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Algorithms: Sorting and Searching

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Actual Run Time for Sorting Algorithms

2:13 with Jay McGavren

Let's compare how long Selection Sort, Quicksort, and Merge Sort take to run on different data sets.

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    I've removed the call to print that displays the sorted list at the end of our 0:00
    selection_sort, quicksort, and merge_sort scripts. 0:04
    That way, it'll still run the sort, but 0:07
    the output won't get in the way of our comparing run times. 0:09
    Let's try running each of these scripts, and see how long it takes. 0:13
    time python selection_sort, we'll do that 0:17
    one first, numbers/10,000.txt. 0:22
    We combine the user and sys results, and that gives us about 6 seconds. 0:30
    Now let's try quicksort, time python 0:35
    quicksort.py numbers/10000.txt. 0:40
    Much faster, less than a second. 0:45
    And finally, time python merge_sort.py numbers/10000.txt. 0:48
    A little longer, but far less than a second. 0:58
    So even on a list with just 10,000 numbers, 1:01
    selection_sort takes many times as long as quicksort and merge_sort. 1:04
    And remember, I ran the selection_sort script on a file with a million numbers. 1:09
    And it took so long that my workspace timed out before it completed. 1:13
    It looks like selection_sort is out of the running as a viable sorting algorithm. 1:18
    It may be easy to understand and implement, but 1:22
    it's just too slow to handle the huge data sets that are out in the real world. 1:25
    Now let's try quicksort and merge_sort on our file with a million numbers, and 1:31
    see how they compare there. 1:35
    time python quicksort.py 1:36
    numbers/1000000.txt. 1:40
    Looks like it took about 11 seconds of CPU time. 1:49
    Now let's try merge_sort, time python 1:53
    merge_sort.py numbers/1000000.txt. 1:57
    That took about 15 seconds of CPU time. 2:05
    It looks like quicksort is marginally faster than merge_sort on this 2:08
    sample data. 2:12

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