/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
/*
* Created on 28-Oct-2004
*/
using System;
using Analyzer = Lucene.Net.Analysis.Analyzer;
using Token = Lucene.Net.Analysis.Token;
using TokenStream = Lucene.Net.Analysis.TokenStream;
using Document = Lucene.Net.Documents.Document;
using IndexReader = Lucene.Net.Index.IndexReader;
using TermFreqVector = Lucene.Net.Index.TermFreqVector;
using TermPositionVector = Lucene.Net.Index.TermPositionVector;
using TermVectorOffsetInfo = Lucene.Net.Index.TermVectorOffsetInfo;
namespace Lucene.Net.Highlight
{
/// Hides implementation issues associated with obtaining a TokenStream for use with
/// the higlighter - can obtain from TermFreqVectors with offsets and (optionally) positions or
/// from Analyzer class reparsing the stored content.
///
/// maharwood
///
public class TokenSources
{
public class StoredTokenStream : TokenStream
{
internal Token[] tokens;
internal int currentToken = 0;
internal StoredTokenStream(Token[] tokens)
{
this.tokens = tokens;
}
public override Token Next()
{
if (currentToken >= tokens.Length)
{
return null;
}
return tokens[currentToken++];
}
}
private class AnonymousClassComparator : System.Collections.IComparer
{
public virtual int Compare(System.Object o1, System.Object o2)
{
Token t1 = (Token) o1;
Token t2 = (Token) o2;
if (t1.StartOffset() > t2.StartOffset())
return 1;
if (t1.StartOffset() < t2.StartOffset())
return - 1;
return 0;
}
}
/// A convenience method that tries a number of approaches to getting a token stream.
/// The cost of finding there are no termVectors in the index is minimal (1000 invocations still
/// registers 0 ms). So this "lazy" (flexible?) approach to coding is probably acceptable
///
/// reader
///
/// docId
///
/// field
///
/// analyzer
///
/// null if field not stored correctly
///
/// IOException
public static TokenStream GetAnyTokenStream(IndexReader reader, int docId, System.String field, Analyzer analyzer)
{
TokenStream ts = null;
TermFreqVector tfv = (TermFreqVector) reader.GetTermFreqVector(docId, field);
if (tfv != null)
{
if (tfv is TermPositionVector)
{
ts = GetTokenStream((TermPositionVector) tfv);
}
}
//No token info stored so fall back to analyzing raw content
if (ts == null)
{
ts = GetTokenStream(reader, docId, field, analyzer);
}
return ts;
}
public static TokenStream GetTokenStream(TermPositionVector tpv)
{
//assumes the worst and makes no assumptions about token position sequences.
return GetTokenStream(tpv, false);
}
/// Low level api.
/// Returns a token stream or null if no offset info available in index.
/// This can be used to feed the highlighter with a pre-parsed token stream
///
/// In my tests the speeds to recreate 1000 token streams using this method are:
/// - with TermVector offset only data stored - 420 milliseconds
/// - with TermVector offset AND position data stored - 271 milliseconds
/// (nb timings for TermVector with position data are based on a tokenizer with contiguous
/// positions - no overlaps or gaps)
/// The cost of not using TermPositionVector to store
/// pre-parsed content and using an analyzer to re-parse the original content:
/// - reanalyzing the original content - 980 milliseconds
///
/// The re-analyze timings will typically vary depending on -
/// 1) The complexity of the analyzer code (timings above were using a
/// stemmer/lowercaser/stopword combo)
/// 2) The number of other fields (Lucene reads ALL fields off the disk
/// when accessing just one document field - can cost dear!)
/// 3) Use of compression on field storage - could be faster cos of compression (less disk IO)
/// or slower (more CPU burn) depending on the content.
///
///
/// tpv
///
/// true if the token position numbers have no overlaps or gaps. If looking
/// to eek out the last drops of performance, set to true. If in doubt, set to false.
///
public static TokenStream GetTokenStream(TermPositionVector tpv, bool tokenPositionsGuaranteedContiguous)
{
//an object used to iterate across an array of tokens
//code to reconstruct the original sequence of Tokens
System.String[] terms = tpv.GetTerms();
int[] freq = tpv.GetTermFrequencies();
int totalTokens = 0;
for (int t = 0; t < freq.Length; t++)
{
totalTokens += freq[t];
}
Token[] tokensInOriginalOrder = new Token[totalTokens];
System.Collections.ArrayList unsortedTokens = null;
for (int t = 0; t < freq.Length; t++)
{
TermVectorOffsetInfo[] offsets = tpv.GetOffsets(t);
if (offsets == null)
{
return null;
}
int[] pos = null;
if (tokenPositionsGuaranteedContiguous)
{
//try get the token position info to speed up assembly of tokens into sorted sequence
pos = tpv.GetTermPositions(t);
}
if (pos == null)
{
//tokens NOT stored with positions or not guaranteed contiguous - must add to list and sort later
if (unsortedTokens == null)
{
unsortedTokens = new System.Collections.ArrayList();
}
for (int tp = 0; tp < offsets.Length; tp++)
{
unsortedTokens.Add(new Token(terms[t], offsets[tp].GetStartOffset(), offsets[tp].GetEndOffset()));
}
}
else
{
//We have positions stored and a guarantee that the token position information is contiguous
// This may be fast BUT wont work if Tokenizers used which create >1 token in same position or
// creates jumps in position numbers - this code would fail under those circumstances
//tokens stored with positions - can use this to index straight into sorted array
for (int tp = 0; tp < pos.Length; tp++)
{
tokensInOriginalOrder[pos[tp]] = new Token(terms[t], offsets[tp].GetStartOffset(), offsets[tp].GetEndOffset());
}
}
}
//If the field has been stored without position data we must perform a sort
if (unsortedTokens != null)
{
tokensInOriginalOrder = (Token[]) unsortedTokens.ToArray(typeof(Token));
Array.Sort(tokensInOriginalOrder, new AnonymousClassComparator());
}
return new StoredTokenStream(tokensInOriginalOrder);
}
public static TokenStream GetTokenStream(IndexReader reader, int docId, System.String field)
{
TermFreqVector tfv = (TermFreqVector) reader.GetTermFreqVector(docId, field);
if (tfv == null)
{
throw new System.ArgumentException(field + " in doc #" + docId + "does not have any term position data stored");
}
if (tfv is TermPositionVector)
{
TermPositionVector tpv = (TermPositionVector) reader.GetTermFreqVector(docId, field);
return GetTokenStream(tpv);
}
throw new System.ArgumentException(field + " in doc #" + docId + "does not have any term position data stored");
}
//convenience method
public static TokenStream GetTokenStream(IndexReader reader, int docId, System.String field, Analyzer analyzer)
{
Document doc = reader.Document(docId);
System.String contents = doc.Get(field);
if (contents == null)
{
throw new System.ArgumentException("Field " + field + " in document #" + docId + " is not stored and cannot be analyzed");
}
return analyzer.TokenStream(field, new System.IO.StringReader(contents));
}
}
}