435 lines
13 KiB
Plaintext
435 lines
13 KiB
Plaintext
# 2009 December 03
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#
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# May you do good and not evil.
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# May you find forgiveness for yourself and forgive others.
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# May you share freely, never taking more than you give.
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#
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#***********************************************************************
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#
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# Brute force (random data) tests for FTS3.
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#
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#-------------------------------------------------------------------------
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#
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# The FTS3 tests implemented in this file focus on testing that FTS3
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# returns the correct set of documents for various types of full-text
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# query. This is done using pseudo-randomly generated data and queries.
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# The expected result of each query is calculated using Tcl code.
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#
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# 1. The database is initialized to contain a single table with three
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# columns. 100 rows are inserted into the table. Each of the three
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# values in each row is a document consisting of between 0 and 100
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# terms. Terms are selected from a vocabulary of $G(nVocab) terms.
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#
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# 2. The following is performed 100 times:
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#
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# a. A row is inserted into the database. The row contents are
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# generated as in step 1. The docid is a pseudo-randomly selected
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# value between 0 and 1000000.
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#
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# b. A psuedo-randomly selected row is updated. One of its columns is
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# set to contain a new document generated in the same way as the
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# documents in step 1.
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#
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# c. A psuedo-randomly selected row is deleted.
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#
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# d. For each of several types of fts3 queries, 10 SELECT queries
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# of the form:
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#
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# SELECT docid FROM <tbl> WHERE <tbl> MATCH '<query>'
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#
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# are evaluated. The results are compared to those calculated by
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# Tcl code in this file. The patterns used for the different query
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# types are:
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#
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# 1. query = <term>
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# 2. query = <prefix>
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# 3. query = "<term> <term>"
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# 4. query = "<term> <term> <term>"
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# 5. query = "<prefix> <prefix> <prefix>"
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# 6. query = <term> NEAR <term>
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# 7. query = <term> NEAR/11 <term> NEAR/11 <term>
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# 8. query = <term> OR <term>
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# 9. query = <term> NOT <term>
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# 10. query = <term> AND <term>
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# 11. query = <term> NEAR <term> OR <term> NEAR <term>
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# 12. query = <term> NEAR <term> NOT <term> NEAR <term>
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# 13. query = <term> NEAR <term> AND <term> NEAR <term>
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#
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# where <term> is a term psuedo-randomly selected from the vocabulary
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# and prefix is the first 2 characters of such a term followed by
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# a "*" character.
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#
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# Every second iteration, steps (a) through (d) above are performed
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# within a single transaction. This forces the queries in (d) to
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# read data from both the database and the in-memory hash table
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# that caches the full-text index entries created by steps (a), (b)
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# and (c) until the transaction is committed.
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#
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# The procedure above is run 5 times, using advisory fts3 node sizes of 50,
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# 500, 1000 and 2000 bytes.
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#
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# After the test using an advisory node-size of 50, an OOM test is run using
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# the database. This test is similar to step (d) above, except that it tests
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# the effects of transient and persistent OOM conditions encountered while
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# executing each query.
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#
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set testdir [file dirname $argv0]
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source $testdir/tester.tcl
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# If this build does not include FTS3, skip the tests in this file.
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#
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ifcapable !fts3 { finish_test ; return }
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source $testdir/fts3_common.tcl
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source $testdir/malloc_common.tcl
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set G(nVocab) 100
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set nVocab 100
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set lVocab [list]
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expr srand(0)
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# Generate a vocabulary of nVocab words. Each word is 3 characters long.
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#
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set lChar {a b c d e f g h i j k l m n o p q r s t u v w x y z}
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for {set i 0} {$i < $nVocab} {incr i} {
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set len [expr int(rand()*3)+2]
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set word [lindex $lChar [expr int(rand()*26)]]
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append word [lindex $lChar [expr int(rand()*26)]]
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if {$len>2} { append word [lindex $lChar [expr int(rand()*26)]] }
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if {$len>3} { append word [lindex $lChar [expr int(rand()*26)]] }
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lappend lVocab $word
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}
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proc random_term {} {
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lindex $::lVocab [expr {int(rand()*$::nVocab)}]
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}
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# Return a document consisting of $nWord arbitrarily selected terms
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# from the $::lVocab list.
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#
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proc generate_doc {nWord} {
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set doc [list]
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for {set i 0} {$i < $nWord} {incr i} {
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lappend doc [random_term]
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}
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return $doc
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}
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# Primitives to update the table.
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#
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unset -nocomplain t1
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proc insert_row {rowid} {
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set a [generate_doc [expr int((rand()*100))]]
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set b [generate_doc [expr int((rand()*100))]]
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set c [generate_doc [expr int((rand()*100))]]
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execsql { INSERT INTO t1(docid, a, b, c) VALUES($rowid, $a, $b, $c) }
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set ::t1($rowid) [list $a $b $c]
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}
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proc delete_row {rowid} {
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execsql { DELETE FROM t1 WHERE rowid = $rowid }
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catch {unset ::t1($rowid)}
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}
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proc update_row {rowid} {
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set cols {a b c}
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set iCol [expr int(rand()*3)]
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set doc [generate_doc [expr int((rand()*100))]]
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lset ::t1($rowid) $iCol $doc
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execsql "UPDATE t1 SET [lindex $cols $iCol] = \$doc WHERE rowid = \$rowid"
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}
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proc simple_phrase {zPrefix} {
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set ret [list]
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set reg [string map {* {[^ ]*}} $zPrefix]
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set reg " $reg "
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foreach key [lsort -integer [array names ::t1]] {
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set value $::t1($key)
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set cnt [list]
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foreach col $value {
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if {[regexp $reg " $col "]} { lappend ret $key ; break }
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}
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}
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#lsort -uniq -integer $ret
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set ret
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}
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# This [proc] is used to test the FTS3 matchinfo() function.
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#
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proc simple_token_matchinfo {zToken} {
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set nDoc(0) 0
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set nDoc(1) 0
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set nDoc(2) 0
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set nHit(0) 0
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set nHit(1) 0
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set nHit(2) 0
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foreach key [array names ::t1] {
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set value $::t1($key)
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set a($key) [list]
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foreach i {0 1 2} col $value {
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set hit [llength [lsearch -all $col $zToken]]
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lappend a($key) $hit
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incr nHit($i) $hit
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if {$hit>0} { incr nDoc($i) }
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}
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}
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set ret [list]
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foreach docid [lsort -integer [array names a]] {
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if { [lindex [lsort -integer $a($docid)] end] } {
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set matchinfo [list 1 3]
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foreach i {0 1 2} hit $a($docid) {
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lappend matchinfo $hit $nHit($i) $nDoc($i)
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}
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lappend ret $docid $matchinfo
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}
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}
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set ret
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}
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proc simple_near {termlist nNear} {
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set ret [list]
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foreach {key value} [array get ::t1] {
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foreach v $value {
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set l [lsearch -exact -all $v [lindex $termlist 0]]
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foreach T [lrange $termlist 1 end] {
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set l2 [list]
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foreach i $l {
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set iStart [expr $i - $nNear - 1]
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set iEnd [expr $i + $nNear + 1]
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if {$iStart < 0} {set iStart 0}
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foreach i2 [lsearch -exact -all [lrange $v $iStart $iEnd] $T] {
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incr i2 $iStart
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if {$i2 != $i} { lappend l2 $i2 }
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}
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}
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set l [lsort -uniq -integer $l2]
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}
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if {[llength $l]} {
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#puts "MATCH($key): $v"
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lappend ret $key
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}
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}
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}
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lsort -unique -integer $ret
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}
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# The following three procs:
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#
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# setup_not A B
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# setup_or A B
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# setup_and A B
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#
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# each take two arguments. Both arguments must be lists of integer values
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# sorted by value. The return value is the list produced by evaluating
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# the equivalent of "A op B", where op is the FTS3 operator NOT, OR or
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# AND.
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#
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proc setop_not {A B} {
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foreach b $B { set n($b) {} }
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set ret [list]
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foreach a $A { if {![info exists n($a)]} {lappend ret $a} }
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return $ret
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}
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proc setop_or {A B} {
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lsort -integer -uniq [concat $A $B]
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}
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proc setop_and {A B} {
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foreach b $B { set n($b) {} }
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set ret [list]
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foreach a $A { if {[info exists n($a)]} {lappend ret $a} }
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return $ret
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}
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proc mit {blob} {
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set scan(littleEndian) i*
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set scan(bigEndian) I*
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binary scan $blob $scan($::tcl_platform(byteOrder)) r
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return $r
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}
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db func mit mit
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set sqlite_fts3_enable_parentheses 1
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foreach nodesize {50 500 1000 2000} {
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catch { array unset ::t1 }
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# Create the FTS3 table. Populate it (and the Tcl array) with 100 rows.
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#
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db transaction {
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catchsql { DROP TABLE t1 }
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execsql "CREATE VIRTUAL TABLE t1 USING fts3(a, b, c)"
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execsql "INSERT INTO t1(t1) VALUES('nodesize=$nodesize')"
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for {set i 0} {$i < 100} {incr i} { insert_row $i }
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}
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for {set iTest 1} {$iTest <= 100} {incr iTest} {
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catchsql COMMIT
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set DO_MALLOC_TEST 0
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set nRep 10
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if {$iTest==100 && $nodesize==50} {
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set DO_MALLOC_TEST 1
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set nRep 2
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}
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# Delete one row, update one row and insert one row.
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#
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set rows [array names ::t1]
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set nRow [llength $rows]
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set iUpdate [lindex $rows [expr {int(rand()*$nRow)}]]
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set iDelete $iUpdate
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while {$iDelete == $iUpdate} {
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set iDelete [lindex $rows [expr {int(rand()*$nRow)}]]
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}
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set iInsert $iUpdate
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while {[info exists ::t1($iInsert)]} {
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set iInsert [expr {int(rand()*1000000)}]
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}
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execsql BEGIN
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insert_row $iInsert
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update_row $iUpdate
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delete_row $iDelete
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if {0==($iTest%2)} { execsql COMMIT }
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if {0==($iTest%2)} {
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do_test fts3rnd-1.$nodesize.$iTest.0 { fts3_integrity_check t1 } ok
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}
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# Pick 10 terms from the vocabulary. Check that the results of querying
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# the database for the set of documents containing each of these terms
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# is the same as the result obtained by scanning the contents of the Tcl
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# array for each term.
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#
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for {set i 0} {$i < 10} {incr i} {
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set term [random_term]
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do_select_test fts3rnd-1.$nodesize.$iTest.1.$i {
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SELECT docid, mit(matchinfo(t1)) FROM t1 WHERE t1 MATCH $term
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} [simple_token_matchinfo $term]
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}
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# This time, use the first two characters of each term as a term prefix
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# to query for. Test that querying the Tcl array produces the same results
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# as querying the FTS3 table for the prefix.
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#
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for {set i 0} {$i < $nRep} {incr i} {
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set prefix [string range [random_term] 0 end-1]
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set match "${prefix}*"
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do_select_test fts3rnd-1.$nodesize.$iTest.2.$i {
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SELECT docid FROM t1 WHERE t1 MATCH $match
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} [simple_phrase $match]
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}
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# Similar to the above, except for phrase queries.
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#
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for {set i 0} {$i < $nRep} {incr i} {
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set term [list [random_term] [random_term]]
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set match "\"$term\""
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do_select_test fts3rnd-1.$nodesize.$iTest.3.$i {
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SELECT docid FROM t1 WHERE t1 MATCH $match
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} [simple_phrase $term]
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}
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# Three word phrases.
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#
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for {set i 0} {$i < $nRep} {incr i} {
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set term [list [random_term] [random_term] [random_term]]
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set match "\"$term\""
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do_select_test fts3rnd-1.$nodesize.$iTest.4.$i {
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SELECT docid FROM t1 WHERE t1 MATCH $match
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} [simple_phrase $term]
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}
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# Three word phrases made up of term-prefixes.
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#
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for {set i 0} {$i < $nRep} {incr i} {
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set query "[string range [random_term] 0 end-1]* "
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append query "[string range [random_term] 0 end-1]* "
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append query "[string range [random_term] 0 end-1]*"
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set match "\"$query\""
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do_select_test fts3rnd-1.$nodesize.$iTest.5.$i {
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SELECT docid FROM t1 WHERE t1 MATCH $match
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} [simple_phrase $query]
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}
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# A NEAR query with terms as the arguments.
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#
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for {set i 0} {$i < $nRep} {incr i} {
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set terms [list [random_term] [random_term]]
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set match [join $terms " NEAR "]
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do_select_test fts3rnd-1.$nodesize.$iTest.6.$i {
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SELECT docid FROM t1 WHERE t1 MATCH $match
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} [simple_near $terms 10]
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}
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# A 3-way NEAR query with terms as the arguments.
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#
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for {set i 0} {$i < $nRep} {incr i} {
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set terms [list [random_term] [random_term] [random_term]]
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set nNear 11
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set match [join $terms " NEAR/$nNear "]
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do_select_test fts3rnd-1.$nodesize.$iTest.7.$i {
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SELECT docid FROM t1 WHERE t1 MATCH $match
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} [simple_near $terms $nNear]
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}
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# Set operations on simple term queries.
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#
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foreach {tn op proc} {
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8 OR setop_or
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9 NOT setop_not
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10 AND setop_and
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} {
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for {set i 0} {$i < $nRep} {incr i} {
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set term1 [random_term]
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set term2 [random_term]
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set match "$term1 $op $term2"
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do_select_test fts3rnd-1.$nodesize.$iTest.$tn.$i {
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SELECT docid FROM t1 WHERE t1 MATCH $match
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} [$proc [simple_phrase $term1] [simple_phrase $term2]]
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}
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}
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# Set operations on NEAR queries.
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#
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foreach {tn op proc} {
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8 OR setop_or
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9 NOT setop_not
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10 AND setop_and
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} {
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for {set i 0} {$i < $nRep} {incr i} {
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set term1 [random_term]
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set term2 [random_term]
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set term3 [random_term]
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set term4 [random_term]
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set match "$term1 NEAR $term2 $op $term3 NEAR $term4"
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do_select_test fts3rnd-1.$nodesize.$iTest.$tn.$i {
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SELECT docid FROM t1 WHERE t1 MATCH $match
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} [$proc \
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[simple_near [list $term1 $term2] 10] \
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[simple_near [list $term3 $term4] 10]
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]
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}
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}
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catchsql COMMIT
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}
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}
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finish_test
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