Window functions in Snowflake - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a window function in Snowflake?

A window function performs a calculation across a set of table rows related to the current row, without collapsing the rows into a single output row.

beginner

What does the OVER() clause do in a Snowflake window function?

The OVER() clause defines the window or set of rows the function operates on, including partitioning and ordering rules.

intermediate

How does PARTITION BY affect a window function in Snowflake?

PARTITION BY divides the data into groups (partitions) so the window function runs separately within each group.

advanced

What is the difference between ROWS and RANGE in window framing?

ROWS counts physical rows relative to the current row; RANGE considers logical values in the order column, including ties.

beginner

Give an example of a common window function in Snowflake and its use.

ROW_NUMBER() assigns a unique number to each row within a partition, useful for ranking or deduplication.

Which clause is mandatory for a window function in Snowflake?

A OVER()

B WHERE

C GROUP BY

D HAVING

The OVER() clause is required to define the window for the function.

What does PARTITION BY do in a window function?

A Filters rows before calculation

B Groups rows for separate calculations

C Sorts rows globally

D Limits output rows

PARTITION BY groups rows so the function runs separately on each group.

Which window function assigns a unique rank to each row?

A SUM()

B AVG()

C ROW_NUMBER()

D COUNT()

ROW_NUMBER() gives a unique number to each row within its partition.

What is the default frame for window functions if not specified?

A ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW

B ROWS BETWEEN CURRENT ROW AND UNBOUNDED FOLLOWING

C RANGE BETWEEN CURRENT ROW AND CURRENT ROW

D RANGE BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING

The default frame is RANGE BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING.

Which keyword defines the order of rows in a window function?

A ORDER BY

B PARTITION BY

C GROUP BY

D FILTER BY

ORDER BY inside OVER() sets the order of rows for the window function.

Explain how window functions differ from aggregate functions in Snowflake.

Think about whether the number of rows changes after applying the function.

Describe how to use PARTITION BY and ORDER BY together in a window function and why.

Consider how you would rank students by class and score.

Practice

1. What does a window function in Snowflake do?

easy

A. Calculates values across rows related to the current row without grouping them into fewer rows

B. Groups rows and reduces the number of rows returned

C. Deletes duplicate rows from the result set

D. Creates a new table from existing data

Solution

1. Step 1: Understand window function purpose

   Window functions perform calculations across a set of rows related to the current row but do not reduce the number of rows returned.

2. Step 2: Compare with grouping

   Unlike GROUP BY, window functions keep all rows visible while calculating values like running totals or ranks.

3. Final Answer:

   Calculates values across rows related to the current row without grouping them into fewer rows -> Option A

4. Quick Check:

   Window functions analyze rows without grouping = A [OK]

Hint: Window functions keep all rows, unlike GROUP BY [OK]

Common Mistakes:

• Confusing window functions with GROUP BY aggregation
• Thinking window functions reduce row count
• Assuming window functions delete duplicates

2. Which of the following is the correct syntax to calculate a running total of sales using a window function in Snowflake?

easy

A. SELECT SUM(sales) GROUP BY region ORDER BY date FROM sales_data;

B. SELECT sales + PREVIOUS(sales) FROM sales_data;

C. SELECT RUNNING_TOTAL(sales) FROM sales_data;

D. SELECT SUM(sales) OVER (PARTITION BY region ORDER BY date) FROM sales_data;

Solution

1. Step 1: Identify correct window function syntax

   SUM(sales) OVER (PARTITION BY region ORDER BY date) correctly calculates a running total partitioned by region and ordered by date.

2. Step 2: Eliminate incorrect options

   SELECT SUM(sales) GROUP BY region ORDER BY date FROM sales_data; uses GROUP BY which reduces rows, not a window function. Options C and D use invalid functions or syntax.

3. Final Answer:

   SELECT SUM(sales) OVER (PARTITION BY region ORDER BY date) FROM sales_data; -> Option D

4. Quick Check:

   SUM() OVER with PARTITION BY and ORDER BY = B [OK]

Hint: Look for SUM() OVER with PARTITION BY and ORDER BY [OK]

Common Mistakes:

• Using GROUP BY instead of OVER clause
• Using non-existent functions like RUNNING_TOTAL
• Omitting ORDER BY in window function

3. Given the table sales with columns region, date, and amount, what is the output of this query?

SELECT region, date, amount, RANK() OVER (PARTITION BY region ORDER BY amount DESC) AS rank FROM sales;

medium

A. Ranks sales amounts within each region from highest to lowest

B. Ranks sales amounts across all regions ignoring region groups

C. Calculates cumulative sum of amounts per region

D. Returns the total number of sales per region

Solution

1. Step 1: Understand RANK() with PARTITION BY and ORDER BY

   RANK() assigns ranks starting at 1 within each partition (region), ordering by amount descending.

2. Step 2: Interpret the query output

   The query shows each sale with its rank in its region based on amount, highest amount ranked 1.

3. Final Answer:

   Ranks sales amounts within each region from highest to lowest -> Option A

4. Quick Check:

   RANK() OVER PARTITION BY region ORDER BY amount DESC = A [OK]

Hint: RANK() with PARTITION BY ranks within groups [OK]

Common Mistakes:

• Thinking RANK() ignores PARTITION BY
• Confusing RANK() with cumulative sum
• Assuming ranks are across all rows without grouping

4. Identify the error in this Snowflake query:

SELECT employee_id, salary, ROW_NUMBER() OVER (ORDER BY salary) PARTITION BY department FROM employees;

medium

A. ORDER BY cannot be used in window functions

B. ROW_NUMBER() cannot be used with ORDER BY

C. PARTITION BY must come before ORDER BY inside OVER()

D. Missing GROUP BY clause for department

Solution

1. Step 1: Check window function clause order

   In Snowflake, PARTITION BY must appear before ORDER BY inside the OVER() clause.

2. Step 2: Identify syntax error

   The query places PARTITION BY after ORDER BY, which is invalid syntax.

3. Final Answer:

   PARTITION BY must come before ORDER BY inside OVER() -> Option C

4. Quick Check:

   PARTITION BY before ORDER BY in OVER() = D [OK]

Hint: PARTITION BY always before ORDER BY in OVER() [OK]

Common Mistakes:

• Placing PARTITION BY after ORDER BY
• Thinking ROW_NUMBER() disallows ORDER BY
• Adding unnecessary GROUP BY for window functions

5. You want to calculate the average sales per region and also show each sale's rank by amount within its region. Which query correctly combines these using window functions?

hard

A. SELECT region, amount, AVG(amount) PARTITION BY region, RANK() ORDER BY amount DESC FROM sales;

B. SELECT region, amount, AVG(amount) OVER (PARTITION BY region) AS avg_region, RANK() OVER (PARTITION BY region ORDER BY amount DESC) AS rank FROM sales;

C. SELECT region, amount, AVG(amount), RANK() FROM sales GROUP BY region ORDER BY amount DESC;

D. SELECT region, amount, AVG(amount) OVER (), RANK() OVER (ORDER BY amount) FROM sales;

Solution

1. Step 1: Use AVG() as window function partitioned by region

   AVG(amount) OVER (PARTITION BY region) calculates average sales per region without grouping rows.

2. Step 2: Use RANK() partitioned by region ordered by amount descending

   RANK() OVER (PARTITION BY region ORDER BY amount DESC) ranks sales within each region.

3. Step 3: Verify query correctness

   SELECT region, amount, AVG(amount) OVER (PARTITION BY region) AS avg_region, RANK() OVER (PARTITION BY region ORDER BY amount DESC) AS rank FROM sales; correctly uses window functions with proper syntax and clauses.

4. Final Answer:

   SELECT region, amount, AVG(amount) OVER (PARTITION BY region) AS avg_region, RANK() OVER (PARTITION BY region ORDER BY amount DESC) AS rank FROM sales; -> Option B

5. Quick Check:

   AVG() and RANK() with PARTITION BY region = C [OK]

Hint: Use OVER(PARTITION BY region) for both AVG and RANK [OK]

Common Mistakes:

• Using GROUP BY instead of window functions
• Incorrect syntax for window functions
• Omitting PARTITION BY for per-region calculations