Partition pruning behavior in PostgreSQL - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to select data only from the partition where the year is 2023.

PostgreSQL

SELECT * FROM sales_[1] WHERE year = 2023;

Drag options to blanks, or click blank then click option'

A2023

B2022

C2021

D2020

Attempts:

3 left

💡 Hint

Common Mistakes

Selecting from the wrong partition year.

Not specifying the partition at all.

To prune partitions and query only the 2023 data, you must specify the partition with year 2023.

2fill in blank

medium

Complete the query to enable partition pruning by filtering on the partition key 'region'.

PostgreSQL

SELECT * FROM sales WHERE region = [1];

Drag options to blanks, or click blank then click option'

A'North'

BNorth

C'north'

Dregion

Attempts:

3 left

💡 Hint

Common Mistakes

Omitting quotes around string values.

Using incorrect case for the region name.

String values in SQL must be enclosed in single quotes. Using 'North' filters the partition key correctly.

3fill in blank

hard

Fix the error in the query to enable partition pruning on the 'month' column.

PostgreSQL

SELECT * FROM sales WHERE month [1] 5;

Drag options to blanks, or click blank then click option'

ABETWEEN

BLIKE

C=

DIN

Attempts:

3 left

💡 Hint

Common Mistakes

Using LIKE operator on numeric columns.

Using IN without parentheses.

Partition pruning requires a direct comparison operator like '=' to filter partitions effectively.

4fill in blank

hard

Fill both blanks to create a query that prunes partitions by year and region.

PostgreSQL

SELECT * FROM sales WHERE year = [1] AND region = [2];

Drag options to blanks, or click blank then click option'

A2023

B'East'

D2022

Attempts:

3 left

💡 Hint

Common Mistakes

Using quotes around the year number.

Omitting quotes around the region string.

Use 2023 for the year and 'East' (with quotes) for the region to prune partitions correctly.

5fill in blank

hard

Fill all three blanks to create a query that prunes partitions by year, region, and month.

PostgreSQL

SELECT * FROM sales WHERE year = [1] AND region = [2] AND month = [3];

Drag options to blanks, or click blank then click option'

A2024

B'West'

C7

D'7'

Attempts:

3 left

💡 Hint

Common Mistakes

Putting quotes around numeric values.

Not quoting string values.

Year and month are numbers, so no quotes. Region is a string and needs quotes.

Practice

1. What is the main purpose of partition pruning in PostgreSQL?

easy

A. To merge all partitions into one table

B. To create new partitions automatically

C. To skip scanning partitions that cannot contain matching rows

D. To backup partitions separately

Solution

1. Step 1: Understand partition pruning concept

   Partition pruning means the database avoids scanning partitions that do not match the query filter.

2. Step 2: Identify the main benefit

   This skipping reduces query time by focusing only on relevant partitions.

3. Final Answer:

   To skip scanning partitions that cannot contain matching rows -> Option C

4. Quick Check:

   Partition pruning = skip irrelevant partitions [OK]

Hint: Partition pruning skips irrelevant partitions to speed queries [OK]

Common Mistakes:

• Thinking pruning merges partitions
• Assuming pruning creates partitions
• Confusing pruning with backup

2. Which of the following WHERE clauses will enable partition pruning on a table partitioned by column region?

easy

A. WHERE region = 'US'

B. WHERE UPPER(region) = 'US'

C. WHERE LENGTH(region) > 2

D. WHERE region LIKE '%US%'

Solution

1. Step 1: Identify pruning conditions

   Partition pruning works best with simple direct comparisons on the partition key.

2. Step 2: Analyze each option

   WHERE region = 'US' uses a direct equality on region, enabling pruning. Options A, B, and D use functions or patterns, preventing pruning.

3. Final Answer:

   WHERE region = 'US' -> Option A

4. Quick Check:

   Simple equality on partition key enables pruning [OK]

Hint: Use simple column = value filters on partition keys [OK]

Common Mistakes:

• Using functions on partition keys disables pruning
• Using LIKE patterns disables pruning
• Assuming any WHERE clause prunes partitions

3. Given a table sales partitioned by year with partitions for 2021 and 2022, what will the query below scan?

SELECT * FROM sales WHERE year = 2021;

medium

A. Only the 2021 partition

B. Both 2021 and 2022 partitions

C. No partitions, query returns empty

D. All partitions plus a full table scan

Solution

1. Step 1: Understand partition pruning with equality filter

   The query filters on year = 2021, which matches exactly one partition.

2. Step 2: Determine scanned partitions

   PostgreSQL will prune and scan only the 2021 partition, skipping 2022.

3. Final Answer:

   Only the 2021 partition -> Option A

4. Quick Check:

   Filter on partition key = value scans matching partition only [OK]

Hint: Equality on partition key scans only matching partition [OK]

Common Mistakes:

• Assuming all partitions scan regardless of filter
• Thinking query returns empty if filter matches a partition
• Believing full table scan always happens

4. You wrote this query on a partitioned table orders partitioned by order_date:

SELECT * FROM orders WHERE EXTRACT(YEAR FROM order_date) = 2023;

Why might partition pruning NOT occur?

medium

A. Partition pruning only works with numeric columns

B. Using a function on the partition key disables pruning

C. The query syntax is invalid

D. Partition pruning requires an index on order_date

Solution

1. Step 1: Identify pruning limitation

   Partition pruning requires direct use of the partition key in filters without wrapping functions.

2. Step 2: Analyze the query

   The query uses EXTRACT(YEAR FROM order_date), a function on the partition key, preventing pruning.

3. Final Answer:

   Using a function on the partition key disables pruning -> Option B

4. Quick Check:

   Functions on partition keys disable pruning [OK]

Hint: Avoid functions on partition keys for pruning [OK]

Common Mistakes:

• Thinking pruning needs indexes
• Believing pruning works with any filter
• Assuming query syntax error causes pruning failure

5. A table events is range partitioned by event_date with monthly partitions. You want to query events in March 2023. Which query will maximize partition pruning?

hard

A. SELECT * FROM events WHERE event_date BETWEEN '2023-02-28' AND '2023-03-31';

B. SELECT * FROM events WHERE EXTRACT(MONTH FROM event_date) = 3 AND EXTRACT(YEAR FROM event_date) = 2023;

C. SELECT * FROM events WHERE TO_CHAR(event_date, 'YYYY-MM') = '2023-03';

D. SELECT * FROM events WHERE event_date >= '2023-03-01' AND event_date < '2023-04-01';

Solution

1. Step 1: Understand pruning with range partitions

   Range partitions work best with direct range conditions on the partition key.

2. Step 2: Evaluate each query

   SELECT * FROM events WHERE event_date >= '2023-03-01' AND event_date < '2023-04-01'; uses a direct range filter on event_date, enabling pruning. Options B and C use functions, disabling pruning. SELECT * FROM events WHERE event_date BETWEEN '2023-02-28' AND '2023-03-31'; includes dates outside March, scanning extra partitions.

3. Final Answer:

   SELECT * FROM events WHERE event_date >= '2023-03-01' AND event_date < '2023-04-01'; -> Option D

4. Quick Check:

   Direct range filters maximize pruning [OK]

Hint: Use direct range filters on partition keys for pruning [OK]

Common Mistakes:

• Using functions disables pruning
• Including extra dates scans more partitions
• Assuming BETWEEN always prunes perfectly