prefix-trie 0.9.3

Prefix trie (tree) datastructure (both a set and a map) that provides exact and longest-prefix matches.
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Prefix-Trie

This crate provides prefix-map and prefix-set collections for IP prefixes and other fixed-width prefix types. PrefixMap is backed by a compact TreeBitMap-style trie and supports exact, longest-prefix, and shortest-prefix matches. The crate supports both IPv4 and IPv6 (from either ipnet, ipnetwork, or cidr). It also supports any tuple (R, u8), where R is any unsigned primitive integer (u8, u16, u32, u64, u128, or usize).

Prefixes are not stored verbatim. They are reconstructed from their trie position when returned from map and set operations, so host bits outside the prefix length are not preserved.

This crate also provides a JointPrefixMap and JointPrefixSet that contains two tables, one for IPv4 and one for IPv6.

Description of the Tree

PrefixMap stores the logical binary prefix trie in multi-bit nodes, as described by W. Eatherton, Z. Dittia, and G. Varghes. Each internal node covers five consecutive binary-trie levels. A node at depth d can hold values for prefixes with lengths d..=d+4, and it has up to 32 child slots for subtries rooted at depth d+5.

Each node stores two bitmaps: one for the value slots that are present in the node, and one for the child slots that are present below it. The allocators store multi-bit nodes and value cells in compact, linearized arrays, which improves cache locality and keeps lookup and traversal decisions local to a node. Physical slots are derived from the bitmaps with a popcount, avoiding one pointer per possible branch.

A stored entry is identified by its path through the trie and by a value bit inside the final multi-bit node. The prefix object passed to insert is not stored alongside the value. Returned prefixes are therefore reconstructed and canonicalized to the prefix length.

Trie Views and Set Operations

TrieView is a trait for immutable, mutable, and composed cursors into a trie. Concrete leaf views are TrieRef (created from &PrefixMap or &PrefixSet) and TrieRefMut (created from mutable references). Both are obtained through the AsView trait: call map.view() for a full-trie view or map.view_at(&prefix) for a non-empty subtrie.

Views and iterators traverse the logical prefix trie in lexicographic order and yield reconstructed owned prefixes together with references or owned values.

Set operations use the same view infrastructure. union, intersection, difference, covering_union, and covering_difference simultaneously traverse the involved trie views and yield results in lexicographic order. Covering variants also report longest-prefix matches from the opposite side where appropriate. The result of such a set operation is not the finished traversal, but a struct that implements TrieView and describes the traversal. Thus, you can chain multiple set operations and compute them in a single traversal of the original tries.

Aggregation

Both PrefixMap and PrefixSet can be aggregated, that is, finding a compact representation of the Trie to yield the same result for longest-prefix matches. For both the map and the set, this library has two versions of the aggregation: aggregate_consistent and aggregate. The consistent variant only drops nodes that are covered by an ancestor. Thus, get_lpm always returns the same value for any prefix. The non-consistent variant is also allowed to merge siblings with the same value. Thus, get_lpm is only guaranteed to return the same value for addresses (i.e., /32 prefixes for IPv4).

The PrefixMap::aggregate, PrefixMap::aggregate_fill, and PrefixMap::aggregate_fill_default implement the Reduced Routing Table Construction algorithm, described by R. P. Draves, C. King, S. Venkatachary, and B. D. Zill. The result is a minimal routing table. The fill variants will also add the root prefix (i.e., 0.0.0.0/0), and fill holes with the default value.

Other operations on the Tree

Most operations are bounded by prefix width, not by the number of stored entries. Let w be the number of bits in the prefix representation, and let h = ceil((w + 1) / 5) be the maximum number of multi-bit nodes on a search path. For IPv4, h <= 7; for IPv6, h <= 26. Let n be the number of stored entries, and let v be the number of trie nodes visited by a traversal.

Operation Complexity
len, is_empty, mem_size O(1)
get, get_mut, contains_key O(h)
get_lpm, get_spm, cover O(h)
entry, insert O(h)
remove, remove_keep_tree O(h)
children, view_at O(h) to create, then linear in the subtrie
iter, keys, values O(n + v) for a complete traversal
retain, clear O(n + v)
remove_children O(h + m) where m is the removed subtrie size
Operations on an occupied map::Entry O(1) after the entry lookup
Inserting through a vacant map::Entry O(h) worst case

There are three removal styles:

  • PrefixMap::remove will remove an entry from the tree and modify the tree structure as if the value was never inserted before. It may remove now-empty multi-bit nodes and compact their allocator blocks.
  • PrefixMap::remove_children will remove all entries that are contained within the given prefix, including entries stored in the same multi-bit node and in child nodes below it.
  • PrefixMap::remove_keep_tree removes only the value and leaves the existing node structure in place, which can make reinserting the same prefix cheaper but may leave empty internal nodes for future traversals to pass through.

Comparison with related projects

ip_network_table-deps-treebitmap provides the same data structure (in the following called TreeBitMap) and uses a similar algorithm. The following table compares the performance and memory of the two libraries, and relates them to the HashMap and BTreeMap of the standard library. Throughput is reported relative to HashMap (1.00x = HashMap speed), with absolute throughput in parentheses. Bold marks the fastest implementation per row. See benches/benchmark.rs for details.

All benchmarks use IPv4 prefixes from a RIPE RIS peer snapshot (1,042,024 IPv4 prefixes or 246,174 IPv6 prefixes). See benches/benchmark.rs and benches/memory.rs for details. The benchmark results below were obtained on an AMD EPYC server.

Benchmark HashMap PrefixMap TreeBitMap BTreeMap
Lookup
-> Random access
---> IPv4 1.00x (7.4 Mops) 1.92x (14.2 Mops) 1.15x (8.5 Mops) 0.46x (3.4 Mops)
---> IPv6 1.00x (11.0 Mops) 0.97x (10.7 Mops) 0.58x (6.4 Mops) 0.45x (4.9 Mops)
-> RIS updates
---> IPv4 1.00x (17.5 Mops) 1.69x (29.5 Mops) 0.78x (13.7 Mops) 0.47x (8.2 Mops)
---> IPv6 1.00x (24.8 Mops) 0.63x (15.7 Mops) 0.33x (8.2 Mops) 0.32x (7.9 Mops)
Insert & Remove
-> Random access
---> IPv4 1.00x (7.4 Mops) 1.04x (7.7 Mops) 0.89x (6.6 Mops) 0.43x (3.2 Mops)
---> IPv6 1.00x (10.8 Mops) 0.48x (5.2 Mops) 0.44x (4.7 Mops) 0.39x (4.3 Mops)
-> RIS updates
---> IPv4 1.00x (17.1 Mops) 0.88x (15.0 Mops) 0.71x (12.2 Mops) 0.47x (8.0 Mops)
---> IPv6 1.00x (25.0 Mops) 0.33x (8.3 Mops) 0.29x (7.3 Mops) 0.31x (7.7 Mops)
Create
-> Random order
---> IPv4 1.00x (7.8 Mops) 1.13x (8.8 Mops) 0.95x (7.4 Mops) 0.55x (4.3 Mops)
---> IPv6 1.00x (11.4 Mops) 0.52x (5.9 Mops) 0.43x (4.9 Mops) 0.42x (4.8 Mops)
-> Sorted order
---> IPv4 1.00x (10.3 Mops) 1.45x (14.9 Mops) 1.04x (10.7 Mops) 0.85x (8.8 Mops)
---> IPv6 1.00x (11.7 Mops) 0.70x (8.2 Mops) 0.55x (6.5 Mops) 0.51x (6.0 Mops)
-> Scattered order
---> IPv4 1.00x (10.3 Mops) 1.02x (10.5 Mops) 0.76x (7.8 Mops) 0.34x (3.4 Mops)
---> IPv6 1.00x (11.6 Mops) 0.59x (6.9 Mops) 0.47x (5.5 Mops) 0.46x (5.4 Mops)
Memory
-> IPv4 26.0 mB 12.0 mB (set: 4.0 mB) 11.0 mB 16.4 mB
-> IPv6 12.5 mB 6.0 mB (set: 4.0 mB) 4.3 mB 8.1 mB

Besides better performance than the TreeBitMap, prefix-trie includes a PrefixSet analogous to std::collections::HashSet. Set operations are exposed through composable trie views, so operations such as union, intersection, difference, covering union, and covering difference can be combined without building temporary maps. prefix-trie has an interface similar to std::collections, and its longest-prefix matching is not limited to individual host addresses.

Migrate to version 0.9

Releases up to 0.8.4 used a binary tree representation. With version 0.9, this crate migrated to a TreeBitMap, described by W. Eatherton, Z. Dittia, and G. Varghes. This yields significant performance improvements (~8x faster inserts & lookups for all IPv4 addresses in internet routing tables) and memory reduction (~4x less).

However, this also comes with significant changes in the interface:

  • Prefixes are reconstructed from the tree location and no longer stored. Consequently, information in the host part of an address is no longer maintained. Additionally, the function yields prefixes by value instead of by reference. If you wish to store the host-bits as well, consider constructing a type PrefixMap<P, (P, T)>.
  • Mutable views of the tree can no longer modify the tree structure (to maintain memory safety). This is because one can construct two mutable views pointing to branches within the same multi-bit node.
  • Thanks to bitmaps, set operations can now be chained (as we can define a common interface for them to return their bitmaps).