Implements the Lucene 4.1 index format, with configurable per-field postings formats.
Lucene 4.1 postings format, which encodes postings in packed integer blocks for fast decode.
Concrete class that reads docId(maybe frq,pos,offset,payloads) list with postings format.
Concrete class that writes docId(maybe frq,pos,offset,payloads) list with postings format.
Lucene 4.1 stored fields format.
This document defines the index file formats used in this version of Lucene.
If you are using a different version of Lucene, please consult the copy of
docs/ that was distributed with
the version you are using.
Apache 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 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 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.
The fundamental concepts in Lucene are index, document, field and term.
An index contains a sequence of documents.
The same sequence of bytes in two different fields is considered a different term. Thus terms are represented as a pair: the string naming the field, and the bytes 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.
java docs for more information on Fields.
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:
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:
Segment info. This contains metadata about a segment, such as the number of documents, what files it uses,
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, unless frequencies are omitted (IndexOptions.DOCS_ONLY)
Term Proximity data. For each term in the dictionary, the positions that the term occurs in each document. Note that this will not exist if all fields in all documents omit position data.
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) may be stored. A term vector consists of term text and term frequency. To add Term Vectors to your index see the
Per-document values. Like stored values, these are also keyed by document number, but are generally intended to be loaded into main memory for fast access. Whereas stored values are generally intended for summary results from searches, per-document values are useful for things like scoring factors.
Deleted documents. An optional file indicating which documents are deleted.
Details on each of these are provided in their linked pages.
All files belonging to a segment have the same name with varying extensions. The extensions correspond to the different file formats described below. When using the Compound File format (default in 1.4 and greater) these files (except for the Segment info file, the Lock file, and Deleted documents file) are collapsed into a single .cfs file (see below for details)
Typically, all segments in an index are stored in a single directory, although this is not required.
As of version 2.1 (lock-less commits), file names are never re-used (there is one exception, "segments.gen", see below). That is, when any file is saved to the Directory it is given a never before used filename. This is achieved using a simple generations approach. For example, the first segments file is segments_1, then segments_2, etc. The generation is a sequential long integer represented in alpha-numeric (base 36) form.
The following table summarizes the names and extensions of the files in Lucene:
||segments.gen, segments_N||Stores information about a commit point|
|Lock File||write.lock||The Write lock prevents multiple IndexWriters from writing to the same file.|
||.si||Stores metadata about a segment|
||.cfs, .cfe||An optional "virtual" file consisting of all the other index files for systems that frequently run out of file handles.|
||.fnm||Stores information about the fields|
||.fdx||Contains pointers to field data|
||.fdt||The stored fields for documents|
||.tim||The term dictionary, stores term info|
||.tip||The index into the Term Dictionary|
||.doc||Contains the list of docs which contain each term along with frequency|
||.pos||Stores position information about where a term occurs in the index|
||.pay||Stores additional per-position metadata information such as character offsets and user payloads|
||.nrm.cfs, .nrm.cfe||Encodes length and boost factors for docs and fields|
||.dv.cfs, .dv.cfe||Encodes additional scoring factors or other per-document information.|
||.tvx||Stores offset into the document data file|
||.tvd||Contains information about each document that has term vectors|
||.tvf||The field level info about term vectors|
||.del||Info about what files are deleted|
Compatibility notes are provided in this document, describing how file formats have changed from prior versions:
Codecapi. Fast per-document storage (
DocValues) was introduced. Normalization factors need no longer be a single byte, they can be any DocValues
type. Terms need not be unicode strings, they can be any byte sequence. Term offsets can optionally be indexed into the postings lists. Payloads can be stored in the term vectors.
When referring to term numbers, Lucene's current implementation uses a Java
int to hold the term index, which means the
maximum number of unique terms in any single index segment is ~2.1 billion
times the term index interval (default 128) = ~274 billion. This is technically
not a limitation of the index file format, just of Lucene's current
Similarly, Lucene uses a Java
int to refer to
document numbers, and the index file format uses an
on-disk to store document numbers. This is a limitation
of both the index file format and the current implementation. Eventually these
should be replaced with either
UInt64 values, or
VInt values which have no limit.
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