Use histogram data to improve the row-count estimates on equality constraints.

FossilOrigin-Name: 6bfc5c69eb22938972bbf4e60179952dc215f770

diff --git a/manifest b/manifest
index 00b3208ed7a866c9a80d8cf4d721b518030b173e..cfa47d73c3b35f9f267d53aaea59367882df66e4 100644 (file)
--- a/manifest
+++ b/manifest
@@ -1,8 +1,8 @@
 -----BEGIN PGP SIGNED MESSAGE-----
 Hash: SHA1
 
-C The\sfirst\sof\sa\splanned\sseries\sof\senhancements\sto\sthe\squery\splanner\sthat\nenable\sit\sto\smake\sbetter\suse\sof\ssqlite_stat2\shistograms\swhen\sthe\stable\nhas\smany\srepeated\svalues.
-D 2011-01-20T02:56:37.736
+C Use\shistogram\sdata\sto\simprove\sthe\srow-count\sestimates\son\sequality\sconstraints.
+D 2011-01-20T16:52:09.439
 F Makefile.arm-wince-mingw32ce-gcc d6df77f1f48d690bd73162294bbba7f59507c72f
 F Makefile.in de6498556d536ae60bb8bb10e8c1ba011448658c
 F Makefile.linux-gcc 91d710bdc4998cb015f39edf3cb314ec4f4d7e23
@@ -243,7 +243,7 @@ F src/vtab.c b297e8fa656ab5e66244ab15680d68db0adbec30
 F src/wal.c dbca424f71678f663a286ab2a98f947af1d412a7
 F src/wal.h c1aac6593a0b02b15dc625987e619edeab39292e
 F src/walker.c 3112bb3afe1d85dc52317cb1d752055e9a781f8f
-F src/where.c 5cd6b88d57bfc816ba7f753a3cdf03686d954b8a
+F src/where.c 068ecc5195578aed083a4314e572de9979a489e5
 F test/aggerror.test a867e273ef9e3d7919f03ef4f0e8c0d2767944f2
 F test/alias.test 4529fbc152f190268a15f9384a5651bbbabc9d87
 F test/all.test 51756962d522e474338e9b2ebb26e7364d4aa125
@@ -256,7 +256,7 @@ F test/analyze.test c1eb87067fc16ece7c07e823d6395fd831b270c5
 F test/analyze2.test f45ac8d54bdad822139e53fc6307fc6b5ee41c69
 F test/analyze3.test 820ddfb7591b49607fbaf77240c7955ac3cabb04
 F test/analyze4.test 757b37875cf9bb528d46f74497bc789c88365045
-F test/analyze5.test 18987796646efdf009ca0b8c8f060874a8fe57fb
+F test/analyze5.test b6bd2e4fbbe2ffcf2a22250b28fb1f2a2ca507de
 F test/async.test ad4ba51b77cd118911a3fe1356b0809da9c108c3
 F test/async2.test bf5e2ca2c96763b4cba3d016249ad7259a5603b6
 F test/async3.test 93edaa9122f498e56ea98c36c72abc407f4fb11e
@@ -900,18 +900,14 @@ F tool/speedtest2.tcl ee2149167303ba8e95af97873c575c3e0fab58ff
 F tool/speedtest8.c 2902c46588c40b55661e471d7a86e4dd71a18224
 F tool/speedtest8inst1.c 293327bc76823f473684d589a8160bde1f52c14e
 F tool/vdbe-compress.tcl d70ea6d8a19e3571d7ab8c9b75cba86d1173ff0f
-P 9660a0a22547656cc3765b673d0cee9e1dd829ef
-R 9ff1bb21abd03a28e074b829beb25c52
-T *bgcolor * #a8c7d3
-T *branch * stat2-enhancement
-T *sym-stat2-enhancement *
-T -sym-trunk *
+P 2cd374cd23fa2fd38f49090d6eeb9b1e521d51d5
+R b298fdc0ea4be4264436dd1639cb2644
 U drh
-Z f191562671825ddb731a0f83c41674dd
+Z 10e84b987d0709faa336295530a6ebae
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 Version: GnuPG v1.4.6 (GNU/Linux)
 
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-=2eyE
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diff --git a/manifest.uuid b/manifest.uuid
index a32e6ed42f8d84f985c44665249d39152c974dc5..cbf97eefeabbdc8455e84286fabbcb770319798b 100644 (file)
--- a/manifest.uuid
+++ b/manifest.uuid
@@ -1 +1 @@
-2cd374cd23fa2fd38f49090d6eeb9b1e521d51d5
\ No newline at end of file
+6bfc5c69eb22938972bbf4e60179952dc215f770
\ No newline at end of file

diff --git a/src/where.c b/src/where.c
index 734f24019ff9a33bd2d91f9d44fc2289c82c4bb2..048ab940a3f038779aca21ef8cae709f8a2b3c92 100644 (file)
--- a/src/where.c
+++ b/src/where.c
@@ -2461,6 +2461,51 @@ range_est_fallback:
   return rc;
 }
 
+#ifdef SQLITE_ENABLE_STAT2
+/*
+** Estimate the number of rows that will be returned based on
+** an equality constraint x=VALUE and where that VALUE occurs in
+** the histogram data.  This only works when x is the left-most
+** column of an index and sqlite_stat2 histogram data is available
+** for that index.
+**
+** Write the estimated row count into *pnRow.  If unable to make
+** an estimate, leave *pnRow unchanged.
+*/
+void whereEqScanEst(
+  Parse *pParse,       /* Parsing & code generating context */
+  Index *p,            /* The index whose left-most column is pTerm */
+  WhereTerm *pTerm,    /* The x=VALUE constraint */
+  double *pnRow        /* Write the revised row estimate here */
+){
+  sqlite3_value *pRhs = 0;  /* VALUE on right-hand side of pTerm */
+  int iLower, iUpper;       /* Range of histogram regions containing pRhs */
+  u8 aff;                   /* Column affinity */
+  int rc;                   /* Subfunction return code */
+  double nRowEst;           /* New estimate of the number of rows */
+
+  assert( p->aSample!=0 );
+  assert( pTerm->eOperator==WO_EQ );
+  aff = p->pTable->aCol[p->aiColumn[0]].affinity;
+  rc = valueFromExpr(pParse, pTerm->pExpr->pRight, aff, &pRhs);
+  if( rc ) goto whereEqScanEst_cancel;
+  rc = whereRangeRegion(pParse, p, pRhs, 0, &iLower);
+  if( rc ) goto whereEqScanEst_cancel;
+  rc = whereRangeRegion(pParse, p, pRhs, 1, &iUpper);
+  if( rc ) goto whereEqScanEst_cancel;
+  if( iLower>=iUpper ){
+    nRowEst = p->aiRowEst[0]/(SQLITE_INDEX_SAMPLES*2);
+    if( nRowEst<*pnRow ) *pnRow = nRowEst;
+  }else{
+    nRowEst = (iUpper-iLower)*p->aiRowEst[0]/SQLITE_INDEX_SAMPLES;
+    *pnRow = nRowEst;
+  }
+
+whereEqScanEst_cancel:
+  sqlite3ValueFree(pRhs);
+}
+#endif /* defined(SQLITE_ENABLE_STAT2) */
+
 
 /*
 ** Find the query plan for accessing a particular table.  Write the
@@ -2624,10 +2669,13 @@ static void bestBtreeIndex(
     int bInEst = 0;
     int nInMul = 1;
     int estBound = 100;
-    int nBound = 0;             /* Number of range constraints seen */
+    int nBound = 0;               /* Number of range constraints seen */
     int bSort = 0;
     int bLookup = 0;
-    WhereTerm *pTerm;           /* A single term of the WHERE clause */
+    WhereTerm *pTerm;             /* A single term of the WHERE clause */
+#ifdef SQLITE_ENABLE_STAT2
+    WhereTerm *pFirstEqTerm = 0;  /* First WO_EQ term */
+#endif
 
     /* Determine the values of nEq and nInMul */
     for(nEq=0; nEq<pProbe->nColumn; nEq++){
@@ -2647,6 +2695,11 @@ static void bestBtreeIndex(
       }else if( pTerm->eOperator & WO_ISNULL ){
         wsFlags |= WHERE_COLUMN_NULL;
       }
+#ifdef SQLITE_ENABLE_STAT2
+      else if( nEq==0 && pProbe->aSample ){
+        pFirstEqTerm = pTerm;
+      }
+#endif
       used |= pTerm->prereqRight;
     }
 
@@ -2723,6 +2776,17 @@ static void bestBtreeIndex(
       nInMul = (int)(nRow / aiRowEst[nEq]);
     }
 
+#ifdef SQLITE_ENABLE_STAT2
+    /* If the constraint is of the form x=VALUE and histogram
+    ** data is available for column x, then it might be possible
+    ** to get a better estimate on the number of rows based on
+    ** VALUE and how common that value is according to the histogram.
+    */
+    if( nRow>(double)1 && nEq==1 && pFirstEqTerm!=0 ){
+      whereEqScanEst(pParse, pProbe, pFirstEqTerm, &nRow);
+    }
+#endif /* SQLITE_ENABLE_STAT2 */
+
     /* Assume constant cost to access a row and logarithmic cost to
     ** do a binary search.  Hence, the initial cost is the number of output
     ** rows plus log2(table-size) times the number of binary searches.

diff --git a/test/analyze5.test b/test/analyze5.test
index 233e7ff9e702b01ca691dce25c23bab1949656fe..da0dbe3eadace18a8572fa3cd9b66070c6fa1a6f 100644 (file)
--- a/test/analyze5.test
+++ b/test/analyze5.test
@@ -214,6 +214,14 @@ foreach {testid where rows} {
  20  {y>=0 AND y<'charlie'}          700
  21  {y>=0 AND y<='charlie'}         900
  22  {y>=0 AND y<'delta'}            900
+ 23  {y>'alpha' AND y<x'00'}         600
+ 24  {y>='bravo' AND y<x'00'}        600
+ 25  {y>'bravo' AND y<x'00'}         300
+ 26  {y>='charlie' AND y<x'00'}      300
+ 27  {y>'charlie' AND y<x'00'}       100
+ 28  {y>='delta' AND y<x'00'}        100
+ 29  {y>'delta' AND y<x'00'}          50
+ 30  {y>='echo' AND y<x'00'}          50
 } {
   do_test analyze5-3.$testid {
     eqp "SELECT * FROM t1 WHERE $where"