This document defines the index file formats used in Lucene version 1.3.

Jakarta Lucene is written in Java, but several efforts are underway to write versions of Lucene in other programming languages. If these versions are to remain compatible with Jakarta 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 Jakarta Lucene 1.3 file formats.

As Lucene's 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.

Inverted Indexing

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.

Types of Fields

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.

Segments

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.

Document Numbers

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.

• 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.

Byte

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.

UInt32

32-bit unsigned integers are written as four bytes, high-order bytes first. UInt32 --> <Byte>4

Uint64

64-bit unsigned integers are written as eight bytes, high-order bytes first. UInt64 --> <Byte>8

VInt

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.

Chars

Lucene writes unicode character sequences using the standard UTF-8 encoding.

String

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.

Segments File

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 --> SegCount, <SegName, SegSize>SegCount SegCount, SegSize --> UInt32 SegName --> String 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.

Lock Files

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.

Deletable File

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 generated on Win32, where a file may not be deleted while it is still open. Deletable --> DeletableCount, <DelableName>DeletableCount DeletableCount --> UInt32 DeletableName --> String

The remaining files are all per-segment, and are thus defined by suffix.

Fields

Field Info Field names are stored in the field info file, with suffix .fnm. FieldInfos (.fnm) --> FieldsCount, <FieldName, FieldBits>FieldsCount FieldsCount --> VInt FieldName --> String FieldBits --> Byte Currently only the low-order bit is used of FieldBits is used. It is one for indexed fields, and zero for non-indexed fields. 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) --> <FieldValuesPosition>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) --> <DocFieldData>SegSize DocFieldData --> FieldCount, <FieldNum, Bits, Value>FieldCount Count --> VInt FieldNum --> VInt Bits --> Byte Value --> String Currently only the low-order bit is used of Bits is used. It is one for tokenized fields, and zero for non-tokenized fields.

Term Dictionary

The term dictionary is represented as two files: The term infos, or tis file. TermInfoFile (.tis)--> TermCount, TermInfos TermCount --> UInt32 TermInfos --> <TermInfo>TermCount TermInfo --> <Term, DocFreq, FreqDelta, ProxDelta> Term --> <PrefixLength, Suffix, FieldNum> Suffix --> String PrefixLength, DocFreq, FreqDelta, ProxDelta --> 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. 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. The term info index, or .tii file. This contains every 128th 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)--> IndexTermCount, TermIndices IndexTermCount --> UInt32 TermIndices --> <TermInfo, IndexDelta>IndexTermCount IndexDelta --> VInt IndexDelta determines the position of this term's TermInfo 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 (or zero for the first term in the file).

Frequencies

The .frq file contains the lists of documents which contain each term, along with the frequency of the term in that document. FreqFile (.frq) --> <TermFreqs>TermCount TermFreqs --> <TermFreq>DocFreq TermFreq --> DocDelta, Freq? DocDelta,Freq --> 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

Positions

The .prx file contains the lists of positions that each term occurs at within documents. ProxFile (.prx) --> <TermPositions>TermCount TermPositions --> <Positions>DocFreq Positions --> <PositionDelta>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

Normalization Factors

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]*) --> <Byte>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.

Deleted Documents

The .del file is optional, and only exists when a segment contains deletions: Deletions (.del) --> ByteCount,BitCount,Bits ByteSize,BitCount --> Uint32 Bits --> <Byte>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.

There are only two places where the code requires that a value be fixed size. These are:

1. The FieldValuesPosition (in the stored field index file, .fdx). This already uses a UInt64, and so is not a problem.

2. The TermCount (in the term info file, .tis). This is written last but is read when the file is first opened, and so is stored at the front. The indexing code first writes an zero here, then overwrites it after the rest of the file has been written. So unless this is stored elsewhere, it must be fixed size and should be changed to a UInt64.

Other than these, all UInt values could be converted to VInt to remove limitations.