gb_trees(3)

MODULE
gb_trees - General Balanced Trees

DESCRIPTION

An efficient implementation of Prof. Arne Andersson's General Balanced Trees. These have no storage overhead compared to unbalaced binary trees, and their performance is in general better than AVL trees.

DATA STRUCTURE

Data structure:

- {Size, Tree}, where `Tree' is composed of nodes of the form:
  - {Key, Value, Smaller, Bigger}, and the "empty tree" node:
  - nil.

There is no attempt to balance trees after deletions. Since deletions do not increase the height of a tree, this should be OK.

Original balance condition h(T) <= ceil(c * log(|T|)) has been changed to the similar (but not quite equivalent) condition 2 ^ h(T) <= |T| ^ c. This should also be OK.

Performance is comparable to the AVL trees in the Erlang book (and faster in general due to less overhead); the difference is that deletion works for these trees, but not for the book's trees. Behaviour is logaritmic (as it should be).

DATA TYPES
gb_tree() = a GB tree

EXPORTS

balance(Tree1) -> Tree2

    Types
        Tree1 = Tree2 = gb_tree()

    Rebalances Tree1. Note that this is rarely necessary, but may be motivated when a large number of nodes have been deleted from the tree without further insertions. Rebalancing could then be forced in order to minimise lookup times, since deletion only does not rebalance the tree.

delete(Key, Tree1) -> Tree2

    Types
        Key = term()
        Tree1 = Tree2 = gb_tree()

    Removes the node with key Key from Tree1; returns new tree. Assumes that the key is present in the tree, crashes otherwise.

delete_any(Key, Tree1) -> Tree2

    Types
        Key = term()
        Tree1 = Tree2 = gb_tree()

    Removes the node with key Key from Tree1 if the key is present in the tree, otherwise does nothing; returns new tree.

empty() -> Tree

    Types
        Tree = gb_tree()

    Returns a new empty tree

enter(Key, Val, Tree1) -> Tree2

    Types
        Key = Val = term()
        Tree1 = Tree2 = gb_tree()

    Inserts Key with value Val into Tree1 if the key is not present in the tree, otherwise updates Key to value Val in Tree1. Returns the new tree.

from_orddict(List) -> Tree

    Types
        List = [{Key, Val}]
        Key = Val = term()
        Tree = gb_tree()

    Turns an ordered list List of key-value tuples into a tree. The list must not contain duplicate keys.

get(Key, Tree) -> Val

    Types
        Key = Val = term()
        Tree = gb_tree()

    Retrieves the value stored with Key in Tree. Assumes that the key is present in the tree, crashes otherwise.

lookup(Key, Tree) -> {value, Val} | none

    Types
        Key = Val = term()
        Tree = gb_tree()

    Looks up Key in Tree; returns {value, Val}, or none if Key is not present.

insert(Key, Val, Tree1) -> Tree2

    Types
        Key = Val = term()
        Tree1 = Tree2 = gb_tree()

    Inserts Key with value Val into Tree1; returns the new tree. Assumes that the key is not present in the tree, crashes otherwise.

is_defined(Key, Tree) -> bool()

    Types
        Tree = gb_tree()

    Returns true if Key is present in Tree, otherwise false.

is_empty(Tree) -> bool()

    Types
        Tree = gb_tree()

    Returns true if Tree is an empty tree, and false otherwise.

iterator(Tree) -> Iter

    Types
        Tree = gb_tree()
        Iter = term()

    Returns an iterator that can be used for traversing the entries of Tree; see next/1. The implementation of this is very efficient; traversing the whole tree using next/1 is only slightly slower than getting the list of all elements using to_list/1 and traversing that. The main advantage of the iterator approach is that it does not require the complete list of all elements to be built in memory at one time.

keys(Tree) -> [Key]

    Types
        Tree = gb_tree()
        Key = term()

    Returns the keys in Tree as an ordered list.

largest(Tree) -> {Key, Val}

    Types
        Tree = gb_tree()
        Key = Val = term()

    Returns {Key, Val}, where Key is the largest key in Tree, and Val is the value associated with this key. Assumes that the tree is nonempty.

next(Iter1) -> {Key, Val, Iter2} | none

    Types
        Iter1 = Iter2 = Key = Val = term()

    Returns {Key, Val, Iter2} where Key is the smallest key referred to by the iterator Iter1, and Iter2 is the new iterator to be used for traversing the remaining nodes, or the atom none if no nodes remain.

size(Tree) -> int()

    Types
        Tree = gb_tree()

    Returns the number of nodes in Tree.

smallest(Tree) -> {Key, Val}

    Types
        Tree = gb_tree()
        Key = Val = term()

    Returns {Key, Val}, where Key is the smallest key in Tree, and Val is the value associated with this key. Assumes that the tree is nonempty.

take_largest(Tree1) -> {Key, Val, Tree2}

    Types
        Tree1 = Tree2 = gb_tree()
        Key = Val = term()

    Returns {Key, Val, Tree2}, where Key is the largest key in Tree1, Val is the value associated with this key, and Tree2 is this tree with the corresponding node deleted. Assumes that the tree is nonempty.

take_smallest(Tree1) -> {Key, Val, Tree2}

    Types
        Tree1 = Tree2 = gb_tree()
        Key = Val = term()

    Returns {Key, Val, Tree2}, where Key is the smallest key in Tree1, Val is the value associated with this key, and Tree2 is this tree with the corresponding node deleted. Assumes that the tree is nonempty.

to_list(Tree) -> [{Key, Val}]

    Types
        Tree = gb_tree()
        Key = Val = term()

    Converts a tree into an ordered list of key-value tuples.

update(Key, Val, Tree1) -> Tree2

    Types
        Key = Val = term()
        Tree1 = Tree2 = gb_tree()

    Updates Key to value Val in Tree1; returns the new tree. Assumes that the key is present in the tree.

values(Tree) -> [Val]

    Types
        Tree = gb_tree()
        Val = term()

    Returns the values in Tree as an ordered list, sorted by their corresponding keys. Duplicates are not removed.

SEE ALSO

gb_sets(3), dict(3)