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dpavlin |
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# -*- Mode: Perl -*- |
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# $Basename: InvertedIndex.pm $ |
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# $Revision: 1.1 $ |
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# Author : Ulrich Pfeifer |
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# Created On : Thu Aug 8 13:05:10 1996 |
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# Last Modified By: Ulrich Pfeifer |
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# Last Modified On: Sat Nov 11 14:36:39 2000 |
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# Language : CPerl |
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# |
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# (C) Copyright 1996-2000, Ulrich Pfeifer |
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# |
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package WAIT::InvertedIndexOld; |
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use strict; |
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use DB_File; |
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use Fcntl; |
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use WAIT::Filter; |
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use Carp; |
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use vars qw(%FUNC); |
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my $O = pack('C', 0xff)."o"; # occurances (document ferquency) |
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# The document frequency is the number of documents a term occurs |
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# in. The idea is that a term occuring in a significant part of the |
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# documents is not too significant. |
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my $M = pack('C', 0xff)."m"; # maxtf (term frequency) |
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# The maximum term frequency of a document is the frequency of the |
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# most frequent term in the document. It is related to the document |
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# length obviously. A document in which the most frequnet term occurs |
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# 100 times is probably much longer than a document whichs most |
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# frequent term occurs five time. |
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sub new { |
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my $type = shift; |
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my %parm = @_; |
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my $self = {}; |
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$self->{file} = $parm{file} or croak "No file specified"; |
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$self->{attr} = $parm{attr} or croak "No attributes specified"; |
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$self->{filter} = $parm{filter}; |
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$self->{'name'} = $parm{'name'}; |
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$self->{records} = 0; |
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for (qw(intervall prefix)) { |
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if (exists $parm{$_}) { |
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if (ref $parm{$_}) { |
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$self->{$_} = [@{$parm{$_}}] # clone |
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} else { |
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$self->{$_} = $parm{$_} |
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} |
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} |
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} |
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bless $self, ref($type) || $type; |
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} |
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sub name {$_[0]->{'name'}} |
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sub _split_pos { |
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my ($text, $pos) = @{$_[0]}; |
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my @result; |
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$text =~ s/(^\s+)// and $pos += length($1); |
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while ($text =~ s/(^\S+)//) { |
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my $word = $1; |
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push @result, [$word, $pos]; |
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$pos += length($word); |
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$text =~ s/(^\s+)// and $pos += length($1); |
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} |
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@result; |
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} |
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sub _xfiltergen { |
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my $filter = pop @_; |
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# Oops, we cannot overrule the user's choice. Other filters may kill |
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# stopwords, such as isotr clobbers "isn't" to "isnt". |
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# if ($filter eq 'stop') { # avoid the slow stopword elimination |
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# return _xfiltergen(@_); # it's cheaper to look them up afterwards |
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# } |
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if (@_) { |
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if ($filter =~ /^split(\d*)/) { |
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if ($1) { |
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"grep(length(\$_->[0])>=$1, map(&WAIT::Filter::split_pos(\$_), " . _xfiltergen(@_) .'))' ; |
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} else { |
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"map(&WAIT::Filter::split_pos(\$_), " . _xfiltergen(@_) .')' ; |
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} |
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} else { |
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"map ([&WAIT::Filter::$filter(\$_->[0]), \$_->[1]]," ._xfiltergen(@_) .')'; |
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} |
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} else { |
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if ($filter =~ /^split(\d*)/) { |
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if ($1) { |
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"grep(length(\$_->[0])>=$1, map(&WAIT::Filter::split_pos(\$_), [\$_[0], 0]))" ; |
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} else { |
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"map(&WAIT::Filter::split_pos(\$_), [\$_[0], 0])" ; |
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} |
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} else { |
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"map ([&WAIT::Filter::$filter(\$_->[0]), \$_->[1]], [\$_[0], 0])"; |
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} |
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} |
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} |
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sub parse_pos { |
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my $self = shift; |
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unless (exists $self->{xfunc}) { |
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$self->{xfunc} = |
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eval sprintf("sub {%s}", _xfiltergen(@{$self->{filter}})); |
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#printf "\nsub{%s}$@\n", _xfiltergen(@{$self->{filter}}); |
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} |
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&{$self->{xfunc}}($_[0]); |
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} |
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sub _filtergen { |
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my $filter = pop @_; |
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if (@_) { |
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"map(&WAIT::Filter::$filter(\$_), " . _filtergen(@_) . ')'; |
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} else { |
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"map(&WAIT::Filter::$filter(\$_), \@_)"; |
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} |
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} |
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sub drop { |
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my $self = shift; |
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if ((caller)[0] eq 'WAIT::Table') { # Table knows about this |
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my $file = $self->{file}; |
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! (!-e $file or unlink $file); |
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} else { # notify our database |
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croak ref($self)."::drop called directly"; |
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} |
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} |
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sub open { |
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my $self = shift; |
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my $file = $self->{file}; |
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if (defined $self->{dbh}) { |
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$self->{dbh}; |
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} else { |
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$self->{func} = |
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eval sprintf("sub {grep /./, %s}", _filtergen(@{$self->{filter}})); |
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$self->{dbh} = tie(%{$self->{db}}, 'DB_File', $file, |
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$self->{mode}, 0664, $DB_BTREE); |
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$self->{cache} = {} |
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if $self->{mode} & O_RDWR; |
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$self->{cdict} = {} |
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if $self->{mode} & O_RDWR; |
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$self->{cached} = 0; |
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} |
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} |
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sub insert { |
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my $self = shift; |
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my $key = shift; |
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my %occ; |
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defined $self->{db} or $self->open; |
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grep $occ{$_}++, &{$self->{func}}(@_); |
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my ($word, $noc); |
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$self->{records}++; |
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while (($word, $noc) = each %occ) { |
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if (defined $self->{cache}->{$word}) { |
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$self->{cdict}->{$O,$word}++; |
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$self->{cache}->{$word} .= pack 'w2', $key, $noc; |
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} else { |
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$self->{cdict}->{$O,$word} = 1; |
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$self->{cache}->{$word} = pack 'w2', $key, $noc; |
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} |
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$self->{cached}++; |
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} |
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# This cache limit should be configurable |
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$self->sync if $self->{cached} > 100_000; |
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my $maxtf = 0; |
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for (values %occ) { |
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$maxtf = $_ if $_ > $maxtf; |
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} |
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$self->{db}->{$M, $key} = $maxtf; |
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} |
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# We sort postings by increasing max term frequency (~ by increasing |
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# document length. This reduces the quality degradation if we process |
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# only the first part of a posting list. |
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sub sort_postings { |
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my $self = shift; |
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my $post = shift; # reference to a hash or packed string |
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if (ref $post) { |
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# we skip the sort part, if the index is not sorted |
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return pack('w*', %$post) unless $self->{reorg}; |
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} else { |
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$post = { unpack 'w*', $post }; |
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} |
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my $r = ''; |
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# Sort posting list by increasing ratio of maximum term frequency (~ |
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# "document length") and term frequency. This rati multipied by the |
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# inverse document frequence gives the score for a term. This sort |
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# order can be exploited for tuning of single term queries. |
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for my $did (sort { $post->{$b} / $self->{db}->{$M, $b} |
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<=> |
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$post->{$a} / $self->{db}->{$M, $a} |
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} keys %$post) { |
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$r .= pack 'w2', $did, $post->{$did}; |
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} |
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#warn sprintf "reorg %d %s\n", scalar keys %$post, join ' ', unpack 'w*', $r; |
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$r; |
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} |
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sub delete { |
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my $self = shift; |
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my $key = shift; |
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my %occ; |
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my $db; |
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defined $self->{db} or $self->open; |
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$db = $self->{db}; |
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$self->sync; |
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$self->{records}--; |
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# less than zero documents in database? |
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_complain('delete of document', $key) and $self->{records} = 0 |
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if $self->{records} < 0; |
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grep $occ{$_}++, &{$self->{func}}(@_); |
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for (keys %occ) {# may reorder posting list |
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my %post = unpack 'w*', $db->{$_}; |
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delete $post{$key}; |
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$db->{$_} = $self->sort_postings(\%post); |
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_complain('delete of term', $_) if $db->{$O,$_}-1 != keys %post; |
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$db->{$O,$_} = scalar keys %post; |
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} |
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delete $db->{$M, $key}; |
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} |
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sub intervall { |
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my ($self, $first, $last) = @_; |
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my $value = ''; |
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my $word = ''; |
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my @result; |
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return unless exists $self->{'intervall'}; |
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defined $self->{db} or $self->open; |
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$self->sync; |
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my $dbh = $self->{dbh}; # for convenience |
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if (ref $self->{'intervall'}) { |
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unless (exists $self->{'ifunc'}) { |
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$self->{'ifunc'} = |
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eval sprintf("sub {grep /./, %s}", _filtergen(@{$self->{intervall}})); |
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} |
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($first) = &{$self->{'ifunc'}}($first) if $first; |
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($last) = &{$self->{'ifunc'}}($last) if $last; |
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} |
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if (defined $first and $first ne '') { # set the cursor to $first |
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$dbh->seq($first, $value, R_CURSOR); |
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} else { |
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$dbh->seq($first, $value, R_FIRST); |
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} |
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# We assume that word do not start with the character \377 |
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# $last = pack 'C', 0xff unless defined $last and $last ne ''; |
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return () if defined $last and $first gt $last; # $first would be after the last word |
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push @result, $first; |
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while (!$dbh->seq($word, $value, R_NEXT)) { |
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# We should limit this to a "resonable" number of words |
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last if (defined $last and $word gt $last) or $word =~ /^($M|$O)/o; |
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push @result, $word; |
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} |
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\@result; # speed |
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} |
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sub prefix { |
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my ($self, $prefix) = @_; |
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my $value = ''; |
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my $word = ''; |
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my @result; |
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return () unless defined $prefix; # Full dictionary requested !! |
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return unless exists $self->{'prefix'}; |
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defined $self->{db} or $self->open; |
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$self->sync; |
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my $dbh = $self->{dbh}; |
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if (ref $self->{'prefix'}) { |
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unless (exists $self->{'pfunc'}) { |
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$self->{'pfunc'} = |
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eval sprintf("sub {grep /./, %s}", _filtergen(@{$self->{prefix}})); |
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} |
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($prefix) = &{$self->{'pfunc'}}($prefix); |
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} |
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if ($dbh->seq($word = $prefix, $value, R_CURSOR)) { |
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return (); |
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} |
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return () if $word !~ /^$prefix/; |
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push @result, $word; |
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while (!$dbh->seq($word, $value, R_NEXT)) { |
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# We should limit this to a "resonable" number of words |
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last if $word !~ /^$prefix/; |
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push @result, $word; |
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} |
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\@result; # speed |
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} |
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=head2 search($query) |
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The search method supports a range of search algorithms. It is |
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recommended to tune the index by calling |
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C<$table-E<gt>set(top=E<gt>1)> B<after> bulk inserting the documents |
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into the table. This is a computing intense operation and all inserts |
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and deletes after this optimization are slightly more expensive. Once |
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reorganized, the index is kept sorted automatically until you switch |
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the optimization off by calling C<$table-E<gt>set(top=E<gt>0)>. |
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When searching a tuned index, a query can be processed faster if the |
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caller requests only the topmost documents. This can be done by |
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passing a C<top =E<gt>> I<n> parameter to the search method. |
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For single term queries, the method returns only the I<n> top ranking |
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documents. For multi term queries two optimized algorithms are |
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available. The first algorithm computes the top n documents |
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approximately but very fast, sacrificing a little bit of precision for |
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speed. The second algorithm computes the topmost I<n> documents |
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precisely. This algorithm is slower and should be used only for small |
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values of I<n>. It can be requested by passing the query attribute |
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C<picky =E<gt> 1>. Both algorithms may return more than I<n> hits. |
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While the picky version might not be faster than the brute force |
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version on average for modest size databases it uses less memory and |
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the processing time is almost linear in the number of query terms, not |
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in the size of the lists. |
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=cut |
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sub search { |
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my $self = shift; |
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my $query = shift; |
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defined $self->{db} or $self->open; |
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$self->sync; |
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$self->search_raw($query, &{$self->{func}}(@_)); # No call to parse() here |
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} |
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sub parse { |
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my $self = shift; |
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defined $self->{db} or $self->open; |
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&{$self->{func}}(@_); |
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} |
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sub keys { |
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my $self = shift; |
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defined $self->{db} or $self->open; |
| 364 |
|
|
keys %{$self->{db}}; |
| 365 |
|
|
} |
| 366 |
|
|
|
| 367 |
|
|
sub search_prefix { |
| 368 |
|
|
my $self = shift; |
| 369 |
|
|
|
| 370 |
|
|
# print "search_prefix(@_)\n"; |
| 371 |
|
|
defined $self->{db} or $self->open; |
| 372 |
|
|
$self->search_raw(map($self->prefix($_), @_)); |
| 373 |
|
|
} |
| 374 |
|
|
|
| 375 |
|
|
sub _complain ($$) { |
| 376 |
|
|
my ($action, $term) = @_; |
| 377 |
|
|
|
| 378 |
|
|
require Carp; |
| 379 |
|
|
Carp::cluck |
| 380 |
|
|
(sprintf("WAIT database inconsistency during $action [%s]: ". |
| 381 |
|
|
"Please rebuild index\n", |
| 382 |
|
|
$term,)); |
| 383 |
|
|
} |
| 384 |
|
|
|
| 385 |
|
|
sub search_raw { |
| 386 |
|
|
my $self = shift; |
| 387 |
|
|
my $query = shift; |
| 388 |
|
|
my %score; |
| 389 |
|
|
|
| 390 |
|
|
# Top $wanted documents must be correct. Zero means all matching |
| 391 |
|
|
# documents. |
| 392 |
|
|
my $wanted = $query->{top}; |
| 393 |
|
|
my $strict = $query->{picky}; |
| 394 |
|
|
|
| 395 |
|
|
# Return at least $minacc documents. Zero means all matching |
| 396 |
|
|
# documents. |
| 397 |
|
|
# my $minacc = $query->{accus} || $wanted; |
| 398 |
|
|
|
| 399 |
|
|
# Open index and flush cache if necessary |
| 400 |
|
|
defined $self->{db} or $self->open; |
| 401 |
|
|
$self->sync; |
| 402 |
|
|
|
| 403 |
|
|
# We keep duplicates |
| 404 |
|
|
my @terms = |
| 405 |
|
|
# Sort words by decreasing document frequency |
| 406 |
|
|
sort { $self->{db}->{$O,$a} <=> $self->{db}->{$O,$b} } |
| 407 |
|
|
# check which words occur in the index. |
| 408 |
|
|
grep { $self->{db}->{$O,$_} } @_; |
| 409 |
|
|
|
| 410 |
|
|
return () unless @terms; # nothing to search for |
| 411 |
|
|
|
| 412 |
|
|
# We special-case one term queries here. If the index was sorted, |
| 413 |
|
|
# choping off the rest of the list will return the same ranking. |
| 414 |
|
|
if ($wanted and @terms == 1) { |
| 415 |
|
|
my $term = shift @terms; |
| 416 |
|
|
my $idf = log($self->{records}/$self->{db}->{$O,$term}); |
| 417 |
|
|
my @res; |
| 418 |
|
|
|
| 419 |
|
|
if ($self->{reorg}) { # or not $query->{picky} |
| 420 |
|
|
@res = unpack "w". int(2*$wanted), $self->{db}->{$term}; |
| 421 |
|
|
} else { |
| 422 |
|
|
@res = unpack 'w*', $self->{db}->{$term}; |
| 423 |
|
|
} |
| 424 |
|
|
|
| 425 |
|
|
for (my $i=1; $i<@res; $i+=2) { |
| 426 |
|
|
$res[$i] /= $self->{db}->{$M, $res[$i-1]} / $idf; |
| 427 |
|
|
} |
| 428 |
|
|
|
| 429 |
|
|
return @res |
| 430 |
|
|
} |
| 431 |
|
|
|
| 432 |
|
|
# We separate exhaustive search here to avoid overhead and make the |
| 433 |
|
|
# code more readable. The block can be removed without changing the |
| 434 |
|
|
# result. |
| 435 |
|
|
unless ($wanted) { |
| 436 |
|
|
for (@terms) { |
| 437 |
|
|
my $df = $self->{db}->{$O,$_}; |
| 438 |
|
|
|
| 439 |
|
|
# The frequency *must* be 1 at least since the posting list is nonempty |
| 440 |
|
|
_complain('search for term', $_) and $df = 1 if $df < 1; |
| 441 |
|
|
|
| 442 |
|
|
# Unpack posting list for current query term $_ |
| 443 |
|
|
my %post = unpack 'w*', $self->{db}->{$_}; |
| 444 |
|
|
|
| 445 |
|
|
_complain('search for term', $_) if $self->{db}->{$O,$_} != keys %post; |
| 446 |
|
|
# This is the inverse document frequency. The log of the inverse |
| 447 |
|
|
# fraction of documents the term occurs in. |
| 448 |
|
|
my $idf = log($self->{records}/$df); |
| 449 |
|
|
for my $did (keys %post) { |
| 450 |
|
|
if (my $freq = $self->{db}->{$M, $did}) { |
| 451 |
|
|
$score{$did} += $post{$did} / $freq * $idf; |
| 452 |
|
|
} |
| 453 |
|
|
} |
| 454 |
|
|
} |
| 455 |
|
|
# warn sprintf "Used %d accumulators\n", scalar keys %score; |
| 456 |
|
|
return %score; |
| 457 |
|
|
} |
| 458 |
|
|
|
| 459 |
|
|
# A sloppy but fast algorithm for multiple term queries. |
| 460 |
|
|
unless ($strict) { |
| 461 |
|
|
for (@terms) { |
| 462 |
|
|
# Unpack posting list for current query term $_ |
| 463 |
|
|
my %post = unpack 'w*', $self->{db}->{$_}; |
| 464 |
|
|
|
| 465 |
|
|
# Lookup the number of documents the term occurs in (document frequency) |
| 466 |
|
|
my $occ = $self->{db}->{$O,$_}; |
| 467 |
|
|
|
| 468 |
|
|
_complain('search for term', $_) if $self->{db}->{$O,$_} != keys %post; |
| 469 |
|
|
# The frequency *must* be 1 at least since the posting list is nonempty |
| 470 |
|
|
_complain('search for term', $_) and $occ = 1 if $occ < 1; |
| 471 |
|
|
|
| 472 |
|
|
# This is the inverse document frequency. The log of the inverse |
| 473 |
|
|
# fraction of documents the term occurs in. |
| 474 |
|
|
my $idf = log($self->{records}/$occ); |
| 475 |
|
|
|
| 476 |
|
|
# If we have a reasonable number of accumulators, change the |
| 477 |
|
|
# loop to iterate over the accumulators. This will compromise |
| 478 |
|
|
# quality for better speed. The algorithm still computes the |
| 479 |
|
|
# exact weights, but the result is not guaranteed to contain the |
| 480 |
|
|
# *best* results. The database might contain documents better |
| 481 |
|
|
# than the worst returned document. |
| 482 |
|
|
|
| 483 |
|
|
# We process the lists in order of increasing length. When the |
| 484 |
|
|
# number of accumulators exceeds $wanted, no new documents are |
| 485 |
|
|
# added, only the ranking/weighting of the seen documents is |
| 486 |
|
|
# improved. The resulting ranking list must be pruned, since only |
| 487 |
|
|
# the top most documents end up near their "optimal" rank. |
| 488 |
|
|
|
| 489 |
|
|
if (keys %score < $wanted) { |
| 490 |
|
|
for my $did (keys %post) { |
| 491 |
|
|
if (my $freq = $self->{db}->{$M, $did}) { |
| 492 |
|
|
$score{$did} += $post{$did} / $freq * $idf; |
| 493 |
|
|
} |
| 494 |
|
|
} |
| 495 |
|
|
} else { |
| 496 |
|
|
for my $did (keys %score) { |
| 497 |
|
|
next unless exists $post{$did}; |
| 498 |
|
|
if (my $freq = $self->{db}->{$M, $did}) { |
| 499 |
|
|
$score{$did} += $post{$did} / $freq * $idf; |
| 500 |
|
|
} |
| 501 |
|
|
} |
| 502 |
|
|
} |
| 503 |
|
|
} |
| 504 |
|
|
return %score; |
| 505 |
|
|
} |
| 506 |
|
|
my @max; $max[$#terms+1]=0; |
| 507 |
|
|
my @idf; |
| 508 |
|
|
|
| 509 |
|
|
# Preparation loop. This extra loop makes sense only when "reorg" |
| 510 |
|
|
# and "wanted" are true. But at the time beeing, keeping the code |
| 511 |
|
|
# for the different search algorithms in one place seems more |
| 512 |
|
|
# desirable than some minor speedup of the brute force version. We |
| 513 |
|
|
# do cache $idf though. |
| 514 |
|
|
|
| 515 |
|
|
for (my $i = $#terms; $i >=0; $i--) { |
| 516 |
|
|
local $_ = $terms[$i]; |
| 517 |
|
|
# Lookup the number of documents the term occurs in (document frequency) |
| 518 |
|
|
my $df = $self->{db}->{$O,$_}; |
| 519 |
|
|
|
| 520 |
|
|
# The frequency *must* be 1 at least since the posting list is nonempty |
| 521 |
|
|
_complain('search for term', $_) and $df = 1 if $df < 1; |
| 522 |
|
|
|
| 523 |
|
|
# This is the inverse document frequency. The log of the inverse |
| 524 |
|
|
# fraction of documents the term occurs in. |
| 525 |
|
|
$idf[$i] = log($self->{records}/$df); |
| 526 |
|
|
|
| 527 |
|
|
my ($did,$occ); |
| 528 |
|
|
if ($self->{reorg}) { |
| 529 |
|
|
($did,$occ) = unpack 'w2', $self->{db}->{$_}; |
| 530 |
|
|
} else { # Maybe this costs more than it helps |
| 531 |
|
|
($did,$occ) = unpack 'w2', $self->sort_postings($self->{db}->{$_}); |
| 532 |
|
|
} |
| 533 |
|
|
my $freq = $self->{db}->{$M, $did}; |
| 534 |
|
|
my $max = $occ/$freq*$idf[$i]; |
| 535 |
|
|
$max[$i] = $max + $max[$i+1]; |
| 536 |
|
|
} |
| 537 |
|
|
|
| 538 |
|
|
# Main loop |
| 539 |
|
|
for my $i (0 .. $#terms) { |
| 540 |
|
|
my $term = $terms[$i]; |
| 541 |
|
|
# Unpack posting list for current query term $term. We loose the |
| 542 |
|
|
# sorting order because the assignment to a hash. |
| 543 |
|
|
my %post = unpack 'w*', $self->{db}->{$term}; |
| 544 |
|
|
|
| 545 |
|
|
_complain('search for term', $term) |
| 546 |
|
|
if $self->{db}->{$O,$term} != keys %post; |
| 547 |
|
|
|
| 548 |
|
|
my $idf = $idf[$i]; |
| 549 |
|
|
my $full; # Need to process all postings |
| 550 |
|
|
my $chop; # Score necessary to enter the ranking list |
| 551 |
|
|
|
| 552 |
|
|
if (# We know that wanted is true since we especial cased the |
| 553 |
|
|
# exhaustive search. |
| 554 |
|
|
|
| 555 |
|
|
$wanted and |
| 556 |
|
|
|
| 557 |
|
|
# We did sort here if necessary in |
| 558 |
|
|
# the preparation loop |
| 559 |
|
|
# $self->{reorg} and |
| 560 |
|
|
|
| 561 |
|
|
scalar keys %score > $wanted) { |
| 562 |
|
|
$chop = (sort { $b <=> $a } values %score)[$wanted]; |
| 563 |
|
|
$full = $max[$i] > $chop; |
| 564 |
|
|
} else { |
| 565 |
|
|
$full = 1; |
| 566 |
|
|
} |
| 567 |
|
|
|
| 568 |
|
|
if ($full) { |
| 569 |
|
|
# We need to inspect the full list. Either $wanted is not given, |
| 570 |
|
|
# the index is not sorted, or we don't have enough accumulators |
| 571 |
|
|
# yet. |
| 572 |
|
|
if (defined $chop) { |
| 573 |
|
|
# We might be able to avoid allocating accumulators |
| 574 |
|
|
for my $did (keys %post) { |
| 575 |
|
|
if (my $freq = $self->{db}->{$M, $did}) { |
| 576 |
|
|
my $wgt = $post{$did} / $freq * $idf; |
| 577 |
|
|
# We add an accumulator if $wgt exeeds $chop |
| 578 |
|
|
if (exists $score{$did} or $wgt > $chop) { |
| 579 |
|
|
$score{$did} += $wgt; |
| 580 |
|
|
} |
| 581 |
|
|
} |
| 582 |
|
|
} |
| 583 |
|
|
} else { |
| 584 |
|
|
# Allocate acumulators for each seen document. |
| 585 |
|
|
for my $did (keys %post) { |
| 586 |
|
|
if (my $freq = $self->{db}->{$M, $did}) { |
| 587 |
|
|
$score{$did} += $post{$did} / $freq * $idf; |
| 588 |
|
|
} |
| 589 |
|
|
} |
| 590 |
|
|
} |
| 591 |
|
|
} else { |
| 592 |
|
|
# Update existing accumulators |
| 593 |
|
|
for my $did (keys %score) { |
| 594 |
|
|
next unless exists $post{$did}; |
| 595 |
|
|
if (my $freq = $self->{db}->{$M, $did}) { |
| 596 |
|
|
$score{$did} += $post{$did} / $freq * $idf; |
| 597 |
|
|
} |
| 598 |
|
|
} |
| 599 |
|
|
} |
| 600 |
|
|
} |
| 601 |
|
|
#warn sprintf "Used %d accumulators\n", scalar keys %score; |
| 602 |
|
|
%score; |
| 603 |
|
|
} |
| 604 |
|
|
|
| 605 |
|
|
sub set { |
| 606 |
|
|
my ($self, $attr, $value) = @_; |
| 607 |
|
|
|
| 608 |
|
|
die "No such indexy attribute: '$attr'" unless $attr eq 'top'; |
| 609 |
|
|
|
| 610 |
|
|
return delete $self->{reorg} if $value == 0; |
| 611 |
|
|
|
| 612 |
|
|
return if $self->{reorg}; # we are sorted already |
| 613 |
|
|
return unless $self->{mode} & O_RDWR; |
| 614 |
|
|
defined $self->{db} or $self->open; |
| 615 |
|
|
|
| 616 |
|
|
$self->sync; |
| 617 |
|
|
while (my($key, $value) = each %{$self->{db}}) { |
| 618 |
|
|
next if $key =~ /^\377[om]/; |
| 619 |
|
|
$self->{db}->{$key} = $self->sort_postings($value); |
| 620 |
|
|
} |
| 621 |
|
|
$self->{reorg} = 1; |
| 622 |
|
|
} |
| 623 |
|
|
|
| 624 |
|
|
sub sync { |
| 625 |
|
|
my $self = shift; |
| 626 |
|
|
|
| 627 |
|
|
if ($self->{mode} & O_RDWR) { |
| 628 |
|
|
print STDERR "Flushing $self->{cached} postings\n" if $self->{cached}; |
| 629 |
|
|
while (my($key, $value) = each %{$self->{cache}}) { |
| 630 |
|
|
if ($self->{reorg}) { |
| 631 |
|
|
$self->{db}->{$key} = $self->sort_postings($self->{db}->{$key} |
| 632 |
|
|
. $value); |
| 633 |
|
|
} else { |
| 634 |
|
|
$self->{db}->{$key} .= $value; |
| 635 |
|
|
} |
| 636 |
|
|
} |
| 637 |
|
|
while (my($key, $value) = each %{$self->{cdict}}) { |
| 638 |
|
|
$self->{db}->{$key} = 0 unless $self->{db}->{$key}; |
| 639 |
|
|
$self->{db}->{$key} += $value; |
| 640 |
|
|
} |
| 641 |
|
|
$self->{cache} = {}; |
| 642 |
|
|
$self->{cdict} = {}; |
| 643 |
|
|
$self->{cached} = 0; |
| 644 |
|
|
} |
| 645 |
|
|
} |
| 646 |
|
|
|
| 647 |
|
|
sub close { |
| 648 |
|
|
my $self = shift; |
| 649 |
|
|
|
| 650 |
|
|
if ($self->{dbh}) { |
| 651 |
|
|
$self->sync; |
| 652 |
|
|
delete $self->{dbh}; |
| 653 |
|
|
untie %{$self->{db}}; |
| 654 |
|
|
delete $self->{db}; |
| 655 |
|
|
delete $self->{func}; |
| 656 |
|
|
delete $self->{cache}; |
| 657 |
|
|
delete $self->{cached}; |
| 658 |
|
|
delete $self->{cdict}; |
| 659 |
|
|
delete $self->{pfunc} if defined $self->{pfunc}; |
| 660 |
|
|
delete $self->{ifunc} if defined $self->{ifunc}; |
| 661 |
|
|
delete $self->{xfunc} if defined $self->{xfunc}; |
| 662 |
|
|
} |
| 663 |
|
|
} |
| 664 |
|
|
|
| 665 |
|
|
1; |
| 666 |
|
|
|
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