Ever found yourself needing to perform complex calculations across a set of table rows in SQL, without resorting to *endless* subqueries? Enter window functions!

Window functions in SQL are like a Swiss army knife—they can do a lot with just a few simple components. Whether you’re calculating running totals, ranking data, or performing cumulative sums, window functions allow you to do it all without the overhead of more complicated queries. That’s why they’re a favorite topic in SQL interviews—mastering them not only shows that you know your SQL but that you can apply it efficiently.

So, if you’re prepping for an SQL interview and want to stand out, knowing your way around window functions is **essential**. Let’s dive into what they are and why they matter.

Before we jump into the interview questions, let’s get clear on what exactly we’re talking about when we say “window functions.”

**Definition**: A window function performs a calculation across a set of table rows that are somehow related to the current row. But unlike aggregate functions, which collapse data into a single result, window functions preserve the rows of your table.

**Components**:

- : Think of this as creating mini-groups within your data. If you’re calculating a running total by department, would ensure that each department’s total is calculated separately.
- : This determines the sequence in which rows are processed within each partition. For example, if you’re ranking sales by date, you’d use .
- : This is the special sauce that turns an ordinary function into a window function. It defines the window or set of rows over which the function operates.

**Common Use Cases**:

- Running Totals: Summing values across rows in a sequence, like cumulative sales.
- Ranking: Assigning a rank or position to rows based on some criteria, such as sales figures.
- Moving Averages: Calculating averages over a specific range of rows, useful in time series analysis.

These are just a few examples, but the flexibility of window functions allows for much more complex operations as well.

- : Calculates the sum of a set of values. As a window function, it’s often used for running totals. For example, would give you a running total of sales within each department.
- : Computes the average of a set of values. In a window function context, it’s great for moving averages, like to calculate a three-month moving average.
- : Counts the number of rows. Used as a window function, it can count cumulative rows, like to count the number of sales records up to each date within a department.
- and : These functions return the maximum or minimum value within a set of rows. When used as window functions, they can track the highest or lowest value seen so far, like .

- **: Assigns a unique sequential integer to rows within a partition. It’s useful for tasks like numbering orders placed by a customer.
- and : These functions rank rows based on a specified order. The difference between them? will leave gaps in the ranking sequence if there are ties, whereas does not. For example, you might rank employees within departments by salary using .
- : Distributes rows into a specified number of roughly equal groups, useful for dividing data into quartiles or deciles. For example, would assign each row to one of four salary quartiles.

- : Retrieves data from the following row within the same result set. It’s perfect for comparing a current row with the next, like to see how sales compare to the next month’s sales.
- : Retrieves data from the preceding row, allowing you to compare the current row with the previous one. For example, can be used to compare this month’s sales to last month’s.
- and : These functions return the first or last value in an ordered set of values. They’re useful for finding the earliest or latest values in a partition, like to find the first sales figure for each department.

As a business analyst on the revenue forecasting team at NVIDIA, you are given a table of NVIDIA transactions in 2021.

Write a query to summarize the total sales revenue for each product line. The product line with the highest revenue should be at the top of the results.

Go to our blog to see more NVIDIA SQL Interview Questions!

Column Name | Type |
---|---|

product_id | integer |

product_name | varchar |

product_line | varchar |

product_id | product_name | product_line |
---|---|---|

1 | Quadro RTX 8000 | GPU |

2 | Quadro RTX 6000 | GPU |

3 | GeForce RTX 3060 | GPU |

4 | BlueField-3 | DPU |

Column Name | Type |
---|---|

transaction_id | integer |

product_id | integer |

amount | integer |

transaction_id | product_id | amount |
---|---|---|

101 | 1 | 5000 |

102 | 2 | 4200 |

103 | 3 | 9000 |

104 | 4 | 7000 |

product_line | total_revenue |
---|---|

GPU | 18200 |

DPU | 7000 |

Solve this NVIDIA Window SQL Question in our interactive coding environment and see an alternate solution!

**Answer**: The clause divides the result set into partitions to which the window function is applied. For example, calculates the total sales for each department. Each department is treated as a separate group for the calculation.

**Answer**:

- assigns a unique sequential number to rows within a partition, without regard for ties.
- gives rows the same rank if they have the same value, but the next rank number skips the number of rows with the same rank.
- also gives the same rank for identical values but does not skip rank numbers.
- Use Cases: for unique numbering, for scenarios where gaps in ranking are acceptable, when you want continuous ranking.

Answer: You can calculate a running total using the function with an clause. For example:

This query calculates a running total of ordered by .

This is the same question as problem #12 in the SQL Chapter of Ace the Data Science Interview!

Assume you're given a table containing data on Amazon customers and their spending on products in different category, write a query to identify the top two highest-grossing products within each category in the year 2022. The output should include the category, product, and total spend.

Column Name | Type |
---|---|

category | string |

product | string |

user_id | integer |

spend | decimal |

transaction_date | timestamp |

category | product | user_id | spend | transaction_date |
---|---|---|---|---|

appliance | refrigerator | 165 | 246.00 | 12/26/2021 12:00:00 |

appliance | refrigerator | 123 | 299.99 | 03/02/2022 12:00:00 |

appliance | washing machine | 123 | 219.80 | 03/02/2022 12:00:00 |

electronics | vacuum | 178 | 152.00 | 04/05/2022 12:00:00 |

electronics | wireless headset | 156 | 249.90 | 07/08/2022 12:00:00 |

electronics | vacuum | 145 | 189.00 | 07/15/2022 12:00:00 |

category | product | total_spend |
---|---|---|

appliance | refrigerator | 299.99 |

appliance | washing machine | 219.80 |

electronics | vacuum | 341.00 |

electronics | wireless headset | 249.90 |

Solve this Amazon SQL Interview Question for FREE on DataLemur and see the additional solution!

And to see more Amazon SQL Interview Questions read our blog!

**Answer**: divides the rows in a result set into a specified number of roughly equal groups. It’s useful for breaking data into quartiles, deciles, etc. For example, divides the data into four quartiles based on .

Psst if these are harder that you expected try this free window function SQL tutorial to get up to speed.

As a data analyst at Uber, it's your job to report the latest metrics for specific groups of Uber users. Some riders create their Uber account the same day they book their first ride; the rider engagement team calls them "in-the-moment" users.

Uber wants to know the average delay between the day of user sign-up and the day of their 2nd ride. Write a query to pull the average 2nd ride delay for "in-the-moment" Uber users. Round the answer to 2-decimal places.

Column Name | Type |
---|---|

user_id | integer |

registration_date | date |

user_id | registration_date |
---|---|

1 | 08/15/2022 |

2 | 08/21/2022 |

Column Name | Type |
---|---|

ride_id | integer |

user_id | integer |

ride_date | date |

ride_id | user_id | ride_date |
---|---|---|

1 | 1 | 08/15/2022 |

2 | 1 | 08/16/2022 |

3 | 2 | 09/20/2022 |

4 | 2 | 09/23/2022 |

average_delay |
---|

1 |

Solve this Uber SQL Interview Question on DataLemur!

To solve more Uber SQL Interview Questions check out this blog.

**Answer**: The clause defines the window or set of rows for the function. within specifies the order of rows, while divides the result set into partitions. The function is applied to each partition separately and within the order specified.

Sometimes, payment transactions are repeated by accident; it could be due to user error, API failure or a retry error that causes a credit card to be charged twice.

Using the transactions table, identify any payments made at the same merchant with the same credit card for the same amount within 10 minutes of each other. Count such repeated payments.

Column Name | Type |
---|---|

transaction_id | integer |

merchant_id | integer |

credit_card_id | integer |

amount | integer |

transaction_timestamp | datetime |

transaction_id | merchant_id | credit_card_id | amount | transaction_timestamp |
---|---|---|---|---|

1 | 101 | 1 | 100 | 09/25/2022 12:00:00 |

2 | 101 | 1 | 100 | 09/25/2022 12:08:00 |

3 | 101 | 1 | 100 | 09/25/2022 12:28:00 |

4 | 102 | 2 | 300 | 09/25/2022 12:00:00 |

6 | 102 | 2 | 400 | 09/25/2022 14:00:00 |

payment_count |
---|

1 |

To solve this Stripe SQL Interview Question and read about how we used a Window Function to get to this final answer go to our site!

Also... if you want to find more Stripe SQL Interview Questions go to our blog.

**Answer**: The clause defines a subset of rows within the window for the function to operate on. For example, would limit the function to just the current row and the one immediately before it. This is critical for calculations like moving averages or cumulative sums, as it dictates the specific rows considered in the calculation.

**Answer**:
Optimizing a query with multiple window functions involves several strategies:

- Indexing: Ensure that the columns used in the and clauses are indexed. This can significantly reduce the time required to sort and partition the data.
- Reducing Data Size: Filter your dataset as early as possible in the query to reduce the number of rows processed by the window functions.
- Minimizing Window Function Usage: Where possible, try to consolidate window functions or calculate certain values outside the window function to reduce overhead.
- Frame Specifications: Use the most specific frame you can ( vs. ) to limit the number of rows considered by the function, thereby improving performance.
- Query Execution Plan: Analyze the execution plan to identify any bottlenecks, and adjust your query accordingly to ensure optimal performance.

The Apple retention team needs your help to investigate buying patterns. Write a query to determine the percentage of buyers who bought AirPods directly after they bought iPhones. Round your answer to a percentage (i.e. 20 for 20%, 50 for 50) with no decimals.

Column Name | Type |
---|---|

transaction_id | integer |

customer_id | integer |

product_name | varchar |

transaction_timestamp | datetime |

transaction_id | customer_id | product_name | transaction_timestamp |
---|---|---|---|

1 | 101 | iPhone | 08/08/2022 00:00:00 |

2 | 101 | AirPods | 08/08/2022 00:00:00 |

5 | 301 | iPhone | 09/05/2022 00:00:00 |

6 | 301 | iPad | 09/06/2022 00:00:00 |

7 | 301 | AirPods | 09/07/2022 00:00:00 |

follow_up_percentage |
---|

50 |

This is a tricky one... to see HOW we got to this answer go to this Apple SQL Interview Question.

If these questions weren't enough solve these additional Apple SQL Interview Questions from our blog.

The best way to prepare for the SQL interview is to practice, practice, practice. Besides solving the earlier SQL interview questions, you should also solve the 200+ SQL coding questions which come from companies like FAANG tech companies and tech startups.

Each exercise has hints to guide you, step-by-step solutions and best of all, there is an interactive coding environment so you can right online code up your SQL query and have it checked. However, if your SQL skills are weak, forget about going right into solving questions – refresh your SQL knowledge with this DataLemur SQL Tutorial.