This crate provides a simple prefix tree for IP prefixes. Any lookup performs longest-prefix
match. This crate supports both IPv4 and IPv6 (from either ipnet
or ipnetwork). It also supports any tuple (R, u8), where
R is any unsigned primitive integer (u8, u16, u32, u64, u128, or usize).
ip_network_table-deps-treebitmap
provides an IP lookup table, similar to PrefixMap.
The following compares the two approaches in the case of dense or sparse maps. Each test case
performs 100'000 modifications or lookups. However, the dense cases randomly pick any IPv4
address, while the sparse case only picks 20 different IPv4 addresses. See benches/benchmark.rs
for more details.
| Operation | Mode | PrefixMap |
treebitmap |
factor |
|---|---|---|---|---|
| Insert & Remove | dense | 31.78ms | 47.52ms | ~1.5x |
| Lookup | dense | 32.36ms | 8.409ms | ~0.25x |
| Insert & Remove | sparse | 6.645ms | 7.329ms | ~1.1x |
| Lookup | sparse | 8.394ms | 12.30ms | ~1.5x |
In addition, prefix-trie includes a PrefixSet analogous to std::collections::HashSet,
including union, intersection and difference operations that are implemented as simultaneous
tree traversals. Further, prefix-trie has an interface similar to std::collections, and
includes methods for accessing all children of a node. Finally, it offers a general
longest-prefix match that is not limited to individual addresses.
The tree is structured as follows: Each node consists of a prefix, a container for a potential
value (Option), and two optional children. Adding a new child, or traversing into the tree is
done as follows: we look at the most significant bit that is not part of the prefix
itself. If it is not set, then we take the left branch, and otherwise, we take the right one.
Any iteration over all elements in the tree is implemented as a graph traversal that will yield elements in lexicographic order.
The library offers set operations of different maps or sets. We implement a union, intersection, difference, and covering_difference. These iterators are implemented using simultaneous tree traversals. They will yield elements in lexicographic order. Whenever appropriate, the yielded items will also include the longest prefix match.
You can create a view of a (sub)-trie. Such a view has an arbitrary node as its root. Any operations on that view will only traverse that node and all its children. You can iterate over all children, search in that sub-trie, and perform set operations on them.
There are several operations one can do on the tree. Regular inserts are handled using the
Entry structure. An Entry is a pointer to a location in the tree to either insert a value or
modify an existing one. Removals however are different.
The following are the computational complexities of the functions, where n is the number of
elements in the tree.
| Operation | Complexity |
|---|---|
entry, insert |
O(log n) |
remove, remove_keep_tree |
O(log n) |
remove_children (calling drop on T) |
O(n) |
get, get_lpm, get_mut |
O(log n) |
retain |
O(n) |
clear (calling drop on T) |
O(n) |
Operations on map::Entry |
O(1) |
len and is_empty |
O(1) |
There are three kinds of removals you! can do:
PrefixMap::removewill remove an entry from the tree and modify the tree structure as if the value was never inserted before.PrefixMap::removewill always exactly revert the operation ofPrefixMap::insert. When only calling this function to remove elements, you are guaranteed that the tree structure is indistinguishable to a different tree where you only inserted elements.PrefixMap::remove_childrenwill remove all entries that are contained within the given prefix. This operation will search for the node with the shortest prefix length that is contained within the given prefix and remove it, including all of its children.PrefixMap::remove_keep_treewill not change anything in the tree structure. It will only remove a value from a node. As soon as you callremove_keep_treeonce on a tree structure, the tree will no longer be optimal.
Migrate to a TreeBitMap, described by W. Eatherton, Z. Dittia, G. Varghes.