Candidate key finding using closure in DBMS Theory - Mini Project: Build & Apply

Candidate Key Finding Using Closure

📖 Scenario: You are working with a database table and want to find which sets of columns can uniquely identify every row. These sets are called candidate keys. To find them, you use the concept of closure of attributes under given functional dependencies.Imagine you have a table of students with columns like StudentID, Name, Course, and Instructor. You want to find which columns or combinations of columns can serve as candidate keys.

🎯 Goal: Build a step-by-step process to find candidate keys by computing the closure of attribute sets under given functional dependencies.You will create the data for attributes and dependencies, set up a target attribute set, compute closure, and finally identify candidate keys.

📋 What You'll Learn

Create a dictionary of functional dependencies with exact keys and values

Create a list of all attributes in the relation

Write code to compute closure of a given attribute set

Identify candidate keys based on closure covering all attributes

💡 Why This Matters

🌍 Real World

Finding candidate keys helps database designers ensure each row in a table can be uniquely identified, which is essential for data integrity.

💼 Career

Database administrators and developers use candidate key analysis to design efficient and normalized database schemas.

1

Setup Functional Dependencies and Attributes

Create a dictionary called fd with these exact functional dependencies: 'A': ['B', 'C'], 'B': ['D'], 'C': ['E']. Also create a list called attributes containing 'A', 'B', 'C', 'D', and 'E'.

DBMS Theory

# Create the dictionary fd and list attributes
# Your code here

Need a hint?

Use a dictionary for functional dependencies where keys are single attributes and values are lists of attributes they determine.

Attributes list should include all attributes mentioned.

2

Set Initial Attribute Set for Closure

Create a list called initial_set containing only the attribute 'A'. This will be the starting point to find closure.

DBMS Theory

fd = {'A': ['B', 'C'], 'B': ['D'], 'C': ['E']}
attributes = ['A', 'B', 'C', 'D', 'E']
# Create initial_set with 'A'
# Your code here

Need a hint?

Just create a list with one element 'A'.

3

Compute Closure of Initial Attribute Set

Write code to compute the closure of initial_set under the functional dependencies fd. Create a set called closure initialized with initial_set. Then repeatedly add attributes to closure if they are functionally determined by any attribute in closure. Use a loop until no new attributes can be added.

DBMS Theory

fd = {'A': ['B', 'C'], 'B': ['D'], 'C': ['E']}
attributes = ['A', 'B', 'C', 'D', 'E']
initial_set = ['A']
# Compute closure of initial_set
# Your code here

Need a hint?

Use a set to hold closure for easy membership checks.

Loop until no new attributes are added.

4

Identify Candidate Key Based on Closure

Check if the closure contains all attributes in attributes. Create a boolean variable called is_candidate_key that is true if closure covers all attributes, otherwise false.

DBMS Theory

fd = {'A': ['B', 'C'], 'B': ['D'], 'C': ['E']}
attributes = ['A', 'B', 'C', 'D', 'E']
initial_set = ['A']
closure = set(initial_set)
changed = True
while changed:
    changed = False
    for attr in list(closure):
        if attr in fd:
            for dep in fd[attr]:
                if dep not in closure:
                    closure.add(dep)
                    changed = True
# Check if closure covers all attributes
# Your code here

Need a hint?

Use the set method issuperset() to check if closure contains all attributes.