Provided by: libkinosearch1-perl_1.01-4build6_amd64 bug

NAME

       KinoSearch1::Docs::FileFormat - overview of invindex file format

OVERVIEW

       It is not necessary to understand the guts of the Lucene-derived "invindex" file format in order to use
       KinoSearch1, but it may be helpful if you are interested in tweaking for high performance, exotic usage,
       or debugging and development.

       On a file system, all the files in an invindex exist in one, flat directory.  Conceptually, the files
       have a hierarchical relationship: an invindex is made up of "segments", each of which is an independent
       inverted index, and each segment is made up of several subsections.

           [invindex]--|
                       |-"segments" file
                       |
                       |-[segments]------|
                                         |--[seg _0]--|
                                         |            |--[postings]
                                         |            |--[stored fields]
                                         |            |--[deletions]
                                         |
                                         |--[seg _1]--|
                                         |            |--[postings]
                                         |            |--[stored fields]
                                         |            |--[deletions]
                                         |
                                         |--[ ... ]---|

       The "segments" file keeps a list of the segments that make up an invindex.  When a new segment is being
       written, KinoSearch1 may put files into the directory, but until the segments file is updated, a Searcher
       reading the index won't know about them.

       Each segment is an independent inverted index.  All the files which belong to a given segment share a
       common prefix which consists of an underscore followed by 1 or more decimal digits: _0, _67, _1058.  A
       fully optimized index has only a single segment.

       In theory there are many files which make up each segment.  However, when you look inside an invindex not
       in the process of being updated, you'll probably see only the segments file and files with either a .cfs
       or .del extension.  The .cfs file, a "compound" file which is consolidated when a segment is finalized,
       "contains" all the other per-segment files.

       Segments are written once, and with the exception of the deletions file, are never modified once written.
       They are deleted when their data is written to new segments during the process of optimization.

A segment's component parts

       Each segment can be said to have four logical parts: postings, stored fields, the deletions file, and the
       term vectors data.

   Stored fields
       The stored fields are organized into two files.

       •   [seg_name].fdx - Field inDeX - pointers to field data

       •   [seg_name].fdt - Field DaTa - the actual stored fields

       When a document turns up as a hit in a search and must be retrieved, KinoSearch1 looks at the Field inDeX
       file to see where in the data file the document's stored fields start, then retrieves all of them from
       the .fdt file in one lump.

           _1.fdx--|
                   |--[doc#0  =>   0]----->_1.fdt--|
                   |                               |--[bodytext]
                   |                               |--[title]
                   |                               |--[url]
                   |--[doc#1  => 305]----->_1.fdt--|             # byte 305
                   |                               |--[bodytext]
                   |                               |--[title]
                   |                               |--[url]
                   |--[...]--------------->_1.fdt--|--[...]

       If a field is marked as "vectorized", its "term vectors" are also stored in the .fdx file.

   Postings
       "Posting" is a technical term from the field of Information Retrieval which refers to an single instance
       of a one term indexing one document.  If you are looking at the index in the back of a book, and you see
       that "freedom" is referenced on pages 8, 86, and 240, that would be three postings, which taken together
       form a "posting list".  The same terminology applies to an index in electronic form.

       The postings data is spread out over 4 main files (not including field normalization data, which we'll
       get to in a moment).  From lowest to highest in the hierarchy, they are...

       [seg_name].prx - PRoXimity data. A list of the positions at which terms appear in any given document.
       The .prx file is just a raw stream of VInts; the document numbers and terms are implicitly indicated by
       files higher up the hierarchy.

       [seg_name].frq - FReQuency data for terms.  If a term has a frequency of 5 in a given document, that
       implies that there will be 5 entries in the .prx file.  The terms themselves are implicitly specified by
       the .tis file.

           _1.frq--|
                   |--[doc#40 => 2]----->_1.prx--|--[54,107]
                   |--[doc#0  => 1]----->_1.prx--|--[6]
                   |--[doc#6  => 1]----->_1.prx--|--[504]
                   |--[doc#36 => 3]----->_1.prx--|--[2,33,747]
                   |--[...]------------->_1.frq--|--[...]

       [seg_name].tis - TermInfoS.  Among the items stored here is the term's doc_freq, which is the number of
       documents the term appears in.  If a term has a doc_freq of 22 in a given collection, that implies that
       there will be 22 corresponding entries in the .frq file.  Terms are ordered lexically, first by field,
       then by term text.

           _1.tis--|
                   |--[...]----------------------->_1.frq--|--[...]
                   |--[bodytext:mule      =>  1]-->_1.frq--|--[doc#40 => 2]
                   |--[bodytext:multitude =>  3]-->_1.frq--|--[doc#0  => 1]
                   |                                       |--[doc#6  => 1]
                   |                                       |--[doc#36 => 3]
                   |--[bodytext:navigate  =>  1]-->_1.frq--|--[doc#21 => 1]
                   |--[...]----------------------->_1.frq--|--[...]
                   |--[title:amendment    => 27]-->_1.frq--|--[doc#21 => 1]
                   |                                       |--[doc#22 => 1]
                   |--[...]----------------------->_1.frq--|--[...]

       [seg_name].tii - TermInfos Index.  This file, which is decompressed and loaded into RAM as soon as the
       IndexReader is initialized, contains a small subset of the .tis data, with pointers to locations in the
       .tis file.  It is used to locate the right general vicinity in the .tis file as quickly as possible.

           _1.tii--|
                   |--[bodytext:a => 20]---------->_1.tis--|--[bodytext:a] # byte 20
                   |                                       |--[bodytext:about]
                   |                                       |--[bodytext:absolute]
                   |                                       |--[...]
                   |--[bodytext:mule => 27065]---->_1.tis--|--[bodytext:mule]
                   |                                       |--[bodytext:multitude]
                   |                                       |--[...]
                   |--[title:amendment => 56992]-->_1.tis--|--[title:amendment]
                                                           |--[...]

       Here's a simplified version of how a search for "freedom" against a given segment plays out:

       1.  The searcher asks the .tii file, "Do you know anything about 'freedom'?"  The .tii file replies,
           "Can't say for sure, but if the .tis file does, 'freedom' is probably somewhere around byte 21008".

       2.  The .tis file tells the searcher "Yes, we have 2 documents which contain 'freedom'.  You'll find them
           in the .frq file starting at byte 66991."

       3.  The .frq file says "document number 40 has 1 'freedom', and document 44 has 8.  If you need to know
           more, like if any 'freedom' is part of the phrase 'freedom of speech', take a look at the .prx file
           starting at..."

       4.  If the searcher is only looking for 'freedom' in isolation, that's where it stops.  It already knows
           enough to assign the documents scores against "freedom", with the 8-freedom document scoring higher
           than the single-freedom document.

   Deletions
       When a document is "deleted" from a segment, it is not actually purged from the postings data and the
       stored fields data right away; it is merely marked as "deleted", via the .del file.  The .del file
       contains a bit vector with one bit for each document in the segment; if bit #254 is set then document 254
       is deleted, and if it turns up in a search it will be masked out.

       It is only when a segment's contents are rewritten to a new segment during the segment-merging process
       that deleted documents truly go away.

   Field Normalization Files
       For the sake of simplicity, the example search scenario above omits the role played the field
       normalization files, or "fieldnorms" for short.  These files have the (theoretical) suffix of .f followed
       by an integer -- .f0, .f1, etc.  Each segment contains one such file for every indexed field.

       By default, the fieldnorms' job is to make sure that a field which is 100 terms long and contains 10
       mentions of the word 'freedom' scores higher than a field which also contains 10 mentions of the word
       'freedom', but is 1000 terms in length.  The idea is that the higher the density of the desired term, the
       more relevant the document.

       The fieldnorms files contain one byte per document per indexed field, and all of them must be loaded into
       RAM before a search can be executed.

Document Numbers

       Document numbers are ephemeral.   They change every time a document gets moved from one segment to a new
       one during optimization.  If you need to assign a primary key to each document, you need to create a
       field and populate it with an externally generated unique identifier.

Not compatible with Java Lucene

       The file format used by KinoSearch1 is closely related to the Lucene compound index format. (The
       technical specification for Lucene's file format is distributed along with Lucene.)  However, indexes
       generated by Lucene and KinoSearch1 are not compatible.

COPYRIGHT

       Copyright 2005-2010 Marvin Humphrey

LICENSE, DISCLAIMER, BUGS, etc.

       See KinoSearch1 version 1.01.