Partitioning best practices in PostgreSQL - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Why use partitioning in PostgreSQL?

Which of the following is the main reason to use table partitioning in PostgreSQL?

A To automatically create indexes on all columns

B To improve query performance by scanning only relevant partitions

C To reduce the size of the database backups

D To enforce foreign key constraints across partitions

💡 Hint

Think about how partitioning helps with large datasets and query speed.

Partitioning helps by dividing a large table into smaller pieces, so queries can scan only the needed partitions, improving performance.

❓ query_result

intermediate

Query result with range partitioning

Given a table sales partitioned by range on sale_date, which query will return sales only from 2023?

PostgreSQL

SELECT * FROM sales WHERE sale_date >= '2023-01-01' AND sale_date < '2024-01-01';

A Returns all sales from 2023 only

B Returns sales from all years

C Returns sales only from 2022

D Returns no rows due to syntax error

💡 Hint

Check the date range in the WHERE clause.

The query filters sales between 2023-01-01 (inclusive) and 2024-01-01 (exclusive), so only 2023 sales are returned.

📝 Syntax

advanced

Correct syntax for creating a list partition

Which of the following CREATE TABLE statements correctly creates a list partition for a table orders partitioned by region?

A CREATE TABLE orders_east PARTITION OF orders FOR VALUES IN ('East');

B CREATE TABLE orders_east PARTITION OF orders VALUES LIST ('East');

C CREATE TABLE orders_east PARTITION OF orders FOR VALUES ('East');

D CREATE TABLE orders_east PARTITION OF orders PARTITION BY LIST ('East');

💡 Hint

Look for the correct syntax to specify list partition values.

The correct syntax uses FOR VALUES IN ('value') to define list partitions.

❓ optimization

advanced

Best practice for partition key choice

Which is the best practice when choosing a partition key for a large table?

A Choose a column that is not indexed

B Choose a column with very few distinct values to reduce partitions

C Choose a column that is rarely used in WHERE clauses

D Choose a column with high cardinality and frequent filtering in queries

💡 Hint

Think about how partition pruning works with query filters.

Choosing a column with many distinct values and often used in filters helps queries scan fewer partitions, improving performance.

🔧 Debug

expert

Why does this partitioned table query fail?

Consider a partitioned table events partitioned by LIST on event_type. The query SELECT * FROM events WHERE event_type = 'login'; returns no rows, but data exists. What is the most likely cause?

A The event_type column is not part of the partition key

B The query syntax is invalid for partitioned tables

C The partition for 'login' was not created or attached properly

D PostgreSQL does not support filtering on partition keys

💡 Hint

Think about how partitions must be created and attached for data to be found.

If the partition for a specific list value is missing or not attached, queries filtering on that value return no rows even if data exists elsewhere.

Practice

1. What is the main benefit of using table partitioning in PostgreSQL?

easy

A. It breaks a large table into smaller, manageable parts to improve performance.

B. It automatically creates backups of the table data.

C. It encrypts the table data for security.

D. It merges multiple tables into one large table.

Solution

1. Step 1: Understand what partitioning does

   Partitioning divides a big table into smaller pieces called partitions.

2. Step 2: Identify the benefit of smaller parts

   Smaller parts make queries faster and data easier to manage.

3. Final Answer:

   It breaks a large table into smaller, manageable parts to improve performance. -> Option A

4. Quick Check:

   Partitioning = smaller parts for performance [OK]

Hint: Partitioning splits big tables for easier handling [OK]

Common Mistakes:

• Thinking partitioning creates backups
• Confusing partitioning with encryption
• Believing partitioning merges tables

2. Which of the following is the correct syntax to create a range partitioned table in PostgreSQL?

easy

A. CREATE TABLE sales (id INT, sale_date DATE) PARTITION BY LIST (sale_date);

B. CREATE TABLE sales PARTITION BY RANGE (sale_date) (id INT, sale_date DATE);

C. CREATE TABLE sales (id INT, sale_date DATE) PARTITION ON RANGE sale_date;

D. CREATE TABLE sales (id INT, sale_date DATE) PARTITION BY RANGE (sale_date);

Solution

1. Step 1: Recall correct partition syntax

   PostgreSQL uses PARTITION BY RANGE (column) after table columns.

2. Step 2: Check each option

   CREATE TABLE sales (id INT, sale_date DATE) PARTITION BY RANGE (sale_date); matches syntax: columns first, then PARTITION BY RANGE.

3. Final Answer:

   CREATE TABLE sales (id INT, sale_date DATE) PARTITION BY RANGE (sale_date); -> Option D

4. Quick Check:

   PARTITION BY RANGE after columns = correct syntax [OK]

Hint: Partition type follows column definitions in CREATE TABLE [OK]

Common Mistakes:

• Placing PARTITION BY before columns
• Using PARTITION ON instead of PARTITION BY
• Confusing RANGE with LIST partition type

3. Given the following partition setup:

CREATE TABLE orders (id INT, region TEXT, order_date DATE) PARTITION BY LIST (region);
CREATE TABLE orders_us PARTITION OF orders FOR VALUES IN ('US');
CREATE TABLE orders_eu PARTITION OF orders FOR VALUES IN ('EU');

What will be the result of this query?

INSERT INTO orders VALUES (1, 'US', '2024-01-01');
SELECT * FROM orders WHERE region = 'US';

medium

A. Returns the row with id 1, region 'US', and date '2024-01-01'.

B. Returns no rows because the partition is missing.

C. Causes a syntax error due to missing partition key.

D. Returns rows from all partitions regardless of region.

Solution

1. Step 1: Understand LIST partitioning by region

   Rows with region 'US' go to orders_us partition.

2. Step 2: Insert and select behavior

   Insert puts row in orders_us; select filters region='US', so row is returned.

3. Final Answer:

   Returns the row with id 1, region 'US', and date '2024-01-01'. -> Option A

4. Quick Check:

   List partition returns matching rows [OK]

Hint: Rows go to partition matching partition key value [OK]

Common Mistakes:

• Assuming insert fails without default partition
• Expecting syntax error on insert
• Thinking select ignores partition keys

4. You have this partitioned table:

CREATE TABLE logs (id SERIAL, log_date DATE) PARTITION BY RANGE (log_date);
CREATE TABLE logs_2023 PARTITION OF logs FOR VALUES FROM ('2023-01-01') TO ('2024-01-01');

Which error will occur if you run this?

INSERT INTO logs (log_date) VALUES ('2022-12-31');

medium

A. ERROR: syntax error near VALUES

B. ERROR: no partition found for row

C. The row is inserted into logs_2023 partition

D. The row is inserted into a default partition automatically

Solution

1. Step 1: Check partition ranges

   logs_2023 covers dates from 2023-01-01 to 2024-01-01 only.

2. Step 2: Insert date outside partition range

   2022-12-31 is before 2023-01-01, so no matching partition exists.

3. Final Answer:

   ERROR: no partition found for row -> Option B

4. Quick Check:

   Insert outside range = no partition error [OK]

Hint: Insert date must fit partition range or error occurs [OK]

Common Mistakes:

• Expecting automatic default partition
• Thinking syntax error occurs
• Assuming row goes to nearest partition

5. You want to optimize queries filtering by user_id and created_at date on a large table. Which partitioning strategy is best practice?

hard

A. Use HASH partitioning on created_at only.

B. Use LIST partitioning on user_id only.

C. Use RANGE partitioning on created_at and subpartition by HASH on user_id.

D. Do not partition; use a single large table with indexes.

Solution

1. Step 1: Analyze query filters

   Queries filter by user_id and created_at, so both should guide partitioning.

2. Step 2: Choose partitioning methods

   RANGE on created_at handles date ranges well; HASH subpartitioning on user_id balances data.

3. Step 3: Evaluate other options

   LIST on user_id alone is inefficient for many users; HASH on created_at is unusual; no partitioning misses benefits.

4. Final Answer:

   Use RANGE partitioning on created_at and subpartition by HASH on user_id. -> Option C

5. Quick Check:

   Combine RANGE and HASH for multi-column filtering [OK]

Hint: Combine RANGE for dates and HASH for IDs for best performance [OK]

Common Mistakes:

• Using LIST for high-cardinality user_id
• Hash partitioning on date column only
• Skipping partitioning on large tables