Lucene 1.4索引文件格式-英文版

Lucene 1.4索引文件格式
原文来自如下:http://lucene.apache.org/java/docs/fileformats.html
读过chris翻译的中文版,英文版应该有空认真读阅.
This document defines the index file formats used in Lucene version 1.4 and above.
Apache Lucene is written in Java, but several efforts are underway to writeversions of Lucene in other programming languages. If these versions are to remain compatible with Apache Lucene, then a language-independent definition of the Lucene index format is required. This document thus attempts to provide a complete and independent definition of the Apache Lucene 1.4 file formats.
As Lucene evolves, this document should evolve. Versions of Lucene in different programming languages should endeavor to agree on file formats, and generate new versions of this document.
Compatibility notes are provided in this document, describing how file formats have changed from prior versions.
The fundamental concepts in Lucene are index, document, field and term.
An index contains a sequence of documents.
A document is a sequence of fields.
A field is a named sequence of terms.
A term is a string.
The same string in two different fields is considered a different term. Thus terms are represented as a pair of strings, the first naming the field, and the second naming text within the field.
The index stores statistics about terms in order to make term-based search more efficient. Lucene‘s index falls into the family of indexes known as an inverted index. This is because it can list, for a term, the documents that contain it. This is the inverse of the natural relationship, in which documents list terms.
In Lucene, fields may be stored, in which case their text is stored in the index literally, in a non-inverted manner. Fields that are inverted are called indexed. A field may be both stored and indexed.
The text of a field may be tokenized into terms to be indexed, or the text of a field may be used literally as a term to be indexed. Most fields are tokenized, but sometimes it is useful for certain identifier fields to be indexed literally.
Lucene indexes may be composed of multiple sub-indexes, or segments. Each segment is a fully independent index, which could be searched separately. Indexes evolve by:
Creating new segments for newly added documents.
Merging existing segments.
Searches may involve multiple segments and/or multiple indexes, each index potentially composed of a set of segments.
Internally, Lucene refers to documents by an integer document number. The first document added to an index is numbered zero, and each subsequent document added gets a number one greater than the previous.
Note that a document‘s number may change, so caution should be taken when storing these numbers outside of Lucene. In particular, numbers may change in the following situations:
The numbers stored in each segment are unique only within the segment, and must be converted before they can be used in a larger context. The standard technique is to allocate each segment a range of values, based on the range of numbers used in that segment. To convert a document number from a segment to an external value, the segment‘s base document number is added. To convert an external value back to a segment-specific value, the segment is identified by the range that the external value is in, and the segment‘s base value is subtracted. For example two five document segments might be combined, so that the first segment has a base value of zero, and the second of five. Document three from the second segment would have an external value of eight.
When documents are deleted, gaps are created in the numbering. These are eventually removed as the index evolves through merging. Deleted documents are dropped when segments are merged. A freshly-merged segment thus has no gaps in its numbering.
Each segment index maintains the following:
Field names. This contains the set of field names used in the index.
Stored Field values. This contains, for each document, a list of attribute-value pairs, where the attributes are field names. These are used to store auxiliary information about the document, such as its title, url, or an identifier to access a database. The set of stored fields are what is returned for each hit when searching. This is keyed by document number.
Term dictionary. A dictionary containing all of the terms used in all of the indexed fields of all of the documents. The dictionary also contains the number of documents which contain the term, and pointers to the term‘s frequency and proximity data.
Term Frequency data. For each term in the dictionary, the numbers of all the documents that contain that term, and the frequency of the term in that document.
Term Proximity data. For each term in the dictionary, the positions that the term occurs in each document.
Normalization factors. For each field in each document, a value is stored that is multiplied into the score for hits on that field.
Term Vectors. For each field in each document, the term vector (sometimes called document vector) is stored. A term vector consists of term text and term frequency.
Deleted documents. An optional file indicating which documents are deleted.
Details on each of these are provided in subsequent sections.
All files belonging to a segment have the same name with varying extensions. The extensions correspond to the different file formats described below.
Typically, all segments in an index are stored in a single directory, although this is not required.
The most primitive type is an eight-bit byte. Files are accessed as sequences of bytes. All other data types are defined as sequences of bytes, so file formats are byte-order independent.
32-bit unsigned integers are written as four bytes, high-order bytes first.
UInt32 --> 4
64-bit unsigned integers are written as eight bytes, high-order bytes first.
UInt64 --> 8
A variable-length format for positive integers is defined where the high-order bit of each byte indicates whether more bytes remain to be read. The low-order seven bits are appended as increasingly more significant bits in the resulting integer value. Thus values from zero to 127 may be stored in a single byte, values from 128 to 16,383 may be stored in two bytes, and so on.
VInt Encoding Example
Value
First byte
Second byte
Third byte
0
00000000
1
00000001
2
00000010
...
127
01111111
128
10000000
00000001
129
10000001
00000001
130
10000010
00000001
...
16,383
11111111
01111111
16,384
10000000
10000000
00000001
16,385
10000001
10000000
00000001
...
This provides compression while still being efficient to decode.
Lucene writes unicode character sequences using Java‘s"modified UTF-8 encoding".
Lucene writes strings as a VInt representing the length, followed by the character data.
String --> VInt, Chars
The files in this section exist one-per-index.
The active segments in the index are stored in the segment info file. An index only has a single file in this format, and it is named "segments". This lists each segment by name, and also contains the size of each segment.
Segments --> Format, Version, SegCount, SegCount
Format, SegCount, SegSize --> UInt32
Version --> UInt64
SegName --> String
Format is -1 in Lucene 1.4.
Version counts how often the index has been changed by adding or deleting documents.
SegName is the name of the segment, and is used as the file name prefix for all of the files that compose the segment‘s index.
SegSize is the number of documents contained in the segment index.
Several files are used to indicate that another process is using an index. Note that these files are not stored in the index directory itself, but rather in the system‘s temporary directory, as indicated in the Java system property "java.io.tmpdir".
When a file named "commit.lock" is present, a process is currently re-writing the "segments" file and deleting outdated segment index files, or a process is reading the "segments" file and opening the files of the segments it names. This lock file prevents files from being deleted by another process after a process has read the "segments" file but before it has managed to open all of the files of the segments named therein.
When a file named "write.lock" is present, a process is currently adding documents to an index, or removing files from that index. This lock file prevents several processes from attempting to modify an index at the same time.
A file named "deletable" contains the names of files that are no longer used by the index, but which could not be deleted. This is only used on Win32, where a file may not be deleted while it is still open. On other platforms the file contains only null bytes.
Deletable --> DeletableCount, DeletableCount
DeletableCount --> UInt32
DeletableName --> String
Starting with Lucene 1.4 the compound file format became default. This is simply a container for all files described in the next section.
Compound (.cfs) --> FileCount, FileCount, FileDataFileCount
FileCount --> VInt
DataOffset --> Long
FileName --> String
FileData --> raw file data
The remaining files are all per-segment, and are thus defined by suffix.
Field Info
Field names are stored in the field info file, with suffix .fnm.
FieldInfos (.fnm) --> FieldsCount, FieldsCount
FieldsCount --> VInt
FieldName --> String
FieldBits --> Byte
The low-order bit is one for indexed fields, and zero for non-indexed fields. The second lowest-order bit is one for fields that have term vectors stored, and zero for fields without term vectors.
Fields are numbered by their order in this file. Thus field zero is the first field in the file, field one the next, and so on. Note that, like document numbers, field numbers are segment relative.
Stored Fields
Stored fields are represented by two files:
The field index, or .fdx file.
This contains, for each document, a pointer to its field data, as follows:
FieldIndex (.fdx) --> SegSize
FieldValuesPosition --> Uint64
This is used to find the location within the field data file of the fields of a particular document. Because it contains fixed-length data, this file may be easily randomly accessed. The position of document n‘s field data is the Uint64 at n*8 in this file.
The field data, or .fdt file.
This contains the stored fields of each document, as follows:
FieldData (.fdt) --> SegSize
DocFieldData --> FieldCount, FieldCount
FieldCount --> VInt
FieldNum --> VInt
Lucene <= 1.4:
Bits --> Byte
Value --> String
Only the low-order bit of Bits is used. It is one for tokenized fields, and zero for non-tokenized fields.
Lucene >= 1.9:
Bits --> Byte
low order bit is one for tokenized fields second bit is one for fields containing binary data third bit is one for fields with compression option enabled (if compression is enabled, the algorithm used is ZLIB)
Value --> String | BinaryValue (depending on Bits)
BinaryValue --> ValueSize, ^ValueSize
ValueSize --> VInt
The term dictionary is represented as two files:
The term infos, or tis file.
TermInfoFile (.tis)--> TIVersion, TermCount, IndexInterval, SkipInterval, TermInfos
TIVersion --> UInt32
TermCount --> UInt64
IndexInterval --> UInt32
SkipInterval --> UInt32
TermInfos --> TermCount
TermInfo -->
Term -->
Suffix --> String
PrefixLength, DocFreq, FreqDelta, ProxDelta, SkipDelta
--> VInt
This file is sorted by Term. Terms are ordered first lexicographically by the term‘s field name, and within that lexicographically by the term‘s text.
TIVersion names the version of the format of this file and is -2 in Lucene 1.4.
Term text prefixes are shared. The PrefixLength is the number of initial characters from the previous term which must be pre-pended to a term‘s suffix in order to form the term‘s text. Thus, if the previous term‘s text was "bone" and the term is "boy", the PrefixLength is two and the suffix is "y".
FieldNumber determines the term‘s field, whose name is stored in the .fdt file.
DocFreq is the count of documents which contain the term.
FreqDelta determines the position of this term‘s TermFreqs within the .frq file. In particular, it is the difference between the position of this term‘s data in that file and the position of the previous term‘s data (or zero, for the first term in the file).
ProxDelta determines the position of this term‘s TermPositions within the .prx file. In particular, it is the difference between the position of this term‘s data in that file and the position of the previous term‘s data (or zero, for the first term in the file.
SkipDelta determines the position of this term‘s SkipData within the .frq file. In particular, it is the number of bytes after TermFreqs that the SkipData starts. In other words, it is the length of the TermFreq data.
The term info index, or .tii file.
This contains every IndexIntervalth entry from the .tis file, along with its location in the "tis" file. This is designed to be read entirely into memory and used to provide random access to the "tis" file.
The structure of this file is very similar to the .tis file, with the addition of one item per record, the IndexDelta.
TermInfoIndex (.tii)--> TIVersion, IndexTermCount, IndexInterval, SkipInterval, TermIndices
TIVersion --> UInt32
IndexTermCount --> UInt64
IndexInterval --> UInt32
SkipInterval --> UInt32
TermIndices --> IndexTermCount
IndexDelta --> VLong
IndexDelta determines the position of this term‘s TermInfo within the .tis file. In particular, it is the difference between the position of this term‘s entry in that file and the position of the previous term‘s entry.
TODO: document skipInterval information
The .frq file contains the lists of documents which contain each term, along with the frequency of the term in that document.
FreqFile (.frq) --> TermCount
TermFreqs --> DocFreq
TermFreq --> DocDelta, Freq?
SkipData --> DocFreq/SkipInterval
SkipDatum --> DocSkip,FreqSkip,ProxSkip
DocDelta,Freq,DocSkip,FreqSkip,ProxSkip --> VInt
TermFreqs are ordered by term (the term is implicit, from the .tis file).
TermFreq entries are ordered by increasing document number.
DocDelta determines both the document number and the frequency. In particular, DocDelta/2 is the difference between this document number and the previous document number (or zero when this is the first document in a TermFreqs). When DocDelta is odd, the frequency is one. When DocDelta is even, the frequency is read as another VInt.
For example, the TermFreqs for a term which occurs once in document seven and three times in document eleven would be the following sequence of VInts:
15, 22, 3
DocSkip records the document number before every SkipIntervalth document in TermFreqs. Document numbers are represented as differences from the previous value in the sequence. FreqSkip and ProxSkip record the position of every SkipIntervalth entry in FreqFile and ProxFile, respectively. File positions are relative to the start of TermFreqs and Positions, to the previous SkipDatum in the sequence.
For example, if DocFreq=35 and SkipInterval=16, then there are two SkipData entries, containing the 15th and 31st document numbers in TermFreqs. The first FreqSkip names the number of bytes after the beginning of TermFreqs that the 16th SkipDatum starts, and the second the number of bytes after that that the 32nd starts. The first ProxSkip names the number of bytes after the beginning of Positions that the 16th SkipDatum starts, and the second the number of bytes after that that the 32nd starts.
The .prx file contains the lists of positions that each term occurs at within documents.
ProxFile (.prx) --> TermCount
TermPositions --> DocFreq
Positions --> Freq
PositionDelta --> VInt
TermPositions are ordered by term (the term is implicit, from the .tis file).
Positions entries are ordered by increasing document number (the document number is implicit from the .frq file).
PositionDelta is the difference between the position of the current occurrence in the document and the previous occurrence (or zero, if this is the first occurrence in this document).
For example, the TermPositions for a term which occurs as the fourth term in one document, and as the fifth and ninth term in a subsequent document, would be the following sequence of VInts:
4, 5, 4
There‘s a norm file for each indexed field with a byte for each document. The .f[0-9]* file contains, for each document, a byte that encodes a value that is multiplied into the score for hits on that field:
Norms (.f[0-9]*) --> SegSize
Each byte encodes a floating point value. Bits 0-2 contain the 3-bit mantissa, and bits 3-8 contain the 5-bit exponent.
These are converted to an IEEE single float value as follows:
If the byte is zero, use a zero float.
Otherwise, set the sign bit of the float to zero;
add 48 to the exponent and use this as the float‘s exponent;
map the mantissa to the high-order 3 bits of the float‘s mantissa; and
set the low-order 21 bits of the float‘s mantissa to zero.
The Document Index or .tvx file.
This contains, for each document, a pointer to the document data in the Document (.tvd) file.
DocumentIndex (.tvx) --> TVXVersionNumDocs
TVXVersion --> Int
DocumentPosition --> UInt64
This is used to find the position of the Document in the .tvd file.
The Document or .tvd file.
This contains, for each document, the number of fields, a list of the fields with term vector info and finally a list of pointers to the field information in the .tvf (Term Vector Fields) file.
Document (.tvd) --> TVDVersionNumDocs
TVDVersion --> Int
NumFields --> VInt
FieldNums --> NumFields
FieldNumDelta --> VInt
FieldPositions --> NumFields
FieldPosition --> VLong
The .tvd file is used to map out the fields that have term vectors stored and where the field information is in the .tvf file.
The Field or .tvf file.
This file contains, for each field that has a term vector stored, a list of the terms and their frequencies.
Field (.tvf) --> TVFVersionNumFields
TVFVersion --> Int
NumTerms --> VInt
NumDistinct --> VInt -- Future Use
TermFreqs --> NumTerms
TermText -->
PrefixLength --> VInt
Suffix --> String
TermFreq --> VInt
Term text prefixes are shared. The PrefixLength is the number of initial characters from the previous term which must be pre-pended to a term‘s suffix in order to form the term‘s text. Thus, if the previous term‘s text was "bone" and the term is "boy", the PrefixLength is two and the suffix is "y".
The .del file is optional, and only exists when a segment contains deletions:
Deletions (.del) --> ByteCount,BitCount,Bits
ByteSize,BitCount --> Uint32
Bits --> ByteCount
ByteCount indicates the number of bytes in Bits. It is typically (SegSize/8)+1.
BitCount indicates the number of bits that are currently set in Bits.
Bits contains one bit for each document indexed. When the bit corresponding to a document number is set, that document is marked as deleted. Bit ordering is from least to most significant. Thus, if Bits contains two bytes, 0x00 and 0x02, then document 9 is marked as deleted.
There are a few places where these file formats limit the maximum number of terms and documents to a 32-bit quantity, or to approximately 4 billion. This is not today a problem, but, in the long term, probably will be. These should therefore be replaced with either UInt64 values, or better yet, with VInt values which have no limit.