# 2009 December 03
#
# May you do good and not evil.
# May you find forgiveness for yourself and forgive others.
# May you share freely, never taking more than you give.
#
#***********************************************************************
#
# Brute force (random data) tests for FTS3.
#
#-------------------------------------------------------------------------
#
# The FTS3 tests implemented in this file focus on testing that FTS3
# returns the correct set of documents for various types of full-text
# query. 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 $testdir/malloc_common.tcl
set G(nVocab) 100
set nVocab 100
set lVocab [list]
expr srand(0)
# Generate a vocabulary of nVocab words. Each word is 3 characters long.
#
set lChar {a b c d e f g h i j k l m n o p q r s t u v w x y z}
for {set i 0} {$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 $doc
}
# Primitives to update the table.
#
unset -nocomplain t1
proc insert_row {rowid} {
set a [generate_doc [expr int((rand()*100))]]
set b [generate_doc [expr int((rand()*100))]]
set c [generate_doc [expr int((rand()*100))]]
execsql { INSERT INTO t1(docid, a, b, c) VALUES($rowid, $a, $b, $c) }
set ::t1($rowid) [list $a $b $c]
}
proc delete_row {rowid} {
execsql { DELETE FROM t1 WHERE rowid = $rowid }
catch {unset ::t1($rowid)}
}
proc update_row {rowid} {
set cols {a b c}
set iCol [expr int(rand()*3)]
set doc [generate_doc [expr int((rand()*100))]]
lset ::t1($rowid) $iCol $doc
execsql "UPDATE t1 SET [lindex $cols $iCol] = \$doc WHERE rowid = \$rowid"
}
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 $res]
]
}
set NUM_TRIALS 100
foreach {nodesize order} {
50 DESC
50 ASC
500 ASC
1000 DESC
2000 ASC
} {
catch { array unset ::t1 }
set testname "$nodesize/$order"
# Create the FTS3 table. Populate it (and the Tcl array) with 100 rows.
#
db transaction {
catchsql { DROP TABLE t1 }
execsql "CREATE VIRTUAL TABLE t1 USING fts4(a, b, c, order=$order)"
execsql "INSERT INTO t1(t1) VALUES('nodesize=$nodesize')"
for {set i 0} {$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 && $nodesize==50} {
set DO_MALLOC_TEST 1
set nRep 2
}
set ::testprefix fts3rnd-1.$testname.$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_select_test 1.$i.asc {
SELECT docid, mit(matchinfo(t1)) FROM t1 WHERE t1 MATCH $term
ORDER BY docid ASC
} [simple_token_matchinfo $term 0]
do_select_test 1.$i.desc {
SELECT docid, mit(matchinfo(t1)) FROM t1 WHERE t1 MATCH $term
ORDER BY docid DESC
} [simple_token_matchinfo $term 1]
}
# This time, use the first two characters of each term as a term prefix
# to query for. Test that querying the Tcl array produces the same results
# as querying the FTS3 table for the prefix.
#
for {set i 0} {$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 $term]
}
# Three word phrases made up of term-prefixes.
#
for {set i 0} {$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