General balanced trees.
This module provides Prof. Arne Andersson's General Balanced Trees. These have no storage overhead compared to unbalanced binary trees, and their performance is better than AVL trees.
This module considers two keys as different if and only if
they do not compare equal (==
).
Data Structure
Trees and iterators are built using opaque data structures that should not be pattern-matched from outside this module.
There is no attempt to balance trees after deletions. As deletions do not increase the height of a tree, this should be OK.
The 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.
Types
tree(Key, Value)
A general balanced tree.
tree() = tree(term(), term())
iter(Key, Value)
A general balanced tree iterator.
iter() = iter(term(), term())
Functions
balance(Tree1) -> Tree2
Tree1 = Tree2 = tree(Key, Value)
Rebalances
. Notice that this is
rarely necessary,
but can be motivated when many nodes have been
deleted from the tree without further insertions. Rebalancing
can then be forced to minimize lookup times, as
deletion does not rebalance the tree.
delete(Key, Tree1) -> Tree2
Tree1 = Tree2 = tree(Key, Value)
Removes the node with key
from
and returns the new tree. Assumes that the
key is present in the tree, crashes otherwise.
delete_any(Key, Tree1) -> Tree2
Tree1 = Tree2 = tree(Key, Value)
Removes the node with key
from
if
the key is present in the tree, otherwise does nothing.
Returns the new tree.
take(Key, Tree1) -> {Value, Tree2}
Tree1 = Tree2 = tree(Key, term())
Key = Value = term()
Returns a value
from node with key
and new
without the node with this value.
Assumes that the node with key is present in the tree,
crashes otherwise.
take_any(Key, Tree1) -> {Value, Tree2} | error
Tree1 = Tree2 = tree(Key, term())
Key = Value = term()
Returns a value
from node with key
and new
without the node with this value.
Returns error
if the node with the key is not present in
the tree.
empty() -> tree()
Returns a new empty tree.
enter(Key, Value, Tree1) -> Tree2
Tree1 = Tree2 = tree(Key, Value)
Inserts
with value
into
if the key is not present in the tree,
otherwise updates
to value
in
. Returns the
new tree.
from_orddict(List) -> Tree
List = [{Key, Value}]
Tree = tree(Key, Value)
Turns an ordered list
of key-value tuples
into a tree. The list must not contain duplicate keys.
get(Key, Tree) -> Value
Tree = tree(Key, Value)
Retrieves the value stored with
in
.
Assumes that the key is present in the tree, crashes
otherwise.
insert(Key, Value, Tree1) -> Tree2
Tree1 = Tree2 = tree(Key, Value)
Inserts
with value
into
and
returns the new tree. Assumes that the key is not present in
the tree, crashes otherwise.
is_defined(Key, Tree) -> boolean()
Tree = tree(Key, Value :: term())
Returns true
if
is present in
, otherwise false
.
iterator(Tree) -> Iter
Returns an iterator that can be used for traversing the
entries of
; 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.
iterator_from(Key, Tree) -> Iter
Returns an iterator that can be used for traversing the
entries of
; see
next/1
.
The difference as compared to the iterator returned by
iterator/1
is that the first key greater than
or equal to
is returned.
largest(Tree) -> {Key, Value}
Tree = tree(Key, Value)
Returns {
, where
is the largest
key in
, and
is
the value associated
with this key. Assumes that the tree is not empty.
lookup(Key, Tree) -> none | {value, Value}
Tree = tree(Key, Value)
Looks up
in
.
Returns {value,
, or none
if
is not present.
map(Function, Tree1) -> Tree2
Function = fun((K :: Key, V1 :: Value1) -> V2 :: Value2)
Tree1 = tree(Key, Value1)
Tree2 = tree(Key, Value2)
Maps function F(
. Returns a
new tree
with the same set of keys as
and the new set of values
.
next(Iter1) -> none | {Key, Value, Iter2}
Iter1 = Iter2 = iter(Key, Value)
Returns {
, where
is the
smallest key referred to by iterator
, and
is the new iterator to be used for
traversing the remaining nodes, or the atom none
if no
nodes remain.
smallest(Tree) -> {Key, Value}
Tree = tree(Key, Value)
Returns {
, where
is the smallest
key in
, and
is
the value associated
with this key. Assumes that the tree is not empty.
take_largest(Tree1) -> {Key, Value, Tree2}
Tree1 = Tree2 = tree(Key, Value)
Returns {
, where
is the
largest key in
,
is the value associated with this key, and
is this tree with the corresponding node deleted. Assumes that the
tree is not empty.
take_smallest(Tree1) -> {Key, Value, Tree2}
Tree1 = Tree2 = tree(Key, Value)
Returns {
, where
is the
smallest key in
,
is the value associated with this key, and
is this tree with the corresponding node deleted. Assumes that the
tree is not empty.
to_list(Tree) -> [{Key, Value}]
Tree = tree(Key, Value)
Converts a tree into an ordered list of key-value tuples.
update(Key, Value, Tree1) -> Tree2
Tree1 = Tree2 = tree(Key, Value)
Updates
to value
in
and
returns the new tree. Assumes that the key is present in the tree.
values(Tree) -> [Value]
Tree = tree(Key :: term(), Value)
Returns the values in
as an ordered list,
sorted by their corresponding keys. Duplicates are not removed.