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bleve/index/upsidedown/analysis.go

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// Copyright (c) 2015 Couchbase, Inc.
2016-10-02 16:13:14 +02:00
//
// Licensed 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.
package upsidedown
import (
"github.com/blevesearch/bleve/analysis"
"github.com/blevesearch/bleve/document"
"github.com/blevesearch/bleve/index"
)
func (udc *UpsideDownCouch) Analyze(d *document.Document) *index.AnalysisResult {
rv := &index.AnalysisResult{
DocID: d.ID,
Rows: make([]index.IndexRow, 0, 100),
}
docIDBytes := []byte(d.ID)
// 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)
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(docIDBytes, 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 {
// 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...)
INDEX FORMAT CHANGE: change back index row value Previously term entries were encoded pairwise (field/term), so you'd have data like: F1/T1 F1/T2 F1/T3 F2/T4 F3/T5 As you can see, even though field 1 has 3 terms, we repeat the F1 part in the encoded data. This is a bit wasteful. In the new format we encode it as a list of terms for each field: F1/T1,T2,T3 F2/T4 F3/T5 When fields have multiple terms, this saves space. In unit tests there is no additional waste even in the case that a field has only a single value. Here are the results of an indexing test case (beer-search): $ benchcmp indexing-before.txt indexing-after.txt benchmark old ns/op new ns/op delta BenchmarkIndexing-4 11275835988 10745514321 -4.70% benchmark old allocs new allocs delta BenchmarkIndexing-4 25230685 22480494 -10.90% benchmark old bytes new bytes delta BenchmarkIndexing-4 4802816224 4741641856 -1.27% And here are the results of a MatchAll search building a facet on the "abv" field: $ benchcmp facet-before.txt facet-after.txt benchmark old ns/op new ns/op delta BenchmarkFacets-4 439762100 228064575 -48.14% benchmark old allocs new allocs delta BenchmarkFacets-4 9460208 3723286 -60.64% benchmark old bytes new bytes delta BenchmarkFacets-4 260784261 151746483 -41.81% Although we expect the index to be smaller in many cases, the beer-search index is about the same in this case. However, this may be due to the underlying storage (boltdb) in this case. Finally, the index version was bumped from 5 to 7, since smolder also used version 6, which could lead to some confusion.
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backIndexTermsEntries := make([]*BackIndexTermsEntry, 0, len(fieldTermFreqs))
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// 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
INDEX FORMAT CHANGE: change back index row value Previously term entries were encoded pairwise (field/term), so you'd have data like: F1/T1 F1/T2 F1/T3 F2/T4 F3/T5 As you can see, even though field 1 has 3 terms, we repeat the F1 part in the encoded data. This is a bit wasteful. In the new format we encode it as a list of terms for each field: F1/T1,T2,T3 F2/T4 F3/T5 When fields have multiple terms, this saves space. In unit tests there is no additional waste even in the case that a field has only a single value. Here are the results of an indexing test case (beer-search): $ benchcmp indexing-before.txt indexing-after.txt benchmark old ns/op new ns/op delta BenchmarkIndexing-4 11275835988 10745514321 -4.70% benchmark old allocs new allocs delta BenchmarkIndexing-4 25230685 22480494 -10.90% benchmark old bytes new bytes delta BenchmarkIndexing-4 4802816224 4741641856 -1.27% And here are the results of a MatchAll search building a facet on the "abv" field: $ benchcmp facet-before.txt facet-after.txt benchmark old ns/op new ns/op delta BenchmarkFacets-4 439762100 228064575 -48.14% benchmark old allocs new allocs delta BenchmarkFacets-4 9460208 3723286 -60.64% benchmark old bytes new bytes delta BenchmarkFacets-4 260784261 151746483 -41.81% Although we expect the index to be smaller in many cases, the beer-search index is about the same in this case. However, this may be due to the underlying storage (boltdb) in this case. Finally, the index version was bumped from 5 to 7, since smolder also used version 6, which could lead to some confusion.
2017-01-24 21:33:54 +01:00
rv.Rows, backIndexTermsEntries = udc.indexField(docIDBytes, includeTermVectors, fieldIndex, fieldLength, tokenFreqs, rv.Rows, backIndexTermsEntries)
}
// build the back index row
INDEX FORMAT CHANGE: change back index row value Previously term entries were encoded pairwise (field/term), so you'd have data like: F1/T1 F1/T2 F1/T3 F2/T4 F3/T5 As you can see, even though field 1 has 3 terms, we repeat the F1 part in the encoded data. This is a bit wasteful. In the new format we encode it as a list of terms for each field: F1/T1,T2,T3 F2/T4 F3/T5 When fields have multiple terms, this saves space. In unit tests there is no additional waste even in the case that a field has only a single value. Here are the results of an indexing test case (beer-search): $ benchcmp indexing-before.txt indexing-after.txt benchmark old ns/op new ns/op delta BenchmarkIndexing-4 11275835988 10745514321 -4.70% benchmark old allocs new allocs delta BenchmarkIndexing-4 25230685 22480494 -10.90% benchmark old bytes new bytes delta BenchmarkIndexing-4 4802816224 4741641856 -1.27% And here are the results of a MatchAll search building a facet on the "abv" field: $ benchcmp facet-before.txt facet-after.txt benchmark old ns/op new ns/op delta BenchmarkFacets-4 439762100 228064575 -48.14% benchmark old allocs new allocs delta BenchmarkFacets-4 9460208 3723286 -60.64% benchmark old bytes new bytes delta BenchmarkFacets-4 260784261 151746483 -41.81% Although we expect the index to be smaller in many cases, the beer-search index is about the same in this case. However, this may be due to the underlying storage (boltdb) in this case. Finally, the index version was bumped from 5 to 7, since smolder also used version 6, which could lead to some confusion.
2017-01-24 21:33:54 +01:00
backIndexRow := NewBackIndexRow(docIDBytes, backIndexTermsEntries, backIndexStoredEntries)
rv.Rows = append(rv.Rows, backIndexRow)
return rv
}