Examples of org.apache.lucene.document.LongField

org.apache.lucene.document.LongField
Field that indexes long values for efficient range filtering and sorting. Here's an example usage:
```
 document.add(new LongField(name, 6L, Field.Store.NO)); 
```
For optimal performance, re-use the LongField and {@link Document} instance for more than one document:
```
 LongField field = new LongField(name, 0L, Field.Store.NO); Document document = new Document(); document.add(field); for(all documents) { ... field.setLongValue(value) writer.addDocument(document); ... } 
```
See also {@link IntField}, {@link FloatField}, {@link DoubleField}. Any type that can be converted to long can also be indexed. For example, date/time values represented by a {@link java.util.Date} can be translated into a longvalue using the {@link java.util.Date#getTime} method. If youdon't need millisecond precision, you can quantize the value, either by dividing the result of {@link java.util.Date#getTime} or using the separate getters(for year, month, etc.) to construct an int or long value.

To perform range querying or filtering against a LongField, use {@link NumericRangeQuery} or {@link NumericRangeFilter}. To sort according to a LongField, use the normal numeric sort types, eg {@link org.apache.lucene.search.SortField.Type#LONG}. LongField values can also be loaded directly from {@link FieldCache}.

You may add the same field name as an LongField to the same document more than once. Range querying and filtering will be the logical OR of all values; so a range query will hit all documents that have at least one value in the range. However sort behavior is not defined. If you need to sort, you should separately index a single-valued LongField.

A LongField will consume somewhat more disk space in the index than an ordinary single-valued field. However, for a typical index that includes substantial textual content per document, this increase will likely be in the noise.

Within Lucene, each numeric value is indexed as a trie structure, where each term is logically assigned to larger and larger pre-defined brackets (which are simply lower-precision representations of the value). The step size between each successive bracket is called the precisionStep, measured in bits. Smaller precisionStep values result in larger number of brackets, which consumes more disk space in the index but may result in faster range search performance. The default value, 4, was selected for a reasonable tradeoff of disk space consumption versus performance. You can create a custom {@link FieldType} and invoke the {@link FieldType#setNumericPrecisionStep} method if you'dlike to change the value. Note that you must also specify a congruent value when creating {@link NumericRangeQuery} or {@link NumericRangeFilter}. For low cardinality fields larger precision steps are good. If the cardinality is < 100, it is fair to use {@link Integer#MAX_VALUE}, which produces one term per value.
For more information on the internals of numeric trie indexing, including the precisionStep configuration, see {@link NumericRangeQuery}. The format of indexed values is described in {@link NumericUtils}.
If you only need to sort by numeric value, and never run range querying/filtering, you can index using a precisionStep of {@link Integer#MAX_VALUE}. This will minimize disk space consumed.

More advanced users can instead use {@link NumericTokenStream} directly, when indexing numbers. Thisclass is a wrapper around this token stream type for easier, more intuitive usage.
@since 2.9

          sf = new StoredField("nf", i);
          typeAnswer = NumericType.INT;
        } else {
          final long l = random().nextLong();
          answer = Long.valueOf(l);
          nf = new LongField("nf", l, Field.Store.NO);
          sf = new StoredField("nf", l);
          typeAnswer = NumericType.LONG;
        }
      }
      doc.add(nf);

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    final double d = random().nextDouble();


    List<Field> fields = Arrays.asList(
        new Field("bytes", bytes, ft),
        new Field("string", string, ft),
        new LongField("long", l, Store.YES),
        new IntField("int", i, Store.YES),
        new FloatField("float", f, Store.YES),
        new DoubleField("double", d, Store.YES)
    );

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        // resolution
        while (parent != null) {
            doc.add(new IntField(ANCESTOR_IDS.key(), parent.getGeonameID(), Field.Store.YES));
            parent = parent.getParent();
        }
        doc.add(new LongField(POPULATION.key(), geoName.getPopulation(), Field.Store.YES));
        // set up sort field based on population and geographic feature type
        if (geoName.getFeatureClass().equals(FeatureClass.P) || geoName.getFeatureCode().name().startsWith("PCL")) {
            if (geoName.getGeonameID() != 2643741) // todo: temporary hack until GeoNames.org fixes the population for City of London
                // boost cities and countries when sorting results by population
                doc.add(new LongField(SORT_POP.key(), geoName.getPopulation() * 11, Field.Store.YES));
        } else {
            // don't boost anything else, because people rarely talk about other stuff
            // (e.g., Washington State's population is more than 10x that of Washington, DC
            // but Washington, DC is mentioned far more frequently than Washington State)
            doc.add(new LongField(SORT_POP.key(), geoName.getPopulation(), Field.Store.YES));
        }
        doc.add(new IntField(HISTORICAL.key(), IndexField.getBooleanIndexValue(geoName.getFeatureCode().isHistorical()), Field.Store.NO));
        doc.add(new StringField(FEATURE_CODE.key(), geoName.getFeatureCode().name(), Field.Store.NO));


        // create a unique Document for each name of this GeoName

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  @Override
  public Iterable<? extends Field> getFieldsForColumn(String family, Column column) {
    String name = getName(family, column.getName());
    long date = parseDate(column.getValue());
    LongField field = new LongField(name, date, _typeNotStored);
    StoredField storedField = new StoredField(name, column.getValue());
    List<Field> fields = new ArrayList<Field>();
    fields.add(field);
    fields.add(storedField);
    return fields;

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  @Override
  public Iterable<? extends Field> getFieldsForSubColumn(String family, Column column, String subName) {
    String name = getName(family, column.getName(), subName);
    long date = parseDate(column.getValue());
    return makeIterable(new LongField(name, date, _typeNotStored));
  }

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    doc.add(new Field("utf8", "Lu\uD834\uDD1Ece\uD834\uDD60ne \u0000 \u2620 ab\ud917\udc17cd", customType2));
    doc.add(new Field("content2", "here is more content with aaa aaa aaa", customType2));
    doc.add(new Field("fie\u2C77ld", "field with non-ascii name", customType2));
    // add numeric fields, to test if flex preserves encoding
    doc.add(new IntField("trieInt", id, Field.Store.NO));
    doc.add(new LongField("trieLong", (long) id, Field.Store.NO));
    // add docvalues fields
    doc.add(new NumericDocValuesField("dvByte", (byte) id));
    byte bytes[] = new byte[] {
      (byte)(id >>> 24), (byte)(id >>> 16),(byte)(id >>> 8),(byte)id
    };

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      document.add(newTextField("english", English.intToEnglish(i), Field.Store.NO));
      document.add(newTextField("oddeven", (i % 2 == 0) ? "even" : "odd", Field.Store.NO));
      document.add(newStringField("byte", "" + ((byte) random().nextInt()), Field.Store.NO));
      document.add(newStringField("short", "" + ((short) random().nextInt()), Field.Store.NO));
      document.add(new IntField("int", random().nextInt(), Field.Store.NO));
      document.add(new LongField("long", random().nextLong(), Field.Store.NO));


      document.add(new FloatField("float", random().nextFloat(), Field.Store.NO));
      document.add(new DoubleField("double", random().nextDouble(), Field.Store.NO));


      document.add(new NumericDocValuesField("intdocvalues", random().nextInt()));

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        fields.put(TITLE_FIELD, new Field(TITLE_FIELD, "", ft));
        fields.put(DATE_FIELD, new Field(DATE_FIELD, "", ft));
        fields.put(ID_FIELD, new StringField(ID_FIELD, "", Field.Store.YES));
        fields.put(NAME_FIELD, new Field(NAME_FIELD, "", ft));


        numericFields.put(DATE_MSEC_FIELD, new LongField(DATE_MSEC_FIELD, 0L, Field.Store.NO));
        numericFields.put(TIME_SEC_FIELD, new IntField(TIME_SEC_FIELD, 0, Field.Store.NO));
        
        doc = new Document();
      } else {
        numericFields = null;

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        switch(type) {
        case INT:
          f = new IntField(name, 0, Field.Store.NO);
          break;
        case LONG:
          f = new LongField(name, 0L, Field.Store.NO);
          break;
        case FLOAT:
          f = new FloatField(name, 0.0F, Field.Store.NO);
          break;
        case DOUBLE:

View Full Code Here

    Directory dir = newDirectory();
    IndexWriterConfig cfg = newIndexWriterConfig(TEST_VERSION_CURRENT, new MockAnalyzer(random()));
    cfg.setMergePolicy(newLogMergePolicy());
    RandomIndexWriter iw = new RandomIndexWriter(random(), dir, cfg);
    Document doc = new Document();
    LongField field = new LongField("f", 0L, Store.YES);
    doc.add(field);
    final long[] values = new long[_TestUtil.nextInt(random(), 1, 10)];
    for (int i = 0; i < values.length; ++i) {
      final long v;
      switch (random().nextInt(10)) {
        case 0:
          v = Long.MIN_VALUE;
          break;
        case 1:
          v = 0;
          break;
        case 2:
          v = Long.MAX_VALUE;
          break;
        default:
          v = _TestUtil.nextLong(random(), -10, 10);
          break;
      }
      values[i] = v;
      if (v == 0 && random().nextBoolean()) {
        // missing
        iw.addDocument(new Document());
      } else {
        field.setLongValue(v);
        iw.addDocument(doc);
      }
    }
    iw.forceMerge(1);
    final DirectoryReader reader = iw.getReader();

View Full Code Here

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