to return a single scalar value.

They are used for summarizing data, such as computing totals, averages, and statistical measures across rows within a window.

To demonstrate a BigQuery window function in action, let's consider an example involving sales employees in a company.

We have a table "EmployeeSales" with data as follows.

We'll use this JSON dataset below to illustrate how window functions can be used to perform advanced data analysis.

Here, we will use the RANK() window function to rank employees by their sales within each region.

Here:

• SELECT: We select the employee, region, and sales columns to include them in our results.
• RANK() OVER (PARTITION BY region ORDER BY sales DESC): This window function ranks employees within each region based on their sales, in descending order. The PARTITION BY clause ensures that ranking restarts within each region.
• FROM owox-analytics.myDataset.employeeSales: Specifies the source table for our query.

In this example, we used the RANK() window function to rank sales employees within each region based on their sales performance. This powerful feature enables detailed and dynamic analysis without collapsing the data into summary rows, thus maintaining the granular details of each employee’s performance.

GROUP BY aggregates data into a single row per group, summarizing data points like sums or averages. In contrast, window functions perform calculations across rows while maintaining the original row structure. You can compute cumulative totals, moving averages, and ranks without losing detailed row-level information.

GROUP BY is for summary results, while window functions allow for detailed, row-wise analysis. Window functions enable complex analytical tasks like running totals and moving averages, enhancing your data insights.

Here is an example that shows the difference between the two functions. 

Let’s aggregate data to summarize it per group. For instance, we might want to find the total sales amount by each salesperson from a sales table.

This query will give us the total sales for each salesperson. The GROUP BY clause groups the rows that have the same value in the sales_person column, and then calculates the sum of the amount for each group.

Window functions, on the other hand, allow you to perform calculations across sets of rows related to the current row while still retaining the individual row structure in the result set. Let's calculate a running total (cumulative sum) of sales for each salesperson, ordered by date.

This query does not aggregate the data into a summary; instead, it adds a new column to each row in the output, showing the running total of sales for each salesperson up to and including that date. This allows you to see both the individual sales amounts and the cumulative totals in the same result set, preserving the granular row-level detail.

BigQuery window functions follow a specific syntax that allows for flexible and powerful data analysis. Understanding this syntax is crucial for effectively leveraging window functions in your queries. Below is the detailed structure of how to declare window functions in BigQuery.

The OVER() clause indicates that you're using a window function. You'd write your query as usual and include the aggregates you want alongside the other column names. Each aggregate is identified with the OVER() clause.

Example:

Suppose you wanted to extract employee sales results and show the average, highest, and lowest sales for each employee within the company.

Your query would look like this:

Here:

• SELECT: Selects the columns employee and sales from the EmployeeSales table.
• AVG(sales) OVER(): Computes the average sales across the entire dataset.
• MIN(sales) OVER(): Finds the lowest sales figure across the entire dataset.
• MAX(sales) OVER(): Finds the highest sales figure across the entire dataset.
• FROM owox-analytics.myDataset.employeeSales: Specifies the table from which the data is being queried.

Using the OVER() clause allows us to compute aggregate values such as average, minimum, and maximum sales without collapsing the data into a single row. This enables us to display these aggregate values alongside each employee's individual sales data.

The PARTITION BY expression is straightforward to understand. It divides the rows into separate chunks (partitions), and the window function is then independently evaluated over each of these partitions. You can even use multiple expressions to partition on multiple fields at once.

Example:

Let's use an example to demonstrate the PARTITION BY clause, this time focusing on employee salary metrics within different company departments.

Here:

• SELECT: Selects the columns employee_id, department, and salary from the employee_data table.
• SUM(salary) OVER(PARTITION BY department): Calculates the total salary for each department by partitioning the data by department.
• RANK() OVER(PARTITION BY department ORDER BY salary DESC): Ranks employees within each department based on their salary in descending order.
• FROM owox-analytics.myDataset.employee_data: Specifies the table from which the data is being queried.

In this example, the PARTITION BY clause creates partitions based on departments. This allows for a detailed employee salary analysis within departments, helping managers manage department budgets.

The ORDER BY clause within the OVER() function uses a different type of sliding window. When you use OVER(ORDER BY column_name), the window includes only those rows where the value of the specified column is less than or equal to that column’s value in the current row. This is particularly useful for calculating running totals and moving averages.

Example:

Let’s use an example involving cumulative sales in a retail environment. We have a table called daily_sales that records sales transactions by date.

In this example, we want to calculate the cumulative sales amount by date.

Here:

• SELECT: Selects the columns sales_date, transaction_id, and amount from the daily_sales table.
• SUM(amount) OVER(ORDER BY sales_date): Calculates the cumulative sales amount up to each date by summing the amount column, ordered by sales_date.
• FROM owox-analytics.myDataset.daily_sales: Specifies the source table.

Using the ORDER BY clause within the OVER() function allows for calculating cumulative metrics, such as running totals. In this example, it helps us track cumulative sales over time, providing a clear view of sales growth.

Window functions in BigQuery enable powerful and flexible data analysis by performing calculations across sets of table rows. Below are some practical examples demonstrating the use of window functions in BigQuery.

The SUM() window function can be used to calculate the grand total of a specific column across all rows in a table. This is useful for obtaining a cumulative sum that spans the entire dataset without aggregating the data into a single row.

Example:

In this example, we will compute the grand total of sales for all departments within a company.

Here’s the sample data from the department_sales table:

Here:

• SELECT: Selects the columns item, sales, and department from the DepartmentSales table.
• SUM(sales) OVER(): Calculates the grand total of sales across all rows in the dataset.
• FROM owox-analytics.myDataset.department_sales: Specifies the source table for the data.

This example demonstrates how to use the SUM() window function with the OVER() clause to compute the grand total of sales across all departments in a company. Maintaining the detailed data for each item allows us to analyze individual and total sales simultaneously.

The SUM() window function can compute subtotals for each category within a table. Using the PARTITION BY clause, the function calculates the sum of values for each partition. This is useful for summarizing data within specific groups while keeping individual row details.

Example

In this example, we will compute the subtotal of sales for each department within a company. This helps us understand each department's sales performance while maintaining detailed data for individual items.

Here’s the sample data from the department_sales table:

Here:

• SELECT: Selects the columns item, sales, and department from the DepartmentSales table.
• PARTITION BY department: Divides the result set into partitions based on each department.
• ORDER BY sales: Orders the rows within each partition by sales.
• ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING: Ensures the sum includes all rows within the partition.
• FROM owox-analytics.myDataset.department_sales: Specifies the source table for the data.

This example demonstrates how to use the SUM() window function with the OVER() clause to compute subtotals for each department. By partitioning the data by department, we can calculate the total sales for each department while maintaining detailed sales data for individual items.

The SUM() window function can compute a cumulative sum for each category within a table. By using the PARTITION BY and ORDER BY clauses, the function calculates the running total of values for each partition, ordered by a specified column.

Example

Let's use an example involving cumulative revenue for different product types within a company.

Here:

• SELECT: Selects the columns product_type, product, sales_date, and revenue from the ProductRevenue table.
• PARTITION BY product_type: Divides the result set into partitions based on each product type.
• ORDER BY sales_date: Orders the rows within each partition by sales date.
• ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW: Ensures the sum includes all rows from the start of the partition up to the current row.
• FROM owox-analytics.myDataset.product_revenue: Specifies the source table for the data.

This example demonstrates using the SUM() window function with the OVER() clause to compute cumulative revenue for each product type. By partitioning the data by product type and ordering it by sales date, we can track the running total of revenue for each product type

The AVG() window function is useful for calculating a moving average, which helps analyze trends over a specific range of rows.

This technique is particularly valuable for identifying patterns and smoothing out short-term fluctuations in data.

Example:

This example calculates the moving average salary for employees based on their employee IDs.

Here:

• SELECT: Selects the columns employee_name, department, and salary from the employees table.
• AVG(salary) OVER (ORDER BY employee_id ROWS BETWEEN 2 PRECEDING AND CURRENT ROW): Calculates the moving average salary over the current row and the two preceding rows, ordered by employee_id.
• FROM owox-analytics.myDataset.employees: Specifies the source table for the data.

This query computes the moving average salary for each employee, providing insights into salary trends and fluctuations within the organization over time.

The COUNT() window function is effective for determining the number of items within a specific range or partition. This is particularly useful for understanding the distribution and size of groups within your data set, such as counting the number of employees in each department.

Example

This example calculates the number of employees within each department.

Here:

• SELECT: Selects the columns employee_name, department, and salary from the employees table.
• COUNT(*) OVER (PARTITION BY department): Counts the number of employees within each department.
• FROM owox-analytics.myDataset.employees: Specifies the source table for the data.

This query calculates the number of employees in each department, clearly understanding departmental size and distribution.

The RANK() window function assigns a rank to each row within a partition of a result set based on a specified order. This function is particularly useful for identifying items' relative standing. It helps highlight top performers and compare values within grouped data.

Example

This example ranks employees within each department based on their salary in descending order.

Here:

• SELECT: Selects the columns employee_name, department, and salary from the employees table.
• RANK() OVER (PARTITION BY department ORDER BY salary DESC): Assigns a rank to each employee within their department based on their salary in descending order.
• FROM owox-analytics.myDataset.employees: Specifies the source table for the data.

This query ranks employees within each department by their salary, helping to identify top earners and overall salary distribution.

The ROW_NUMBER() window function assigns unique sequential integers to rows within a partition of a result set. This is useful for easier referencing and detailed analysis of individual rows, particularly when you need a clear identifier for each row within grouped data.

Example

This example demonstrates assigning row numbers to sales records within different sales regions for better organization and analysis.

Here:

1. SELECT: Selects the columns sales_person, region, and revenue from the sales table.
2. ROW_NUMBER() OVER (PARTITION BY region ORDER BY sales_id): Assigns a unique sequential number to each sales record within their region, ordered by sales_id.
3. FROM owox-analytics.myDataset.sale_person_data: Specifies the source table for the data.

This query assigns row numbers to sales records within each region, providing a unique identifier for each row. This helps in organizing and referencing sales data within different regions for detailed analysis.

The PERCENT_RANK() window function is used to determine percentiles within a dataset, which is useful for finding the relative standing of values, such as the 90th percentile of test scores. This can help identify the distribution of data and highlight key performance metrics.

Example

In this example, we calculate the percentile rank of sales revenue for each sales person within their region.

Here:

• SELECT: Selects the columns sales_person, region, and revenue from the sales table.
• PERCENT_RANK() OVER (PARTITION BY region ORDER BY revenue): Calculates the percentile rank of each sales person’s revenue within their region.
• FROM owox-analytics.myDataset.sale_person_data: Specifies the source table for the data.

This query computes the percentile rank of each salesperson’s revenue within their region, providing insights into how individual revenues compare to others in the same region.

The LEAD() and LAG() window functions allow for comparing current values with previous and future values, which is useful for detecting changes or growth, such as in financial or event sequence data.

Example

In this example, we analyze the previous and next sales revenue for each sales person within their region.

Here:

• SELECT: Selects the columns sales_person, region, and revenue from the sales table.
• LAG(revenue, 1) OVER (PARTITION BY region ORDER BY revenue): Accesses the revenue of the previous row within the same region, ordered by revenue.
• LEAD(revenue, 1) OVER (PARTITION BY region ORDER BY revenue): Accesses the revenue of the next row within the same region, ordered by revenue.
• FROM owox-analytics.myDataset.sale_person_data: Specifies the source table for the data.

This query provides insights into each salesperson's previous and next sales revenue within their region, allowing for a comparative analysis of revenue progression.

The FIRST_VALUE() and LAST_VALUE() window functions return the first and last values in an ordered partition. This is useful for identifying key events, such as the first and last sales date of a product.

Example

Here is a table with data we will use to analyze and demonstrate the functions.

With the following syntax, we identify each region's first and last sales revenue.

Here:

• SELECT: Selects the columns sales_person, region, and revenue from the sales table.
• FIRST_VALUE(revenue) OVER (PARTITION BY region ORDER BY revenue): Retrieves the first revenue within each region.
• LAST_VALUE(revenue) OVER (PARTITION BY region ORDER BY revenue): Retrieves the last revenue within each region.
• FROM owox-analytics.myDataset.sale_person_data: Specifies the source table for the data.

This query identifies the first and last sales revenue within each region, providing a range of revenues within the region to better understand revenue distribution.

The LEAD() function can be used to calculate the following time for an event, such as time to purchase after a marketing campaign. This is useful for understanding the effectiveness of events or campaigns.

Example

In this example, we calculate the time to the next promotion for employees within their department.

Here:

• SELECT: Selects the columns employee_name, department, and promotion_date from the employee_promotions table.
• LEAD(promotion_date, 1) OVER (PARTITION BY department ORDER BY promotion_date): Calculates the date of the next promotion.
• FROM owox-analytics.myDataset.employee_promotions: Specifies the source table for the data.

This query calculates the date to the next promotion for employees within their department, providing insights into promotion timelines and the effectiveness of career advancement strategies.

The STDDEV() window function computes the standard deviation of values within a partition, which is useful for understanding data variability, such as salary distributions within a department.

Example

In this example, we calculate the standard deviation of salaries within each department to understand salary variability.

Here:

• SELECT: Select the columns employee_name, department, and salary from the employees' table.
• STDDEV(salary) OVER (PARTITION BY department): Computes the standard deviation of salaries within each department.
• FROM owox-analytics.myDataset.employees: Specifies the source table for the data.

This query calculates the standard deviation of salaries within each department, providing insights into salary variability and helping to identify departments with higher salary dispersion.

Window functions in BigQuery offer powerful tools for performing complex calculations and analyses over data partitions. These advanced use cases enable deeper insights and more nuanced data interpretations, which are essential for data-driven decision-making.

Many use cases for window functions involve calculating rolling metrics between dates, providing insights into trends over specific time periods. These functions are ideal for tasks like moving averages or cumulative sums within a defined range.

Example

In this example, we will calculate the total sales amount for each product in 3-day and 5-day sliding windows.

Here:

• SELECT: Selects the columns product_id, sale_date, and sale_amount from the product_sales table.
• SUM(sale_amount) OVER (PARTITION BY product_id ORDER BY UNIX_DATE(sale_date) RANGE BETWEEN 3 PRECEDING AND CURRENT ROW): Calculates the cumulative sum of sales for each product over the last 3 days, including the current day.
• SUM(sale_amount) OVER (PARTITION BY product_id ORDER BY UNIX_DATE(sale_date) RANGE BETWEEN 5 PRECEDING AND CURRENT ROW): Calculates the cumulative sum of sales for each product over the last 5 days, including the current day.
• FROM owox-analytics.myDataset.product_sales: Specifies the source table for the data.

This query uses BigQuery window functions to calculate rolling sales totals within different time intervals. This technique can be extended to other metrics and time intervals to suit various analytical needs.

💡 Want to master date manipulation and analysis in BigQuery? Check out our latest guide for a comprehensive overview of DATE functions, including syntax, usage, and practical examples.

In many cases, you might want to filter the results of a Window Function. This can be achieved using the QUALIFY clause in BigQuery. The QUALIFY clause filters the results of window functions, including only rows that meet the specified condition. This is particularly useful for refining data analysis and obtaining focused results from complex queries.

Example

In this example, we will filter and keep only the top 3 salespersons based on their total sales in each region.

Here:

• SELECT: Selects the columns salesperson, region, and total_sales from the sales table.
• RANK() OVER (PARTITION BY region ORDER BY sales_amount DESC): Calculates the rank of each salesperson within their region based on the sales_amount, in descending order.
• FROM owox-analytics.myDataset.salesperson_data: Specifies the source table for the data.
• QUALIFY position <= 3: Filters the results to include only the top 3 salespersons in each region.

This query uses the QUALIFY clause to filter and retain only the top 3 salespersons based on their total sales within each region. By combining the RANK() window function with the QUALIFY clause, we can efficiently filter and analyze top-performing individuals or entities within specific groups.

The QUALIFY clause can be effectively used for deduplicating data in a BigQuery table. This is particularly useful when you want to keep only the most recent record for each unique identifier, ensuring that your dataset does not contain any redundant entries.

Example: In this example, we will deduplicate records by keeping only the latest entry for each userId based on the transactionDate.

Here:

• SELECT userId, transactionId, transactionDate: Selects the useId, transactionId, and transactionDate columns from the specified table.

• ROW_NUMBER() OVER (PARTITION BY userId ORDER BY transactionDate DESC): Assigns a unique row number to each record within each partition of userId, ordered by transactionDate in descending order. The most recent event for each user will have a row number of 1.

• QUALIFY ROW_NUMBER() OVER (PARTITION BY userId ORDER BY transactionDate DESC) = Filters the results to include only the records with a row number of 1, which represents the latest event for each user_id. This effectively removes duplicate records, retaining only the most recent entry for each user.

Using unnamed and unaliased window functions with the QUALIFY clause allows you to filter results directly based on the output of window functions without needing to include additional columns in your output. This makes your queries more concise and focused on the necessary data.

Example: In this example, we will filter and keep only the top 5 employees based on their salary without including the ranking in the output.

Here:

• SELECT employee_name, salary: Selects the name and salary columns from the employees table.

• RANK() OVER (ORDER BY salary DESC): Calculates the rank of each employee based on their salary in descending order.

• QUALIFY RANK() OVER (ORDER BY salary DESC) <= 5: Filters the results to include only employees who are ranked 5 or higher based on their salary.

Named windows in BigQuery allow you to define a window frame clause once and reuse it across multiple window functions. This improves query readability and maintainability by avoiding redundancy. You can efficiently manage and standardize complex analytical calculations over defined data partitions by applying named windows.

Example

Let's say we want to analyze the sales performance of different products.

Here:

• SELECT: Selects the columns product_id, sale_date, and sales_amount from the product_sales table.
• LAG(sales_amount) OVER(PARTITION BY product_id ORDER BY sale_date): Retrieves the sales amount of the previous day for each product.
• FIRST_VALUE(sales_amount) OVER(PARTITION BY product_id ORDER BY sale_date): Retrieves the amount of the first sale within the partition for each product.
• SUM(sales_amount) OVER(yearly_ordered_sales): Computes the cumulative sum of sales for each product over the years.
• AVG(sales_amount) OVER(yearly_ordered_sales): Computes the cumulative average sales for each product over the years.
• WINDOW: Defines named windows yearly_ordered_sales. The yearly_ordered_sales window partitions the data by product_id.

This example demonstrates how to use named windows in BigQuery to efficiently perform multiple window function calculations. Defining and reusing named windows allows you to streamline your queries and maintain consistent analysis across different metrics.

Using window functions in BigQuery can significantly enhance your data analysis capabilities, but it's important to follow best practices to ensure optimal performance and accuracy. Implementing these strategies can lead to more efficient and maintainable queries.

When using navigation functions in BigQuery, there are times when NULL values need to be ignored to ensure accurate results. For instance, if you're looking for the first and second available preceding value, you might want to ignore the NULLs. This can be achieved using the IGNORE NULLS value expression.

Example

In this example, we ignore NULL values when looking for each product's previous two non-null sales amounts.

This query demonstrates how to use the IGNORE NULLS value expression to skip NULL values in navigation functions, ensuring that the results reflect meaningful data points.

When working with window functions, it’s essential to include only the necessary columns and expressions in your queries. This practice minimizes the data processed, reducing computational load and improving query performance. Focusing on relevant data ensures that the results are both efficient and accurate.

Example

In this example, we calculate the average salary within each department while including only the necessary columns.

By including only the necessary columns (department and salary), this query reduces the data processed and focuses on the relevant information needed to calculate each department's average salary. This practice enhances query performance and ensures efficient data analysis.

Applying filters before using window functions can significantly reduce the amount of data processed, leading to more efficient and faster queries. By filtering out unnecessary rows early in the query, you can minimize the computational load and focus only on relevant data.

Example

In this example, we filter sales data to include only transactions from date 2023-09-01 before calculating the cumulative sales for each product.

By applying a filter to include only relevant transactions from 2023-09-01, this query reduces the amount of data processed by the window function, resulting in more efficient and faster computation.

Unlike aggregate functions, window functions allow you to perform calculations across sets of rows related to the current row without collapsing the result into a single output. This makes window functions ideal for tasks such as ranking, running totals, and moving averages, providing detailed insights and maintaining row-level detail.

Example

In this example, we use window functions to calculate running totals of sales amounts for each product.

This query demonstrates the use of window functions to solve the problem of calculating running totals. By using the SUM() window function, we maintain the granularity of each sale while providing cumulative sales amounts.

Being explicit when aliasing columns and naming windows in BigQuery enhances the readability and maintainability of your SQL queries. Clear and descriptive aliases help to understand the purpose of each column and the logic behind window functions, making it easier to debug and optimize your code.

Example

In this example, we will analyze the sales data and use clear aliases and named windows to improve the query's readability and maintainability.

By being explicit in aliasing columns and naming windows, this query becomes more readable and easier to maintain. Descriptive aliases indicate the purpose of each calculated column. Named windows like yearly_sales and three_day_sales_window help to simplify the reuse of window definitions, making the query structure clear and logical.

Referring to official documentation is crucial for ensuring accuracy and best practices when working with window functions in BigQuery. The documentation provides detailed information on syntax, function behavior, and edge cases, which helps write correct and efficient queries. Staying updated with the documentation ensures that you leverage the full potential of BigQuery's capabilities and avoid common pitfalls.

Understanding the order of clause execution in SQL queries is essential for writing efficient and accurate queries. In BigQuery, the execution order for various clauses (e.g., FROM, WHERE, GROUP BY, HAVING, WINDOW, SELECT, ORDER BY) impacts how the data is processed and filtered. Knowing this order helps in structuring queries correctly and avoiding logical errors.

Example

In this example, we use multiple clauses to filter, group, and order sales data while calculating cumulative sales using window functions.

Understanding the order of clause execution ensures that the query processes data correctly, leading to accurate results. Properly structuring queries according to this order helps avoid logical errors and optimizes performance.

BigQuery offers a rich set of functions beyond window functions. Exploring and mastering these functions can significantly enhance your data analysis capabilities.

• Conditional Expressions: Used to perform logic-based operations and return values based on specified conditions (e.g., CASE, IF).
• String Functions: Functions that manipulate and format string data (e.g., CONCAT, SUBSTRING, UPPER, LOWER).
• Conversion Functions: Convert data from one type to another (e.g., CAST, SAFE_CAST).
• Array Functions: Functions to create, manipulate, and query arrays (e.g., ARRAY_AGG, UNNEST, ARRAY_LENGTH).
• Timestamp Functions: Functions that handle and manipulate timestamp data (e.g., CURRENT_TIMESTAMP, TIMESTAMP_ADD, FORMAT_TIMESTAMP).
• Data Manipulation Language: SQL statements used to manage data within tables (e.g., INSERT, UPDATE, DELETE).
• Datetime Functions: Functions that work with date and time data types (e.g., CURRENT_DATE, DATE_ADD, EXTRACT).

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