Space complexity and time complexity are two different aspects used to analyze the performance of algorithms.

• Definition: Time complexity is a measure of the amount of time an algorithm takes to complete as a function of the input size.
• Focus: It is concerned with how the computational time of an algorithm increases with the size of the input.
• Example: If you have an algorithm that sorts an array of numbers, the time complexity might indicate how the time taken to sort the array grows as the number of elements in the array increases.

• Definition: Space complexity is a measure of the amount of memory an algorithm uses as a function of the input size.
• Focus: It is concerned with how the memory requirements of an algorithm increase with the size of the input.
• Example: In the case of a recursive algorithm, the space complexity might indicate how much memory is required to store the call stack as the recursion depth increases.

• Efficiency: DSA primarily focuses on time complexity because, in many applications, the efficiency of an algorithm in terms of time directly impacts the responsiveness and performance of a system.
• Resource Utilization: Time is often a critical resource, especially in real-time systems, where responses need to be generated quickly.
• Comparisons: Time complexity provides a standardized metric to compare and analyze different algorithms independently of hardware or specific programming language details.
• Scalability: As the size of data sets and problem instances grows, understanding how an algorithm scales in terms of time is crucial for predicting performance.

• Time Complexity Example: Linear search has a time complexity of O(n), where n is the size of the array. As the array size grows, the time taken for a linear search grows linearly.
• Space Complexity Example: Linear search has a space complexity of O(1), constant space, because it doesn't require additional memory proportional to the input size. It only needs a few variables regardless of the array size.