Python – IronPython performance

A lot of people are saying that once you get to the size where speed is actually a concern that HashSet<T> will always beat List<T>, but that depends on what you are doing.

Let's say you have a List<T> that will only ever have on average 5 items in it. Over a large number of cycles, if a single item is added or removed each cycle, you may well be better off using a List<T>.

I did a test for this on my machine, and, well, it has to be very very small to get an advantage from List<T>. For a list of short strings, the advantage went away after size 5, for objects after size 20.

1 item LIST strs time: 617ms
1 item HASHSET strs time: 1332ms

2 item LIST strs time: 781ms
2 item HASHSET strs time: 1354ms

3 item LIST strs time: 950ms
3 item HASHSET strs time: 1405ms

4 item LIST strs time: 1126ms
4 item HASHSET strs time: 1441ms

5 item LIST strs time: 1370ms
5 item HASHSET strs time: 1452ms

6 item LIST strs time: 1481ms
6 item HASHSET strs time: 1418ms

7 item LIST strs time: 1581ms
7 item HASHSET strs time: 1464ms

8 item LIST strs time: 1726ms
8 item HASHSET strs time: 1398ms

9 item LIST strs time: 1901ms
9 item HASHSET strs time: 1433ms

1 item LIST objs time: 614ms
1 item HASHSET objs time: 1993ms

4 item LIST objs time: 837ms
4 item HASHSET objs time: 1914ms

7 item LIST objs time: 1070ms
7 item HASHSET objs time: 1900ms

10 item LIST objs time: 1267ms
10 item HASHSET objs time: 1904ms

13 item LIST objs time: 1494ms
13 item HASHSET objs time: 1893ms

16 item LIST objs time: 1695ms
16 item HASHSET objs time: 1879ms

19 item LIST objs time: 1902ms
19 item HASHSET objs time: 1950ms

22 item LIST objs time: 2136ms
22 item HASHSET objs time: 1893ms

25 item LIST objs time: 2357ms
25 item HASHSET objs time: 1826ms

28 item LIST objs time: 2555ms
28 item HASHSET objs time: 1865ms

31 item LIST objs time: 2755ms
31 item HASHSET objs time: 1963ms

34 item LIST objs time: 3025ms
34 item HASHSET objs time: 1874ms

37 item LIST objs time: 3195ms
37 item HASHSET objs time: 1958ms

40 item LIST objs time: 3401ms
40 item HASHSET objs time: 1855ms

43 item LIST objs time: 3618ms
43 item HASHSET objs time: 1869ms

46 item LIST objs time: 3883ms
46 item HASHSET objs time: 2046ms

49 item LIST objs time: 4218ms
49 item HASHSET objs time: 1873ms

Here is that data displayed as a graph:

Here's the code:

static void Main(string[] args)
{
    int times = 10000000;


    for (int listSize = 1; listSize < 10; listSize++)
    {
        List<string> list = new List<string>();
        HashSet<string> hashset = new HashSet<string>();

        for (int i = 0; i < listSize; i++)
        {
            list.Add("string" + i.ToString());
            hashset.Add("string" + i.ToString());
        }

        Stopwatch timer = new Stopwatch();
        timer.Start();
        for (int i = 0; i < times; i++)
        {
            list.Remove("string0");
            list.Add("string0");
        }
        timer.Stop();
        Console.WriteLine(listSize.ToString() + " item LIST strs time: " + timer.ElapsedMilliseconds.ToString() + "ms");


        timer = new Stopwatch();
        timer.Start();
        for (int i = 0; i < times; i++)
        {
            hashset.Remove("string0");
            hashset.Add("string0");
        }
        timer.Stop();
        Console.WriteLine(listSize.ToString() + " item HASHSET strs time: " + timer.ElapsedMilliseconds.ToString() + "ms");
        Console.WriteLine();
    }


    for (int listSize = 1; listSize < 50; listSize+=3)
    {
        List<object> list = new List<object>();
        HashSet<object> hashset = new HashSet<object>();

        for (int i = 0; i < listSize; i++)
        {
            list.Add(new object());
            hashset.Add(new object());
        }

        object objToAddRem = list[0];

        Stopwatch timer = new Stopwatch();
        timer.Start();
        for (int i = 0; i < times; i++)
        {
            list.Remove(objToAddRem);
            list.Add(objToAddRem);
        }
        timer.Stop();
        Console.WriteLine(listSize.ToString() + " item LIST objs time: " + timer.ElapsedMilliseconds.ToString() + "ms");



        timer = new Stopwatch();
        timer.Start();
        for (int i = 0; i < times; i++)
        {
            hashset.Remove(objToAddRem);
            hashset.Add(objToAddRem);
        }
        timer.Stop();
        Console.WriteLine(listSize.ToString() + " item HASHSET objs time: " + timer.ElapsedMilliseconds.ToString() + "ms");
        Console.WriteLine();
    }

    Console.ReadLine();
}

Several tips:

1. Put inserts/updates in a transaction.
2. For older versions of SQLite - Consider a less paranoid journal mode (pragma journal_mode). There is NORMAL, and then there is OFF, which can significantly increase insert speed if you're not too worried about the database possibly getting corrupted if the OS crashes. If your application crashes the data should be fine. Note that in newer versions, the OFF/MEMORY settings are not safe for application level crashes.
3. Playing with page sizes makes a difference as well (PRAGMA page_size). Having larger page sizes can make reads and writes go a bit faster as larger pages are held in memory. Note that more memory will be used for your database.
4. If you have indices, consider calling CREATE INDEX after doing all your inserts. This is significantly faster than creating the index and then doing your inserts.
5. You have to be quite careful if you have concurrent access to SQLite, as the whole database is locked when writes are done, and although multiple readers are possible, writes will be locked out. This has been improved somewhat with the addition of a WAL in newer SQLite versions.
6. Take advantage of saving space...smaller databases go faster. For instance, if you have key value pairs, try making the key an INTEGER PRIMARY KEY if possible, which will replace the implied unique row number column in the table.
7. If you are using multiple threads, you can try using the shared page cache, which will allow loaded pages to be shared between threads, which can avoid expensive I/O calls.
8. Don't use !feof(file)!

I've also asked similar questions here and here.