func (t *TextField) Analyze() (int, analysis.TokenFrequencies) {
var tokens analysis.TokenStream
if t.analyzer != nil {
bytesToAnalyze := t.Value()
if t.options.IsStored() {
// need to copy
bytesCopied := make([]byte, len(bytesToAnalyze))
copy(bytesCopied, bytesToAnalyze)
bytesToAnalyze = bytesCopied
}
tokens = t.analyzer.Analyze(bytesToAnalyze)
} else {
tokens = analysis.TokenStream{
&analysis.Token{
Start: 0,
End: len(t.value),
Term: t.value,
Position: 1,
Type: analysis.AlphaNumeric,
},
}
}
fieldLength := len(tokens) // number of tokens in this doc field
tokenFreqs := analysis.TokenFrequency(tokens, t.arrayPositions, t.options.IncludeTermVectors())
return fieldLength, tokenFreqs
}
func (n *DateTimeField) Analyze() (int, analysis.TokenFrequencies) {
tokens := make(analysis.TokenStream, 0)
tokens = append(tokens, &analysis.Token{
Start: 0,
End: len(n.value),
Term: n.value,
Position: 1,
Type: analysis.DateTime,
})
original, err := n.value.Int64()
if err == nil {
shift := DefaultDateTimePrecisionStep
for shift < 64 {
shiftEncoded, err := numeric_util.NewPrefixCodedInt64(original, shift)
if err != nil {
break
}
token := analysis.Token{
Start: 0,
End: len(shiftEncoded),
Term: shiftEncoded,
Position: 1,
Type: analysis.DateTime,
}
tokens = append(tokens, &token)
shift += DefaultDateTimePrecisionStep
}
}
fieldLength := len(tokens)
tokenFreqs := analysis.TokenFrequency(tokens, n.arrayPositions)
return fieldLength, tokenFreqs
}
func (b *BooleanField) Analyze() (int, analysis.TokenFrequencies) {
tokens := make(analysis.TokenStream, 0)
tokens = append(tokens, &analysis.Token{
Start: 0,
End: len(b.value),
Term: b.value,
Position: 1,
Type: analysis.Boolean,
})
fieldLength := len(tokens)
tokenFreqs := analysis.TokenFrequency(tokens, b.arrayPositions, b.options.IncludeTermVectors())
return fieldLength, tokenFreqs
}
func BenchmarkAnalysis(b *testing.B) {
for i := 0; i < b.N; i++ {
cache := registry.NewCache()
analyzer, err := cache.AnalyzerNamed(standard_analyzer.Name)
if err != nil {
b.Fatal(err)
}
ts := analyzer.Analyze(bleveWikiArticle)
freqs := analysis.TokenFrequency(ts, nil, true)
if len(freqs) != 511 {
b.Errorf("expected %d freqs, got %d", 511, len(freqs))
}
}
}
func (t *TextField) Analyze() (int, analysis.TokenFrequencies) {
var tokens analysis.TokenStream
if t.analyzer != nil {
tokens = t.analyzer.Analyze(t.Value())
} else {
tokens = analysis.TokenStream{
&analysis.Token{
Start: 0,
End: len(t.value),
Term: t.value,
Position: 1,
Type: analysis.AlphaNumeric,
},
}
}
fieldLength := len(tokens) // number of tokens in this doc field
tokenFreqs := analysis.TokenFrequency(tokens)
return fieldLength, tokenFreqs
}
func (udc *SmolderingCouch) Analyze(d *document.Document) *index.AnalysisResult {
rv := &index.AnalysisResult{
DocID: d.ID,
Rows: make([]index.IndexRow, 0, 100),
}
docNumBytes := EncodeUvarintAscending(nil, d.Number)
// track our back index entries
backIndexStoredEntries := make([]*BackIndexStoreEntry, 0)
// information we collate as we merge fields with same name
fieldTermFreqs := make(map[uint16]analysis.TokenFrequencies)
fieldLengths := make(map[uint16]int)
fieldIncludeTermVectors := make(map[uint16]bool)
fieldNames := make(map[uint16]string)
// set the value for the _id field
idBytes := []byte(d.ID)
fieldTermFreqs[0] = analysis.TokenFrequency(analysis.TokenStream{
&analysis.Token{
Term: idBytes,
Position: 1,
Start: 0,
End: len(d.ID),
},
}, nil, false)
// store the _id field as well
f := document.NewTextField("_id", nil, []byte(idBytes))
rv.Rows, backIndexStoredEntries = udc.storeField(docNumBytes, f, 0, rv.Rows, backIndexStoredEntries)
analyzeField := func(field document.Field, storable bool) {
fieldIndex, newFieldRow := udc.fieldIndexOrNewRow(field.Name())
if newFieldRow != nil {
rv.Rows = append(rv.Rows, newFieldRow)
}
fieldNames[fieldIndex] = field.Name()
if field.Options().IsIndexed() {
fieldLength, tokenFreqs := field.Analyze()
existingFreqs := fieldTermFreqs[fieldIndex]
if existingFreqs == nil {
fieldTermFreqs[fieldIndex] = tokenFreqs
} else {
existingFreqs.MergeAll(field.Name(), tokenFreqs)
fieldTermFreqs[fieldIndex] = existingFreqs
}
fieldLengths[fieldIndex] += fieldLength
fieldIncludeTermVectors[fieldIndex] = field.Options().IncludeTermVectors()
}
if storable && field.Options().IsStored() {
rv.Rows, backIndexStoredEntries = udc.storeField(docNumBytes, field, fieldIndex, rv.Rows, backIndexStoredEntries)
}
}
// walk all the fields, record stored fields now
// place information about indexed fields into map
// this collates information across fields with
// same names (arrays)
for _, field := range d.Fields {
analyzeField(field, true)
}
if len(d.CompositeFields) > 0 {
for fieldIndex, tokenFreqs := range fieldTermFreqs {
if fieldIndex == 0 {
// dont add id to any composite field
continue
}
// see if any of the composite fields need this
for _, compositeField := range d.CompositeFields {
compositeField.Compose(fieldNames[fieldIndex], fieldLengths[fieldIndex], tokenFreqs)
}
}
for _, compositeField := range d.CompositeFields {
analyzeField(compositeField, false)
}
}
rowsCapNeeded := len(rv.Rows) + 1
for _, tokenFreqs := range fieldTermFreqs {
rowsCapNeeded += len(tokenFreqs)
}
rv.Rows = append(make([]index.IndexRow, 0, rowsCapNeeded), rv.Rows...)
backIndexTermsEntries := make([]*BackIndexTermsEntry, 0, len(fieldTermFreqs))
// walk through the collated information and process
// once for each indexed field (unique name)
for fieldIndex, tokenFreqs := range fieldTermFreqs {
fieldLength := fieldLengths[fieldIndex]
includeTermVectors := fieldIncludeTermVectors[fieldIndex]
// encode this field
rv.Rows, backIndexTermsEntries = udc.indexField(docNumBytes, includeTermVectors, fieldIndex, fieldLength, tokenFreqs, rv.Rows, backIndexTermsEntries)
}
// build the back index row
backIndexRow := NewBackIndexRow(docNumBytes, backIndexTermsEntries, backIndexStoredEntries)
rv.Rows = append(rv.Rows, backIndexRow)
return rv
}
