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bleve/index/upsidedown/analysis.go
Marty Schoch 606fd6344b 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 15:39:38 -05:00

111 lines
3.6 KiB
Go

// Copyright (c) 2015 Couchbase, Inc.
//
// 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...)
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(docIDBytes, includeTermVectors, fieldIndex, fieldLength, tokenFreqs, rv.Rows, backIndexTermsEntries)
}
// build the back index row
backIndexRow := NewBackIndexRow(docIDBytes, backIndexTermsEntries, backIndexStoredEntries)
rv.Rows = append(rv.Rows, backIndexRow)
return rv
}