blist 0.9.9

a list-like type with better asymptotic performance and similar performance on small lists

The BList is a type that looks, acts, and quacks like a Python list, but has better performance for many (but not all) use cases. The use cases where the BList is slightly slower than Python’s list are as follows (O(log n) vs. O(1)):

1. A large list that never changes length.
2. A large lists where inserts and deletes are only at the end of the list (LIFO).

With that disclaimer out of the way, here are some of the use cases where the BLists is dramatically faster than the built-in list:

1. Insertion into or removal from a large list (O(log n) vs. O(n))
2. Taking large slices of large lists (O(log n) vs O(n))
3. Making shallow copies of large lists (O(1) vs. O(n))
4. Changing large slices of large lists (O(log n + log k) vs. O(n + k))
5. Multiplying a list to make a large, sparse list (O(log k) vs. O(kn))

You’ve probably noticed that we keep referring to “large lists”. For small lists, BLists and the built-in list have very similar performance.

So you can see the performance of the BList in more detail, several performance graphs available at the following link: http://stutzbachenterprises.com/blist/

Example usage:

>>> from blist import *
>>> x = blist([0])             # x is a BList with one element
>>> x *= 2**29                 # x is a BList with > 500 million elements
>>> x.append(5)                # append to x
>>> y = x[4:-234234]           # Take a 500 million element slice from x
>>> del x[3:1024]              # Delete a few thousand elements from x

For comparison, on most systems the built-in list just raises MemoryError and calls it a day.

The BList has two key features that allow it to pull off this performance:

1. Internally, a B+Tree is a wide, squat tree. Each node has a maximum of 128 children. If the entire list contains 128 or fewer objects, then there is only one node, which simply contains an array of the objects. In other words, for short lists, a BList works just like Python’s array-based list() type. Thus, it has the same good performance on small lists.
2. The BList type features transparent copy-on-write. If a non-root node needs to be copied (as part of a getslice, copy, setslice, etc.), the node is shared between multiple parents instead of being copied. If it needs to be modified later, it will be copied at that time. This is completely behind-the-scenes; from the user’s point of view, the BList works just like a regular Python list.