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Overview
Comment:Fix the generation of sqlite3_rtree_query_info.iRowid and add test cases to verify that it is fixed.
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Timelines: family | ancestors | descendants | both | rtree-enhancements
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SHA1: eba95ead49f8f8ce45d400186562ff0066537c5c
User & Date: drh 2014-04-21 18:13:37.363
Context
2014-04-25
16:29
Enhance the sqlite3_rtree_query_info object to report on the number of elements in the priority queue at each level. (Closed-Leaf check-in: f7dad408dd user: drh tags: rtree-enhancements)
2014-04-21
18:13
Fix the generation of sqlite3_rtree_query_info.iRowid and add test cases to verify that it is fixed. (check-in: eba95ead49 user: drh tags: rtree-enhancements)
15:53
Be sure to initialize the sqlite3_rtree_query_info.iRowid field for the leaves of the R-Tree when doing a query callback search. (check-in: 4394693882 user: drh tags: rtree-enhancements)
Changes
Unified Diff Ignore Whitespace Patch
Changes to ext/rtree/rtree.c.
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  int nCoord = pInfo->nCoord;                           /* No. of coordinates */
  int rc;                                             /* Callback return code */
  sqlite3_rtree_dbl aCoord[RTREE_MAX_DIMENSIONS*2];   /* Decoded coordinates */

  assert( pConstraint->op==RTREE_MATCH || pConstraint->op==RTREE_QUERY );
  assert( nCoord==2 || nCoord==4 || nCoord==6 || nCoord==8 || nCoord==10 );

  if( pConstraint->op==RTREE_MATCH && pSearch->iLevel==1 ){
    pInfo->iRowid = readInt64(pCellData);
  }
  pCellData += 8;
  for(i=0; i<nCoord; i++, pCellData += 4){
    RTREE_DECODE_COORD(eInt, pCellData, aCoord[i]);
  }
  if( pConstraint->op==RTREE_MATCH ){








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  int nCoord = pInfo->nCoord;                           /* No. of coordinates */
  int rc;                                             /* Callback return code */
  sqlite3_rtree_dbl aCoord[RTREE_MAX_DIMENSIONS*2];   /* Decoded coordinates */

  assert( pConstraint->op==RTREE_MATCH || pConstraint->op==RTREE_QUERY );
  assert( nCoord==2 || nCoord==4 || nCoord==6 || nCoord==8 || nCoord==10 );

  if( pConstraint->op==RTREE_QUERY && pSearch->iLevel==1 ){
    pInfo->iRowid = readInt64(pCellData);
  }
  pCellData += 8;
  for(i=0; i<nCoord; i++, pCellData += 4){
    RTREE_DECODE_COORD(eInt, pCellData, aCoord[i]);
  }
  if( pConstraint->op==RTREE_MATCH ){
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Changes to ext/rtree/rtreeE.test.
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# This causes the 200s to sort before the 100s and the 0s to sort before
# last.
#
do_execsql_test rtreeE-1.4 {
  SELECT id FROM rt1 WHERE id MATCH Qcircle(0,0,1000,3) AND id%100==0
} {200 100 0}











# Construct a large 2-D RTree with thousands of random entries.
#
do_test rtreeE-2.1 {
  db eval {
    CREATE TABLE t2(id,x0,x1,y0,y1);
    CREATE VIRTUAL TABLE rt2 USING rtree(id,x0,x1,y0,y1);
    BEGIN;








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# This causes the 200s to sort before the 100s and the 0s to sort before
# last.
#
do_execsql_test rtreeE-1.4 {
  SELECT id FROM rt1 WHERE id MATCH Qcircle(0,0,1000,3) AND id%100==0
} {200 100 0}

# Exclude odd rowids on a depth-first search
do_execsql_test rtreeE-1.5 {
  SELECT id FROM rt1 WHERE id MATCH Qcircle(0,0,1000,4) ORDER BY +id
} {0 2 4 6 8 10 12 14 16 18 20 22 24 100 102 104 106 108 110 112 114 116 118 120 122 124 200 202 204 206 208 210 212 214 216 218 220 222 224}

# Exclude odd rowids on a breadth-first search.
do_execsql_test rtreeE-1.6 {
  SELECT id FROM rt1 WHERE id MATCH Qcircle(0,0,1000,5) ORDER BY +id
} {0 2 4 6 8 10 12 14 16 18 20 22 24 100 102 104 106 108 110 112 114 116 118 120 122 124 200 202 204 206 208 210 212 214 216 218 220 222 224}

# Construct a large 2-D RTree with thousands of random entries.
#
do_test rtreeE-2.1 {
  db eval {
    CREATE TABLE t2(id,x0,x1,y0,y1);
    CREATE VIRTUAL TABLE rt2 USING rtree(id,x0,x1,y0,y1);
    BEGIN;
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Changes to src/test_rtree.c.
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  if( pCircle->eScoreType==1 ){
    /* Depth first search */
    p->rScore = p->iLevel;
  }else if( pCircle->eScoreType==2 ){
    /* Breadth first search */
    p->rScore = 100 - p->iLevel;
  }else{
    /* Depth-first search, except sort the leaf nodes by area with
    ** the largest area first */
    if( p->iLevel==1 ){
      p->rScore = 1.0 - (xmax-xmin)*(ymax-ymin)/pCircle->mxArea;
      if( p->rScore<0.01 ) p->rScore = 0.01;
    }else{
      p->rScore = 0.0;
    }








  }
  if( nWithin==0 ){
    p->eWithin = NOT_WITHIN;
  }else if( nWithin>=4 ){
    p->eWithin = FULLY_WITHIN;
  }else{
    p->eWithin = PARTLY_WITHIN;








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  if( pCircle->eScoreType==1 ){
    /* Depth first search */
    p->rScore = p->iLevel;
  }else if( pCircle->eScoreType==2 ){
    /* Breadth first search */
    p->rScore = 100 - p->iLevel;
  }else if( pCircle->eScoreType==3 ){
    /* Depth-first search, except sort the leaf nodes by area with
    ** the largest area first */
    if( p->iLevel==1 ){
      p->rScore = 1.0 - (xmax-xmin)*(ymax-ymin)/pCircle->mxArea;
      if( p->rScore<0.01 ) p->rScore = 0.01;
    }else{
      p->rScore = 0.0;
    }
  }else if( pCircle->eScoreType==4 ){
    /* Depth-first search, except exclude odd rowids */
    p->rScore = p->iLevel;
    if( p->iRowid&1 ) nWithin = 0;
  }else{
    /* Breadth-first search, except exclude odd rowids */
    p->rScore = 100 - p->iLevel;
    if( p->iRowid&1 ) nWithin = 0;
  }
  if( nWithin==0 ){
    p->eWithin = NOT_WITHIN;
  }else if( nWithin>=4 ){
    p->eWithin = FULLY_WITHIN;
  }else{
    p->eWithin = PARTLY_WITHIN;
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