X-Git-Url: https://git.openstreetmap.org./nominatim.git/blobdiff_plain/c42273a4db2d7b4fe05a0be9210901d35e038887..b427fc79656124cd91475ac26016f5865fbc04f3:/nominatim/api/search/db_searches.py diff --git a/nominatim/api/search/db_searches.py b/nominatim/api/search/db_searches.py index f0d75ad1..555819e7 100644 --- a/nominatim/api/search/db_searches.py +++ b/nominatim/api/search/db_searches.py @@ -7,16 +7,245 @@ """ Implementation of the acutal database accesses for forward search. """ +from typing import List, Tuple, AsyncIterator, Dict, Any, Callable, cast import abc +import sqlalchemy as sa + +from nominatim.typing import SaFromClause, SaScalarSelect, SaColumn, \ + SaExpression, SaSelect, SaLambdaSelect, SaRow, SaBind from nominatim.api.connection import SearchConnection -from nominatim.api.types import SearchDetails +from nominatim.api.types import SearchDetails, DataLayer, GeometryFormat, Bbox import nominatim.api.results as nres from nominatim.api.search.db_search_fields import SearchData, WeightedCategories +from nominatim.db.sqlalchemy_types import Geometry, IntArray + +#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. + """ + return {'limit': details.max_results, + 'min_rank': details.min_rank, + 'max_rank': details.max_rank, + 'viewbox': details.viewbox, + 'viewbox2': details.viewbox_x2, + 'near': details.near, + 'near_radius': details.near_radius, + 'excluded': details.excluded, + 'countries': details.countries} + + +LIMIT_PARAM: SaBind = sa.bindparam('limit') +MIN_RANK_PARAM: SaBind = sa.bindparam('min_rank') +MAX_RANK_PARAM: SaBind = sa.bindparam('max_rank') +VIEWBOX_PARAM: SaBind = sa.bindparam('viewbox', type_=Geometry) +VIEWBOX2_PARAM: SaBind = sa.bindparam('viewbox2', type_=Geometry) +NEAR_PARAM: SaBind = sa.bindparam('near', type_=Geometry) +NEAR_RADIUS_PARAM: SaBind = sa.bindparam('near_radius') +COUNTRIES_PARAM: SaBind = sa.bindparam('countries') + + +def filter_by_area(sql: SaSelect, t: SaFromClause, + details: SearchDetails, avoid_index: bool = False) -> SaSelect: + """ Apply SQL statements for filtering by viewbox and near point, + if applicable. + """ + if details.near is not None and details.near_radius is not None: + if details.near_radius < 0.1 and not avoid_index: + sql = sql.where(t.c.geometry.within_distance(NEAR_PARAM, NEAR_RADIUS_PARAM)) + else: + sql = sql.where(t.c.geometry.ST_Distance(NEAR_PARAM) <= NEAR_RADIUS_PARAM) + if details.viewbox is not None and details.bounded_viewbox: + sql = sql.where(t.c.geometry.intersects(VIEWBOX_PARAM, + use_index=not avoid_index and + details.viewbox.area < 0.2)) + + return sql + + +def _exclude_places(t: SaFromClause) -> Callable[[], SaExpression]: + return lambda: t.c.place_id.not_in(sa.bindparam('excluded')) + + +def _select_placex(t: SaFromClause) -> SaSelect: + return 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.housenumber, t.c.postcode, t.c.country_code, + 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')) + + +def _add_geometry_columns(sql: SaLambdaSelect, col: SaColumn, details: SearchDetails) -> SaSelect: + out = [] + + if details.geometry_simplification > 0.0: + col = sa.func.ST_SimplifyPreserveTopology(col, details.geometry_simplification) + + if details.geometry_output & GeometryFormat.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, 7).label('geometry_kml')) + if details.geometry_output & GeometryFormat.SVG: + out.append(sa.func.ST_AsSVG(col, 0, 7).label('geometry_svg')) + + return sql.add_columns(*out) + + +def _make_interpolation_subquery(table: SaFromClause, inner: SaFromClause, + numerals: List[int], details: SearchDetails) -> SaScalarSelect: + all_ids = sa.func.ArrayAgg(table.c.place_id) + sql = sa.select(all_ids).where(table.c.parent_place_id == inner.c.place_id) + + if len(numerals) == 1: + sql = sql.where(sa.between(numerals[0], table.c.startnumber, table.c.endnumber))\ + .where((numerals[0] - table.c.startnumber) % table.c.step == 0) + else: + sql = sql.where(sa.or_( + *(sa.and_(sa.between(n, table.c.startnumber, table.c.endnumber), + (n - table.c.startnumber) % table.c.step == 0) + for n in numerals))) + + if details.excluded: + sql = sql.where(_exclude_places(table)) + + return sql.scalar_subquery() + + +def _filter_by_layer(table: SaFromClause, layers: DataLayer) -> SaColumn: + orexpr: List[SaExpression] = [] + if layers & DataLayer.ADDRESS and layers & DataLayer.POI: + orexpr.append(no_index(table.c.rank_address).between(1, 30)) + elif layers & DataLayer.ADDRESS: + orexpr.append(no_index(table.c.rank_address).between(1, 29)) + orexpr.append(sa.func.IsAddressPoint(table)) + elif layers & DataLayer.POI: + orexpr.append(sa.and_(no_index(table.c.rank_address) == 30, + table.c.class_.not_in(('place', 'building')))) + + if layers & DataLayer.MANMADE: + exclude = [] + if not layers & DataLayer.RAILWAY: + exclude.append('railway') + if not layers & DataLayer.NATURAL: + exclude.extend(('natural', 'water', 'waterway')) + orexpr.append(sa.and_(table.c.class_.not_in(tuple(exclude)), + no_index(table.c.rank_address) == 0)) + else: + include = [] + if layers & DataLayer.RAILWAY: + include.append('railway') + if layers & DataLayer.NATURAL: + include.extend(('natural', 'water', 'waterway')) + orexpr.append(sa.and_(table.c.class_.in_(tuple(include)), + no_index(table.c.rank_address) == 0)) + + if len(orexpr) == 1: + return orexpr[0] + + return sa.or_(*orexpr) + + +def _interpolated_position(table: SaFromClause, nr: SaColumn) -> SaColumn: + pos = sa.cast(nr - table.c.startnumber, sa.Float) / (table.c.endnumber - table.c.startnumber) + return sa.case( + (table.c.endnumber == table.c.startnumber, table.c.linegeo.ST_Centroid()), + else_=table.c.linegeo.ST_LineInterpolatePoint(pos)).label('centroid') + + +async def _get_placex_housenumbers(conn: SearchConnection, + place_ids: List[int], + details: SearchDetails) -> AsyncIterator[nres.SearchResult]: + t = conn.t.placex + sql = _select_placex(t).add_columns(t.c.importance)\ + .where(t.c.place_id.in_(place_ids)) + + if details.geometry_output: + sql = _add_geometry_columns(sql, t.c.geometry, details) + + for row in await conn.execute(sql): + result = nres.create_from_placex_row(row, nres.SearchResult) + assert result + result.bbox = Bbox.from_wkb(row.bbox) + yield result + + +def _int_list_to_subquery(inp: List[int]) -> 'sa.Subquery': + """ Create a subselect that returns the given list of integers + as rows in the column 'nr'. + """ + vtab = sa.func.JsonArrayEach(sa.type_coerce(inp, sa.JSON))\ + .table_valued(sa.column('value', type_=sa.JSON)) + return sa.select(sa.cast(sa.cast(vtab.c.value, sa.Text), sa.Integer).label('nr')).subquery() + + +async def _get_osmline(conn: SearchConnection, place_ids: List[int], + numerals: List[int], + details: SearchDetails) -> AsyncIterator[nres.SearchResult]: + t = conn.t.osmline + + values = _int_list_to_subquery(numerals) + sql = sa.select(t.c.place_id, t.c.osm_id, + t.c.parent_place_id, t.c.address, + values.c.nr.label('housenumber'), + _interpolated_position(t, values.c.nr), + t.c.postcode, t.c.country_code)\ + .where(t.c.place_id.in_(place_ids))\ + .join(values, values.c.nr.between(t.c.startnumber, t.c.endnumber)) + + if details.geometry_output: + sub = sql.subquery() + sql = _add_geometry_columns(sa.select(sub), sub.c.centroid, details) + + for row in await conn.execute(sql): + result = nres.create_from_osmline_row(row, nres.SearchResult) + assert result + yield result + + +async def _get_tiger(conn: SearchConnection, place_ids: List[int], + numerals: List[int], osm_id: int, + details: SearchDetails) -> AsyncIterator[nres.SearchResult]: + t = conn.t.tiger + values = _int_list_to_subquery(numerals) + sql = sa.select(t.c.place_id, t.c.parent_place_id, + sa.literal('W').label('osm_type'), + sa.literal(osm_id).label('osm_id'), + values.c.nr.label('housenumber'), + _interpolated_position(t, values.c.nr), + t.c.postcode)\ + .where(t.c.place_id.in_(place_ids))\ + .join(values, values.c.nr.between(t.c.startnumber, t.c.endnumber)) + + if details.geometry_output: + sub = sql.subquery() + sql = _add_geometry_columns(sa.select(sub), sub.c.centroid, details) + + for row in await conn.execute(sql): + result = nres.create_from_tiger_row(row, nres.SearchResult) + assert result + yield result + class AbstractSearch(abc.ABC): """ Encapuslation of a single lookup in the database. """ + SEARCH_PRIO: int = 2 def __init__(self, penalty: float) -> None: self.penalty = penalty @@ -42,7 +271,97 @@ class NearSearch(AbstractSearch): details: SearchDetails) -> nres.SearchResults: """ Find results for the search in the database. """ - return nres.SearchResults([]) + results = nres.SearchResults() + base = await self.search.lookup(conn, details) + + if not base: + return results + + base.sort(key=lambda r: (r.accuracy, r.rank_search)) + max_accuracy = base[0].accuracy + 0.5 + if base[0].rank_address == 0: + min_rank = 0 + max_rank = 0 + elif base[0].rank_address < 26: + min_rank = 1 + max_rank = min(25, base[0].rank_address + 4) + else: + min_rank = 26 + max_rank = 30 + base = nres.SearchResults(r for r in base if r.source_table == nres.SourceTable.PLACEX + and r.accuracy <= max_accuracy + and r.bbox and r.bbox.area < 20 + and r.rank_address >= min_rank + and r.rank_address <= max_rank) + + if base: + baseids = [b.place_id for b in base[:5] if b.place_id] + + for category, penalty in self.categories: + await self.lookup_category(results, conn, baseids, category, penalty, details) + if len(results) >= details.max_results: + break + + return results + + + async def lookup_category(self, results: nres.SearchResults, + conn: SearchConnection, ids: List[int], + category: Tuple[str, str], penalty: float, + details: SearchDetails) -> None: + """ Find places of the given category near the list of + place ids and add the results to 'results'. + """ + table = await conn.get_class_table(*category) + + tgeom = conn.t.placex.alias('pgeom') + + if table is None: + # No classtype table available, do a simplified lookup in placex. + table = conn.t.placex + sql = sa.select(table.c.place_id, + sa.func.min(tgeom.c.centroid.ST_Distance(table.c.centroid)) + .label('dist'))\ + .join(tgeom, table.c.geometry.intersects(tgeom.c.centroid.ST_Expand(0.01)))\ + .where(table.c.class_ == category[0])\ + .where(table.c.type == category[1]) + else: + # Use classtype table. We can afford to use a larger + # radius for the lookup. + sql = sa.select(table.c.place_id, + sa.func.min(tgeom.c.centroid.ST_Distance(table.c.centroid)) + .label('dist'))\ + .join(tgeom, + table.c.centroid.ST_CoveredBy( + sa.case((sa.and_(tgeom.c.rank_address > 9, + tgeom.c.geometry.is_area()), + tgeom.c.geometry), + else_ = tgeom.c.centroid.ST_Expand(0.05)))) + + inner = sql.where(tgeom.c.place_id.in_(ids))\ + .group_by(table.c.place_id).subquery() + + t = conn.t.placex + sql = _select_placex(t).add_columns((-inner.c.dist).label('importance'))\ + .join(inner, inner.c.place_id == t.c.place_id)\ + .order_by(inner.c.dist) + + 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 = sql.where(_exclude_places(t)) + if details.layers is not None: + sql = sql.where(_filter_by_layer(t, details.layers)) + + sql = sql.limit(LIMIT_PARAM) + for row in await conn.execute(sql, _details_to_bind_params(details)): + result = nres.create_from_placex_row(row, nres.SearchResult) + assert result + result.accuracy = self.penalty + penalty + result.bbox = Bbox.from_wkb(row.bbox) + results.append(result) + class PoiSearch(AbstractSearch): @@ -50,7 +369,7 @@ class PoiSearch(AbstractSearch): """ def __init__(self, sdata: SearchData) -> None: super().__init__(sdata.penalty) - self.categories = sdata.qualifiers + self.qualifiers = sdata.qualifiers self.countries = sdata.countries @@ -58,12 +377,80 @@ class PoiSearch(AbstractSearch): details: SearchDetails) -> nres.SearchResults: """ Find results for the search in the database. """ - return nres.SearchResults([]) + bind_params = _details_to_bind_params(details) + t = conn.t.placex + + rows: List[SaRow] = [] + + if details.near and details.near_radius is not None and details.near_radius < 0.2: + # simply search in placex table + def _base_query() -> SaSelect: + return _select_placex(t) \ + .add_columns((-t.c.centroid.ST_Distance(NEAR_PARAM)) + .label('importance'))\ + .where(t.c.linked_place_id == None) \ + .where(t.c.geometry.within_distance(NEAR_PARAM, NEAR_RADIUS_PARAM)) \ + .order_by(t.c.centroid.ST_Distance(NEAR_PARAM)) \ + .limit(LIMIT_PARAM) + + classtype = self.qualifiers.values + if len(classtype) == 1: + cclass, ctype = classtype[0] + sql: SaLambdaSelect = sa.lambda_stmt(lambda: _base_query() + .where(t.c.class_ == cclass) + .where(t.c.type == ctype)) + else: + sql = _base_query().where(sa.or_(*(sa.and_(t.c.class_ == cls, t.c.type == typ) + for cls, typ in classtype))) + + if self.countries: + sql = sql.where(t.c.country_code.in_(self.countries.values)) + + if details.viewbox is not None and details.bounded_viewbox: + sql = sql.where(t.c.geometry.intersects(VIEWBOX_PARAM)) + + rows.extend(await conn.execute(sql, bind_params)) + else: + # use the class type tables + for category in self.qualifiers.values: + table = await conn.get_class_table(*category) + if table is not None: + sql = _select_placex(t)\ + .add_columns(t.c.importance)\ + .join(table, t.c.place_id == table.c.place_id)\ + .where(t.c.class_ == category[0])\ + .where(t.c.type == category[1]) + + if details.viewbox is not None and details.bounded_viewbox: + sql = sql.where(table.c.centroid.intersects(VIEWBOX_PARAM)) + + if details.near and details.near_radius is not None: + sql = sql.order_by(table.c.centroid.ST_Distance(NEAR_PARAM))\ + .where(table.c.centroid.within_distance(NEAR_PARAM, + NEAR_RADIUS_PARAM)) + + if self.countries: + sql = sql.where(t.c.country_code.in_(self.countries.values)) + + sql = sql.limit(LIMIT_PARAM) + rows.extend(await conn.execute(sql, bind_params)) + + results = nres.SearchResults() + for row in rows: + result = nres.create_from_placex_row(row, nres.SearchResult) + assert result + result.accuracy = self.penalty + self.qualifiers.get_penalty((row.class_, row.type)) + result.bbox = Bbox.from_wkb(row.bbox) + results.append(result) + + return results class CountrySearch(AbstractSearch): """ Search for a country name or country code. """ + SEARCH_PRIO = 0 + def __init__(self, sdata: SearchData) -> None: super().__init__(sdata.penalty) self.countries = sdata.countries @@ -73,7 +460,82 @@ class CountrySearch(AbstractSearch): details: SearchDetails) -> nres.SearchResults: """ Find results for the search in the database. """ - return nres.SearchResults([]) + t = conn.t.placex + + ccodes = self.countries.values + sql = _select_placex(t)\ + .add_columns(t.c.importance)\ + .where(t.c.country_code.in_(ccodes))\ + .where(t.c.rank_address == 4) + + if details.geometry_output: + sql = _add_geometry_columns(sql, t.c.geometry, details) + + if details.excluded: + sql = sql.where(_exclude_places(t)) + + sql = filter_by_area(sql, t, details) + + results = nres.SearchResults() + for row in await conn.execute(sql, _details_to_bind_params(details)): + result = nres.create_from_placex_row(row, nres.SearchResult) + assert result + result.accuracy = self.penalty + self.countries.get_penalty(row.country_code, 5.0) + result.bbox = Bbox.from_wkb(row.bbox) + results.append(result) + + if not results: + results = await self.lookup_in_country_table(conn, details) + + if results: + details.min_rank = min(5, details.max_rank) + details.max_rank = min(25, details.max_rank) + + return results + + + async def lookup_in_country_table(self, conn: SearchConnection, + details: SearchDetails) -> nres.SearchResults: + """ Look up the country in the fallback country tables. + """ + # Avoid the fallback search when this is a more search. Country results + # usually are in the first batch of results and it is not possible + # to exclude these fallbacks. + if details.excluded: + return nres.SearchResults() + + t = conn.t.country_name + tgrid = conn.t.country_grid + + sql = sa.select(tgrid.c.country_code, + tgrid.c.geometry.ST_Centroid().ST_Collect().ST_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) + + sql = filter_by_area(sql, tgrid, details, avoid_index=True) + + sub = sql.subquery('grid') + + sql = sa.select(t.c.country_code, + t.c.name.merge(t.c.derived_name).label('name'), + 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) + + return results + class PostcodeSearch(AbstractSearch): @@ -91,12 +553,69 @@ class PostcodeSearch(AbstractSearch): details: SearchDetails) -> nres.SearchResults: """ Find results for the search in the database. """ - return nres.SearchResults([]) + t = conn.t.postcode + pcs = self.postcodes.values + + sql = sa.select(t.c.place_id, t.c.parent_place_id, + t.c.rank_search, t.c.rank_address, + t.c.postcode, t.c.country_code, + t.c.geometry.label('centroid'))\ + .where(t.c.postcode.in_(pcs)) + + if details.geometry_output: + sql = _add_geometry_columns(sql, t.c.geometry, details) + + penalty: SaExpression = sa.literal(self.penalty) + + if details.viewbox is not None and not details.bounded_viewbox: + penalty += sa.case((t.c.geometry.intersects(VIEWBOX_PARAM), 0.0), + (t.c.geometry.intersects(VIEWBOX2_PARAM), 0.5), + else_=1.0) + + if details.near is not None: + sql = sql.order_by(t.c.geometry.ST_Distance(NEAR_PARAM)) + + sql = filter_by_area(sql, t, details) + + if self.countries: + sql = sql.where(t.c.country_code.in_(self.countries.values)) + + if details.excluded: + sql = sql.where(_exclude_places(t)) + + if self.lookups: + assert len(self.lookups) == 1 + tsearch = conn.t.search_name + sql = sql.where(tsearch.c.place_id == t.c.parent_place_id)\ + .where((tsearch.c.name_vector + tsearch.c.nameaddress_vector) + .contains(sa.type_coerce(self.lookups[0].tokens, + IntArray))) + + for ranking in self.rankings: + penalty += ranking.sql_penalty(conn.t.search_name) + penalty += sa.case(*((t.c.postcode == v, p) for v, p in self.postcodes), + else_=1.0) + + + sql = sql.add_columns(penalty.label('accuracy')) + sql = sql.order_by('accuracy').limit(LIMIT_PARAM) + + results = nres.SearchResults() + for row in await conn.execute(sql, _details_to_bind_params(details)): + result = nres.create_from_postcode_row(row, nres.SearchResult) + assert result + result.accuracy = row.accuracy + results.append(result) + + return results + class PlaceSearch(AbstractSearch): """ Generic search for an address or named place. """ + SEARCH_PRIO = 1 + def __init__(self, extra_penalty: float, sdata: SearchData, expected_count: int) -> None: super().__init__(sdata.penalty + extra_penalty) self.countries = sdata.countries @@ -112,4 +631,186 @@ class PlaceSearch(AbstractSearch): details: SearchDetails) -> nres.SearchResults: """ Find results for the search in the database. """ - return nres.SearchResults([]) + 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) + + penalty: SaExpression = sa.literal(self.penalty) + for ranking in self.rankings: + penalty += ranking.sql_penalty(tsearch) + + for lookup in self.lookups: + sql = sql.where(lookup.sql_condition(tsearch)) + + if self.countries: + sql = sql.where(tsearch.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: + # Many results expected. Restrict by 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)) + .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)))\ + .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.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_=0.75001-(sa.cast(tsearch.c.search_rank, sa.Float())/40))) + sql = sql.add_columns(t.c.importance) + + + sql = sql.add_columns(penalty.label('accuracy')) + + if self.expected_count < 10000: + sql = sql.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() + + # Housenumbers from placex + thnr = conn.t.placex.alias('hnr') + pid_list = sa.func.ArrayAgg(thnr.c.place_id) + place_sql = sa.select(pid_list)\ + .where(thnr.c.parent_place_id == inner.c.place_id)\ + .where(sa.func.RegexpWord(hnr_list, thnr.c.housenumber))\ + .where(thnr.c.linked_place_id == None)\ + .where(thnr.c.indexed_status == 0) + + if details.excluded: + place_sql = place_sql.where(thnr.c.place_id.not_in(sa.bindparam('excluded'))) + 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() and len(n) < 8] + interpol_sql: SaColumn + tiger_sql: SaColumn + if numerals and \ + (not self.qualifiers or ('place', 'house') in self.qualifiers.values): + # Housenumbers from interpolations + interpol_sql = _make_interpolation_subquery(conn.t.osmline, inner, + numerals, details) + # Housenumbers from Tiger + tiger_sql = sa.case((inner.c.country_code == 'us', + _make_interpolation_subquery(conn.t.tiger, inner, + numerals, details) + ), else_=None) + else: + interpol_sql = sa.null() + tiger_sql = sa.null() + + unsort = sa.select(inner, place_sql.scalar_subquery().label('placex_hnr'), + interpol_sql.label('interpol_hnr'), + tiger_sql.label('tiger_hnr')).subquery('unsort') + sql = sa.select(unsort)\ + .order_by(sa.case((unsort.c.placex_hnr != None, 1), + (unsort.c.interpol_hnr != None, 2), + (unsort.c.tiger_hnr != None, 3), + else_=4), + unsort.c.accuracy) + else: + sql = sql.where(t.c.linked_place_id == None)\ + .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)) + + sql = sql.limit(LIMIT_PARAM) + + results = nres.SearchResults() + for row in await conn.execute(sql, _details_to_bind_params(details)): + result = nres.create_from_placex_row(row, nres.SearchResult) + assert result + result.bbox = Bbox.from_wkb(row.bbox) + result.accuracy = row.accuracy + if self.housenumbers and row.rank_address < 30: + if row.placex_hnr: + subs = _get_placex_housenumbers(conn, row.placex_hnr, details) + elif row.interpol_hnr: + subs = _get_osmline(conn, row.interpol_hnr, numerals, details) + elif row.tiger_hnr: + subs = _get_tiger(conn, row.tiger_hnr, numerals, row.osm_id, details) + else: + subs = None + + if subs is not None: + async for sub in subs: + assert sub.housenumber + sub.accuracy = result.accuracy + if not any(nr in self.housenumbers.values + for nr in sub.housenumber.split(';')): + sub.accuracy += 0.6 + results.append(sub) + + # Only add the street as a result, if it meets all other + # filter conditions. + if (not details.excluded or result.place_id not in details.excluded)\ + and (not self.qualifiers or result.category in self.qualifiers.values)\ + and result.rank_address >= details.min_rank: + result.accuracy += 1.0 # penalty for missing housenumber + results.append(result) + else: + results.append(result) + + return results