How do you solve Average Time of Process per Machine on LeetCode?

Average Time of Process per Machine (LeetCode #1661) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n) time complexity and O(n) space complexity. Key insight: Understanding the relationship between 'start' and 'end' activities is crucial for calculating the total time accurately.

What is the time complexity of Average Time of Process per Machine?

The optimal solution for Average Time of Process per Machine runs in O(n) time and uses O(n) space. The complexity is O(n) because we make a single pass through the activities, storing and calculating values in constant time for each entry.

What is the brute force solution for Average Time of Process per Machine?

The brute force approach for Average Time of Process per Machine is Brute Force, with O(n²) time complexity and O(1) space complexity. In this approach, we will iterate through the entire activity log for each machine and calculate the total time for each process by finding the corresponding 'start' and 'end' timestamps. This method is straightforward but inefficient as it checks each pair multiple times. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Average Time of Process per Machine?

Average Time of Process per Machine uses the following concepts: Database. The recommended patterns to study are: Hash Map, Array.

What are the interview tips for Average Time of Process per Machine?

Clearly explain your thought process and how you plan to tackle the problem before diving into code. Consider edge cases, such as machines with no processes or processes that start and end at the same timestamp. Practice writing clean and efficient code, as readability is important in interviews.

What are common mistakes when solving Average Time of Process per Machine?

Failing to correctly match 'start' and 'end' activities, which can lead to incorrect time calculations. Not handling multiple processes for the same machine correctly, which can result in incorrect averages.

#1661

Average Time of Process per Machine

Easy

DatabaseHash MapArray

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(n²)	O(n)
Space	O(1)	O(n)

💡

Intuition

Time O(n)Space O(n)

This approach leverages a single pass through the activity log to efficiently calculate the total time and count of processes for each machine. By using a dictionary to track start times, we can directly compute the duration when we encounter an 'end' activity.

⚙️

Algorithm

6 steps

1Step 1: Initialize two dictionaries to store total time and count of processes for each machine.
2Step 2: Iterate through each activity in the log.
3Step 3: For 'start' activities, store the timestamp in a dictionary with a key of (machine_id, process_id).
4Step 4: For 'end' activities, calculate the duration using the stored 'start' timestamp, update the total time and count for the machine.
5Step 5: After processing all activities, compute the average time for each machine.
6Step 6: Return the results as a table.

solution.py18 lines

1def average_time_per_machine(activities):
2    from collections import defaultdict
3    total_time = defaultdict(float)
4    count = defaultdict(int)
5    start_time = {}
6
7    for activity in activities:
8        machine_id, process_id, activity_type, timestamp = activity
9        if activity_type == 'start':
10            start_time[(machine_id, process_id)] = timestamp
11        elif activity_type == 'end':
12            total_time[machine_id] += timestamp - start_time[(machine_id, process_id)]
13            count[machine_id] += 1
14
15    result = []
16    for machine_id in total_time:
17        result.append((machine_id, total_time[machine_id] / count[machine_id]))
18    return result

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Complexity note: The complexity is O(n) because we make a single pass through the activities, storing and calculating values in constant time for each entry.

1Understanding the relationship between 'start' and 'end' activities is crucial for calculating the total time accurately.
2Using dictionaries (or hash maps) allows for efficient lookups and updates, significantly improving performance.

Solutions and explanations are original Tejav content. Problem titles © LeetCode — use the LeetCode button above for the full problem statement.