Bitmap index scan behavior in PostgreSQL - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to perform a bitmap index scan on the table 'employees' using the index 'idx_salary'.

PostgreSQL

EXPLAIN SELECT * FROM employees WHERE salary [1] 50000;

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

A=

B<

C>

DLIKE

Attempts:

3 left

💡 Hint

Common Mistakes

Using '=' limits the scan to exact matches, not a range.

Using 'LIKE' is for pattern matching, not numeric comparison.

The query uses a condition 'salary > 50000' which can utilize a bitmap index scan on the 'idx_salary' index.

2fill in blank

medium

Complete the code to explain the query plan that uses a bitmap index scan on 'idx_age' for ages less than 30.

PostgreSQL

EXPLAIN SELECT * FROM users WHERE age [1] 30;

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

A>

B<

C=

D>=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '>' or '>=' selects ages greater than or equal to 30, which is incorrect here.

Using '=' only matches age exactly 30, not a range.

The condition 'age < 30' allows PostgreSQL to use a bitmap index scan on 'idx_age' to find all users younger than 30.

3fill in blank

hard

Fix the error in the query to enable bitmap index scan on 'idx_status' for status 'active'.

PostgreSQL

EXPLAIN SELECT * FROM accounts WHERE status [1] 'active';

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

A=

BLIKE

C!=

DIN

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'LIKE' is for pattern matching and may not use the index efficiently.

Using '!=' excludes the value and won't use the index for this condition.

The '=' operator is correct for exact match on 'status' to use bitmap index scan on 'idx_status'.

4fill in blank

hard

Fill both blanks to create a bitmap index scan query filtering 'orders' by 'order_date' after 2023-01-01 and 'status' equals 'shipped'.

PostgreSQL

EXPLAIN SELECT * FROM orders WHERE order_date [1] '2023-01-01' AND status [2] 'shipped';

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

A>

B=

C<

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' for order_date would select before the date, not after.

Using '!=' for status excludes 'shipped' and won't use the index properly.

The '>' operator filters orders after '2023-01-01' and '=' matches status exactly, both suitable for bitmap index scans.

5fill in blank

hard

Fill all three blanks to write a bitmap index scan query on 'products' filtering by 'category' equals 'books', 'price' less than 20, and 'stock' greater than 0.

PostgreSQL

EXPLAIN SELECT * FROM products WHERE category [1] 'books' AND price [2] 20 AND stock [3] 0;

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

A=

B<

C>

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '!=' excludes the desired values and prevents index usage.

Mixing up '<' and '>' operators changes the filter logic.

The '=' operator matches category exactly, '<' filters price less than 20, and '>' filters stock greater than 0, all suitable for bitmap index scans.

Practice

1. What is the main purpose of a Bitmap Index Scan in PostgreSQL?

easy

A. To delete rows using bitmap operations

B. To create a bitmap of matching row locations from indexes for efficient retrieval

C. To update rows in the table based on index values

D. To directly fetch rows from the table without using indexes

Solution

1. Step 1: Understand Bitmap Index Scan role

   Bitmap Index Scan creates a bitmap representing matching row positions using indexes.

2. Step 2: Differentiate from other scans

   It does not fetch rows directly but prepares a bitmap for efficient row retrieval later.

3. Final Answer:

   To create a bitmap of matching row locations from indexes for efficient retrieval -> Option B

4. Quick Check:

   Bitmap Index Scan = bitmap of row locations [OK]

Hint: Bitmap Index Scan builds a map of rows to fetch [OK]

Common Mistakes:

• Confusing bitmap scan with direct table scan
• Thinking bitmap scan updates or deletes rows
• Assuming bitmap scan fetches rows immediately

2. Which of the following is the correct syntax to perform a Bitmap Index Scan in a PostgreSQL EXPLAIN query output?

easy

A. Index Scan on table_name (cost=0.29..8.31 rows=5 width=12)

B. Bitmap Heap Scan on index_name (cost=0.29..8.31 rows=5 width=12)

C. Bitmap Index Scan on index_name (cost=0.29..8.31 rows=5 width=12)

D. Seq Scan on index_name (cost=0.29..8.31 rows=5 width=12)

Solution

1. Step 1: Identify Bitmap Index Scan syntax

   Bitmap Index Scan appears as "Bitmap Index Scan on index_name" in EXPLAIN output.

2. Step 2: Differentiate from other scans

   Bitmap Heap Scan fetches rows using bitmap, Index Scan and Seq Scan are different methods.

3. Final Answer:

   Bitmap Index Scan on index_name (cost=0.29..8.31 rows=5 width=12) -> Option C

4. Quick Check:

   Bitmap Index Scan syntax = Bitmap Index Scan on index_name (cost=0.29..8.31 rows=5 width=12) [OK]

Hint: Look for 'Bitmap Index Scan on' in EXPLAIN output [OK]

Common Mistakes:

• Confusing Bitmap Heap Scan with Bitmap Index Scan
• Choosing Index Scan or Seq Scan syntax incorrectly
• Misreading EXPLAIN output keywords

3. Given a table employees with an index on department_id, what will the Bitmap Index Scan do when you run:
EXPLAIN SELECT * FROM employees WHERE department_id = 5;?

medium

A. It creates a bitmap of row locations where department_id = 5, then fetches those rows efficiently

B. It scans the entire table sequentially without using the index

C. It updates the rows where department_id = 5

D. It deletes rows where department_id = 5

Solution

1. Step 1: Understand Bitmap Index Scan on condition

   The scan uses the index on department_id to find matching rows and creates a bitmap of their locations.

2. Step 2: Explain how rows are fetched

   Using the bitmap, it fetches only those rows efficiently from the table, avoiding full scan.

3. Final Answer:

   It creates a bitmap of row locations where department_id = 5, then fetches those rows efficiently -> Option A

4. Quick Check:

   Bitmap Index Scan finds matching rows then fetches [OK]

Hint: Bitmap Index Scan finds and fetches matching rows efficiently [OK]

Common Mistakes:

• Thinking it scans the whole table sequentially
• Confusing scan with update or delete operations
• Assuming it fetches rows without bitmap

4. You see a query plan with Bitmap Index Scan followed by Bitmap Heap Scan, but the query is running very slowly. What could be a likely cause?

medium

A. The index does not exist on the queried column

B. The table is empty, so no rows are fetched

C. The query is missing a WHERE clause

D. The bitmap is too large because the condition matches too many rows, causing inefficient heap fetch

Solution

1. Step 1: Analyze Bitmap Index Scan and Bitmap Heap Scan behavior

   Bitmap Index Scan creates a bitmap of matching rows; Bitmap Heap Scan fetches rows using that bitmap.

2. Step 2: Understand performance impact of large bitmap

   If too many rows match, the bitmap is large, causing many random disk accesses and slowing the query.

3. Final Answer:

   The bitmap is too large because the condition matches too many rows, causing inefficient heap fetch -> Option D

4. Quick Check:

   Large bitmap = slow Bitmap Heap Scan [OK]

Hint: Large bitmap means many rows matched, slowing fetch [OK]

Common Mistakes:

• Assuming missing index causes Bitmap Index Scan
• Thinking missing WHERE clause causes Bitmap Index Scan
• Believing empty table causes slow scan

5. You want to optimize a query that uses Bitmap Index Scan but runs slowly because it matches many rows. Which approach can improve performance?

hard

A. Add more selective WHERE conditions to reduce matching rows before Bitmap Index Scan

B. Drop the index to force a sequential scan

C. Increase the work_mem setting to allow larger bitmaps in memory

D. Rewrite the query to use a JOIN instead of WHERE clause

Solution

1. Step 1: Understand Bitmap Index Scan memory usage

   Bitmap Index Scan builds a bitmap in memory; if too large, it spills to disk, slowing performance.

2. Step 2: Improve performance by adding selective conditions

   Adding more selective WHERE conditions reduces matching rows, making bitmap smaller and faster.

3. Step 3: Evaluate other options

   Increasing work_mem helps but may not be sufficient; dropping index or rewriting query may not improve bitmap scan efficiency.

4. Final Answer:

   Add more selective WHERE conditions to reduce matching rows before Bitmap Index Scan -> Option A

5. Quick Check:

   More selective WHERE = smaller bitmap = faster scan [OK]

Hint: Add selective WHERE clauses to reduce bitmap size [OK]

Common Mistakes:

• Dropping index thinking it helps performance
• Assuming increasing work_mem always solves slowness
• Believing rewriting query always improves bitmap scan