Add comment to fts3rnd.test to explain how the test works.
FossilOrigin-Name: 6b740c7cd57d618623ed028be4213dfef860054a
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dan
@@ -1,8 +1,5 @@
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-----BEGIN PGP SIGNED MESSAGE-----
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Hash: SHA1
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C Extensive\sedits\sto\sthe\scomments\sin\sthe\ssqlite.h.in\ssource\sfile\sto\sidentify\ntestable\sstatements\sof\struth\sabout\sthe\sC-language\sinterface.
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D 2009-12-11T03:44:19
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C Add\scomment\sto\sfts3rnd.test\sto\sexplain\show\sthe\stest\sworks.
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D 2009-12-11T07:07:36
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F Makefile.arm-wince-mingw32ce-gcc fcd5e9cd67fe88836360bb4f9ef4cb7f8e2fb5a0
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F Makefile.in c5827ead754ab32b9585487177c93bb00b9497b3
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F Makefile.linux-gcc d53183f4aa6a9192d249731c90dbdffbd2c68654
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@@ -405,7 +402,7 @@ F test/fts3expr.test 05dab77387801e4900009917bb18f556037d82da
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F test/fts3expr2.test 18da930352e5693eaa163a3eacf96233b7290d1a
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F test/fts3malloc.test d02ee86b21edd2b43044e0d6dfdcd26cb6efddcb
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F test/fts3near.test dc196dd17b4606f440c580d45b3d23aa975fd077
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F test/fts3rnd.test d7fe25493aa76f5010df0a6dbfa4dfa14f537c26
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F test/fts3rnd.test b1fd9a0b8bd95014b1c2cb9d5a8f27b5b3afc50d
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F test/func.test af106ed834001738246d276659406823e35cde7b
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F test/func2.test 772d66227e4e6684b86053302e2d74a2500e1e0f
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F test/fuzz.test a4174c3009a3e2c2e14b31b364ebf7ddb49de2c9
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@@ -781,14 +778,7 @@ F tool/speedtest2.tcl ee2149167303ba8e95af97873c575c3e0fab58ff
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F tool/speedtest8.c 2902c46588c40b55661e471d7a86e4dd71a18224
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F tool/speedtest8inst1.c 293327bc76823f473684d589a8160bde1f52c14e
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F tool/vdbe-compress.tcl d70ea6d8a19e3571d7ab8c9b75cba86d1173ff0f
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U drh
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P ea884e1ed8dba1aa0f3cf68fc71923954983f6c6
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R ae6e3fceaf15353a41abf5fd7eaec6f2
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U dan
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Z ad28381e408238f8547c0374a8ed6615
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+1
-1
@@ -1 +1 @@
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ea884e1ed8dba1aa0f3cf68fc71923954983f6c6
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6b740c7cd57d618623ed028be4213dfef860054a
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+68
-1
@@ -9,6 +9,72 @@
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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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@@ -17,12 +83,13 @@ source $testdir/tester.tcl
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ifcapable !fts3 { finish_test ; return }
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source $testdir/fts3_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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