General Programming Flashcards
Inheritance
Definition: Inheritance is a mechanism where a new class (child/subclass) derives properties and behaviors (methods and fields) from an existing class (parent/superclass).
Relationship: It represents an “is-a” relationship. For example, a Dog class can inherit from an Animal class because a dog is a type of animal.
Reusability: Promotes code reuse by allowing the child class to inherit and extend the functionality of the parent class.
Composition
Definition: Composition is a design principle where a class is composed of one or more objects of other classes. It represents a “has-a” relationship.
Relationship: It represents a “has-a” relationship. For example, a Car class can have an Engine object because a car has an engine.
Reusability: Achieves code reuse by combining smaller, reusable components.
Big O Notation
Big O Notation is a way to describe the time complexity or space complexity of an algorithm. It measures how the runtime or memory usage of an algorithm grows as the input size grows. It helps developers understand the efficiency of their code, especially when dealing with large datasets.
Time Complexity: How the runtime of an algorithm increases as the input size increases.
Space Complexity: How much memory an algorithm uses as the input size increases.
Big O focuses on the worst-case scenario and ignores constants and lower-order terms, as they become insignificant for large inputs.
Big O(1)
Constant time. The runtime does not depend on the input size.
Example: Accessing an element in an array by index.
Big O(log n)
Logarithmic time. The runtime grows logarithmically as the input size increases.
Example: Binary search.
Big O(n)
Linear time. The runtime grows linearly with the input size.
Example: Looping through an array.
Big O(n log n)
Linearithmic time. Common in efficient sorting algorithms.
Example: Merge sort, Quick sort.
Big O(n²)
Quadratic time. The runtime grows exponentially with the input size.
Example: Nested loops.
Big O(2ⁿ)
Exponential time. The runtime doubles with each additional input.
Example: Recursive Fibonacci.
Dirty Write
Definition: A dirty write occurs when one transaction overwrites uncommitted changes made by another transaction.
Scenario:
Transaction A modifies a row but has not yet committed.
Transaction B modifies the same row before Transaction A commits or rolls back.
If Transaction A rolls back, the changes made by Transaction B are based on invalid data.
Dirty Read
Definition: A dirty read occurs when one transaction reads uncommitted changes made by another transaction.
Scenario:
Transaction A modifies a row but has not yet committed.
Transaction B reads the modified row before Transaction A commits or rolls back.
If Transaction A rolls back, Transaction B has read data that never officially existed.
