#pylint: disable=singleton-comparison,not-callable
#pylint: disable=too-many-branches,too-many-arguments,too-many-locals,too-many-statements
+def no_index(expr: SaColumn) -> SaColumn:
+ """ Wrap the given expression, so that the query planner will
+ refrain from using the expression for index lookup.
+ """
+ return sa.func.coalesce(sa.null(), expr) # pylint: disable=not-callable
+
+
def _details_to_bind_params(details: SearchDetails) -> Dict[str, Any]:
""" Create a dictionary from search parameters that can be used
as bind parameter for SQL execute.
t.c.housenumber, t.c.postcode, t.c.country_code,
t.c.importance, t.c.wikipedia,
t.c.parent_place_id, t.c.rank_address, t.c.rank_search,
+ t.c.linked_place_id, t.c.admin_level,
t.c.centroid,
t.c.geometry.ST_Expand(0).label('bbox'))
col = sa.func.ST_SimplifyPreserveTopology(col, details.geometry_simplification)
if details.geometry_output & GeometryFormat.GEOJSON:
- out.append(sa.func.ST_AsGeoJSON(col).label('geometry_geojson'))
+ out.append(sa.func.ST_AsGeoJSON(col, 7).label('geometry_geojson'))
if details.geometry_output & GeometryFormat.TEXT:
out.append(sa.func.ST_AsText(col).label('geometry_text'))
if details.geometry_output & GeometryFormat.KML:
- out.append(sa.func.ST_AsKML(col).label('geometry_kml'))
+ out.append(sa.func.ST_AsKML(col, 7).label('geometry_kml'))
if details.geometry_output & GeometryFormat.SVG:
- out.append(sa.func.ST_AsSVG(col).label('geometry_svg'))
+ out.append(sa.func.ST_AsSVG(col, 0, 7).label('geometry_svg'))
return sql.add_columns(*out)
def _filter_by_layer(table: SaFromClause, layers: DataLayer) -> SaColumn:
orexpr: List[SaExpression] = []
if layers & DataLayer.ADDRESS and layers & DataLayer.POI:
- orexpr.append(table.c.rank_address.between(1, 30))
+ orexpr.append(no_index(table.c.rank_address).between(1, 30))
elif layers & DataLayer.ADDRESS:
- orexpr.append(table.c.rank_address.between(1, 29))
- orexpr.append(sa.and_(table.c.rank_address == 30,
+ orexpr.append(no_index(table.c.rank_address).between(1, 29))
+ orexpr.append(sa.and_(no_index(table.c.rank_address) == 30,
sa.or_(table.c.housenumber != None,
table.c.address.has_key('addr:housename'))))
elif layers & DataLayer.POI:
- orexpr.append(sa.and_(table.c.rank_address == 30,
+ orexpr.append(sa.and_(no_index(table.c.rank_address) == 30,
table.c.class_.not_in(('place', 'building'))))
if layers & DataLayer.MANMADE:
if not layers & DataLayer.NATURAL:
exclude.extend(('natural', 'water', 'waterway'))
orexpr.append(sa.and_(table.c.class_.not_in(tuple(exclude)),
- table.c.rank_address == 0))
+ no_index(table.c.rank_address) == 0))
else:
include = []
if layers & DataLayer.RAILWAY:
if layers & DataLayer.NATURAL:
include.extend(('natural', 'water', 'waterway'))
orexpr.append(sa.and_(table.c.class_.in_(tuple(include)),
- table.c.rank_address == 0))
+ no_index(table.c.rank_address) == 0))
if len(orexpr) == 1:
return orexpr[0]
"""
table = await conn.get_class_table(*category)
- t = conn.t.placex.alias('p')
+ t = conn.t.placex
tgeom = conn.t.placex.alias('pgeom')
sql = _select_placex(t).where(tgeom.c.place_id.in_(ids))\
else_ = tgeom.c.centroid.ST_Expand(0.05))))\
.order_by(tgeom.c.centroid.ST_Distance(table.c.centroid))
- sql = sql.where(t.c.rank_address.between(MIN_RANK_PARAM, MAX_RANK_PARAM))
+ sql = sql.where(no_index(t.c.rank_address).between(MIN_RANK_PARAM, MAX_RANK_PARAM))
if details.countries:
sql = sql.where(t.c.country_code.in_(COUNTRIES_PARAM))
if details.excluded:
sql = sa.select(tgrid.c.country_code,
tgrid.c.geometry.ST_Centroid().ST_Collect().ST_Centroid()
- .label('centroid'))\
+ .label('centroid'),
+ tgrid.c.geometry.ST_Collect().ST_Expand(0).label('bbox'))\
.where(tgrid.c.country_code.in_(self.countries.values))\
.group_by(tgrid.c.country_code)
+ sa.func.coalesce(t.c.derived_name,
sa.cast('', type_=conn.t.types.Composite))
).label('name'),
- sub.c.centroid)\
+ sub.c.centroid, sub.c.bbox)\
.join(sub, t.c.country_code == sub.c.country_code)
+ if details.geometry_output:
+ sql = _add_geometry_columns(sql, sub.c.centroid, details)
+
results = nres.SearchResults()
for row in await conn.execute(sql, _details_to_bind_params(details)):
result = nres.create_from_country_row(row, nres.SearchResult)
assert result
+ result.bbox = Bbox.from_wkb(row.bbox)
result.accuracy = self.penalty + self.countries.get_penalty(row.country_code, 5.0)
results.append(result)
sql = sql.where(t.c.geometry.intersects(VIEWBOX_PARAM))
else:
penalty += sa.case((t.c.geometry.intersects(VIEWBOX_PARAM), 0.0),
- (t.c.geometry.intersects(VIEWBOX2_PARAM), 1.0),
- else_=2.0)
+ (t.c.geometry.intersects(VIEWBOX2_PARAM), 0.5),
+ else_=1.0)
if details.near is not None:
if details.near_radius is not None:
sql: SaLambdaSelect = sa.lambda_stmt(lambda:
sa.select(t.c.place_id, t.c.osm_type, t.c.osm_id, t.c.name,
t.c.class_, t.c.type,
- t.c.address, t.c.extratags,
+ t.c.address, t.c.extratags, t.c.admin_level,
t.c.housenumber, t.c.postcode, t.c.country_code,
t.c.wikipedia,
t.c.parent_place_id, t.c.rank_address, t.c.rank_search,
sql = sql.where(tsearch.c.centroid.intersects(VIEWBOX_PARAM))
else:
sql = sql.where(tsearch.c.centroid.ST_Intersects_no_index(VIEWBOX_PARAM))
+ elif self.expected_count >= 10000:
+ if details.viewbox.area < 0.5:
+ sql = sql.where(tsearch.c.centroid.intersects(VIEWBOX2_PARAM))
+ else:
+ sql = sql.where(tsearch.c.centroid.ST_Intersects_no_index(VIEWBOX2_PARAM))
else:
penalty += sa.case((t.c.geometry.intersects(VIEWBOX_PARAM), 0.0),
- (t.c.geometry.intersects(VIEWBOX2_PARAM), 1.0),
- else_=2.0)
+ (t.c.geometry.intersects(VIEWBOX2_PARAM), 0.5),
+ else_=1.0)
if details.near is not None:
if details.near_radius is not None:
.label('importance'))
sql = sql.order_by(sa.desc(sa.text('importance')))
else:
- sql = sql.order_by(penalty - sa.case((tsearch.c.importance > 0, tsearch.c.importance),
- else_=0.75001-(sa.cast(tsearch.c.search_rank, sa.Float())/40)))
+ if self.expected_count < 10000\
+ or (details.viewbox is not None and details.viewbox.area < 0.5):
+ sql = sql.order_by(
+ penalty - sa.case((tsearch.c.importance > 0, tsearch.c.importance),
+ else_=0.75001-(sa.cast(tsearch.c.search_rank, sa.Float())/40)))
sql = sql.add_columns(t.c.importance)
- sql = sql.add_columns(penalty.label('accuracy'))\
- .order_by(sa.text('accuracy'))
+ sql = sql.add_columns(penalty.label('accuracy'))
+
+ if self.expected_count < 10000:
+ sql = sql.order_by(sa.text('accuracy'))
if self.housenumbers:
hnr_regexp = f"\\m({'|'.join(self.housenumbers.values)})\\M"
if self.qualifiers:
place_sql = place_sql.where(self.qualifiers.sql_restrict(thnr))
- numerals = [int(n) for n in self.housenumbers.values if n.isdigit()]
+ numerals = [int(n) for n in self.housenumbers.values
+ if n.isdigit() and len(n) < 8]
interpol_sql: SaColumn
tiger_sql: SaColumn
if numerals and \
projects / nominatim.git / blobdiff