[sql] Remove SQLite 3.10.2 reference directory.

third_party/sqlite/sqlite-src-3100200/test/fts3rnd.test

-# 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