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Overview
Comment: | Add fts5rnd1.test, a modified version of fts3rnd.test that works with fts5. |
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Timelines: | family | ancestors | descendants | both | trunk |
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SHA1: |
29d07b13f05ccc4f8b29a27b7efc186f |
User & Date: | dan 2012-12-29 15:16:49.408 |
Context
2012-12-30
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11:45 | Add support for prefix queries to fts5. Still no support for prefix indexes, just prefix queries using the regular term index. check-in: dd018f834a user: dan tags: trunk | |
2012-12-29
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15:16 | Add fts5rnd1.test, a modified version of fts3rnd.test that works with fts5. check-in: 29d07b13f0 user: dan tags: trunk | |
09:56 | Add support for the AND, OR and NOT operators to fts5. check-in: c2efd983b0 user: dan tags: trunk | |
Changes
Changes to src/fts5.c.
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1670 1671 1672 1673 1674 1675 1676 | if( pNext->nList==0 ){ pTrim->nList = 0; }else{ int bEof = 0; int nTrail = nNear + (pNext->nToken-1) + 1; int nLead = nNear + (pTrim->nToken-1) + 1; | | < | < | < | | | < | | < | > > | < < < < | 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 | if( pNext->nList==0 ){ pTrim->nList = 0; }else{ int bEof = 0; int nTrail = nNear + (pNext->nToken-1) + 1; int nLead = nNear + (pTrim->nToken-1) + 1; InstanceList near; InstanceList in; InstanceList out; fts5InstanceListInit(pNext->aList, pNext->nList, &near); fts5InstanceListInit(pTrim->aList, pTrim->nList, &in); fts5InstanceListInit(pTrim->aList, pTrim->nList, &out); fts5InstanceListNext(&near); fts5InstanceListNext(&in); while( bEof==0 ){ if( fts5IsNear(&near, &in, nTrail) || fts5IsNear(&in, &near, nLead) ){ /* The current position is a match. Append an entry to the output ** and advance the input cursor. */ fts5InstanceListAppend(&out, in.iCol, in.iWeight, in.iOff); bEof = fts5InstanceListNext(&in); }else{ if( near.iCol<in.iCol || (near.iCol==in.iCol && near.iOff<in.iOff) ){ bEof = fts5InstanceListNext(&near); }else if( near.iCol==in.iCol && near.iOff==in.iOff ){ bEof = fts5InstanceListNext(&in); if( fts5IsNear(&near, &in, nTrail) ){ fts5InstanceListAppend(&out, near.iCol, near.iWeight, near.iOff); } }else{ bEof = fts5InstanceListNext(&in); } } } pTrim->nList = out.iList; |
︙ | ︙ |
Changes to test/fts5query1.test.
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78 79 80 81 82 83 84 85 86 87 | 9 {SELECT x FROM t1 WHERE t1 MATCH 'b+c+d NEAR/1 g+h'} {2 3} 10 {SELECT x FROM t1 WHERE t1 MATCH 'a AND d'} {1 2} 11 {SELECT x FROM t1 WHERE t1 MATCH 'a OR d'} {1 2 3 4} 12 {SELECT x FROM t1 WHERE t1 MATCH 'a NOT d'} {4} } { do_execsql_test 3.$tn $stmt $res } finish_test | > > > > > > > > > > > | 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 | 9 {SELECT x FROM t1 WHERE t1 MATCH 'b+c+d NEAR/1 g+h'} {2 3} 10 {SELECT x FROM t1 WHERE t1 MATCH 'a AND d'} {1 2} 11 {SELECT x FROM t1 WHERE t1 MATCH 'a OR d'} {1 2 3 4} 12 {SELECT x FROM t1 WHERE t1 MATCH 'a NOT d'} {4} } { do_execsql_test 3.$tn $stmt $res } do_execsql_test 4.0 { CREATE TABLE t2(docid PRIMARY KEY, a, b, c); CREATE INDEX i2 ON t2 USING fts5(); INSERT INTO t2 VALUES(136895, 'qkfl my qkfl krag gw', NULL, NULL); } breakpoint do_execsql_test 4.1 { SELECT docid FROM t2 WHERE t2 MATCH 'qkfl NEAR/2 gw'; } {136895} finish_test |
Added test/fts5rnd1.test.
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This is done using pseudo-randomly generated data and queries. # The expected result of each query is calculated using Tcl code. # # 1. The database is initialized to contain a single table with three # columns. 100 rows are inserted into the table. Each of the three # values in each row is a document consisting of between 0 and 100 # terms. Terms are selected from a vocabulary of $G(nVocab) terms. # # 2. The following is performed 100 times: # # a. A row is inserted into the database. The row contents are # generated as in step 1. The docid is a pseudo-randomly selected # value between 0 and 1000000. # # b. A psuedo-randomly selected row is updated. One of its columns is # set to contain a new document generated in the same way as the # documents in step 1. # # c. A psuedo-randomly selected row is deleted. # # d. For each of several types of fts3 queries, 10 SELECT queries # of the form: # # SELECT docid FROM <tbl> WHERE <tbl> MATCH '<query>' # # are evaluated. The results are compared to those calculated by # Tcl code in this file. The patterns used for the different query # types are: # # 1. query = <term> # 2. query = <prefix> # 3. query = "<term> <term>" # 4. query = "<term> <term> <term>" # 5. query = "<prefix> <prefix> <prefix>" # 6. query = <term> NEAR <term> # 7. query = <term> NEAR/11 <term> NEAR/11 <term> # 8. query = <term> OR <term> # 9. query = <term> NOT <term> # 10. query = <term> AND <term> # 11. query = <term> NEAR <term> OR <term> NEAR <term> # 12. query = <term> NEAR <term> NOT <term> NEAR <term> # 13. query = <term> NEAR <term> AND <term> NEAR <term> # # where <term> is a term psuedo-randomly selected from the vocabulary # and prefix is the first 2 characters of such a term followed by # a "*" character. # # Every second iteration, steps (a) through (d) above are performed # within a single transaction. This forces the queries in (d) to # read data from both the database and the in-memory hash table # that caches the full-text index entries created by steps (a), (b) # and (c) until the transaction is committed. # # The procedure above is run 5 times, using advisory fts3 node sizes of 50, # 500, 1000 and 2000 bytes. # # After the test using an advisory node-size of 50, an OOM test is run using # the database. This test is similar to step (d) above, except that it tests # the effects of transient and persistent OOM conditions encountered while # executing each query. # set testdir [file dirname $argv0] source $testdir/tester.tcl # If this build does not include FTS3, skip the tests in this file. # #ifcapable !fts3 { finish_test ; return } source $testdir/fts3_common.tcl source $testdir/malloc_common.tcl set G(nVocab) 100 set nVocab 100 set lVocab [list] expr srand(0) # Generate a vocabulary of nVocab words. Each word is between 2 and 4 # characters long. # set lChar {a b c d e f g h i j k l m n o p q r s t u v w x y z} for {set i 0} {$i < $nVocab} {incr i} { set len [expr int(rand()*3)+2] set word [lindex $lChar [expr int(rand()*26)]] append word [lindex $lChar [expr int(rand()*26)]] if {$len>2} { append word [lindex $lChar [expr int(rand()*26)]] } if {$len>3} { append word [lindex $lChar [expr int(rand()*26)]] } lappend lVocab $word } proc random_term {} { lindex $::lVocab [expr {int(rand()*$::nVocab)}] } # Return a document consisting of $nWord arbitrarily selected terms # from the $::lVocab list. # proc generate_doc {nWord} { set doc [list] for {set i 0} {$i < $nWord} {incr i} { lappend doc [random_term] } return $doc } # Primitives to update the table. # unset -nocomplain t1 proc insert_row {docid} { set a [generate_doc [expr int((rand()*100))]] set b [generate_doc [expr int((rand()*100))]] set c [generate_doc [expr int((rand()*100))]] execsql { INSERT INTO t1(docid, a, b, c) VALUES($docid, $a, $b, $c) } set ::t1($docid) [list $a $b $c] } proc delete_row {docid} { execsql { DELETE FROM t1 WHERE docid = $docid } catch {unset ::t1($docid)} } proc update_row {docid} { set cols {a b c} set iCol [expr int(rand()*3)] set doc [generate_doc [expr int((rand()*100))]] lset ::t1($docid) $iCol $doc execsql "UPDATE t1 SET [lindex $cols $iCol] = \$doc WHERE docid = \$docid" } proc simple_phrase {zPrefix} { set ret [list] set reg [string map {* {[^ ]*}} $zPrefix] set reg " $reg " foreach key [lsort -integer [array names ::t1]] { set value $::t1($key) set cnt [list] foreach col $value { if {[regexp $reg " $col "]} { lappend ret $key ; break } } } #lsort -uniq -integer $ret set ret } # This [proc] is used to test the FTS3 matchinfo() function. # proc simple_token_matchinfo {zToken bDesc} { set nDoc(0) 0 set nDoc(1) 0 set nDoc(2) 0 set nHit(0) 0 set nHit(1) 0 set nHit(2) 0 set dir -inc if {$bDesc} { set dir -dec } foreach key [array names ::t1] { set value $::t1($key) set a($key) [list] foreach i {0 1 2} col $value { set hit [llength [lsearch -all $col $zToken]] lappend a($key) $hit incr nHit($i) $hit if {$hit>0} { incr nDoc($i) } } } set ret [list] foreach docid [lsort -integer $dir [array names a]] { if { [lindex [lsort -integer $a($docid)] end] } { set matchinfo [list 1 3] foreach i {0 1 2} hit $a($docid) { lappend matchinfo $hit $nHit($i) $nDoc($i) } lappend ret $docid $matchinfo } } set ret } proc simple_near {termlist nNear} { set ret [list] foreach {key value} [array get ::t1] { foreach v $value { set l [lsearch -exact -all $v [lindex $termlist 0]] foreach T [lrange $termlist 1 end] { set l2 [list] foreach i $l { set iStart [expr $i - $nNear - 1] set iEnd [expr $i + $nNear + 1] if {$iStart < 0} {set iStart 0} foreach i2 [lsearch -exact -all [lrange $v $iStart $iEnd] $T] { incr i2 $iStart if {$i2 != $i} { lappend l2 $i2 } } } set l [lsort -uniq -integer $l2] } if {[llength $l]} { #puts "MATCH($key): $v" lappend ret $key } } } lsort -unique -integer $ret } # The following three procs: # # setup_not A B # setup_or A B # setup_and A B # # each take two arguments. Both arguments must be lists of integer values # sorted by value. The return value is the list produced by evaluating # the equivalent of "A op B", where op is the FTS3 operator NOT, OR or # AND. # proc setop_not {A B} { foreach b $B { set n($b) {} } set ret [list] foreach a $A { if {![info exists n($a)]} {lappend ret $a} } return $ret } proc setop_or {A B} { lsort -integer -uniq [concat $A $B] } proc setop_and {A B} { foreach b $B { set n($b) {} } set ret [list] foreach a $A { if {[info exists n($a)]} {lappend ret $a} } return $ret } proc mit {blob} { set scan(littleEndian) i* set scan(bigEndian) I* binary scan $blob $scan($::tcl_platform(byteOrder)) r return $r } db func mit mit set sqlite_fts3_enable_parentheses 1 proc do_orderbydocid_test {tn sql res} { uplevel [list do_select_test $tn.asc "$sql ORDER BY docid ASC" $res] uplevel [list do_select_test $tn.desc "$sql ORDER BY docid DESC" \ [lsort -int -dec $res] ] } #------------------------------------------------------------------------- # set NUM_TRIALS 10 catch { array unset ::t1 } # Create the fts5 table. Populate it (and the Tcl array) with 100 rows. # db transaction { execsql { DROP TABLE IF EXISTS t1; CREATE TABLE t1(docid PRIMARY KEY, a, b, c); CREATE INDEX i1 ON t1 USING fts5(); } for {set i 0} {$i < 100} {incr i} { insert_row $i } } for {set iTest 1} {$iTest <= $NUM_TRIALS} {incr iTest} { catchsql COMMIT set DO_MALLOC_TEST 0 set nRep 10 # if {$iTest==100 && $nodesize==50} { # set DO_MALLOC_TEST 1 # set nRep 2 # } set ::testprefix fts5rnd-1.$iTest # Delete one row, update one row and insert one row. # set rows [array names ::t1] set nRow [llength $rows] set iUpdate [lindex $rows [expr {int(rand()*$nRow)}]] set iDelete $iUpdate while {$iDelete == $iUpdate} { set iDelete [lindex $rows [expr {int(rand()*$nRow)}]] } set iInsert $iUpdate while {[info exists ::t1($iInsert)]} { set iInsert [expr {int(rand()*1000000)}] } execsql BEGIN insert_row $iInsert update_row $iUpdate delete_row $iDelete if {0==($iTest%2)} { execsql COMMIT } if {0==($iTest%2)} { #do_test 0 { fts3_integrity_check t1 } ok } # Pick 10 terms from the vocabulary. Check that the results of querying # the database for the set of documents containing each of these terms # is the same as the result obtained by scanning the contents of the Tcl # array for each term. # for {set i 0} {$i < 10} {incr i} { set term [random_term] do_orderbydocid_test 1.$i.asc { SELECT docid FROM t1 WHERE t1 MATCH $term } [simple_phrase $term] do_orderbydocid_test 1.$i.desc { SELECT docid FROM t1 WHERE t1 MATCH $term } [simple_phrase $term] } # This time, use the first two characters of each term as a term prefix # to query for. Test that querying the Tcl array produces the same results # as querying the FTS3 table for the prefix. # puts "todo: prefix query support" # for {set i 0} {$i < $nRep} {incr i} { # set prefix [string range [random_term] 0 end-1] # set match "${prefix}*" # do_orderbydocid_test 2.$i { # SELECT docid FROM t1 WHERE t1 MATCH $match # } [simple_phrase $match] # } # Similar to the above, except for phrase queries. # for {set i 0} {$i < $nRep} {incr i} { set term [list [random_term] [random_term]] set match "\"$term\"" do_orderbydocid_test 3.$i { SELECT docid FROM t1 WHERE t1 MATCH $match } [simple_phrase $term] } # Three word phrases. # for {set i 0} {$i < $nRep} {incr i} { set term [list [random_term] [random_term] [random_term]] set match "\"$term\"" do_orderbydocid_test 4.$i { SELECT docid FROM t1 WHERE t1 MATCH $match } [simple_phrase $term] } # Three word phrases made up of term-prefixes. # puts "todo: prefix query support" # for {set i 0} {$i < $nRep} {incr i} { # set query "[string range [random_term] 0 end-1]* " # append query "[string range [random_term] 0 end-1]* " # append query "[string range [random_term] 0 end-1]*" # # set match "\"$query\"" # do_orderbydocid_test 5.$i { # SELECT docid FROM t1 WHERE t1 MATCH $match # } [simple_phrase $query] # } # A NEAR query with terms as the arguments: # # ... MATCH '$term1 NEAR $term2' ... # for {set i 0} {$i < $nRep} {incr i} { set terms [list [random_term] [random_term]] set match [join $terms " NEAR "] do_orderbydocid_test 6.$i { SELECT docid FROM t1 WHERE t1 MATCH $match } [simple_near $terms 10] } # A 3-way NEAR query with terms as the arguments. # for {set i 0} {$i < $nRep} {incr i} { set terms [list [random_term] [random_term] [random_term]] set nNear 11 set match [join $terms " NEAR/$nNear "] do_orderbydocid_test 7.$i { SELECT docid FROM t1 WHERE t1 MATCH $match } [simple_near $terms $nNear] } # Set operations on simple term queries. # foreach {tn op proc} { 8 OR setop_or 9 NOT setop_not 10 AND setop_and } { for {set i 0} {$i < $nRep} {incr i} { set term1 [random_term] set term2 [random_term] set match "$term1 $op $term2" do_orderbydocid_test $tn.$i { SELECT docid FROM t1 WHERE t1 MATCH $match } [$proc [simple_phrase $term1] [simple_phrase $term2]] } } # Set operations on NEAR queries. # foreach {tn op proc} { 11 OR setop_or 12 NOT setop_not 13 AND setop_and } { for {set i 0} {$i < $nRep} {incr i} { set term1 [random_term] set term2 [random_term] set term3 [random_term] set term4 [random_term] set match "$term1 NEAR $term2 $op $term3 NEAR $term4" do_orderbydocid_test $tn.$i { SELECT docid FROM t1 WHERE t1 MATCH $match } [$proc \ [simple_near [list $term1 $term2] 10] \ [simple_near [list $term3 $term4] 10] ] } } catchsql COMMIT } finish_test |