mirror of
https://github.com/osm-search/Nominatim.git
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266 lines
11 KiB
Python
266 lines
11 KiB
Python
# SPDX-License-Identifier: GPL-3.0-or-later
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#
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# This file is part of Nominatim. (https://nominatim.org)
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#
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# Copyright (C) 2023 by the Nominatim developer community.
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# For a full list of authors see the git log.
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"""
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Exporting a Nominatim database to SQlite.
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"""
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from typing import Set, Any
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import datetime as dt
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import logging
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from pathlib import Path
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import sqlalchemy as sa
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from nominatim.typing import SaSelect, SaRow
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from nominatim.db.sqlalchemy_types import Geometry, IntArray
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from nominatim.api.search.query_analyzer_factory import make_query_analyzer
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import nominatim.api as napi
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LOG = logging.getLogger()
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async def convert(project_dir: Path, outfile: Path, options: Set[str]) -> None:
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""" Export an existing database to sqlite. The resulting database
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will be usable against the Python frontend of Nominatim.
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"""
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api = napi.NominatimAPIAsync(project_dir)
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try:
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outapi = napi.NominatimAPIAsync(project_dir,
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{'NOMINATIM_DATABASE_DSN': f"sqlite:dbname={outfile}",
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'NOMINATIM_DATABASE_RW': '1'})
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try:
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async with api.begin() as src, outapi.begin() as dest:
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writer = SqliteWriter(src, dest, options)
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await writer.write()
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finally:
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await outapi.close()
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finally:
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await api.close()
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class SqliteWriter:
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""" Worker class which creates a new SQLite database.
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"""
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def __init__(self, src: napi.SearchConnection,
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dest: napi.SearchConnection, options: Set[str]) -> None:
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self.src = src
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self.dest = dest
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self.options = options
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async def write(self) -> None:
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""" Create the database structure and copy the data from
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the source database to the destination.
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"""
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LOG.warning('Setting up spatialite')
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await self.dest.execute(sa.select(sa.func.InitSpatialMetaData(True, 'WGS84')))
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await self.create_tables()
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await self.copy_data()
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if 'search' in self.options:
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await self.create_word_table()
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await self.create_indexes()
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async def create_tables(self) -> None:
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""" Set up the database tables.
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"""
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LOG.warning('Setting up tables')
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if 'search' not in self.options:
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self.dest.t.meta.remove(self.dest.t.search_name)
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else:
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await self.create_class_tables()
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await self.dest.connection.run_sync(self.dest.t.meta.create_all)
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# Convert all Geometry columns to Spatialite geometries
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for table in self.dest.t.meta.sorted_tables:
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for col in table.c:
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if isinstance(col.type, Geometry):
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await self.dest.execute(sa.select(
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sa.func.RecoverGeometryColumn(table.name, col.name, 4326,
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col.type.subtype.upper(), 'XY')))
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async def create_class_tables(self) -> None:
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""" Set up the table that serve class/type-specific geometries.
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"""
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sql = sa.text("""SELECT tablename FROM pg_tables
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WHERE tablename LIKE 'place_classtype_%'""")
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for res in await self.src.execute(sql):
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for db in (self.src, self.dest):
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sa.Table(res[0], db.t.meta,
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sa.Column('place_id', sa.BigInteger),
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sa.Column('centroid', Geometry))
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async def create_word_table(self) -> None:
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""" Create the word table.
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This table needs the property information to determine the
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correct format. Therefore needs to be done after all other
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data has been copied.
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"""
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await make_query_analyzer(self.src)
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await make_query_analyzer(self.dest)
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src = self.src.t.meta.tables['word']
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dest = self.dest.t.meta.tables['word']
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await self.dest.connection.run_sync(dest.create)
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LOG.warning("Copying word table")
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async_result = await self.src.connection.stream(sa.select(src))
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async for partition in async_result.partitions(10000):
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data = [{k: getattr(r, k) for k in r._fields} for r in partition]
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await self.dest.execute(dest.insert(), data)
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await self.dest.connection.run_sync(sa.Index('idx_word_woken', dest.c.word_token).create)
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async def copy_data(self) -> None:
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""" Copy data for all registered tables.
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"""
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def _getfield(row: SaRow, key: str) -> Any:
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value = getattr(row, key)
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if isinstance(value, dt.datetime):
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if value.tzinfo is not None:
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value = value.astimezone(dt.timezone.utc)
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return value
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for table in self.dest.t.meta.sorted_tables:
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LOG.warning("Copying '%s'", table.name)
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async_result = await self.src.connection.stream(self.select_from(table.name))
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async for partition in async_result.partitions(10000):
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data = [{('class_' if k == 'class' else k): _getfield(r, k)
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for k in r._fields}
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for r in partition]
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await self.dest.execute(table.insert(), data)
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# Set up a minimal copy of pg_tables used to look up the class tables later.
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pg_tables = sa.Table('pg_tables', self.dest.t.meta,
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sa.Column('schemaname', sa.Text, default='public'),
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sa.Column('tablename', sa.Text))
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await self.dest.connection.run_sync(pg_tables.create)
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data = [{'tablename': t} for t in self.dest.t.meta.tables]
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await self.dest.execute(pg_tables.insert().values(data))
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async def create_indexes(self) -> None:
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""" Add indexes necessary for the frontend.
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"""
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# reverse place node lookup needs an extra table to simulate a
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# partial index with adaptive buffering.
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await self.dest.execute(sa.text(
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""" CREATE TABLE placex_place_node_areas AS
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SELECT place_id, ST_Expand(geometry,
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14.0 * exp(-0.2 * rank_search) - 0.03) as geometry
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FROM placex
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WHERE rank_address between 5 and 25
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and osm_type = 'N'
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and linked_place_id is NULL """))
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await self.dest.execute(sa.select(
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sa.func.RecoverGeometryColumn('placex_place_node_areas', 'geometry',
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4326, 'GEOMETRY', 'XY')))
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await self.dest.execute(sa.select(sa.func.CreateSpatialIndex(
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'placex_place_node_areas', 'geometry')))
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# Remaining indexes.
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await self.create_spatial_index('country_grid', 'geometry')
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await self.create_spatial_index('placex', 'geometry')
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await self.create_spatial_index('osmline', 'linegeo')
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await self.create_spatial_index('tiger', 'linegeo')
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await self.create_index('placex', 'place_id')
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await self.create_index('placex', 'parent_place_id')
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await self.create_index('placex', 'rank_address')
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await self.create_index('addressline', 'place_id')
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await self.create_index('postcode', 'place_id')
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await self.create_index('osmline', 'place_id')
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await self.create_index('tiger', 'place_id')
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if 'search' in self.options:
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await self.create_spatial_index('postcode', 'geometry')
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await self.create_spatial_index('search_name', 'centroid')
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await self.create_index('search_name', 'place_id')
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await self.create_index('osmline', 'parent_place_id')
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await self.create_index('tiger', 'parent_place_id')
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await self.create_search_index()
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for t in self.dest.t.meta.tables:
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if t.startswith('place_classtype_'):
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await self.dest.execute(sa.select(
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sa.func.CreateSpatialIndex(t, 'centroid')))
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async def create_spatial_index(self, table: str, column: str) -> None:
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""" Create a spatial index on the given table and column.
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"""
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await self.dest.execute(sa.select(
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sa.func.CreateSpatialIndex(getattr(self.dest.t, table).name, column)))
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async def create_index(self, table_name: str, column: str) -> None:
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""" Create a simple index on the given table and column.
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"""
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table = getattr(self.dest.t, table_name)
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await self.dest.connection.run_sync(
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sa.Index(f"idx_{table}_{column}", getattr(table.c, column)).create)
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async def create_search_index(self) -> None:
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""" Create the tables and indexes needed for word lookup.
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"""
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LOG.warning("Creating reverse search table")
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rsn = sa.Table('reverse_search_name', self.dest.t.meta,
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sa.Column('word', sa.Integer()),
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sa.Column('column', sa.Text()),
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sa.Column('places', IntArray))
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await self.dest.connection.run_sync(rsn.create)
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tsrc = self.src.t.search_name
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for column in ('name_vector', 'nameaddress_vector'):
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sql = sa.select(sa.func.unnest(getattr(tsrc.c, column)).label('word'),
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sa.func.ArrayAgg(tsrc.c.place_id).label('places'))\
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.group_by('word')
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async_result = await self.src.connection.stream(sql)
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async for partition in async_result.partitions(100):
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data = []
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for row in partition:
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row.places.sort()
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data.append({'word': row.word,
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'column': column,
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'places': row.places})
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await self.dest.execute(rsn.insert(), data)
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await self.dest.connection.run_sync(
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sa.Index('idx_reverse_search_name_word', rsn.c.word).create)
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def select_from(self, table: str) -> SaSelect:
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""" Create the SQL statement to select the source columns and rows.
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"""
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columns = self.src.t.meta.tables[table].c
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if table == 'placex':
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# SQLite struggles with Geometries that are larger than 5MB,
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# so simplify those.
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return sa.select(*(c for c in columns if not isinstance(c.type, Geometry)),
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sa.func.ST_AsText(columns.centroid).label('centroid'),
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sa.func.ST_AsText(
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sa.case((sa.func.ST_MemSize(columns.geometry) < 5000000,
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columns.geometry),
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else_=sa.func.ST_SimplifyPreserveTopology(
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columns.geometry, 0.0001)
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)).label('geometry'))
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sql = sa.select(*(sa.func.ST_AsText(c).label(c.name)
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if isinstance(c.type, Geometry) else c for c in columns))
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return sql
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