Hash indexes in DBMS Theory - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a hash index on the column 'user_id'.

DBMS Theory

CREATE INDEX idx_user_id ON users USING [1] (user_id);

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

Abtree

Bgist

Cgin

Dhash

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'btree' which is the default but not a hash index.

Using 'gist' or 'gin' which are for other index types.

The hash keyword specifies that the index uses a hash structure, which is efficient for equality searches.

2fill in blank

medium

Complete the code to query a table using a hash index efficiently.

DBMS Theory

SELECT * FROM orders WHERE order_id [1] 12345;

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

A>

B=

C<

DLIKE

Attempts:

3 left

💡 Hint

Common Mistakes

Using range operators like '>' or '<' which hash indexes do not support efficiently.

Using 'LIKE' which is for pattern matching.

Hash indexes are optimized for equality comparisons using the = operator.

3fill in blank

hard

Fix the error in the statement to create a hash index in PostgreSQL.

DBMS Theory

CREATE INDEX idx_email ON customers USING [1] (email);

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

Ahash

Bbtree

Ctext

Dindex

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'text' which is a data type, not an index type.

Using 'index' which is not a valid index method.

PostgreSQL supports hash indexes using the hash keyword. Using 'btree' creates a different index type, and 'text' or 'index' are invalid here.

4fill in blank

hard

Fill both blanks to create a hash index and ensure it is used for equality searches.

DBMS Theory

CREATE INDEX idx_product_id ON products USING [1] (product_id);
SELECT * FROM products WHERE product_id [2] 100;

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

Ahash

B=

C>

Dbtree

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'btree' for the index type when the question asks for a hash index.

Using '>' instead of '=' in the WHERE clause.

The first blank must be 'hash' to create a hash index. The second blank must be '=' because hash indexes support equality comparisons.

5fill in blank

hard

Fill all three blanks to create a hash index, query with equality, and explain the limitation of hash indexes.

DBMS Theory

CREATE INDEX idx_customer_id ON customers USING [1] (customer_id);
SELECT * FROM customers WHERE customer_id [2] 500;
-- Note: Hash indexes do not support [3] searches efficiently.

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

Ahash

B=

Crange

Dbtree

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'btree' instead of 'hash' for the index type.

Using operators other than '=' for querying hash indexes.

Not recognizing that range queries are inefficient with hash indexes.

Hash indexes are created with 'hash' and used with '=' for equality searches. They do not support 'range' searches efficiently, which is a key limitation.

Practice

1. What is the primary purpose of a hash index in a database?

easy

A. To store data in sorted order

B. To speed up range queries

C. To compress data for storage

D. To speed up exact key lookups

Solution

1. Step 1: Understand the function of hash indexes

   Hash indexes convert keys into hash values to quickly find exact matches.

2. Step 2: Compare with other index types

   Unlike B-tree indexes, hash indexes do not support range queries or sorting.

3. Final Answer:

   To speed up exact key lookups -> Option D

4. Quick Check:

   Hash index = exact key lookup speed [OK]

Hint: Hash indexes are for exact matches, not ranges [OK]

Common Mistakes:

• Thinking hash indexes support range queries
• Confusing hash indexes with sorted indexes
• Assuming hash indexes compress data

2. Which of the following is the correct syntax to create a hash index on a column named user_id in SQL (assuming the database supports hash indexes)?

easy

A. CREATE INDEX idx_user ON users USING HASH (user_id);

B. CREATE HASH INDEX idx_user ON users (user_id);

C. CREATE INDEX idx_user ON users (user_id) HASH;

D. CREATE INDEX idx_user HASH ON users (user_id);

Solution

1. Step 1: Recall standard SQL syntax for hash indexes

   The correct syntax uses CREATE INDEX with USING HASH to specify the index type.

2. Step 2: Analyze each option

   CREATE INDEX idx_user ON users USING HASH (user_id); correctly places USING HASH after the index name and before the column list.

3. Final Answer:

   CREATE INDEX idx_user ON users USING HASH (user_id); -> Option A

4. Quick Check:

   Syntax for hash index = CREATE INDEX ... USING HASH ... [OK]

Hint: Use 'USING HASH' after index name to specify hash index [OK]

Common Mistakes:

• Placing HASH keyword incorrectly
• Omitting USING keyword
• Using non-standard syntax unsupported by SQL

3. Consider the following SQL query on a table with a hash index on email column:

SELECT * FROM users WHERE email = '[e-mail]';

What is the expected behavior of the database when using the hash index?

medium

A. It performs a range scan on the email column

B. It scans the entire table because hash indexes do not support equality

C. It performs a fast exact match lookup using the hash index

D. It returns an error because hash indexes cannot be used in WHERE clauses

Solution

1. Step 1: Understand hash index usage in equality queries

   Hash indexes are designed to quickly find rows matching an exact key value.

2. Step 2: Analyze the query condition

   The WHERE clause uses equality on the indexed column, so the hash index is used efficiently.

3. Final Answer:

   It performs a fast exact match lookup using the hash index -> Option C

4. Quick Check:

   Equality query + hash index = fast lookup [OK]

Hint: Hash indexes speed up exact equality queries [OK]

Common Mistakes:

• Thinking hash indexes do full table scans
• Confusing hash index with range scan
• Assuming hash indexes cause errors in queries

4. A developer created a hash index on the phone_number column but notices that queries with LIKE '%1234' are slow. What is the most likely reason?

medium

A. The hash index is corrupted and needs rebuilding

B. Hash indexes do not support pattern matching or partial searches

C. The database does not support hash indexes on numeric columns

D. The query optimizer ignores all indexes for LIKE queries

Solution

1. Step 1: Understand hash index limitations

   Hash indexes only support exact key lookups, not pattern matching or partial searches.

2. Step 2: Analyze the query pattern

   The LIKE '%1234' pattern searches for suffix matches, which hash indexes cannot optimize.

3. Final Answer:

   Hash indexes do not support pattern matching or partial searches -> Option B

4. Quick Check:

   Hash index ≠ pattern matching support [OK]

Hint: Hash indexes only speed exact matches, not LIKE patterns [OK]

Common Mistakes:

• Assuming hash indexes speed up all LIKE queries
• Blaming index corruption without evidence
• Thinking numeric columns can't have hash indexes

5. You want to optimize a database table for fast lookups on a customer_id column, but also need to efficiently query ranges of order_date. Which indexing strategy is best?

hard

A. Create a hash index on customer_id and a B-tree index on order_date

B. Create hash indexes on both customer_id and order_date

C. Create a B-tree index on customer_id and no index on order_date

D. Create no indexes and rely on full table scans

Solution

1. Step 1: Match index types to query needs

   Hash indexes are best for exact key lookups like on customer_id.

2. Step 2: Use B-tree indexes for range queries

   B-tree indexes efficiently support range queries, so use it on order_date.

3. Final Answer:

   Create a hash index on customer_id and a B-tree index on order_date -> Option A

4. Quick Check:

   Hash for exact, B-tree for range queries [OK]

Hint: Use hash for exact keys, B-tree for ranges [OK]

Common Mistakes:

• Using hash index for range queries
• Not indexing columns needed for fast queries
• Relying on full scans for large tables