Example usage for org.apache.lucene.index DirectoryReader open

List of usage examples for org.apache.lucene.index DirectoryReader open

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Introduction

In this page you can find the example usage for org.apache.lucene.index DirectoryReader open.

Prototype

public static DirectoryReader open(final IndexCommit commit) throws IOException

Source Link

Document

Expert: returns an IndexReader reading the index in the given IndexCommit .

Usage

From source file:com.github.tteofili.looseen.MinHashClassifier.java

License:Apache License

@Override
public ClassificationResult<BytesRef> assignClass(String text) throws IOException {
    DirectoryReader reader = DirectoryReader.open(directory);
    IndexSearcher searcher = new IndexSearcher(reader);
    try {/*from   w w w.  j  a va 2s . c  om*/
        int k = 3;
        TopDocs topDocs = searcher.search(buildQuery(TEXT_FIELD, text, min, hashCount, hashSize), k);
        if (topDocs.totalHits > 0) {
            return buildListFromTopDocs(searcher, CLASS_FIELD, topDocs, k).get(0);
            //                Document document = reader.document(topDocs.scoreDocs[0].doc);
            //                String category = document.getField(CLASS_FIELD).stringValue();
            //                return new ClassificationResult<>(new BytesRef(category), topDocs.getMaxScore());
        } else {
            return null;
        }
    } finally {
        reader.close();
    }
}

From source file:com.github.tteofili.looseen.Test20NewsgroupsClassification.java

License:Apache License

@Test
public void test20Newsgroups() throws Exception {

    String indexProperty = System.getProperty("index");
    if (indexProperty != null) {
        try {/*from w w  w .  j a  va2s .c o  m*/
            index = Boolean.valueOf(indexProperty);
        } catch (Exception e) {
            // ignore
        }
    }

    String splitProperty = System.getProperty("split");
    if (splitProperty != null) {
        try {
            split = Boolean.valueOf(splitProperty);
        } catch (Exception e) {
            // ignore
        }
    }

    Path mainIndexPath = Paths.get(INDEX + "/original");
    Directory directory = FSDirectory.open(mainIndexPath);
    Path trainPath = Paths.get(INDEX + "/train");
    Path testPath = Paths.get(INDEX + "/test");
    Path cvPath = Paths.get(INDEX + "/cv");
    FSDirectory cv = null;
    FSDirectory test = null;
    FSDirectory train = null;
    IndexReader testReader = null;
    if (split) {
        cv = FSDirectory.open(cvPath);
        test = FSDirectory.open(testPath);
        train = FSDirectory.open(trainPath);
    }

    if (index) {
        delete(mainIndexPath);
        if (split) {
            delete(trainPath, testPath, cvPath);
        }
    }

    IndexReader reader = null;
    List<Classifier<BytesRef>> classifiers = new LinkedList<>();
    try {
        Analyzer analyzer = new StandardAnalyzer();
        if (index) {

            System.out.format("Indexing 20 Newsgroups...%n");

            long startIndex = System.currentTimeMillis();
            IndexWriter indexWriter = new IndexWriter(directory, new IndexWriterConfig(analyzer));

            buildIndex(new File(PREFIX + "/20n/20_newsgroups"), indexWriter);

            long endIndex = System.currentTimeMillis();
            System.out.format("Indexed %d pages in %ds %n", indexWriter.maxDoc(),
                    (endIndex - startIndex) / 1000);

            indexWriter.close();

        }

        if (split && !index) {
            reader = DirectoryReader.open(train);
        } else {
            reader = DirectoryReader.open(directory);
        }

        if (index && split) {
            // split the index
            System.out.format("Splitting the index...%n");

            long startSplit = System.currentTimeMillis();
            DatasetSplitter datasetSplitter = new DatasetSplitter(0.1, 0);
            datasetSplitter.split(reader, train, test, cv, analyzer, false, CATEGORY_FIELD, BODY_FIELD,
                    SUBJECT_FIELD, CATEGORY_FIELD);
            reader.close();
            reader = DirectoryReader.open(train); // using the train index from now on
            long endSplit = System.currentTimeMillis();
            System.out.format("Splitting done in %ds %n", (endSplit - startSplit) / 1000);
        }

        final long startTime = System.currentTimeMillis();

        classifiers.add(new KNearestNeighborClassifier(reader, new ClassicSimilarity(), analyzer, null, 1, 0, 0,
                CATEGORY_FIELD, BODY_FIELD));
        classifiers.add(new KNearestNeighborClassifier(reader, null, analyzer, null, 1, 0, 0, CATEGORY_FIELD,
                BODY_FIELD));
        classifiers.add(new KNearestNeighborClassifier(reader, new ClassicSimilarity(), analyzer, null, 3, 0, 0,
                CATEGORY_FIELD, BODY_FIELD));
        classifiers.add(new KNearestNeighborClassifier(reader, new AxiomaticF1EXP(), analyzer, null, 3, 0, 0,
                CATEGORY_FIELD, BODY_FIELD));
        classifiers.add(new KNearestNeighborClassifier(reader, new AxiomaticF1LOG(), analyzer, null, 3, 0, 0,
                CATEGORY_FIELD, BODY_FIELD));
        classifiers.add(new KNearestNeighborClassifier(reader, new LMDirichletSimilarity(), analyzer, null, 3,
                1, 1, CATEGORY_FIELD, BODY_FIELD));
        classifiers.add(new KNearestNeighborClassifier(reader, new LMJelinekMercerSimilarity(0.3f), analyzer,
                null, 3, 1, 1, CATEGORY_FIELD, BODY_FIELD));
        classifiers.add(new KNearestNeighborClassifier(reader, null, analyzer, null, 3, 1, 1, CATEGORY_FIELD,
                BODY_FIELD));
        classifiers.add(new KNearestNeighborClassifier(reader,
                new DFRSimilarity(new BasicModelG(), new AfterEffectB(), new NormalizationH1()), analyzer, null,
                3, 1, 1, CATEGORY_FIELD, BODY_FIELD));
        classifiers.add(new KNearestNeighborClassifier(reader,
                new DFRSimilarity(new BasicModelP(), new AfterEffectL(), new NormalizationH3()), analyzer, null,
                3, 1, 1, CATEGORY_FIELD, BODY_FIELD));
        classifiers.add(new KNearestNeighborClassifier(reader,
                new IBSimilarity(new DistributionSPL(), new LambdaDF(), new Normalization.NoNormalization()),
                analyzer, null, 3, 1, 1, CATEGORY_FIELD, BODY_FIELD));
        classifiers.add(new KNearestNeighborClassifier(reader,
                new IBSimilarity(new DistributionLL(), new LambdaTTF(), new NormalizationH1()), analyzer, null,
                3, 1, 1, CATEGORY_FIELD, BODY_FIELD));
        classifiers.add(new MinHashClassifier(reader, BODY_FIELD, CATEGORY_FIELD, 15, 1, 100));
        classifiers.add(new MinHashClassifier(reader, BODY_FIELD, CATEGORY_FIELD, 30, 3, 300));
        classifiers.add(new MinHashClassifier(reader, BODY_FIELD, CATEGORY_FIELD, 10, 1, 100));
        classifiers.add(new KNearestFuzzyClassifier(reader, new LMJelinekMercerSimilarity(0.3f), analyzer, null,
                1, CATEGORY_FIELD, BODY_FIELD));
        classifiers.add(new KNearestFuzzyClassifier(reader,
                new IBSimilarity(new DistributionLL(), new LambdaTTF(), new NormalizationH1()), analyzer, null,
                1, CATEGORY_FIELD, BODY_FIELD));
        classifiers.add(new KNearestFuzzyClassifier(reader, new ClassicSimilarity(), analyzer, null, 1,
                CATEGORY_FIELD, BODY_FIELD));
        classifiers.add(new KNearestFuzzyClassifier(reader, new ClassicSimilarity(), analyzer, null, 3,
                CATEGORY_FIELD, BODY_FIELD));
        classifiers
                .add(new KNearestFuzzyClassifier(reader, null, analyzer, null, 1, CATEGORY_FIELD, BODY_FIELD));
        classifiers
                .add(new KNearestFuzzyClassifier(reader, null, analyzer, null, 3, CATEGORY_FIELD, BODY_FIELD));
        classifiers.add(new KNearestFuzzyClassifier(reader, new AxiomaticF1EXP(), analyzer, null, 3,
                CATEGORY_FIELD, BODY_FIELD));
        classifiers.add(new KNearestFuzzyClassifier(reader, new AxiomaticF1LOG(), analyzer, null, 3,
                CATEGORY_FIELD, BODY_FIELD));
        classifiers.add(new BM25NBClassifier(reader, analyzer, null, CATEGORY_FIELD, BODY_FIELD));
        classifiers.add(new CachingNaiveBayesClassifier(reader, analyzer, null, CATEGORY_FIELD, BODY_FIELD));
        classifiers.add(new SimpleNaiveBayesClassifier(reader, analyzer, null, CATEGORY_FIELD, BODY_FIELD));

        int maxdoc;

        if (split) {
            testReader = DirectoryReader.open(test);
            maxdoc = testReader.maxDoc();
        } else {
            maxdoc = reader.maxDoc();
        }

        System.out.format("Starting evaluation on %d docs...%n", maxdoc);

        ExecutorService service = Executors.newCachedThreadPool();
        List<Future<String>> futures = new LinkedList<>();
        for (Classifier<BytesRef> classifier : classifiers) {
            testClassifier(reader, startTime, testReader, service, futures, classifier);
        }
        for (Future<String> f : futures) {
            System.out.println(f.get());
        }

        Thread.sleep(10000);
        service.shutdown();

    } finally {
        if (reader != null) {
            reader.close();
        }
        directory.close();
        if (test != null) {
            test.close();
        }
        if (train != null) {
            train.close();
        }
        if (cv != null) {
            cv.close();
        }
        if (testReader != null) {
            testReader.close();
        }

        for (Classifier c : classifiers) {
            if (c instanceof Closeable) {
                ((Closeable) c).close();
            }
        }
    }
}

From source file:com.github.tteofili.looseen.TestWikipediaClassification.java

License:Apache License

@Test
public void testItalianWikipedia() throws Exception {

    String indexProperty = System.getProperty("index");
    if (indexProperty != null) {
        try {//  w  w  w  .j  a  v a2s.c o m
            index = Boolean.valueOf(indexProperty);
        } catch (Exception e) {
            // ignore
        }
    }

    String splitProperty = System.getProperty("split");
    if (splitProperty != null) {
        try {
            split = Boolean.valueOf(splitProperty);
        } catch (Exception e) {
            // ignore
        }
    }

    Path mainIndexPath = Paths.get(INDEX + "/original");
    Directory directory = FSDirectory.open(mainIndexPath);
    Path trainPath = Paths.get(INDEX + "/train");
    Path testPath = Paths.get(INDEX + "/test");
    Path cvPath = Paths.get(INDEX + "/cv");
    FSDirectory cv = null;
    FSDirectory test = null;
    FSDirectory train = null;
    DirectoryReader testReader = null;
    if (split) {
        cv = FSDirectory.open(cvPath);
        test = FSDirectory.open(testPath);
        train = FSDirectory.open(trainPath);
    }

    if (index) {
        delete(mainIndexPath);
        if (split) {
            delete(trainPath, testPath, cvPath);
        }
    }

    IndexReader reader = null;
    try {
        Collection<String> stopWordsList = Arrays.asList("di", "a", "da", "in", "per", "tra", "fra", "il", "lo",
                "la", "i", "gli", "le");
        CharArraySet stopWords = new CharArraySet(stopWordsList, true);
        Analyzer analyzer = new ItalianAnalyzer(stopWords);
        if (index) {

            System.out.format("Indexing Italian Wikipedia...%n");

            long startIndex = System.currentTimeMillis();
            IndexWriter indexWriter = new IndexWriter(directory, new IndexWriterConfig(analyzer));

            importWikipedia(new File(PREFIX + "/itwiki/itwiki-20150405-pages-meta-current1.xml"), indexWriter);
            importWikipedia(new File(PREFIX + "/itwiki/itwiki-20150405-pages-meta-current2.xml"), indexWriter);
            importWikipedia(new File(PREFIX + "/itwiki/itwiki-20150405-pages-meta-current3.xml"), indexWriter);
            importWikipedia(new File(PREFIX + "/itwiki/itwiki-20150405-pages-meta-current4.xml"), indexWriter);

            long endIndex = System.currentTimeMillis();
            System.out.format("Indexed %d pages in %ds %n", indexWriter.maxDoc(),
                    (endIndex - startIndex) / 1000);

            indexWriter.close();

        }

        if (split && !index) {
            reader = DirectoryReader.open(train);
        } else {
            reader = DirectoryReader.open(directory);
        }

        if (index && split) {
            // split the index
            System.out.format("Splitting the index...%n");

            long startSplit = System.currentTimeMillis();
            DatasetSplitter datasetSplitter = new DatasetSplitter(0.1, 0);
            for (LeafReaderContext context : reader.leaves()) {
                datasetSplitter.split(context.reader(), train, test, cv, analyzer, false, CATEGORY_FIELD,
                        TEXT_FIELD, CATEGORY_FIELD);
            }
            reader.close();
            reader = DirectoryReader.open(train); // using the train index from now on
            long endSplit = System.currentTimeMillis();
            System.out.format("Splitting done in %ds %n", (endSplit - startSplit) / 1000);
        }

        final long startTime = System.currentTimeMillis();

        List<Classifier<BytesRef>> classifiers = new LinkedList<>();
        classifiers.add(new KNearestNeighborClassifier(reader, new ClassicSimilarity(), analyzer, null, 1, 0, 0,
                CATEGORY_FIELD, TEXT_FIELD));
        classifiers.add(new KNearestNeighborClassifier(reader, new BM25Similarity(), analyzer, null, 1, 0, 0,
                CATEGORY_FIELD, TEXT_FIELD));
        classifiers.add(new KNearestNeighborClassifier(reader, null, analyzer, null, 1, 0, 0, CATEGORY_FIELD,
                TEXT_FIELD));
        classifiers.add(new KNearestNeighborClassifier(reader, new LMDirichletSimilarity(), analyzer, null, 3,
                1, 1, CATEGORY_FIELD, TEXT_FIELD));
        classifiers.add(new KNearestNeighborClassifier(reader, new LMJelinekMercerSimilarity(0.3f), analyzer,
                null, 3, 1, 1, CATEGORY_FIELD, TEXT_FIELD));
        classifiers.add(new KNearestNeighborClassifier(reader, new ClassicSimilarity(), analyzer, null, 3, 0, 0,
                CATEGORY_FIELD, TEXT_FIELD));
        classifiers.add(new KNearestNeighborClassifier(reader, new ClassicSimilarity(), analyzer, null, 3, 1, 1,
                CATEGORY_FIELD, TEXT_FIELD));
        classifiers.add(new KNearestNeighborClassifier(reader,
                new DFRSimilarity(new BasicModelG(), new AfterEffectB(), new NormalizationH1()), analyzer, null,
                3, 1, 1, CATEGORY_FIELD, TEXT_FIELD));
        classifiers.add(new KNearestNeighborClassifier(reader,
                new DFRSimilarity(new BasicModelP(), new AfterEffectL(), new NormalizationH3()), analyzer, null,
                3, 1, 1, CATEGORY_FIELD, TEXT_FIELD));
        classifiers.add(new KNearestNeighborClassifier(reader,
                new IBSimilarity(new DistributionSPL(), new LambdaDF(), new Normalization.NoNormalization()),
                analyzer, null, 3, 1, 1, CATEGORY_FIELD, TEXT_FIELD));
        classifiers.add(new KNearestNeighborClassifier(reader,
                new IBSimilarity(new DistributionLL(), new LambdaTTF(), new NormalizationH1()), analyzer, null,
                3, 1, 1, CATEGORY_FIELD, TEXT_FIELD));
        classifiers.add(new MinHashClassifier(reader, TEXT_FIELD, CATEGORY_FIELD, 5, 1, 100));
        classifiers.add(new MinHashClassifier(reader, TEXT_FIELD, CATEGORY_FIELD, 10, 1, 100));
        classifiers.add(new MinHashClassifier(reader, TEXT_FIELD, CATEGORY_FIELD, 15, 1, 100));
        classifiers.add(new MinHashClassifier(reader, TEXT_FIELD, CATEGORY_FIELD, 15, 3, 100));
        classifiers.add(new MinHashClassifier(reader, TEXT_FIELD, CATEGORY_FIELD, 15, 3, 300));
        classifiers.add(new MinHashClassifier(reader, TEXT_FIELD, CATEGORY_FIELD, 5, 3, 100));
        classifiers.add(new KNearestFuzzyClassifier(reader, new ClassicSimilarity(), analyzer, null, 3,
                CATEGORY_FIELD, TEXT_FIELD));
        classifiers.add(new KNearestFuzzyClassifier(reader, new ClassicSimilarity(), analyzer, null, 1,
                CATEGORY_FIELD, TEXT_FIELD));
        classifiers.add(new KNearestFuzzyClassifier(reader, new BM25Similarity(), analyzer, null, 3,
                CATEGORY_FIELD, TEXT_FIELD));
        classifiers.add(new KNearestFuzzyClassifier(reader, new BM25Similarity(), analyzer, null, 1,
                CATEGORY_FIELD, TEXT_FIELD));
        classifiers.add(new BM25NBClassifier(reader, analyzer, null, CATEGORY_FIELD, TEXT_FIELD));
        classifiers.add(new CachingNaiveBayesClassifier(reader, analyzer, null, CATEGORY_FIELD, TEXT_FIELD));
        classifiers.add(new SimpleNaiveBayesClassifier(reader, analyzer, null, CATEGORY_FIELD, TEXT_FIELD));

        int maxdoc;

        if (split) {
            testReader = DirectoryReader.open(test);
            maxdoc = testReader.maxDoc();
        } else {
            maxdoc = reader.maxDoc();
        }

        System.out.format("Starting evaluation on %d docs...%n", maxdoc);

        ExecutorService service = Executors.newCachedThreadPool();
        List<Future<String>> futures = new LinkedList<>();
        for (Classifier<BytesRef> classifier : classifiers) {

            final IndexReader finalReader = reader;
            final DirectoryReader finalTestReader = testReader;
            futures.add(service.submit(() -> {
                ConfusionMatrixGenerator.ConfusionMatrix confusionMatrix;
                if (split) {
                    confusionMatrix = ConfusionMatrixGenerator.getConfusionMatrix(finalTestReader, classifier,
                            CATEGORY_FIELD, TEXT_FIELD, 60000 * 30);
                } else {
                    confusionMatrix = ConfusionMatrixGenerator.getConfusionMatrix(finalReader, classifier,
                            CATEGORY_FIELD, TEXT_FIELD, 60000 * 30);
                }

                final long endTime = System.currentTimeMillis();
                final int elapse = (int) (endTime - startTime) / 1000;

                return " * " + classifier + " \n    * accuracy = " + confusionMatrix.getAccuracy()
                        + "\n    * precision = " + confusionMatrix.getPrecision() + "\n    * recall = "
                        + confusionMatrix.getRecall() + "\n    * f1-measure = " + confusionMatrix.getF1Measure()
                        + "\n    * avgClassificationTime = " + confusionMatrix.getAvgClassificationTime()
                        + "\n    * time = " + elapse + " (sec)\n ";
            }));

        }
        for (Future<String> f : futures) {
            System.out.println(f.get());
        }

        Thread.sleep(10000);
        service.shutdown();

    } finally {
        try {
            if (reader != null) {
                reader.close();
            }
            if (directory != null) {
                directory.close();
            }
            if (test != null) {
                test.close();
            }
            if (train != null) {
                train.close();
            }
            if (cv != null) {
                cv.close();
            }
            if (testReader != null) {
                testReader.close();
            }
        } catch (Throwable e) {
            e.printStackTrace();
        }
    }
}

From source file:com.github.wxiaoqi.search.lucene.LuceneDao.java

License:Open Source License

public TableResultResponse<IndexObject> page(Integer pageNumber, Integer pageSize, String keyword) {

    IndexReader indexReader = null;/*from   w  w  w  . ja  v a  2s  .  com*/
    TableResultResponse<IndexObject> pageQuery = null;
    List<IndexObject> searchResults = new ArrayList<>();
    try {
        indexReader = DirectoryReader.open(this.getDirectory());
        IndexSearcher indexSearcher = new IndexSearcher(indexReader);
        Query query = QueryUtil.query(keyword, this.getAnalyzer(), "title", "descripton");
        ScoreDoc lastScoreDoc = this.getLastScoreDoc(pageNumber, pageSize, query, indexSearcher);
        /*?documentsearchAfter */
        TopDocs topDocs = indexSearcher.searchAfter(lastScoreDoc, query, pageSize);
        Highlighter highlighter = this.addStringHighlighter(query);
        log.info("??{}", keyword);
        log.info("{}", topDocs.totalHits);
        for (ScoreDoc scoreDoc : topDocs.scoreDocs) {
            int docID = scoreDoc.doc;
            float score = scoreDoc.score;
            Document document = indexSearcher.doc(docID);
            IndexObject indexObject = DocumentUtil.document2IndexObject(this.getAnalyzer(), highlighter,
                    document, score);
            searchResults.add(indexObject);
            log.info("" + score);
        }
        Collections.sort(searchResults);
        pageQuery = new TableResultResponse<>(topDocs.totalHits, searchResults);

    } catch (Exception e) {
        e.printStackTrace();
    } finally {
        try {
            indexReader.close();
        } catch (IOException e) {
            e.printStackTrace();
        }
    }
    return pageQuery;
}

From source file:com.globalsight.ling.lucene.HighFreqTerms.java

License:Apache License

public static void main(String[] args) throws Exception {
    IndexReader reader = null;/* w  ww .  j ava 2s. c  o  m*/
    if (args.length == 1) {
        SimpleFSDirectory fsd = new SimpleFSDirectory(new File(args[0]));
        reader = DirectoryReader.open(fsd);
    } else {
        usage();
        System.exit(1);
    }

    TermInfoQueue tiq = new TermInfoQueue(numTerms);
    //TODO: IS field right?
    String field = IndexDocument.TEXT;
    Terms terms = reader.getTermVector(0, field);
    //TermEnum terms = reader.terms();
    TermsEnum termsEnum = terms.iterator(null);

    BytesRef next = null;

    while ((next = termsEnum.next()) != null) {
        tiq.insertWithOverflow(new TermInfo(new Term(field, termsEnum.term()), termsEnum.docFreq()));
    }

    while (tiq.size() != 0) {
        TermInfo termInfo = (TermInfo) tiq.pop();
        System.out.println(termInfo.term + " " + termInfo.docFreq);
    }

    reader.close();
}

From source file:com.globalsight.ling.tm2.lucene.LuceneCache.java

License:Apache License

/**
 * For one dir searcher//ww w.ja  va2  s . c  o  m
 * 
 * @param path
 */
public static LuceneCache getLuceneCache(File path) throws IOException {
    if (path == null) {
        return null;
    }

    String p = path.getPath();

    if (cache.containsKey(p)) {
        LuceneCache lc = cache.get(p);
        return lc;
    }

    NIOFSDirectory dir = new NIOFSDirectory(path);
    if (dir != null && DirectoryReader.indexExists(dir)) {
        // if (IndexWriter.isLocked(dir))
        // {
        // IndexWriter.unlock(dir);
        // }

        // clean lock
        // dir.clearLock(name);
        IndexReader iR = DirectoryReader.open(dir);
        IndexSearcher iS = new IndexSearcher(iR);
        LuceneCache lc = new LuceneCache(p, iR, iS);

        iR.addReaderClosedListener(lc);
        cache.put(p, lc);

        return lc;
    } else {
        return null;
    }
}

From source file:com.gmail.mosoft521.luceneDemo.SearchFiles.java

License:Apache License

/**
 * Simple command-line based search demo.
 *//* w ww . jav  a  2  s .c o m*/
public static void main(String[] args) throws Exception {
    String usage = "Usage:\tjava org.apache.lucene.demo.SearchFiles [-index dir] [-field f] [-repeat n] [-queries file] [-query string] [-raw] [-paging hitsPerPage]\n\nSee http://lucene.apache.org/core/4_1_0/demo/ for details.";
    if (args.length > 0 && ("-h".equals(args[0]) || "-help".equals(args[0]))) {
        System.out.println(usage);
        System.exit(0);
    }

    String index = "index";
    String field = "contents";
    String queries = null;
    int repeat = 0;
    boolean raw = false;
    String queryString = null;
    int hitsPerPage = 10;

    for (int i = 0; i < args.length; i++) {
        if ("-index".equals(args[i])) {
            index = args[i + 1];
            i++;
        } else if ("-field".equals(args[i])) {
            field = args[i + 1];
            i++;
        } else if ("-queries".equals(args[i])) {
            queries = args[i + 1];
            i++;
        } else if ("-query".equals(args[i])) {
            queryString = args[i + 1];
            i++;
        } else if ("-repeat".equals(args[i])) {
            repeat = Integer.parseInt(args[i + 1]);
            i++;
        } else if ("-raw".equals(args[i])) {
            raw = true;
        } else if ("-paging".equals(args[i])) {
            hitsPerPage = Integer.parseInt(args[i + 1]);
            if (hitsPerPage <= 0) {
                System.err.println("There must be at least 1 hit per page.");
                System.exit(1);
            }
            i++;
        }
    }

    IndexReader reader = DirectoryReader.open(FSDirectory.open(new File(index)));
    IndexSearcher searcher = new IndexSearcher(reader);
    // :Post-Release-Update-Version.LUCENE_XY:
    Analyzer analyzer = new StandardAnalyzer(Version.LUCENE_48);

    BufferedReader in = null;
    if (queries != null) {
        in = new BufferedReader(new InputStreamReader(new FileInputStream(queries), StandardCharsets.UTF_8));
    } else {
        in = new BufferedReader(new InputStreamReader(System.in, StandardCharsets.UTF_8));
    }
    // :Post-Release-Update-Version.LUCENE_XY:
    QueryParser parser = new QueryParser(Version.LUCENE_48, field, analyzer);
    while (true) {
        if (queries == null && queryString == null) { // prompt the user
            System.out.println("Enter query: ");
        }

        String line = queryString != null ? queryString : in.readLine();

        if (line == null || line.length() == -1) {
            break;
        }

        line = line.trim();
        if (line.length() == 0) {
            break;
        }

        Query query = parser.parse(line);
        System.out.println("Searching for: " + query.toString(field));

        if (repeat > 0) { // repeat & time as benchmark
            Date start = new Date();
            for (int i = 0; i < repeat; i++) {
                searcher.search(query, null, 100);
            }
            Date end = new Date();
            System.out.println("Time: " + (end.getTime() - start.getTime()) + "ms");
        }

        doPagingSearch(in, searcher, query, hitsPerPage, raw, queries == null && queryString == null);

        if (queryString != null) {
            break;
        }
    }
    reader.close();
}

From source file:com.google.gerrit.lucene.WrappableSearcherManager.java

License:Apache License

/**
 * Creates and returns a new SearcherManager from the given {@link Directory}.
 * @param dir the directory to open the DirectoryReader on.
 * @param searcherFactory An optional {@link SearcherFactory}. Pass
 *        <code>null</code> if you don't require the searcher to be warmed
 *        before going live or other custom behavior.
 *
 * @throws IOException if there is a low-level I/O error
 *///  w  w w . j  a v a2s  .  c om
public WrappableSearcherManager(Directory dir, SearcherFactory searcherFactory) throws IOException {
    if (searcherFactory == null) {
        searcherFactory = new SearcherFactory();
    }
    this.searcherFactory = searcherFactory;
    current = getSearcher(searcherFactory, DirectoryReader.open(dir));
}

From source file:com.google.gerrit.server.change.ReviewerSuggestionCache.java

License:Apache License

private IndexSearcher index() throws IOException, OrmException {
    RAMDirectory idx = new RAMDirectory();
    IndexWriterConfig config = new IndexWriterConfig(new StandardAnalyzer(CharArraySet.EMPTY_SET));
    config.setOpenMode(OpenMode.CREATE);

    try (IndexWriter writer = new IndexWriter(idx, config)) {
        for (Account a : db.get().accounts().all()) {
            if (a.isActive()) {
                addAccount(writer, a);/*w  ww. jav a2  s  .co  m*/
            }
        }
    }

    return new IndexSearcher(DirectoryReader.open(idx));
}

From source file:com.google.gerrit.server.documentation.QueryDocumentationExecutor.java

License:Apache License

@Inject
public QueryDocumentationExecutor() {
    try {//from  w  w w. ja va  2s .c o  m
        Directory dir = readIndexDirectory();
        if (dir == null) {
            searcher = null;
            parser = null;
            return;
        }
        IndexReader reader = DirectoryReader.open(dir);
        searcher = new IndexSearcher(reader);
        parser = new QueryParser(Constants.DOC_FIELD, new StandardAnalyzer());
    } catch (IOException e) {
        log.error("Cannot initialize documentation full text index", e);
        searcher = null;
        parser = null;
    }
}