SQLite

    pParse->nErr++;
    goto begin_table_error;
  }
  pTable->zName = zName;
  pTable->iPKey = -1;
  pTable->pSchema = db->aDb[iDb].pSchema;
  pTable->nRef = 1;
  pTable->nRowEst = 1000000;
  assert( pParse->pNewTable==0 );
  pParse->pNewTable = pTable;

  /* If this is the magic sqlite_sequence table used by autoincrement,
  ** then record a pointer to this table in the main database structure
  ** so that INSERT can find the table easily.
  */

** routine goes through and adds the types and collations.
**
** This routine requires that all identifiers in the SELECT
** statement be resolved.
*/
static void selectAddColumnTypeAndCollation(
  Parse *pParse,        /* Parsing contexts */
  int nCol,             /* Number of columns */
  Column *aCol,         /* List of columns */
  Select *pSelect       /* SELECT used to determine types and collations */
){
  sqlite3 *db = pParse->db;
  NameContext sNC;
  Column *pCol;
  CollSeq *pColl;
  int i;
  Expr *p;
  struct ExprList_item *a;


  assert( pSelect!=0 );
  assert( (pSelect->selFlags & SF_Resolved)!=0 );
  assert( nCol==pSelect->pEList->nExpr || db->mallocFailed );
  if( db->mallocFailed ) return;
  memset(&sNC, 0, sizeof(sNC));
  sNC.pSrcList = pSelect->pSrc;
  a = pSelect->pEList->a;
  for(i=0, pCol=aCol; i<nCol; i++, pCol++){
    p = a[i].pExpr;
    pCol->zType = sqlite3DbStrDup(db, columnType(&sNC, p, 0, 0, 0));


    pCol->affinity = sqlite3ExprAffinity(p);
    if( pCol->affinity==0 ) pCol->affinity = SQLITE_AFF_NONE;
    pColl = sqlite3ExprCollSeq(pParse, p);
    if( pColl ){
      pCol->zColl = sqlite3DbStrDup(db, pColl->zName);
    }
  }

}

/*
** Given a SELECT statement, generate a Table structure that describes
** the result set of that SELECT.
*/
Table *sqlite3ResultSetOfSelect(Parse *pParse, Select *pSelect){

    return 0;
  }
  /* The sqlite3ResultSetOfSelect() is only used n contexts where lookaside
  ** is disabled */
  assert( db->lookaside.bEnabled==0 );
  pTab->nRef = 1;
  pTab->zName = 0;
  pTab->nRowEst = 1000000;
  selectColumnsFromExprList(pParse, pSelect->pEList, &pTab->nCol, &pTab->aCol);
  selectAddColumnTypeAndCollation(pParse, pTab->nCol, pTab->aCol, pSelect);
  pTab->iPKey = -1;
  if( db->mallocFailed ){
    sqlite3DeleteTable(db, pTab);
    return 0;
  }
  return pTab;
}

      /* A sub-query in the FROM clause of a SELECT */
      assert( pSel!=0 );
      assert( pFrom->pTab==0 );
      sqlite3WalkSelect(pWalker, pSel);
      pFrom->pTab = pTab = sqlite3DbMallocZero(db, sizeof(Table));
      if( pTab==0 ) return WRC_Abort;
      pTab->nRef = 1;
      pTab->zName = sqlite3MPrintf(db, "sqlite_subquery_%p_", (void*)pTab);
      while( pSel->pPrior ){ pSel = pSel->pPrior; }
      selectColumnsFromExprList(pParse, pSel->pEList, &pTab->nCol, &pTab->aCol);
      pTab->iPKey = -1;
      pTab->nRowEst = 1000000;
      pTab->tabFlags |= TF_Ephemeral;
#endif
    }else{
      /* An ordinary table or view name in the FROM clause */
      assert( pFrom->pTab==0 );
      pFrom->pTab = pTab = sqlite3LocateTableItem(pParse, 0, pFrom);
      if( pTab==0 ) return WRC_Abort;

    for(i=0, pFrom=pTabList->a; i<pTabList->nSrc; i++, pFrom++){
      Table *pTab = pFrom->pTab;
      if( ALWAYS(pTab!=0) && (pTab->tabFlags & TF_Ephemeral)!=0 ){
        /* A sub-query in the FROM clause of a SELECT */
        Select *pSel = pFrom->pSelect;
        assert( pSel );
        while( pSel->pPrior ) pSel = pSel->pPrior;
        selectAddColumnTypeAndCollation(pParse, pTab->nCol, pTab->aCol, pSel);
      }
    }
  }
  return WRC_Continue;
}
#endif

** rows visited by a factor of 16.
*/
static int whereRangeScanEst(
  Parse *pParse,       /* Parsing & code generating context */
  WhereLoopBuilder *pBuilder,
  WhereTerm *pLower,   /* Lower bound on the range. ex: "x>123" Might be NULL */
  WhereTerm *pUpper,   /* Upper bound on the range. ex: "x<455" Might be NULL */
  LogEst *pnOut        /* IN/OUT: Number of rows visited */
){
  int rc = SQLITE_OK;
  int nOut = (int)*pnOut;

  LogEst nNew;

#ifdef SQLITE_ENABLE_STAT3_OR_STAT4
  Index *p = pBuilder->pNew->u.btree.pIndex;
  int nEq = pBuilder->pNew->u.btree.nEq;

  if( p->nSample>0
   && nEq==pBuilder->nRecValid
   && nEq<p->nSampleCol
   && OptimizationEnabled(pParse->db, SQLITE_Stat3) 
  ){
    UnpackedRecord *pRec = pBuilder->pRec;

        nNew = sqlite3LogEst(iUpper - iLower);
      }else{
        nNew = 10;        assert( 10==sqlite3LogEst(2) );
      }
      if( nNew<nOut ){
        nOut = nNew;
      }
      *pnOut = (LogEst)nOut;
      WHERETRACE(0x100, ("range scan regions: %u..%u  est=%d\n",
                         (u32)iLower, (u32)iUpper, nOut));
      return SQLITE_OK;
    }
  }
#else
  UNUSED_PARAMETER(pParse);

  }
  if( pUpper ){
    nNew -= 20;        assert( 20==sqlite3LogEst(4) );
    nOut--;
  }
  if( nNew<10 ) nNew = 10;
  if( nNew<nOut ) nOut = nNew;
  *pnOut = (LogEst)nOut;
  return rc;
}

#ifdef SQLITE_ENABLE_STAT3_OR_STAT4
/*
** Estimate the number of rows that will be returned based on
** an equality constraint x=VALUE and where that VALUE occurs in

      pTop = pTerm;
      pBtm = (pNew->wsFlags & WHERE_BTM_LIMIT)!=0 ?
                     pNew->aLTerm[pNew->nLTerm-2] : 0;
    }
    if( pNew->wsFlags & WHERE_COLUMN_RANGE ){
      /* Adjust nOut and rRun for STAT3 range values */
      assert( pNew->nOut==saved_nOut );
      whereRangeScanEst(pParse, pBuilder, pBtm, pTop, &pNew->nOut);






    }
#ifdef SQLITE_ENABLE_STAT3_OR_STAT4
    if( nInMul==0 
     && pProbe->nSample 
     && pNew->u.btree.nEq<=pProbe->nSampleCol
     && OptimizationEnabled(db, SQLITE_Stat3) 
    ){

  struct SrcList_item *pItem;
  
  pWC = pBuilder->pWC;
  if( pWInfo->wctrlFlags & WHERE_AND_ONLY ) return SQLITE_OK;
  pWCEnd = pWC->a + pWC->nTerm;
  pNew = pBuilder->pNew;
  memset(&sSum, 0, sizeof(sSum));



  for(pTerm=pWC->a; pTerm<pWCEnd && rc==SQLITE_OK; pTerm++){
    if( (pTerm->eOperator & WO_OR)!=0
     && (pTerm->u.pOrInfo->indexable & pNew->maskSelf)!=0 
    ){
      WhereClause * const pOrWC = &pTerm->u.pOrInfo->wc;
      WhereTerm * const pOrWCEnd = &pOrWC->a[pOrWC->nTerm];
      WhereTerm *pOrTerm;
      int once = 1;
      int i, j;
    
      pItem = pWInfo->pTabList->a + pNew->iTab;
      iCur = pItem->iCursor;
      sSubBuild = *pBuilder;
      sSubBuild.pOrderBy = 0;
      sSubBuild.pOrSet = &sCur;

      for(pOrTerm=pOrWC->a; pOrTerm<pOrWCEnd; pOrTerm++){
        if( (pOrTerm->eOperator & WO_AND)!=0 ){
          sSubBuild.pWC = &pOrTerm->u.pAndInfo->wc;

  /* Precompute the cost of sorting the final result set, if the caller
  ** to sqlite3WhereBegin() was concerned about sorting */
  rSortCost = 0;
  if( pWInfo->pOrderBy==0 || nRowEst==0 ){
    aFrom[0].isOrderedValid = 1;
  }else{
    /* TUNING: Estimated cost of sorting is N*log2(N) where N is the
    ** number of output rows. */


    rSortCost = nRowEst + estLog(nRowEst) + 55;
    WHERETRACE(0x002,("---- sort cost=%-3d\n", rSortCost));
  }

  /* Compute successively longer WherePaths using the previous generation
  ** of WherePaths as the basis for the next.  Keep track of the mxChoice
  ** best paths at each generation */

} [concat {-60.06 {} {}} {-39.24 {} encompass -1}]

# EVIDENCE-OF: R-44414-54710 There is a row in the cartesian product
# dataset formed by combining each unique combination of a row from the
# left-hand and right-hand datasets.
#
do_join_test e_select-1.4.2.1 {
  SELECT * FROM x2 %JOIN% x3
} [list -60.06 {} {}      -39.24 {} encompass -1                 \
        -60.06 {} {}      presenting 51 reformation dignified    \
        -60.06 {} {}      conducting -87.24 37.56 {}             \
        -60.06 {} {}      coldest -96 dramatists 82.3            \
        -60.06 {} {}      alerting {} -93.79 {}                  \

        -58 {} 1.21       -39.24 {} encompass -1                 \
        -58 {} 1.21       presenting 51 reformation dignified    \
        -58 {} 1.21       conducting -87.24 37.56 {}             \
        -58 {} 1.21       coldest -96 dramatists 82.3            \
        -58 {} 1.21       alerting {} -93.79 {}                  \

]
# TODO: Come back and add a few more like the above.

# EVIDENCE-OF: R-20659-43267 In other words, if the left-hand dataset
# consists of Nlhs rows of Mlhs columns, and the right-hand dataset of
# Nrhs rows of Mrhs columns, then the cartesian product is a dataset of
# Nlhs.Nrhs rows, each containing Mlhs+Mrhs columns.

  catchsql {
    UPDATE t1 INDEXED BY t1b SET a=a+100
     WHERE (+b IS NULL AND c NOT NULL AND d NOT NULL)
        OR (b NOT NULL AND c IS NULL AND d NOT NULL)
        OR (b NOT NULL AND c NOT NULL AND d IS NULL)
  }
} {1 {no query solution}}
ifcapable stat4||stat3 {
  # When STAT3 is enabled, the "b NOT NULL" terms get translated
  # into b>NULL, which can be satified by the index t1b.  It is a very
  # expensive way to do the query, but it works, and so a solution is possible.
  do_test where9-6.8.3-stat4 {
    catchsql {
      UPDATE t1 INDEXED BY t1b SET a=a+100
       WHERE (b IS NULL AND c NOT NULL AND d NOT NULL)
          OR (b NOT NULL AND c IS NULL AND d NOT NULL)
          OR (b NOT NULL AND c NOT NULL AND d IS NULL)
    }
  } {0 {}}
  do_test where9-6.8.4-stat4 {
    catchsql {
      DELETE FROM t1 INDEXED BY t1b
       WHERE (b IS NULL AND c NOT NULL AND d NOT NULL)
          OR (b NOT NULL AND c IS NULL AND d NOT NULL)
          OR (b NOT NULL AND c NOT NULL AND d IS NULL)
    }
  } {0 {}}
} else {
  do_test where9-6.8.3 {
    catchsql {
      UPDATE t1 INDEXED BY t1b SET a=a+100
       WHERE (b IS NULL AND c NOT NULL AND d NOT NULL)
          OR (b NOT NULL AND c IS NULL AND d NOT NULL)
          OR (b NOT NULL AND c NOT NULL AND d IS NULL)
    }