Partition pruning behavior in PostgreSQL - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ query_result

intermediate

Partition pruning with static query filters

Given a partitioned table sales partitioned by region, which query will prune partitions correctly and return only rows from the north region?

PostgreSQL

SELECT * FROM sales WHERE region = 'north';

A SELECT * FROM sales WHERE region LIKE '%north%';

B SELECT * FROM sales WHERE region IN ('north', 'south');

C SELECT * FROM sales WHERE region = 'north';

D SELECT * FROM sales WHERE region <> 'south';

💡 Hint

Partition pruning works best with exact matches on the partition key.

Only option C uses an exact equality condition on the partition key 'region', allowing PostgreSQL to prune partitions and scan only the 'north' partition. Other options use patterns or ranges that prevent pruning.

🧠 Conceptual

intermediate

Understanding partition pruning timing

When does PostgreSQL perform partition pruning during query execution?

A At query planning time for static filters and at execution time for parameterized queries.

B Only at query execution time regardless of query type.

C Only at query planning time for all queries.

D After the query finishes scanning all partitions.

💡 Hint

Think about how static values and parameters affect planning.

PostgreSQL prunes partitions at planning time when filters are static constants. For parameterized queries, pruning happens at execution time when parameter values are known.

📝 Syntax

advanced

Which query syntax enables effective partition pruning with a parameter?

Given a partitioned table orders partitioned by order_date, which query syntax will allow PostgreSQL to prune partitions when using a parameter for the date filter?

PostgreSQL

Prepare a query to select orders for a specific date using a parameter.

A SELECT * FROM orders WHERE order_date = $1;

B SELECT * FROM orders WHERE order_date::text = $1;

C SELECT * FROM orders WHERE EXTRACT(YEAR FROM order_date) = $1;

D SELECT * FROM orders WHERE order_date > $1 - INTERVAL '1 day';

💡 Hint

Partition pruning requires direct comparison on the partition key without transformations.

Option A uses a direct equality comparison on the partition key with a parameter, enabling pruning. Options A, B, and C apply transformations or functions that prevent pruning.

🔧 Debug

advanced

Why does this query scan all partitions despite a filter?

A table events is partitioned by event_type. The query SELECT * FROM events WHERE event_type = 'click' OR event_type = 'view'; scans all partitions. Why?

A Because the table is not actually partitioned.

B Because the OR condition prevents pruning; only single equality filters prune partitions.

C Because the query uses string literals instead of parameters.

D Because the partition key is not indexed.

💡 Hint

Consider how multiple conditions affect pruning.

Partition pruning in PostgreSQL works best with simple equality conditions on the partition key. Using OR with multiple values disables pruning, causing all partitions to be scanned.

❓ optimization

expert

Optimizing partition pruning for range partitions

Given a table logs partitioned by range on log_date, which query will maximize partition pruning efficiency for retrieving logs from January 2024?

A SELECT * FROM logs WHERE log_date BETWEEN '2024-01-01' AND '2024-01-31';

B SELECT * FROM logs WHERE TO_CHAR(log_date, 'YYYY-MM') = '2024-01';

C SELECT * FROM logs WHERE EXTRACT(MONTH FROM log_date) = 1 AND EXTRACT(YEAR FROM log_date) = 2024;

D SELECT * FROM logs WHERE log_date >= '2024-01-01' AND log_date < '2024-02-01';

💡 Hint

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

Option D uses direct range conditions on the partition key, allowing PostgreSQL to prune partitions efficiently. Options A, B, and D use functions or inclusive ranges that may prevent pruning or scan extra partitions.

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