Working with BigQuery ROW_NUMBER Function Made Easy
BigQuery is a fully managed data warehouse that lets users manage, manipulate, and analyze their data using a variety of in-built features, including business intelligence, machine learning, and geospatial analysis. The serverless nature of BigQuery means that you can easily run SQL queries to figure out how your organization is performing, without requiring any infrastructure management.
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The powerful analysis engine in BigQuery lets organizations query petabytes of data within minutes. Since it’s owned by Google, BigQuery works best if you are working on the Google Cloud Platform. However, it also works seamlessly with other platforms. If you need a solution for analyzing nested data, BigQuery ROW_NUMBER is a fantastic choice. The BI Engine and BigQuery ML are both capable of analyzing tons of data quickly and efficiently.
BigQuery also supports a variety of numbering functions. At their core, numbering functions are simply a subgroup of analytic functions. Numbering functions simply assign integer values to each row, depending on their position in the specified window. For example, take a look at the table below:
Now, it’s important to understand the ROW_NUMBER function better before you go ahead.
Table of Contents
- What is BigQuery ROW_NUMBER Function?
- Examples of BigQuery ROW_NUMBER Function
- How To Overcome the Resources Exceeded Error
What is the BigQuery ROW_NUMBER Function?
The BigQuery ROW_NUMBER is function is one of the most commonly used functions in SQL. It was made available in SQL Server 2005, and all later versions too. When analyzing a results grid, Bigquery ROW_NUMBER simply adds a discrete incrementing number to the order.
To determine the order, you must use the ORDER BY expression. This determines the order in which row numbers are added. In most cases, one or more columns are generally stated in the ORDER BY expression. But you can also use a more complicated expression or add a sub-query too.
As a result, the function generates a constantly increasing integral value that begins at 1, with a higher value assigned for each subsequent row.
More importantly, the ROW_NUMBER can also be paired with the PARTITION BY clause. So, when the partition limit is crossed, the counter is reset, and begins from 1 again. Partitions can have several values, such as 1,2, 3, and so on, and when it’s reset, the counter starts from 1, 2, and 3 again.
To put it simply, the ROW_NUMBER generates a series of temporary values that are assigned to figures, and is calculated dynamically based on when the query is executed. Both the ROW_NUMBER and the RANK function are generally similar.
However, whereas the ROW_NUMBER gives a sequence of values starting from 1 (with 1 added incrementally), the RANK function repeats values that are tied.
For people who already have experience with Oracle, the ROW_NUM function might be a bit more familiar. It’s very similar in nature to a Pseudo-Column, except this one’s a bit more dynamic.
Keep in mind that the ROW_NUMBER is an analytic function. Analytic functions are those which compute values over groups of rows, returning a single result for each of the rows. This function is commonly used for implanting top or bottom-N reports.
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Examples of the BigQuery ROW_NUMBER Function
To better understand the BigQuery ROW_NUMBER function, here’s a simple example.
The sample table for
office.employees assigns a number to each row based on when an employee left the office.
SELECT office_id, last_name, employee_id, ROW_NUMBER () OVER (PARTITION BY department_id ORDER BY employee_id) AS work_id FROM employees;
Now, if you were to run the following inner-N query, it’ll simply select all rows from the table shown above but returns only rows between 5-20.
SELECT last_name FROM (SLEECT last_name, ROW_NUMBER () OVER (ORDER BY last_name) R FROM employees) Where R BETWEEN 5 and 20;
This is a simple way to sort the data. You can also use the OVER clause in order to manipulate the data and get another clause in it. For instance, in the following example, we can get a list of all customers for an organization by projecting several columns, including the OrderID, the OrderDate, the OrderNumber, the TotalDue, and ROW_NUMBER.
For the purposes of this example, the ROW_NUMBER function shall be applied to the CustID column. The values continue until the table reaches its end. Keep in mind that since the ORDER BY clause is not used this query, the order of the CustID column is not specific.
USE BusinessSales2021; GO SELECT ROW_NUMBER () OVER ( ORDER BY CustID) AS RowNum, CustID, OrderID, OrderDate, OrderNumber TotalDue From TotalSales.SalesOrderHeader;
You can also use the ROW_NUMBER function with the PARTITION by clause. For instance ,if you add that on the CustID and OrderDate fields, the output will change. Let’s carry on from the example given above.
Use BusinessSales2021; GO Select ROW_NUMBER () OVER (PARTITION BY CustID, DATEADD (MONTH, DATEDIFF (Month, 0, OrderDate), 0) ODER BY SubTotal DESC) AS MonthlyOrders, CustID, OrderID, OrderDate, OrderNumber Subtotal, TotalDue FROM TotalSales.SalesOrderHeader;
In this query, a partition is created for the OrderDate and the CustID. For each unique combination, the ROW_Number will repeat itself. This makes it easy for data analysts to figure out which order placed more than one order on the same day. As you can see, CustID 1200 placed two orders in the month of December 2021.
How To Overcome the Resources Exceeded Error
ROW_NUMBER is a commonly used function, but from time to time, you might get the error “Resources Exceeded” when using it. As the volume of your data continues to increase, there might be an excess number of elements in your dataset to use ORDER BY to bring them in a single partition.
However, to avoid this issue, you should consider using ARRAY_AGG(), since the ORDER BY is capable of dropping all of the data, apart from the top record on each GROUP BY.
Obviously, the query is likely to run a bit slower, but at least you’ll be able to circumvent the “Resources Exceeded” error.
It’s important to note that ARRAY_AGG () is an aggregate function. These functions simply summarize rows within a group, condensing them to a single value.
How Versatile is ROW_NUMBER?
ROW_NUMBER can be leveraged for use in a variety of different situations, such as:
- Identifying quality gaps in your data
- Minimizing the available data
- Ranking values in datasets
- Optimizing sessionization
- Managing queries associated with preferences.
However, when you think about it, ROW_NUMBER is more than just a traditional function. It’s actually a window function with peculiar properties. And, before we go further, it’s important to talk about the arguments that the function can take.
So, if you take a look at ROW_NUMBER’s conventional syntax, you’ll realize it doesn’t take direct arguments. The OVER clause must be used for adding an argument to the ROW_NUMBER clause. This argument is then referred to as a window.
The window simply gives definition to a subset of the data that must be used for computing data. Apart from the OVER clause, other arguments that can be used with this function include partitions, rows, and orders.
Typically, ROW_NUMBER is used for ranking different records. While there are other functions that can be used, such as DENSE_RANK and RANK, they are all slightly different. The reason why ROW_NUMBER is unique is because it returns a unique and constantly increasing ranking for each of the records.
This ultimately becomes one of the function’s greatest advantages. Since data analysts already know that only one record can exist for each value of a ROW_NUMBER, they don’t have to worry about cardinalities when grouping the different queries together.
As mentioned in the article, BigQuery ROW_NUMBER is one of the many functions that you can use to gain better visibility over your data and manipulate it to offer valuable insights into your business performance. If you are using BigQuery to store your data, you should absolutely consider adding a no-code data pipeline to seamlessly pull your data into one location.
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