FILTER clause for conditional aggregation in PostgreSQL - Time & Space Complexity

Time Complexity: FILTER clause for conditional aggregation

O(n)

Understanding Time Complexity

We want to understand how the time to run a query with a FILTER clause changes as the data grows.

Specifically, how does filtering inside aggregation affect performance?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

SELECT department_id,
       COUNT(*) FILTER (WHERE status = 'active') AS active_count,
       COUNT(*) FILTER (WHERE status = 'inactive') AS inactive_count
FROM employees
GROUP BY department_id;
    

This query counts active and inactive employees per department using FILTER inside aggregation.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Scanning each row in the employees table once.
• How many times: Once per row, checking conditions and grouping.

How Execution Grows With Input

As the number of employees grows, the query checks each row once to decide which count to increase.

Input Size (n)	Approx. Operations
10	About 10 row checks
100	About 100 row checks
1000	About 1000 row checks

Pattern observation: The work grows directly with the number of rows.

Final Time Complexity

Time Complexity: O(n)

This means the time grows linearly with the number of rows in the table.

Common Mistake

[X] Wrong: "Using FILTER makes the query run multiple times for each condition."

[OK] Correct: The database scans each row once and applies all FILTER conditions during that scan, so it does not repeat the whole query multiple times.

Interview Connect

Understanding how conditional aggregation scales helps you write efficient queries and explain your reasoning clearly in interviews.

Self-Check

"What if we replaced FILTER with CASE statements inside COUNT? How would the time complexity change?"

Practice

1. What does the FILTER clause do in a PostgreSQL aggregate function?

easy

A. It filters the entire table before aggregation starts.

B. It applies a condition to include only certain rows in the aggregation.

C. It changes the grouping columns in the query.

D. It sorts the results after aggregation.

Solution

1. Step 1: Understand the purpose of FILTER in aggregation

   The FILTER clause allows you to specify a condition inside an aggregate function to include only rows that meet that condition.

2. Step 2: Compare with other options

   FILTER does not filter the whole table before aggregation, nor does it change grouping or sort results. It only limits rows counted or summed inside the aggregate.

3. Final Answer:

   It applies a condition to include only certain rows in the aggregation. -> Option B

4. Quick Check:

   FILTER = condition inside aggregate [OK]

Hint: FILTER applies conditions inside aggregates, not on whole table [OK]

Common Mistakes:

• Thinking FILTER filters rows before aggregation
• Confusing FILTER with WHERE clause
• Assuming FILTER changes GROUP BY columns

2. Which of the following is the correct syntax to count only rows where status = 'active' using FILTER in PostgreSQL?

easy

A. SELECT COUNT(*) FILTER (WHERE status = 'active') FROM users;

B. SELECT COUNT(*) FROM users FILTER (WHERE status = 'active');

C. SELECT COUNT(*) WHERE FILTER status = 'active' FROM users;

D. SELECT COUNT(*) FILTER WHERE status = 'active' FROM users;

Solution

1. Step 1: Recall FILTER syntax in aggregate functions

   The correct syntax places FILTER with parentheses and a WHERE clause inside the aggregate function: COUNT(*) FILTER (WHERE condition).

2. Step 2: Check each option

   SELECT COUNT(*) FILTER (WHERE status = 'active') FROM users; matches the correct syntax. The other options misuse FILTER placement or syntax.

3. Final Answer:

   SELECT COUNT(*) FILTER (WHERE status = 'active') FROM users; -> Option A

4. Quick Check:

   FILTER syntax = FILTER (WHERE condition) [OK]

Hint: Use FILTER (WHERE condition) inside aggregate parentheses [OK]

Common Mistakes:

• Omitting parentheses after FILTER
• Placing FILTER outside aggregate function
• Using FILTER without WHERE keyword

3. Given the table sales with columns region and amount, what will this query return?

SELECT region, SUM(amount) FILTER (WHERE amount > 100) AS big_sales FROM sales GROUP BY region;

medium

A. Sum of all sales amounts per region.

B. Count of sales with amount greater than 100 per region.

C. Sum of sales amounts less than or equal to 100 per region.

D. Sum of sales amounts greater than 100 per region.

Solution

1. Step 1: Understand the FILTER condition in SUM

   The FILTER clause limits the SUM to only rows where amount > 100.

2. Step 2: Analyze the GROUP BY effect

   The query groups rows by region, then sums only amounts above 100 in each group.

3. Final Answer:

   Sum of sales amounts greater than 100 per region. -> Option D

4. Quick Check:

   SUM with FILTER (WHERE amount > 100) = sum of big sales [OK]

Hint: FILTER limits rows inside SUM to amount > 100 [OK]

Common Mistakes:

• Thinking it sums all amounts, ignoring FILTER
• Confusing SUM with COUNT
• Ignoring GROUP BY effect

4. Identify the error in this query:

SELECT department, COUNT(*) FILTER (status = 'active') FROM employees GROUP BY department;

medium

A. Missing WHERE keyword inside FILTER clause.

B. GROUP BY should include status column.

C. COUNT(*) cannot be used with FILTER.

D. FILTER clause must be outside aggregate function.

Solution

1. Step 1: Check FILTER clause syntax

   FILTER requires a WHERE keyword before the condition: FILTER (WHERE condition).

2. Step 2: Verify other parts

   GROUP BY on department is correct. COUNT(*) works with FILTER. FILTER must be inside aggregate function.

3. Final Answer:

   Missing WHERE keyword inside FILTER clause. -> Option A

4. Quick Check:

   FILTER syntax needs WHERE [OK]

Hint: Always include WHERE inside FILTER parentheses [OK]

Common Mistakes:

• Omitting WHERE in FILTER
• Placing FILTER outside aggregate
• Adding unnecessary columns to GROUP BY

5. You want to find the total sales and the total sales above 500 for each store in one query. Which query correctly uses FILTER to achieve this?

hard

A. SELECT store, SUM(amount) FILTER (WHERE amount > 500) AS total_sales, SUM(amount) AS big_sales FROM sales GROUP BY store;

B. SELECT store, SUM(amount) AS total_sales, COUNT(amount) FILTER (WHERE amount > 500) AS big_sales FROM sales GROUP BY store;

C. SELECT store, SUM(amount) AS total_sales, SUM(amount) FILTER (WHERE amount > 500) AS big_sales FROM sales GROUP BY store;

D. SELECT store, COUNT(amount) AS total_sales, SUM(amount) FILTER (WHERE amount > 500) AS big_sales FROM sales GROUP BY store;

Solution

1. Step 1: Identify total sales calculation

   Total sales is the sum of all amounts per store, so SUM(amount) without filter is correct.

2. Step 2: Identify big sales calculation

   Big sales are sums of amounts greater than 500, so SUM(amount) FILTER (WHERE amount > 500) is correct.

3. Step 3: Check options

   SELECT store, SUM(amount) AS total_sales, SUM(amount) FILTER (WHERE amount > 500) AS big_sales FROM sales GROUP BY store; correctly assigns total_sales and big_sales. The other options mix sums and counts incorrectly or swap columns.

4. Final Answer:

   SELECT store, SUM(amount) AS total_sales, SUM(amount) FILTER (WHERE amount > 500) AS big_sales FROM sales GROUP BY store; -> Option C

5. Quick Check:

   SUM with and without FILTER for totals [OK]

Hint: Use SUM(amount) and SUM(amount) FILTER (WHERE ...) together [OK]

Common Mistakes:

• Swapping filtered and unfiltered sums
• Using COUNT instead of SUM for amounts
• Misplacing FILTER clause