gb_trees
General Balanced Trees
An efficient implementation of Prof. Arne Andersson's General Balanced Trees. These have no storage overhead compared to unbalanced binary trees, and their performance is in general better than AVL trees.
This module considers two keys as different if and only if
they do not compare equal (==
).
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 logarithmic (as it should be).
DATA TYPES
gb_tree() = a GB tree
Functions
balance(Tree1) -> Tree2
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
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
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
Tree = gb_tree()
Returns a new empty tree
enter(Key, Val, Tree1) -> Tree2
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
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
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
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
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()
Tree = gb_tree()
Returns true
if Key
is present in Tree
,
otherwise false
.
is_empty(Tree) -> bool()
Tree = gb_tree()
Returns true
if Tree
is an empty tree, and
false
otherwise.
iterator(Tree) -> Iter
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]
Tree = gb_tree()
Key = term()
Returns the keys in Tree
as an ordered list.
largest(Tree) -> {Key, Val}
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.
map(Function, Tree1) -> Tree2
Function = fun(K, V1) -> V2
Tree1 = Tree2 = gb_tree()
maps the function F(K, V1) -> V2 to all key-value pairs of the tree Tree1 and returns a new tree Tree2 with the same set of keys as Tree1 and the new set of values V2.
next(Iter1) -> {Key, Val, Iter2} | none
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()
Tree = gb_tree()
Returns the number of nodes in Tree
.
smallest(Tree) -> {Key, Val}
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}
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}
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}]
Tree = gb_tree()
Key = Val = term()
Converts a tree into an ordered list of key-value tuples.
update(Key, Val, Tree1) -> Tree2
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]
Tree = gb_tree()
Val = term()
Returns the values in Tree
as an ordered list, sorted
by their corresponding keys. Duplicates are not removed.