self.expected_count = expected_count
- async def lookup(self, conn: SearchConnection,
- details: SearchDetails) -> nres.SearchResults:
- """ Find results for the search in the database.
+ def _inner_search_name_cte(self, conn: SearchConnection,
+ details: SearchDetails) -> 'sa.CTE':
+ """ Create a subquery that preselects the rows in the search_name
+ table.
"""
- t = conn.t.placex
- tsearch = conn.t.search_name
-
- sql: SaLambdaSelect = sa.lambda_stmt(lambda:
- _select_placex(t).where(t.c.place_id == tsearch.c.place_id))
-
-
- if details.geometry_output:
- sql = _add_geometry_columns(sql, t.c.geometry, details)
+ t = conn.t.search_name
penalty: SaExpression = sa.literal(self.penalty)
for ranking in self.rankings:
- penalty += ranking.sql_penalty(tsearch)
+ penalty += ranking.sql_penalty(t)
+
+ sql = sa.select(t.c.place_id, t.c.search_rank, t.c.address_rank,
+ t.c.country_code, t.c.centroid,
+ t.c.name_vector, t.c.nameaddress_vector,
+ sa.case((t.c.importance > 0, t.c.importance),
+ else_=0.40001-(sa.cast(t.c.search_rank, sa.Float())/75))
+ .label('importance'),
+ penalty.label('penalty'))
for lookup in self.lookups:
- sql = sql.where(lookup.sql_condition(tsearch))
+ sql = sql.where(lookup.sql_condition(t))
if self.countries:
- sql = sql.where(tsearch.c.country_code.in_(self.countries.values))
+ sql = sql.where(t.c.country_code.in_(self.countries.values))
if self.postcodes:
# if a postcode is given, don't search for state or country level objects
- sql = sql.where(tsearch.c.address_rank > 9)
- tpc = conn.t.postcode
- pcs = self.postcodes.values
- if self.expected_count > 5000:
+ sql = sql.where(t.c.address_rank > 9)
+ if self.expected_count > 10000:
# Many results expected. Restrict by postcode.
+ tpc = conn.t.postcode
sql = sql.where(sa.select(tpc.c.postcode)
- .where(tpc.c.postcode.in_(pcs))
- .where(tsearch.c.centroid.within_distance(tpc.c.geometry, 0.12))
+ .where(tpc.c.postcode.in_(self.postcodes.values))
+ .where(t.c.centroid.within_distance(tpc.c.geometry, 0.4))
.exists())
- # Less results, only have a preference for close postcodes
- pc_near = sa.select(sa.func.min(tpc.c.geometry.ST_Distance(tsearch.c.centroid)))\
+ if details.viewbox is not None:
+ if details.bounded_viewbox:
+ sql = sql.where(t.c.centroid
+ .intersects(VIEWBOX_PARAM,
+ use_index=details.viewbox.area < 0.2))
+ elif not self.postcodes and not self.housenumbers and self.expected_count >= 10000:
+ sql = sql.where(t.c.centroid
+ .intersects(VIEWBOX2_PARAM,
+ use_index=details.viewbox.area < 0.5))
+
+ if details.near is not None and details.near_radius is not None:
+ if details.near_radius < 0.1:
+ sql = sql.where(t.c.centroid.within_distance(NEAR_PARAM,
+ NEAR_RADIUS_PARAM))
+ else:
+ sql = sql.where(t.c.centroid
+ .ST_Distance(NEAR_PARAM) < NEAR_RADIUS_PARAM)
+
+ if self.housenumbers:
+ sql = sql.where(t.c.address_rank.between(16, 30))
+ else:
+ if details.excluded:
+ sql = sql.where(_exclude_places(t))
+ if details.min_rank > 0:
+ sql = sql.where(sa.or_(t.c.address_rank >= MIN_RANK_PARAM,
+ t.c.search_rank >= MIN_RANK_PARAM))
+ if details.max_rank < 30:
+ sql = sql.where(sa.or_(t.c.address_rank <= MAX_RANK_PARAM,
+ t.c.search_rank <= MAX_RANK_PARAM))
+
+ inner = sql.limit(10000).order_by(sa.desc(sa.text('importance'))).subquery()
+
+ sql = sa.select(inner.c.place_id, inner.c.search_rank, inner.c.address_rank,
+ inner.c.country_code, inner.c.centroid, inner.c.importance,
+ inner.c.penalty)
+
+ # If the query is not an address search or has a geographic preference,
+ # preselect most important items to restrict the number of places
+ # that need to be looked up in placex.
+ if not self.housenumbers\
+ and (details.viewbox is None or details.bounded_viewbox)\
+ and (details.near is None or details.near_radius is not None)\
+ and not self.qualifiers:
+ sql = sql.add_columns(sa.func.first_value(inner.c.penalty - inner.c.importance)
+ .over(order_by=inner.c.penalty - inner.c.importance)
+ .label('min_penalty'))
+
+ inner = sql.subquery()
+
+ sql = sa.select(inner.c.place_id, inner.c.search_rank, inner.c.address_rank,
+ inner.c.country_code, inner.c.centroid, inner.c.importance,
+ inner.c.penalty)\
+ .where(inner.c.penalty - inner.c.importance < inner.c.min_penalty + 0.5)
+
+ return sql.cte('searches')
+
+
+ async def lookup(self, conn: SearchConnection,
+ details: SearchDetails) -> nres.SearchResults:
+ """ Find results for the search in the database.
+ """
+ t = conn.t.placex
+ tsearch = self._inner_search_name_cte(conn, details)
+
+ sql = _select_placex(t).join(tsearch, t.c.place_id == tsearch.c.place_id)
+
+ if details.geometry_output:
+ sql = _add_geometry_columns(sql, t.c.geometry, details)
+
+ penalty: SaExpression = tsearch.c.penalty
+
+ if self.postcodes:
+ tpc = conn.t.postcode
+ pcs = self.postcodes.values
+
+ pc_near = sa.select(sa.func.min(tpc.c.geometry.ST_Distance(t.c.centroid)))\
.where(tpc.c.postcode.in_(pcs))\
.scalar_subquery()
penalty += sa.case((t.c.postcode.in_(pcs), 0.0),
else_=sa.func.coalesce(pc_near, cast(SaColumn, 2.0)))
- if details.viewbox is not None:
- if details.bounded_viewbox:
- sql = sql.where(tsearch.c.centroid
- .intersects(VIEWBOX_PARAM,
- use_index=details.viewbox.area < 0.2))
- elif not self.postcodes and not self.housenumbers and self.expected_count >= 10000:
- sql = sql.where(tsearch.c.centroid
- .intersects(VIEWBOX2_PARAM,
- use_index=details.viewbox.area < 0.5))
- else:
- penalty += sa.case((t.c.geometry.intersects(VIEWBOX_PARAM, use_index=False), 0.0),
- (t.c.geometry.intersects(VIEWBOX2_PARAM, use_index=False), 0.5),
- else_=1.0)
+ if details.viewbox is not None and not details.bounded_viewbox:
+ penalty += sa.case((t.c.geometry.intersects(VIEWBOX_PARAM, use_index=False), 0.0),
+ (t.c.geometry.intersects(VIEWBOX2_PARAM, use_index=False), 0.5),
+ else_=1.0)
if details.near is not None:
- if details.near_radius is not None:
- if details.near_radius < 0.1:
- sql = sql.where(tsearch.c.centroid.within_distance(NEAR_PARAM,
- NEAR_RADIUS_PARAM))
- else:
- sql = sql.where(tsearch.c.centroid
- .ST_Distance(NEAR_PARAM) < NEAR_RADIUS_PARAM)
sql = sql.add_columns((-tsearch.c.centroid.ST_Distance(NEAR_PARAM))
.label('importance'))
sql = sql.order_by(sa.desc(sa.text('importance')))
else:
- 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_=0.40001-(sa.cast(tsearch.c.search_rank, sa.Float())/75)))
- sql = sql.add_columns(t.c.importance)
-
+ sql = sql.order_by(penalty - tsearch.c.importance)
+ sql = sql.add_columns(tsearch.c.importance)
- sql = sql.add_columns(penalty.label('accuracy'))
- if self.expected_count < 10000:
- sql = sql.order_by(sa.text('accuracy'))
+ sql = sql.add_columns(penalty.label('accuracy'))\
+ .order_by(sa.text('accuracy'))
if self.housenumbers:
hnr_list = '|'.join(self.housenumbers.values)
- sql = sql.where(tsearch.c.address_rank.between(16, 30))\
- .where(sa.or_(tsearch.c.address_rank < 30,
- sa.func.RegexpWord(hnr_list, t.c.housenumber)))
-
- # Cross check for housenumbers, need to do that on a rather large
- # set. Worst case there are 40.000 main streets in OSM.
- inner = sql.limit(10000).subquery()
+ inner = sql.where(sa.or_(tsearch.c.address_rank < 30,
+ sa.func.RegexpWord(hnr_list, t.c.housenumber)))\
+ .subquery()
# Housenumbers from placex
thnr = conn.t.placex.alias('hnr')
.where(t.c.indexed_status == 0)
if self.qualifiers:
sql = sql.where(self.qualifiers.sql_restrict(t))
- if details.excluded:
- sql = sql.where(_exclude_places(tsearch))
- if details.min_rank > 0:
- sql = sql.where(sa.or_(tsearch.c.address_rank >= MIN_RANK_PARAM,
- tsearch.c.search_rank >= MIN_RANK_PARAM))
- if details.max_rank < 30:
- sql = sql.where(sa.or_(tsearch.c.address_rank <= MAX_RANK_PARAM,
- tsearch.c.search_rank <= MAX_RANK_PARAM))
if details.layers is not None:
sql = sql.where(_filter_by_layer(t, details.layers))
([20], [101, 100])])
def test_lookup_all_match(self, apiobj, frontend, lookup_type, rank, res):
lookup = FieldLookup('name_vector', [1,2], lookup_type)
- ranking = FieldRanking('name_vector', 0.9, [RankedTokens(0.0, rank)])
+ ranking = FieldRanking('name_vector', 0.4, [RankedTokens(0.0, rank)])
results = run_search(apiobj, frontend, 0.1, [lookup], [ranking])
@pytest.mark.parametrize('lookup_type', [LookupAll, Restrict])
def test_lookup_all_partial_match(self, apiobj, frontend, lookup_type):
lookup = FieldLookup('name_vector', [1,20], lookup_type)
- ranking = FieldRanking('name_vector', 0.9, [RankedTokens(0.0, [21])])
+ ranking = FieldRanking('name_vector', 0.4, [RankedTokens(0.0, [21])])
results = run_search(apiobj, frontend, 0.1, [lookup], [ranking])
([20], [101, 100])])
def test_lookup_any_match(self, apiobj, frontend, rank, res):
lookup = FieldLookup('name_vector', [11,21], LookupAny)
- ranking = FieldRanking('name_vector', 0.9, [RankedTokens(0.0, rank)])
+ ranking = FieldRanking('name_vector', 0.4, [RankedTokens(0.0, rank)])
results = run_search(apiobj, frontend, 0.1, [lookup], [ranking])
def test_lookup_any_partial_match(self, apiobj, frontend):
lookup = FieldLookup('name_vector', [20], LookupAll)
- ranking = FieldRanking('name_vector', 0.9, [RankedTokens(0.0, [21])])
+ ranking = FieldRanking('name_vector', 0.4, [RankedTokens(0.0, [21])])
results = run_search(apiobj, frontend, 0.1, [lookup], [ranking])
@pytest.mark.parametrize('cc,res', [('us', 100), ('mx', 101)])
def test_lookup_restrict_country(self, apiobj, frontend, cc, res):
lookup = FieldLookup('name_vector', [1,2], LookupAll)
- ranking = FieldRanking('name_vector', 0.9, [RankedTokens(0.0, [10])])
+ ranking = FieldRanking('name_vector', 0.4, [RankedTokens(0.0, [10])])
results = run_search(apiobj, frontend, 0.1, [lookup], [ranking], ccodes=[cc])
def test_lookup_restrict_placeid(self, apiobj, frontend):
lookup = FieldLookup('name_vector', [1,2], LookupAll)
- ranking = FieldRanking('name_vector', 0.9, [RankedTokens(0.0, [10])])
+ ranking = FieldRanking('name_vector', 0.4, [RankedTokens(0.0, [10])])
results = run_search(apiobj, frontend, 0.1, [lookup], [ranking],
details=SearchDetails(excluded=[101]))
napi.GeometryFormat.TEXT])
def test_return_geometries(self, apiobj, frontend, geom):
lookup = FieldLookup('name_vector', [20], LookupAll)
- ranking = FieldRanking('name_vector', 0.9, [RankedTokens(0.0, [21])])
+ ranking = FieldRanking('name_vector', 0.4, [RankedTokens(0.0, [21])])
results = run_search(apiobj, frontend, 0.1, [lookup], [ranking],
details=SearchDetails(geometry_output=geom))
centroid=(5.6, 4.3))
lookup = FieldLookup('name_vector', [55], LookupAll)
- ranking = FieldRanking('name_vector', 0.9, [RankedTokens(0.0, [21])])
+ ranking = FieldRanking('name_vector', 0.4, [RankedTokens(0.0, [21])])
results = run_search(apiobj, frontend, 0.1, [lookup], [ranking],
details=SearchDetails(geometry_output=napi.GeometryFormat.GEOJSON,
def test_prefer_near(self, apiobj, frontend):
lookup = FieldLookup('name_vector', [1, 2], LookupAll)
- ranking = FieldRanking('name_vector', 0.9, [RankedTokens(0.0, [21])])
+ ranking = FieldRanking('name_vector', 0.4, [RankedTokens(0.0, [21])])
api = frontend(apiobj, options=APIOPTIONS)
results = run_search(api, None, 0.1, [lookup], [ranking])
apiobj.add_placex(place_id=991, class_='highway', type='service',
rank_search=27, rank_address=27,
postcode='11221',
- centroid=(10.1, 10.1),
- geometry='LINESTRING(9.995 10.1, 10.005 10.1)')
- apiobj.add_search_name(991, names=[111], centroid=(10.1, 10.1),
+ centroid=(10.3, 10.3),
+ geometry='LINESTRING(9.995 10.3, 10.005 10.3)')
+ apiobj.add_search_name(991, names=[111], centroid=(10.3, 10.3),
search_rank=27, address_rank=27)
apiobj.add_postcode(place_id=100, country_code='ch', postcode='11225',
geometry='POINT(10 10)')
projects / nominatim.git / commitdiff