package org.apache.lucene.search.suggest.fst;
import java.io.BufferedInputStream;
import java.io.BufferedOutputStream;
import java.io.File;
import java.io.FileInputStream;
import java.io.FileOutputStream;
import java.io.IOException;
import java.io.InputStream;
import java.io.OutputStream;
import java.util.ArrayList;
import java.util.Collections;
import java.util.Comparator;
import java.util.List;
import org.apache.lucene.util.IOUtils;
import org.apache.lucene.util.IntsRef;
import org.apache.lucene.util.fst.Builder;
import org.apache.lucene.util.fst.FST;
import org.apache.lucene.util.fst.FST.Arc;
import org.apache.lucene.util.fst.NoOutputs;
import org.apache.lucene.util.fst.Outputs;
import org.apache.lucene.search.suggest.Lookup;
import org.apache.lucene.search.suggest.tst.TSTLookup;
import org.apache.lucene.search.spell.TermFreqIterator;
import org.apache.lucene.store.InputStreamDataInput;
import org.apache.lucene.store.OutputStreamDataOutput;
/**
* Finite state automata based implementation of {@link Lookup} query
* suggestion/ autocomplete interface.
*
* <h2>Implementation details</h2>
*
* <p>The construction step in {@link #build(TermFreqIterator)} works as follows:
* <ul>
* <li>A set of input terms (String) and weights (float) is given.</li>
* <li>The range of weights is determined and then all weights are discretized into a fixed set
* of values ({@link #buckets}).
* Note that this means that minor changes in weights may be lost during automaton construction.
* In general, this is not a big problem because the "priorities" of completions can be split
* into a fixed set of classes (even as rough as: very frequent, frequent, baseline, marginal).
* If you need exact, fine-grained weights, use {@link TSTLookup} instead.<li>
* <li>All terms in the input are preprended with a synthetic pseudo-character being the weight
* of that term. For example a term <code>abc</code> with a discretized weight equal '1' would
* become <code>1abc</code>.</li>
* <li>The terms are sorted by their raw value of utf16 character values (including the synthetic
* term in front).</li>
* <li>A finite state automaton ({@link FST}) is constructed from the input. The root node has
* arcs labeled with all possible weights. We cache all these arcs, highest-weight first.</li>
* </ul>
*
* <p>At runtime, in {@link #lookup(String, boolean, int)}, the automaton is utilized as follows:
* <ul>
* <li>For each possible term weight encoded in the automaton (cached arcs from the root above),
* starting with the highest one, we descend along the path of the input key. If the key is not
* a prefix of a sequence in the automaton (path ends prematurely), we exit immediately.
* No completions.
* <li>Otherwise, we have found an internal automaton node that ends the key. <b>The entire
* subautomaton (all paths) starting from this node form the key's completions.</b> We start
* the traversal of this subautomaton. Every time we reach a final state (arc), we add a single
* suggestion to the list of results (the weight of this suggestion is constant and equal to the
* root path we started from). The tricky part is that because automaton edges are sorted and
* we scan depth-first, we can terminate the entire procedure as soon as we collect enough
* suggestions the user requested.
* <li>In case the number of suggestions collected in the step above is still insufficient,
* we proceed to the next (smaller) weight leaving the root node and repeat the same
* algorithm again.
* </li>
* </ul>
*
* <h2>Runtime behavior and performance characteristic</h2>
*
* <p>The algorithm described above is optimized for finding suggestions to short prefixes
* in a top-weights-first order. This is probably the most common use case: it allows
* presenting suggestions early and sorts them by the global frequency (and then alphabetically).
*
* <p>If there is an exact match in the automaton, it is returned first on the results
* list (even with by-weight sorting).
*
* <p>Note that the maximum lookup time for <b>any prefix</b>
* is the time of descending to the subtree, plus traversal of the subtree up to the number
* of requested suggestions (because they are already presorted by weight on the root level
* and alphabetically at any node level).
*
* <p>To order alphabetically only (no ordering by priorities), use identical term weights
* for all terms. Alphabetical suggestions are returned even if non-constant weights are
* used, but the algorithm for doing this is suboptimal.
*
* <p>"alphabetically" in any of the documentation above indicates utf16 codepoint order,
* nothing else.
*/
public class FSTLookup extends Lookup {
public FSTLookup() {
this(10, true);
}
public FSTLookup(int buckets, boolean exactMatchFirst) {
this.buckets = buckets;
this.exactMatchFirst = exactMatchFirst;
}
/** A structure for a single entry (for sorting/ preprocessing). */
private static class Entry {
char [] term;
float weight;
public Entry(char [] term, float freq) {
this.term = term;
this.weight = freq;
}
}
/** Serialized automaton file name (storage). */
public static final String FILENAME = "fst.dat";
/** An empty result. */
private static final List<LookupResult> EMPTY_RESULT = Collections.emptyList();
/**
* The number of separate buckets for weights (discretization). The more buckets,
* the more fine-grained term weights (priorities) can be assigned. The speed of lookup
* will not decrease for prefixes which have highly-weighted completions (because these
* are filled-in first), but will decrease significantly for low-weighted terms (but
* these should be infrequent, so it is all right).
*
* <p>The number of buckets must be within [1, 255] range.
*/
private final int buckets;
/**
* If <code>true</code>, exact suggestions are returned first, even if they are prefixes
* of other strings in the automaton (possibly with larger weights).
*/
private final boolean exactMatchFirst;
/**
* Finite state automaton encoding all the lookup terms. See class
* notes for details.
*/
private FST<Object> automaton;
/**
* An array of arcs leaving the root automaton state and encoding weights of all
* completions in their sub-trees.
*/
private Arc<Object> [] rootArcs;
/* */
@Override
public void build(TermFreqIterator tfit) throws IOException {
// Buffer the input because we will need it twice: for calculating
// weights distribution and for the actual automata building.
List<Entry> entries = new ArrayList<Entry>();
while (tfit.hasNext()) {
String term = tfit.next();
char [] termChars = new char [term.length() + 1]; // add padding for weight.
for (int i = 0; i < term.length(); i++)
termChars[i + 1] = term.charAt(i);
entries.add(new Entry(termChars, tfit.freq()));
}
// Distribute weights into at most N buckets. This is a form of discretization to
// limit the number of possible weights so that they can be efficiently encoded in the
// automaton.
//
// It is assumed the distribution of weights is _linear_ so proportional division
// of [min, max] range will be enough here. Other approaches could be to sort
// weights and divide into proportional ranges.
if (entries.size() > 0) {
redistributeWeightsProportionalMinMax(entries, buckets);
encodeWeightPrefix(entries);
}
// Build the automaton (includes input sorting) and cache root arcs in order from the highest,
// to the lowest weight.
this.automaton = buildAutomaton(entries);
cacheRootArcs();
}
/**
* Cache the root node's output arcs starting with completions with the highest weights.
*/
@SuppressWarnings("unchecked")
private void cacheRootArcs() throws IOException {
if (automaton != null) {
List<Arc<Object>> rootArcs = new ArrayList<Arc<Object>>();
Arc<Object> arc = automaton.getFirstArc(new Arc<Object>());
automaton.readFirstTargetArc(arc, arc);
while (true) {
rootArcs.add(new Arc<Object>().copyFrom(arc));
if (arc.isLast())
break;
automaton.readNextArc(arc);
}
Collections.reverse(rootArcs); // we want highest weights first.
this.rootArcs = rootArcs.toArray(new Arc[rootArcs.size()]);
}
}
/**
* Not implemented.
*/
@Override
public boolean add(String key, Object value) {
// This implementation does not support ad-hoc additions (all input
// must be sorted for the builder).
return false;
}
/**
* Get the (approximated) weight of a single key (if there is a perfect match
* for it in the automaton).
*
* @return Returns the approximated weight of the input key or <code>null</code>
* if not found.
*/
@Override
public Float get(String key) {
return getExactMatchStartingFromRootArc(0, key);
}
/**
* Returns the first exact match by traversing root arcs, starting from
* the arc <code>i</code>.
*
* @param i The first root arc index in {@link #rootArcs} to consider when
* matching.
*/
private Float getExactMatchStartingFromRootArc(int i, String key) {
// Get the UTF-8 bytes representation of the input key.
try {
final FST.Arc<Object> scratch = new FST.Arc<Object>();
for (; i < rootArcs.length; i++) {
final FST.Arc<Object> rootArc = rootArcs[i];
final FST.Arc<Object> arc = scratch.copyFrom(rootArc);
// Descend into the automaton using the key as prefix.
if (descendWithPrefix(arc, key)) {
automaton.readFirstTargetArc(arc, arc);
if (arc.label == FST.END_LABEL) {
// Prefix-encoded weight.
return rootArc.label / (float) buckets;
}
}
}
} catch (IOException e) {
// Should never happen, but anyway.
throw new RuntimeException(e);
}
return null;
}
/**
* Lookup autocomplete suggestions to <code>key</code>.
*
* @param key The prefix to which suggestions should be sought.
* @param onlyMorePopular Return most popular suggestions first. This is the default
* behavior for this implementation. Setting it to <code>false</code> has no effect (use
* constant term weights to sort alphabetically only).
* @param num At most this number of suggestions will be returned.
* @return Returns the suggestions, sorted by their approximated weight first (decreasing)
* and then alphabetically (utf16 codepoint order).
*/
@Override
public List<LookupResult> lookup(String key, boolean onlyMorePopular, int num) {
if (key.length() == 0 || automaton == null) {
// Keep the result an ArrayList to keep calls monomorphic.
return EMPTY_RESULT;
}
try {
if (!onlyMorePopular && rootArcs.length > 1) {
// We could emit a warning here (?). An optimal strategy for alphabetically sorted
// suggestions would be to add them with a constant weight -- this saves unnecessary
// traversals and sorting.
return lookupSortedAlphabetically(key, num);
} else {
return lookupSortedByWeight(key, num, false);
}
} catch (IOException e) {
// Should never happen, but anyway.
throw new RuntimeException(e);
}
}
/**
* Lookup suggestions sorted alphabetically <b>if weights are not constant</b>. This
* is a workaround: in general, use constant weights for alphabetically sorted result.
*/
private List<LookupResult> lookupSortedAlphabetically(String key, int num) throws IOException {
// Greedily get num results from each weight branch.
List<LookupResult> res = lookupSortedByWeight(key, num, true);
// Sort and trim.
Collections.sort(res, new Comparator<LookupResult>() {
// not till java6 @Override
public int compare(LookupResult o1, LookupResult o2) {
return o1.key.compareTo(o2.key);
}
});
if (res.size() > num) {
res = res.subList(0, num);
}
return res;
}
/**
* Lookup suggestions sorted by weight (descending order).
*
* @param collectAll If <code>true</code>, the routine terminates immediately when <code>num</code>
* suggestions have been collected. If <code>false</code>, it will collect suggestions from
* all weight arcs (needed for {@link #lookupSortedAlphabetically}.
*/
private ArrayList<LookupResult> lookupSortedByWeight(String key, int num, boolean collectAll) throws IOException {
// Don't overallocate the results buffers. This also serves the purpose of allowing
// the user of this class to request all matches using Integer.MAX_VALUE as the number
// of results.
final ArrayList<LookupResult> res = new ArrayList<LookupResult>(Math.min(10, num));
final StringBuilder output = new StringBuilder(key);
final int matchLength = key.length() - 1;
for (int i = 0; i < rootArcs.length; i++) {
final FST.Arc<Object> rootArc = rootArcs[i];
final FST.Arc<Object> arc = new FST.Arc<Object>().copyFrom(rootArc);
// Descend into the automaton using the key as prefix.
if (descendWithPrefix(arc, key)) {
// Prefix-encoded weight.
final float weight = rootArc.label / (float) buckets;
// A subgraph starting from the current node has the completions
// of the key prefix. The arc we're at is the last key's byte,
// so we will collect it too.
output.setLength(matchLength);
if (collect(res, num, weight, output, arc) && !collectAll) {
// We have enough suggestions to return immediately. Keep on looking for an
// exact match, if requested.
if (exactMatchFirst) {
if (!checkExistingAndReorder(res, key)) {
Float exactMatchWeight = getExactMatchStartingFromRootArc(i, key);
if (exactMatchWeight != null) {
// Insert as the first result and truncate at num.
while (res.size() >= num) {
res.remove(res.size() - 1);
}
res.add(0, new LookupResult(key, exactMatchWeight));
}
}
}
break;
}
}
}
return res;
}
/**
* Checks if the list of {@link LookupResult}s already has a <code>key</code>. If so,
* reorders that {@link LookupResult} to the first position.
*
* @return Returns <code>true<code> if and only if <code>list</code> contained <code>key</code>.
*/
private boolean checkExistingAndReorder(ArrayList<LookupResult> list, String key) {
// We assume list does not have duplicates (because of how the FST is created).
for (int i = list.size(); --i >= 0;) {
if (key.equals(list.get(i).key)) {
// Key found. Unless already at i==0, remove it and push up front so that the ordering
// remains identical with the exception of the exact match.
list.add(0, list.remove(i));
return true;
}
}
return false;
}
/**
* Descend along the path starting at <code>arc</code> and going through
* bytes in <code>utf8</code> argument.
*
* @param arc The starting arc. This argument is modified in-place.
* @param term The term to descend with.
* @return If <code>true</code>, <code>arc</code> will be set to the arc matching
* last byte of <code>utf8</code>. <code>false</code> is returned if no such
* prefix <code>utf8</code> exists.
*/
private boolean descendWithPrefix(Arc<Object> arc, String term) throws IOException {
final int max = term.length();
for (int i = 0; i < max; i++) {
if (automaton.findTargetArc(term.charAt(i) & 0xffff, arc, arc) == null) {
// No matching prefixes, return an empty result.
return false;
}
}
return true;
}
/**
* Recursive collect lookup results from the automaton subgraph starting at <code>arc</code>.
*
* @param num Maximum number of results needed (early termination).
* @param weight Weight of all results found during this collection.
*/
private boolean collect(List<LookupResult> res, int num, float weight, StringBuilder output, Arc<Object> arc) throws IOException {
output.append((char) arc.label);
automaton.readFirstTargetArc(arc, arc);
while (true) {
if (arc.label == FST.END_LABEL) {
res.add(new LookupResult(output.toString(), weight));
if (res.size() >= num)
return true;
} else {
int save = output.length();
if (collect(res, num, weight, output, new Arc<Object>().copyFrom(arc))) {
return true;
}
output.setLength(save);
}
if (arc.isLast()) {
break;
}
automaton.readNextArc(arc);
}
return false;
}
/**
* Builds the final automaton from a list of entries.
*/
private FST<Object> buildAutomaton(List<Entry> entries) throws IOException {
if (entries.size() == 0)
return null;
// Sort by utf16 (raw char value)
final Comparator<Entry> comp = new Comparator<Entry>() {
public int compare(Entry o1, Entry o2) {
char [] ch1 = o1.term;
char [] ch2 = o2.term;
int len1 = ch1.length;
int len2 = ch2.length;
int max = Math.min(len1, len2);
for (int i = 0; i < max; i++) {
int v = ch1[i] - ch2[i];
if (v != 0) return v;
}
return len1 - len2;
}
};
Collections.sort(entries, comp);
// Avoid duplicated identical entries, if possible. This is required because
// it breaks automaton construction otherwise.
int len = entries.size();
int j = 0;
for (int i = 1; i < len; i++) {
if (comp.compare(entries.get(j), entries.get(i)) != 0) {
entries.set(++j, entries.get(i));
}
}
entries = entries.subList(0, j + 1);
// Build the automaton.
final Outputs<Object> outputs = NoOutputs.getSingleton();
final Object empty = outputs.getNoOutput();
final Builder<Object> builder =
new Builder<Object>(FST.INPUT_TYPE.BYTE4, outputs);
final IntsRef scratchIntsRef = new IntsRef(10);
for (Entry e : entries) {
final int termLength = scratchIntsRef.length = e.term.length;
scratchIntsRef.grow(termLength);
final int [] ints = scratchIntsRef.ints;
final char [] chars = e.term;
for (int i = termLength; --i >= 0;) {
ints[i] = chars[i];
}
builder.add(scratchIntsRef, empty);
}
return builder.finish();
}
/**
* Prepends the entry's weight to each entry, encoded as a single byte, so that the
* root automaton node fans out to all possible priorities, starting with the arc that has
* the highest weights.
*/
private void encodeWeightPrefix(List<Entry> entries) {
for (Entry e : entries) {
int weight = (int) e.weight;
assert (weight >= 0 && weight <= buckets) :
"Weight out of range: " + weight + " [" + buckets + "]";
// There should be a single empty char reserved in front for the weight.
e.term[0] = (char) weight;
}
}
/**
* Split [min, max] range into buckets, reassigning weights. Entries' weights are
* remapped to [0, buckets] range (so, buckets + 1 buckets, actually).
*/
private void redistributeWeightsProportionalMinMax(List<Entry> entries, int buckets) {
float min = entries.get(0).weight;
float max = min;
for (Entry e : entries) {
min = Math.min(e.weight, min);
max = Math.max(e.weight, max);
}
final float range = max - min;
for (Entry e : entries) {
e.weight = (int) (buckets * ((e.weight - min) / range)); // int cast equiv. to floor()
}
}
/**
* Deserialization from disk.
*/
@Override
public synchronized boolean load(File storeDir) throws IOException {
File data = new File(storeDir, FILENAME);
if (!data.exists() || !data.canRead()) {
return false;
}
InputStream is = new BufferedInputStream(new FileInputStream(data));
try {
this.automaton = new FST<Object>(new InputStreamDataInput(is), NoOutputs.getSingleton());
cacheRootArcs();
} finally {
IOUtils.close(is);
}
return true;
}
/**
* Serialization to disk.
*/
@Override
public synchronized boolean store(File storeDir) throws IOException {
if (!storeDir.exists() || !storeDir.isDirectory() || !storeDir.canWrite()) {
return false;
}
if (this.automaton == null)
return false;
File data = new File(storeDir, FILENAME);
OutputStream os = new BufferedOutputStream(new FileOutputStream(data));
try {
this.automaton.save(new OutputStreamDataOutput(os));
} finally {
IOUtils.close(os);
}
return true;
}
}