1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214
use std::cmp;
use std::collections::{BTreeSet, VecDeque};
use std::fmt;
use std::mem::size_of;
use std::ops::{Index, IndexMut};
use crate::ahocorasick::MatchKind;
use crate::automaton::Automaton;
use crate::classes::{ByteClassBuilder, ByteClasses};
use crate::error::Result;
use crate::prefilter::{self, opposite_ascii_case, Prefilter, PrefilterObj};
use crate::state_id::{dead_id, fail_id, usize_to_state_id, StateID};
use crate::Match;
/// The identifier for a pattern, which is simply the position of the pattern
/// in the sequence of patterns given by the caller.
pub type PatternID = usize;
/// The length of a pattern, in bytes.
pub type PatternLength = usize;
/// An Aho-Corasick automaton, represented as an NFA.
///
/// This is the classical formulation of Aho-Corasick, which involves building
/// up a prefix trie of a given set of patterns, and then wiring up failure
/// transitions between states in order to guarantee linear time matching. The
/// standard formulation is, technically, an NFA because of these failure
/// transitions. That is, one can see them as enabling the automaton to be in
/// multiple states at once. Indeed, during search, it is possible to check
/// the transitions on multiple states for a single input byte.
///
/// This particular implementation not only supports the standard style of
/// matching, but also provides a mode for choosing leftmost-first or
/// leftmost-longest match semantics. When a leftmost mode is chosen, some
/// failure transitions that would otherwise be added are elided. See
/// the documentation of `MatchKind` for more details and examples on how the
/// match semantics may differ.
///
/// If one wants a DFA, then it is necessary to first build an NFA and convert
/// it into a DFA. Note, however, that because we've constrained ourselves to
/// matching literal patterns, this does not need to use subset construction
/// for determinization. Instead, the DFA has at most a number of states
/// equivalent to the number of NFA states. The only real difference between
/// them is that all failure transitions are followed and pre-computed. This
/// uses much more memory, but also executes searches more quickly.
#[derive(Clone)]
pub struct NFA<S> {
/// The match semantics built into this NFA.
match_kind: MatchKind,
/// The start state id as an index into `states`.
start_id: S,
/// The length, in bytes, of the longest pattern in this automaton. This
/// information is useful for keeping correct buffer sizes when searching
/// on streams.
max_pattern_len: usize,
/// The total number of patterns added to this automaton, including
/// patterns that may never be matched.
pattern_count: usize,
/// The number of bytes of heap used by this NFA's transition table.
heap_bytes: usize,
/// A prefilter for quickly skipping to candidate matches, if pertinent.
prefilter: Option<PrefilterObj>,
/// Whether this automaton anchors all matches to the start of input.
anchored: bool,
/// A set of equivalence classes in terms of bytes. We compute this while
/// building the NFA, but don't use it in the NFA's states. Instead, we
/// use this for building the DFA. We store it on the NFA since it's easy
/// to compute while visiting the patterns.
byte_classes: ByteClasses,
/// A set of states. Each state defines its own transitions, a fail
/// transition and a set of indices corresponding to matches.
///
/// The first state is always the fail state, which is used only as a
/// sentinel. Namely, in the final NFA, no transition into the fail state
/// exists. (Well, they do, but they aren't followed. Instead, the state's
/// failure transition is followed.)
///
/// The second state (index 1) is always the dead state. Dead states are
/// in every automaton, but only used when leftmost-{first,longest} match
/// semantics are enabled. Specifically, they instruct search to stop
/// at specific points in order to report the correct match location. In
/// the standard Aho-Corasick construction, there are no transitions to
/// the dead state.
///
/// The third state (index 2) is generally intended to be the starting or
/// "root" state.
states: Vec<State<S>>,
}
impl<S: StateID> NFA<S> {
/// Returns the equivalence classes of bytes found while constructing
/// this NFA.
///
/// Note that the NFA doesn't actually make use of these equivalence
/// classes. Instead, these are useful for building the DFA when desired.
pub fn byte_classes(&self) -> &ByteClasses {
&self.byte_classes
}
/// Returns a prefilter, if one exists.
pub fn prefilter_obj(&self) -> Option<&PrefilterObj> {
self.prefilter.as_ref()
}
/// Returns the total number of heap bytes used by this NFA's transition
/// table.
pub fn heap_bytes(&self) -> usize {
self.heap_bytes
+ self.prefilter.as_ref().map_or(0, |p| p.as_ref().heap_bytes())
}
/// Return the length of the longest pattern in this automaton.
pub fn max_pattern_len(&self) -> usize {
self.max_pattern_len
}
/// Return the total number of patterns added to this automaton.
pub fn pattern_count(&self) -> usize {
self.pattern_count
}
/// Returns the total number of states in this NFA.
pub fn state_len(&self) -> usize {
self.states.len()
}
/// Returns the matches for the given state.
pub fn matches(&self, id: S) -> &[(PatternID, PatternLength)] {
&self.states[id.to_usize()].matches
}
/// Returns an iterator over all transitions in the given state according
/// to the given equivalence classes, including transitions to `fail_id()`.
/// The number of transitions returned is always equivalent to the number
/// of equivalence classes.
pub fn iter_all_transitions<F: FnMut(u8, S)>(
&self,
byte_classes: &ByteClasses,
id: S,
f: F,
) {
self.states[id.to_usize()].trans.iter_all(byte_classes, f);
}
/// Returns the failure transition for the given state.
pub fn failure_transition(&self, id: S) -> S {
self.states[id.to_usize()].fail
}
/// Returns the next state for the given state and input byte.
///
/// Note that this does not follow failure transitions. As such, the id
/// returned may be `fail_id`.
pub fn next_state(&self, current: S, input: u8) -> S {
self.states[current.to_usize()].next_state(input)
}
fn state(&self, id: S) -> &State<S> {
&self.states[id.to_usize()]
}
fn state_mut(&mut self, id: S) -> &mut State<S> {
&mut self.states[id.to_usize()]
}
fn start(&self) -> &State<S> {
self.state(self.start_id)
}
fn start_mut(&mut self) -> &mut State<S> {
let id = self.start_id;
self.state_mut(id)
}
fn iter_transitions_mut(&mut self, id: S) -> IterTransitionsMut<'_, S> {
IterTransitionsMut::new(self, id)
}
fn copy_matches(&mut self, src: S, dst: S) {
let (src, dst) =
get_two_mut(&mut self.states, src.to_usize(), dst.to_usize());
dst.matches.extend_from_slice(&src.matches);
}
fn copy_empty_matches(&mut self, dst: S) {
let start_id = self.start_id;
self.copy_matches(start_id, dst);
}
fn add_dense_state(&mut self, depth: usize) -> Result<S> {
let trans = Transitions::Dense(Dense::new());
let id = usize_to_state_id(self.states.len())?;
self.states.push(State {
trans,
// Anchored automatons do not have any failure transitions.
fail: if self.anchored { dead_id() } else { self.start_id },
depth,
matches: vec![],
});
Ok(id)
}
fn add_sparse_state(&mut self, depth: usize) -> Result<S> {
let trans = Transitions::Sparse(vec![]);
let id = usize_to_state_id(self.states.len())?;
self.states.push(State {
trans,
// Anchored automatons do not have any failure transitions.
fail: if self.anchored { dead_id() } else { self.start_id },
depth,
matches: vec![],
});
Ok(id)
}
}
impl<S: StateID> Automaton for NFA<S> {
type ID = S;
fn match_kind(&self) -> &MatchKind {
&self.match_kind
}
fn anchored(&self) -> bool {
self.anchored
}
fn prefilter(&self) -> Option<&dyn Prefilter> {
self.prefilter.as_ref().map(|p| p.as_ref())
}
fn start_state(&self) -> S {
self.start_id
}
fn is_valid(&self, id: S) -> bool {
id.to_usize() < self.states.len()
}
fn is_match_state(&self, id: S) -> bool {
self.states[id.to_usize()].is_match()
}
fn get_match(
&self,
id: S,
match_index: usize,
end: usize,
) -> Option<Match> {
let state = match self.states.get(id.to_usize()) {
None => return None,
Some(state) => state,
};
state.matches.get(match_index).map(|&(id, len)| Match {
pattern: id,
len,
end,
})
}
fn match_count(&self, id: S) -> usize {
self.states[id.to_usize()].matches.len()
}
fn next_state(&self, mut current: S, input: u8) -> S {
// This terminates since:
//
// 1. `State.fail` never points to fail_id().
// 2. All `State.fail` values point to a state closer to `start`.
// 3. The start state has no transitions to fail_id().
loop {
let state = &self.states[current.to_usize()];
let next = state.next_state(input);
if next != fail_id() {
return next;
}
current = state.fail;
}
}
}
/// A representation of an NFA state for an Aho-Corasick automaton.
///
/// It contains the transitions to the next state, a failure transition for
/// cases where there exists no other transition for the current input byte,
/// the matches implied by visiting this state (if any) and the depth of this
/// state. The depth of a state is simply the distance from it to the start
/// state in the automaton, where the depth of the start state is 0.
#[derive(Clone, Debug)]
pub struct State<S> {
trans: Transitions<S>,
fail: S,
matches: Vec<(PatternID, PatternLength)>,
// TODO: Strictly speaking, this isn't needed for searching. It's only
// used when building an NFA that supports leftmost match semantics. We
// could drop this from the state and dynamically build a map only when
// computing failure transitions, but it's not clear which is better.
// Benchmark this.
depth: usize,
}
impl<S: StateID> State<S> {
fn heap_bytes(&self) -> usize {
self.trans.heap_bytes()
+ (self.matches.len() * size_of::<(PatternID, PatternLength)>())
}
fn add_match(&mut self, i: PatternID, len: PatternLength) {
self.matches.push((i, len));
}
fn is_match(&self) -> bool {
!self.matches.is_empty()
}
fn next_state(&self, input: u8) -> S {
self.trans.next_state(input)
}
fn set_next_state(&mut self, input: u8, next: S) {
self.trans.set_next_state(input, next);
}
}
/// Represents the transitions for a single dense state.
///
/// The primary purpose here is to encapsulate index access. Namely, since a
/// dense representation always contains 256 elements, all values of `u8` are
/// valid indices.
#[derive(Clone, Debug)]
struct Dense<S>(Vec<S>);
impl<S> Dense<S>
where
S: StateID,
{
fn new() -> Self {
Dense(vec![fail_id(); 256])
}
#[inline]
fn len(&self) -> usize {
self.0.len()
}
}
impl<S> Index<u8> for Dense<S> {
type Output = S;
#[inline]
fn index(&self, i: u8) -> &S {
// SAFETY: This is safe because all dense transitions have
// exactly 256 elements, so all u8 values are valid indices.
&self.0[i as usize]
}
}
impl<S> IndexMut<u8> for Dense<S> {
#[inline]
fn index_mut(&mut self, i: u8) -> &mut S {
// SAFETY: This is safe because all dense transitions have
// exactly 256 elements, so all u8 values are valid indices.
&mut self.0[i as usize]
}
}
/// A representation of transitions in an NFA.
///
/// Transitions have either a sparse representation, which is slower for
/// lookups but uses less memory, or a dense representation, which is faster
/// for lookups but uses more memory. In the sparse representation, the absence
/// of a state implies a transition to `fail_id()`. Transitions to `dead_id()`
/// are still explicitly represented.
///
/// For the NFA, by default, we use a dense representation for transitions for
/// states close to the start state because it's likely these are the states
/// that will be most frequently visited.
#[derive(Clone, Debug)]
enum Transitions<S> {
Sparse(Vec<(u8, S)>),
Dense(Dense<S>),
}
impl<S: StateID> Transitions<S> {
fn heap_bytes(&self) -> usize {
match *self {
Transitions::Sparse(ref sparse) => {
sparse.len() * size_of::<(u8, S)>()
}
Transitions::Dense(ref dense) => dense.len() * size_of::<S>(),
}
}
fn next_state(&self, input: u8) -> S {
match *self {
Transitions::Sparse(ref sparse) => {
for &(b, id) in sparse {
if b == input {
return id;
}
}
fail_id()
}
Transitions::Dense(ref dense) => dense[input],
}
}
fn set_next_state(&mut self, input: u8, next: S) {
match *self {
Transitions::Sparse(ref mut sparse) => {
match sparse.binary_search_by_key(&input, |&(b, _)| b) {
Ok(i) => sparse[i] = (input, next),
Err(i) => sparse.insert(i, (input, next)),
}
}
Transitions::Dense(ref mut dense) => {
dense[input] = next;
}
}
}
/// Iterate over transitions in this state while skipping over transitions
/// to `fail_id()`.
fn iter<F: FnMut(u8, S)>(&self, mut f: F) {
match *self {
Transitions::Sparse(ref sparse) => {
for &(b, id) in sparse {
f(b, id);
}
}
Transitions::Dense(ref dense) => {
for b in AllBytesIter::new() {
let id = dense[b];
if id != fail_id() {
f(b, id);
}
}
}
}
}
/// Iterate over all transitions in this state according to the given
/// equivalence classes, including transitions to `fail_id()`.
fn iter_all<F: FnMut(u8, S)>(&self, classes: &ByteClasses, mut f: F) {
if classes.is_singleton() {
match *self {
Transitions::Sparse(ref sparse) => {
sparse_iter(sparse, f);
}
Transitions::Dense(ref dense) => {
for b in AllBytesIter::new() {
f(b, dense[b]);
}
}
}
} else {
// In this case, we only want to yield a single byte for each
// equivalence class.
match *self {
Transitions::Sparse(ref sparse) => {
let mut last_class = None;
sparse_iter(sparse, |b, next| {
let class = classes.get(b);
if last_class != Some(class) {
last_class = Some(class);
f(b, next);
}
})
}
Transitions::Dense(ref dense) => {
for b in classes.representatives() {
f(b, dense[b]);
}
}
}
}
}
}
/// Iterator over transitions in a state, skipping transitions to `fail_id()`.
///
/// This abstracts over the representation of NFA transitions, which may be
/// either in a sparse or dense representation.
///
/// This somewhat idiosyncratically borrows the NFA mutably, so that when one
/// is iterating over transitions, the caller can still mutate the NFA. This
/// is useful when creating failure transitions.
#[derive(Debug)]
struct IterTransitionsMut<'a, S: StateID> {
nfa: &'a mut NFA<S>,
state_id: S,
cur: usize,
}
impl<'a, S: StateID> IterTransitionsMut<'a, S> {
fn new(nfa: &'a mut NFA<S>, state_id: S) -> IterTransitionsMut<'a, S> {
IterTransitionsMut { nfa, state_id, cur: 0 }
}
fn nfa(&mut self) -> &mut NFA<S> {
self.nfa
}
}
impl<'a, S: StateID> Iterator for IterTransitionsMut<'a, S> {
type Item = (u8, S);
fn next(&mut self) -> Option<(u8, S)> {
match self.nfa.states[self.state_id.to_usize()].trans {
Transitions::Sparse(ref sparse) => {
if self.cur >= sparse.len() {
return None;
}
let i = self.cur;
self.cur += 1;
Some(sparse[i])
}
Transitions::Dense(ref dense) => {
while self.cur < dense.len() {
// There are always exactly 255 transitions in dense repr.
debug_assert!(self.cur < 256);
let b = self.cur as u8;
let id = dense[b];
self.cur += 1;
if id != fail_id() {
return Some((b, id));
}
}
None
}
}
}
}
/// A simple builder for configuring the NFA construction of Aho-Corasick.
#[derive(Clone, Debug)]
pub struct Builder {
dense_depth: usize,
match_kind: MatchKind,
prefilter: bool,
anchored: bool,
ascii_case_insensitive: bool,
}
impl Default for Builder {
fn default() -> Builder {
Builder {
dense_depth: 2,
match_kind: MatchKind::default(),
prefilter: true,
anchored: false,
ascii_case_insensitive: false,
}
}
}
impl Builder {
pub fn new() -> Builder {
Builder::default()
}
pub fn build<I, P, S: StateID>(&self, patterns: I) -> Result<NFA<S>>
where
I: IntoIterator<Item = P>,
P: AsRef<[u8]>,
{
Compiler::new(self)?.compile(patterns)
}
pub fn match_kind(&mut self, kind: MatchKind) -> &mut Builder {
self.match_kind = kind;
self
}
pub fn dense_depth(&mut self, depth: usize) -> &mut Builder {
self.dense_depth = depth;
self
}
pub fn prefilter(&mut self, yes: bool) -> &mut Builder {
self.prefilter = yes;
self
}
pub fn anchored(&mut self, yes: bool) -> &mut Builder {
self.anchored = yes;
self
}
pub fn ascii_case_insensitive(&mut self, yes: bool) -> &mut Builder {
self.ascii_case_insensitive = yes;
self
}
}
/// A compiler uses a builder configuration and builds up the NFA formulation
/// of an Aho-Corasick automaton. This roughly corresponds to the standard
/// formulation described in textbooks.
#[derive(Debug)]
struct Compiler<'a, S: StateID> {
builder: &'a Builder,
prefilter: prefilter::Builder,
nfa: NFA<S>,
byte_classes: ByteClassBuilder,
}
impl<'a, S: StateID> Compiler<'a, S> {
fn new(builder: &'a Builder) -> Result<Compiler<'a, S>> {
Ok(Compiler {
builder,
prefilter: prefilter::Builder::new(builder.match_kind)
.ascii_case_insensitive(builder.ascii_case_insensitive),
nfa: NFA {
match_kind: builder.match_kind,
start_id: usize_to_state_id(2)?,
max_pattern_len: 0,
pattern_count: 0,
heap_bytes: 0,
prefilter: None,
anchored: builder.anchored,
byte_classes: ByteClasses::singletons(),
states: vec![],
},
byte_classes: ByteClassBuilder::new(),
})
}
fn compile<I, P>(mut self, patterns: I) -> Result<NFA<S>>
where
I: IntoIterator<Item = P>,
P: AsRef<[u8]>,
{
self.add_state(0)?; // the fail state, which is never entered
self.add_state(0)?; // the dead state, only used for leftmost
self.add_state(0)?; // the start state
self.build_trie(patterns)?;
self.add_start_state_loop();
self.add_dead_state_loop();
if !self.builder.anchored {
self.fill_failure_transitions();
}
self.close_start_state_loop();
self.nfa.byte_classes = self.byte_classes.build();
if !self.builder.anchored {
self.nfa.prefilter = self.prefilter.build();
}
self.calculate_size();
Ok(self.nfa)
}
/// This sets up the initial prefix trie that makes up the Aho-Corasick
/// automaton. Effectively, it creates the basic structure of the
/// automaton, where every pattern given has a path from the start state to
/// the end of the pattern.
fn build_trie<I, P>(&mut self, patterns: I) -> Result<()>
where
I: IntoIterator<Item = P>,
P: AsRef<[u8]>,
{
'PATTERNS: for (pati, pat) in patterns.into_iter().enumerate() {
let pat = pat.as_ref();
self.nfa.max_pattern_len =
cmp::max(self.nfa.max_pattern_len, pat.len());
self.nfa.pattern_count += 1;
let mut prev = self.nfa.start_id;
let mut saw_match = false;
for (depth, &b) in pat.iter().enumerate() {
// When leftmost-first match semantics are requested, we
// specifically stop adding patterns when a previously added
// pattern is a prefix of it. We avoid adding it because
// leftmost-first semantics imply that the pattern can never
// match. This is not just an optimization to save space! It
// is necessary for correctness. In fact, this is the only
// difference in the automaton between the implementations for
// leftmost-first and leftmost-longest.
saw_match = saw_match || self.nfa.state(prev).is_match();
if self.builder.match_kind.is_leftmost_first() && saw_match {
// Skip to the next pattern immediately. This avoids
// incorrectly adding a match after this loop terminates.
continue 'PATTERNS;
}
// Add this byte to our equivalence classes. We don't use these
// for NFA construction. These are instead used only if we're
// building a DFA. They would technically be useful for the
// NFA, but it would require a second pass over the patterns.
self.byte_classes.set_range(b, b);
if self.builder.ascii_case_insensitive {
let b = opposite_ascii_case(b);
self.byte_classes.set_range(b, b);
}
// If the transition from prev using the current byte already
// exists, then just move through it. Otherwise, add a new
// state. We track the depth here so that we can determine
// how to represent transitions. States near the start state
// use a dense representation that uses more memory but is
// faster. Other states use a sparse representation that uses
// less memory but is slower.
let next = self.nfa.state(prev).next_state(b);
if next != fail_id() {
prev = next;
} else {
let next = self.add_state(depth + 1)?;
self.nfa.state_mut(prev).set_next_state(b, next);
if self.builder.ascii_case_insensitive {
let b = opposite_ascii_case(b);
self.nfa.state_mut(prev).set_next_state(b, next);
}
prev = next;
}
}
// Once the pattern has been added, log the match in the final
// state that it reached.
self.nfa.state_mut(prev).add_match(pati, pat.len());
// ... and hand it to the prefilter builder, if applicable.
if self.builder.prefilter {
self.prefilter.add(pat);
}
}
Ok(())
}
/// This routine creates failure transitions according to the standard
/// textbook formulation of the Aho-Corasick algorithm, with a couple small
/// tweaks to support "leftmost" semantics.
///
/// Building failure transitions is the most interesting part of building
/// the Aho-Corasick automaton, because they are what allow searches to
/// be performed in linear time. Specifically, a failure transition is
/// a single transition associated with each state that points back to
/// the longest proper suffix of the pattern being searched. The failure
/// transition is followed whenever there exists no transition on the
/// current state for the current input byte. If there is no other proper
/// suffix, then the failure transition points back to the starting state.
///
/// For example, let's say we built an Aho-Corasick automaton with the
/// following patterns: 'abcd' and 'cef'. The trie looks like this:
///
/// ```ignore
/// a - S1 - b - S2 - c - S3 - d - S4*
/// /
/// S0 - c - S5 - e - S6 - f - S7*
/// ```
///
/// At this point, it should be fairly straight-forward to see how this
/// trie can be used in a simplistic way. At any given position in the
/// text we're searching (called the "subject" string), all we need to do
/// is follow the transitions in the trie by consuming one transition for
/// each byte in the subject string. If we reach a match state, then we can
/// report that location as a match.
///
/// The trick comes when searching a subject string like 'abcef'. We'll
/// initially follow the transition from S0 to S1 and wind up in S3 after
/// observng the 'c' byte. At this point, the next byte is 'e' but state
/// S3 has no transition for 'e', so the search fails. We then would need
/// to restart the search at the next position in 'abcef', which
/// corresponds to 'b'. The match would fail, but the next search starting
/// at 'c' would finally succeed. The problem with this approach is that
/// we wind up searching the subject string potentially many times. In
/// effect, this makes the algorithm have worst case `O(n * m)` complexity,
/// where `n ~ len(subject)` and `m ~ len(all patterns)`. We would instead
/// like to achieve a `O(n + m)` worst case complexity.
///
/// This is where failure transitions come in. Instead of dying at S3 in
/// the first search, the automaton can instruct the search to move to
/// another part of the automaton that corresponds to a suffix of what
/// we've seen so far. Recall that we've seen 'abc' in the subject string,
/// and the automaton does indeed have a non-empty suffix, 'c', that could
/// potentially lead to another match. Thus, the actual Aho-Corasick
/// automaton for our patterns in this case looks like this:
///
/// ```ignore
/// a - S1 - b - S2 - c - S3 - d - S4*
/// / /
/// / ----------------
/// / /
/// S0 - c - S5 - e - S6 - f - S7*
/// ```
///
/// That is, we have a failure transition from S3 to S5, which is followed
/// exactly in cases when we are in state S3 but see any byte other than
/// 'd' (that is, we've "failed" to find a match in this portion of our
/// trie). We know we can transition back to S5 because we've already seen
/// a 'c' byte, so we don't need to re-scan it. We can then pick back up
/// with the search starting at S5 and complete our match.
///
/// Adding failure transitions to a trie is fairly simple, but subtle. The
/// key issue is that you might have multiple failure transition that you
/// need to follow. For example, look at the trie for the patterns
/// 'abcd', 'b', 'bcd' and 'cd':
///
/// ```ignore
/// - a - S1 - b - S2* - c - S3 - d - S4*
/// / / /
/// / ------- -------
/// / / /
/// S0 --- b - S5* - c - S6 - d - S7*
/// \ /
/// \ --------
/// \ /
/// - c - S8 - d - S9*
/// ```
///
/// The failure transitions for this trie are defined from S2 to S5,
/// S3 to S6 and S6 to S8. Moreover, state S2 needs to track that it
/// corresponds to a match, since its failure transition to S5 is itself
/// a match state.
///
/// Perhaps simplest way to think about adding these failure transitions
/// is recursively. That is, if you know the failure transitions for every
/// possible previous state that could be visited (e.g., when computing the
/// failure transition for S3, you already know the failure transitions
/// for S0, S1 and S2), then you can simply follow the failure transition
/// of the previous state and check whether the incoming transition is
/// defined after following the failure transition.
///
/// For example, when determining the failure state for S3, by our
/// assumptions, we already know that there is a failure transition from
/// S2 (the previous state) to S5. So we follow that transition and check
/// whether the transition connecting S2 to S3 is defined. Indeed, it is,
/// as there is a transition from S5 to S6 for the byte 'c'. If no such
/// transition existed, we could keep following the failure transitions
/// until we reach the start state, which is the failure transition for
/// every state that has no corresponding proper suffix.
///
/// We don't actually use recursion to implement this, but instead, use a
/// breadth first search of the automaton. Our base case is the start
/// state, whose failure transition is just a transition to itself.
///
/// When building a leftmost automaton, we proceed as above, but only
/// include a subset of failure transitions. Namely, we omit any failure
/// transitions that appear after a match state in the trie. This is
/// because failure transitions always point back to a proper suffix of
/// what has been seen so far. Thus, following a failure transition after
/// a match implies looking for a match that starts after the one that has
/// already been seen, which is of course therefore not the leftmost match.
///
/// N.B. I came up with this algorithm on my own, and after scouring all of
/// the other AC implementations I know of (Perl, Snort, many on GitHub).
/// I couldn't find any that implement leftmost semantics like this.
/// Perl of course needs leftmost-first semantics, but they implement it
/// with a seeming hack at *search* time instead of encoding it into the
/// automaton. There are also a couple Java libraries that support leftmost
/// longest semantics, but they do it by building a queue of matches at
/// search time, which is even worse than what Perl is doing. ---AG
fn fill_failure_transitions(&mut self) {
let kind = self.match_kind();
// Initialize the queue for breadth first search with all transitions
// out of the start state. We handle the start state specially because
// we only want to follow non-self transitions. If we followed self
// transitions, then this would never terminate.
let mut queue = VecDeque::new();
let mut seen = self.queued_set();
let mut it = self.nfa.iter_transitions_mut(self.nfa.start_id);
while let Some((_, next)) = it.next() {
// Skip anything we've seen before and any self-transitions on the
// start state.
if next == it.nfa().start_id || seen.contains(next) {
continue;
}
queue.push_back(next);
seen.insert(next);
// Under leftmost semantics, if a state immediately following
// the start state is a match state, then we never want to
// follow its failure transition since the failure transition
// necessarily leads back to the start state, which we never
// want to do for leftmost matching after a match has been
// found.
//
// We apply the same logic to non-start states below as well.
if kind.is_leftmost() && it.nfa().state(next).is_match() {
it.nfa().state_mut(next).fail = dead_id();
}
}
while let Some(id) = queue.pop_front() {
let mut it = self.nfa.iter_transitions_mut(id);
while let Some((b, next)) = it.next() {
if seen.contains(next) {
// The only way to visit a duplicate state in a transition
// list is when ASCII case insensitivity is enabled. In
// this case, we want to skip it since it's redundant work.
// But it would also end up duplicating matches, which
// results in reporting duplicate matches in some cases.
// See the 'acasei010' regression test.
continue;
}
queue.push_back(next);
seen.insert(next);
// As above for start states, under leftmost semantics, once
// we see a match all subsequent states should have no failure
// transitions because failure transitions always imply looking
// for a match that is a suffix of what has been seen so far
// (where "seen so far" corresponds to the string formed by
// following the transitions from the start state to the
// current state). Under leftmost semantics, we specifically do
// not want to allow this to happen because we always want to
// report the match found at the leftmost position.
//
// The difference between leftmost-first and leftmost-longest
// occurs previously while we build the trie. For
// leftmost-first, we simply omit any entries that would
// otherwise require passing through a match state.
//
// Note that for correctness, the failure transition has to be
// set to the dead state for ALL states following a match, not
// just the match state itself. However, by setting the failure
// transition to the dead state on all match states, the dead
// state will automatically propagate to all subsequent states
// via the failure state computation below.
if kind.is_leftmost() && it.nfa().state(next).is_match() {
it.nfa().state_mut(next).fail = dead_id();
continue;
}
let mut fail = it.nfa().state(id).fail;
while it.nfa().state(fail).next_state(b) == fail_id() {
fail = it.nfa().state(fail).fail;
}
fail = it.nfa().state(fail).next_state(b);
it.nfa().state_mut(next).fail = fail;
it.nfa().copy_matches(fail, next);
}
// If the start state is a match state, then this automaton can
// match the empty string. This implies all states are match states
// since every position matches the empty string, so copy the
// matches from the start state to every state. Strictly speaking,
// this is only necessary for overlapping matches since each
// non-empty non-start match state needs to report empty matches
// in addition to its own. For the non-overlapping case, such
// states only report the first match, which is never empty since
// it isn't a start state.
if !kind.is_leftmost() {
it.nfa().copy_empty_matches(id);
}
}
}
/// Returns a set that tracked queued states.
///
/// This is only necessary when ASCII case insensitivity is enabled, since
/// it is the only way to visit the same state twice. Otherwise, this
/// returns an inert set that nevers adds anything and always reports
/// `false` for every member test.
fn queued_set(&self) -> QueuedSet<S> {
if self.builder.ascii_case_insensitive {
QueuedSet::active()
} else {
QueuedSet::inert()
}
}
/// Set the failure transitions on the start state to loop back to the
/// start state. This effectively permits the Aho-Corasick automaton to
/// match at any position. This is also required for finding the next
/// state to terminate, namely, finding the next state should never return
/// a fail_id.
///
/// This must be done after building the initial trie, since trie
/// construction depends on transitions to `fail_id` to determine whether a
/// state already exists or not.
fn add_start_state_loop(&mut self) {
let start_id = self.nfa.start_id;
let start = self.nfa.start_mut();
for b in AllBytesIter::new() {
if start.next_state(b) == fail_id() {
start.set_next_state(b, start_id);
}
}
}
/// Remove the start state loop by rewriting any transitions on the start
/// state back to the start state with transitions to the dead state.
///
/// The loop is only closed when two conditions are met: the start state
/// is a match state and the match kind is leftmost-first or
/// leftmost-longest. (Alternatively, if this is an anchored automaton,
/// then the start state is always closed, regardless of aforementioned
/// conditions.)
///
/// The reason for this is that under leftmost semantics, a start state
/// that is also a match implies that we should never restart the search
/// process. We allow normal transitions out of the start state, but if
/// none exist, we transition to the dead state, which signals that
/// searching should stop.
fn close_start_state_loop(&mut self) {
if self.builder.anchored
|| (self.match_kind().is_leftmost() && self.nfa.start().is_match())
{
let start_id = self.nfa.start_id;
let start = self.nfa.start_mut();
for b in AllBytesIter::new() {
if start.next_state(b) == start_id {
start.set_next_state(b, dead_id());
}
}
}
}
/// Sets all transitions on the dead state to point back to the dead state.
/// Normally, missing transitions map back to the failure state, but the
/// point of the dead state is to act as a sink that can never be escaped.
fn add_dead_state_loop(&mut self) {
let dead = self.nfa.state_mut(dead_id());
for b in AllBytesIter::new() {
dead.set_next_state(b, dead_id());
}
}
/// Computes the total amount of heap used by this NFA in bytes.
fn calculate_size(&mut self) {
let mut size = 0;
for state in &self.nfa.states {
size += size_of::<State<S>>() + state.heap_bytes();
}
self.nfa.heap_bytes = size;
}
/// Add a new state to the underlying NFA with the given depth. The depth
/// is used to determine how to represent the transitions.
///
/// If adding the new state would overflow the chosen state ID
/// representation, then this returns an error.
fn add_state(&mut self, depth: usize) -> Result<S> {
if depth < self.builder.dense_depth {
self.nfa.add_dense_state(depth)
} else {
self.nfa.add_sparse_state(depth)
}
}
/// Returns the match kind configured on the underlying builder.
fn match_kind(&self) -> MatchKind {
self.builder.match_kind
}
}
/// A set of state identifiers used to avoid revisiting the same state multiple
/// times when filling in failure transitions.
///
/// This set has an "inert" and an "active" mode. When inert, the set never
/// stores anything and always returns `false` for every member test. This is
/// useful to avoid the performance and memory overhead of maintaining this
/// set when it is not needed.
#[derive(Debug)]
struct QueuedSet<S> {
set: Option<BTreeSet<S>>,
}
impl<S: StateID> QueuedSet<S> {
/// Return an inert set that returns `false` for every state ID membership
/// test.
fn inert() -> QueuedSet<S> {
QueuedSet { set: None }
}
/// Return an active set that tracks state ID membership.
fn active() -> QueuedSet<S> {
QueuedSet { set: Some(BTreeSet::new()) }
}
/// Inserts the given state ID into this set. (If the set is inert, then
/// this is a no-op.)
fn insert(&mut self, state_id: S) {
if let Some(ref mut set) = self.set {
set.insert(state_id);
}
}
/// Returns true if and only if the given state ID is in this set. If the
/// set is inert, this always returns false.
fn contains(&self, state_id: S) -> bool {
match self.set {
None => false,
Some(ref set) => set.contains(&state_id),
}
}
}
/// An iterator over every byte value.
///
/// We use this instead of (0..256).map(|b| b as u8) because this optimizes
/// better in debug builds.
///
/// We also use this instead of 0..=255 because we're targeting Rust 1.24 and
/// inclusive range syntax was stabilized in Rust 1.26. We can get rid of this
/// once our MSRV is Rust 1.26 or newer.
#[derive(Debug)]
struct AllBytesIter(u16);
impl AllBytesIter {
fn new() -> AllBytesIter {
AllBytesIter(0)
}
}
impl Iterator for AllBytesIter {
type Item = u8;
fn next(&mut self) -> Option<Self::Item> {
if self.0 >= 256 {
None
} else {
let b = self.0 as u8;
self.0 += 1;
Some(b)
}
}
}
impl<S: StateID> fmt::Debug for NFA<S> {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
writeln!(f, "NFA(")?;
writeln!(f, "match_kind: {:?}", self.match_kind)?;
writeln!(f, "prefilter: {:?}", self.prefilter)?;
writeln!(f, "{}", "-".repeat(79))?;
for (id, s) in self.states.iter().enumerate() {
let mut trans = vec![];
s.trans.iter(|byte, next| {
// The start state has a bunch of uninteresting transitions
// back into itself. It's questionable to hide them since they
// are critical to understanding the automaton, but they are
// very noisy without better formatting for contiugous ranges
// to the same state.
if id == self.start_id.to_usize() && next == self.start_id {
return;
}
// Similarly, the dead state has a bunch of uninteresting
// transitions too.
if id == dead_id() {
return;
}
trans.push(format!("{} => {}", escape(byte), next.to_usize()));
});
writeln!(f, "{:04}: {}", id, trans.join(", "))?;
let matches: Vec<String> = s
.matches
.iter()
.map(|&(pattern_id, _)| pattern_id.to_string())
.collect();
writeln!(f, " matches: {}", matches.join(", "))?;
writeln!(f, " fail: {}", s.fail.to_usize())?;
writeln!(f, " depth: {}", s.depth)?;
}
writeln!(f, "{}", "-".repeat(79))?;
writeln!(f, ")")?;
Ok(())
}
}
/// Iterate over all possible byte transitions given a sparse set.
fn sparse_iter<S: StateID, F: FnMut(u8, S)>(trans: &[(u8, S)], mut f: F) {
let mut byte = 0u16;
for &(b, id) in trans {
while byte < (b as u16) {
f(byte as u8, fail_id());
byte += 1;
}
f(b, id);
byte += 1;
}
for b in byte..256 {
f(b as u8, fail_id());
}
}
/// Safely return two mutable borrows to two different locations in the given
/// slice.
///
/// This panics if i == j.
fn get_two_mut<T>(xs: &mut [T], i: usize, j: usize) -> (&mut T, &mut T) {
assert!(i != j, "{} must not be equal to {}", i, j);
if i < j {
let (before, after) = xs.split_at_mut(j);
(&mut before[i], &mut after[0])
} else {
let (before, after) = xs.split_at_mut(i);
(&mut after[0], &mut before[j])
}
}
/// Return the given byte as its escaped string form.
fn escape(b: u8) -> String {
use std::ascii;
String::from_utf8(ascii::escape_default(b).collect::<Vec<_>>()).unwrap()
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn scratch() {
let nfa: NFA<usize> = Builder::new()
.dense_depth(0)
// .match_kind(MatchKind::LeftmostShortest)
// .match_kind(MatchKind::LeftmostLongest)
.match_kind(MatchKind::LeftmostFirst)
// .build(&["abcd", "ce", "b"])
// .build(&["ab", "bc"])
// .build(&["b", "bcd", "ce"])
// .build(&["abc", "bx"])
// .build(&["abc", "bd", "ab"])
// .build(&["abcdefghi", "hz", "abcdefgh"])
// .build(&["abcd", "bce", "b"])
.build(&["abcdefg", "bcde", "bcdef"])
.unwrap();
println!("{:?}", nfa);
}
}