mirror of
https://github.com/osm-search/Nominatim.git
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422 lines
14 KiB
Python
422 lines
14 KiB
Python
"""
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Tokenizer implementing normalisation as used before Nominatim 4.
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"""
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from collections import OrderedDict
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import logging
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import re
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import shutil
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import psycopg2
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import psycopg2.extras
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from nominatim.db.connection import connect
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from nominatim.db import properties
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from nominatim.db import utils as db_utils
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from nominatim.db.sql_preprocessor import SQLPreprocessor
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from nominatim.errors import UsageError
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DBCFG_NORMALIZATION = "tokenizer_normalization"
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DBCFG_MAXWORDFREQ = "tokenizer_maxwordfreq"
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LOG = logging.getLogger()
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def create(dsn, data_dir):
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""" Create a new instance of the tokenizer provided by this module.
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"""
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return LegacyTokenizer(dsn, data_dir)
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def _install_module(config_module_path, src_dir, module_dir):
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""" Copies the PostgreSQL normalisation module into the project
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directory if necessary. For historical reasons the module is
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saved in the '/module' subdirectory and not with the other tokenizer
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data.
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The function detects when the installation is run from the
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build directory. It doesn't touch the module in that case.
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"""
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# Custom module locations are simply used as is.
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if config_module_path:
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LOG.info("Using custom path for database module at '%s'", config_module_path)
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return config_module_path
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# Compatibility mode for builddir installations.
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if module_dir.exists() and src_dir.samefile(module_dir):
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LOG.info('Running from build directory. Leaving database module as is.')
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return module_dir
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# In any other case install the module in the project directory.
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if not module_dir.exists():
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module_dir.mkdir()
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destfile = module_dir / 'nominatim.so'
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shutil.copy(str(src_dir / 'nominatim.so'), str(destfile))
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destfile.chmod(0o755)
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LOG.info('Database module installed at %s', str(destfile))
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return module_dir
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def _check_module(module_dir, conn):
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""" Try to use the PostgreSQL module to confirm that it is correctly
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installed and accessible from PostgreSQL.
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"""
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with conn.cursor() as cur:
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try:
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cur.execute("""CREATE FUNCTION nominatim_test_import_func(text)
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RETURNS text AS '{}/nominatim.so', 'transliteration'
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LANGUAGE c IMMUTABLE STRICT;
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DROP FUNCTION nominatim_test_import_func(text)
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""".format(module_dir))
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except psycopg2.DatabaseError as err:
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LOG.fatal("Error accessing database module: %s", err)
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raise UsageError("Database module cannot be accessed.") from err
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class LegacyTokenizer:
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""" The legacy tokenizer uses a special PostgreSQL module to normalize
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names and queries. The tokenizer thus implements normalization through
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calls to the database.
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"""
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def __init__(self, dsn, data_dir):
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self.dsn = dsn
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self.data_dir = data_dir
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self.normalization = None
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def init_new_db(self, config):
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""" Set up a new tokenizer for the database.
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This copies all necessary data in the project directory to make
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sure the tokenizer remains stable even over updates.
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"""
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module_dir = _install_module(config.DATABASE_MODULE_PATH,
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config.lib_dir.module,
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config.project_dir / 'module')
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self.normalization = config.TERM_NORMALIZATION
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with connect(self.dsn) as conn:
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_check_module(module_dir, conn)
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self._save_config(conn, config)
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conn.commit()
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self.update_sql_functions(config)
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self._init_db_tables(config)
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def init_from_project(self):
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""" Initialise the tokenizer from the project directory.
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"""
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with connect(self.dsn) as conn:
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self.normalization = properties.get_property(conn, DBCFG_NORMALIZATION)
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def update_sql_functions(self, config):
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""" Reimport the SQL functions for this tokenizer.
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"""
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with connect(self.dsn) as conn:
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max_word_freq = properties.get_property(conn, DBCFG_MAXWORDFREQ)
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modulepath = config.DATABASE_MODULE_PATH or \
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str((config.project_dir / 'module').resolve())
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sqlp = SQLPreprocessor(conn, config)
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sqlp.run_sql_file(conn, 'tokenizer/legacy_tokenizer.sql',
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max_word_freq=max_word_freq,
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modulepath=modulepath)
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def migrate_database(self, config):
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""" Initialise the project directory of an existing database for
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use with this tokenizer.
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This is a special migration function for updating existing databases
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to new software versions.
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"""
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module_dir = _install_module(config.DATABASE_MODULE_PATH,
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config.lib_dir.module,
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config.project_dir / 'module')
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with connect(self.dsn) as conn:
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_check_module(module_dir, conn)
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self._save_config(conn, config)
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def name_analyzer(self):
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""" Create a new analyzer for tokenizing names and queries
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using this tokinzer. Analyzers are context managers and should
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be used accordingly:
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```
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with tokenizer.name_analyzer() as analyzer:
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analyser.tokenize()
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```
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When used outside the with construct, the caller must ensure to
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call the close() function before destructing the analyzer.
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Analyzers are not thread-safe. You need to instantiate one per thread.
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"""
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return LegacyNameAnalyzer(self.dsn)
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def _init_db_tables(self, config):
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""" Set up the word table and fill it with pre-computed word
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frequencies.
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"""
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with connect(self.dsn) as conn:
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sqlp = SQLPreprocessor(conn, config)
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sqlp.run_sql_file(conn, 'tokenizer/legacy_tokenizer_tables.sql')
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conn.commit()
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LOG.warning("Precomputing word tokens")
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db_utils.execute_file(self.dsn, config.lib_dir.data / 'words.sql')
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def _save_config(self, conn, config):
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""" Save the configuration that needs to remain stable for the given
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database as database properties.
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"""
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properties.set_property(conn, DBCFG_NORMALIZATION, self.normalization)
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properties.set_property(conn, DBCFG_MAXWORDFREQ, config.MAX_WORD_FREQUENCY)
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class LegacyNameAnalyzer:
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""" The legacy analyzer uses the special Postgresql module for
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splitting names.
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Each instance opens a connection to the database to request the
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normalization.
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"""
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def __init__(self, dsn):
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self.conn = connect(dsn).connection
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self.conn.autocommit = True
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psycopg2.extras.register_hstore(self.conn)
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self._cache = _TokenCache(self.conn)
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def __enter__(self):
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return self
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def __exit__(self, exc_type, exc_value, traceback):
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self.close()
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def close(self):
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""" Free all resources used by the analyzer.
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"""
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if self.conn:
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self.conn.close()
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self.conn = None
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def add_postcodes_from_db(self):
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""" Add postcodes from the location_postcode table to the word table.
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"""
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with self.conn.cursor() as cur:
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cur.execute("""SELECT count(create_postcode_id(pc))
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FROM (SELECT distinct(postcode) as pc
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FROM location_postcode) x""")
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def process_place(self, place):
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""" Determine tokenizer information about the given place.
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Returns a JSON-serialisable structure that will be handed into
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the database via the token_info field.
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"""
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token_info = _TokenInfo(self._cache)
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token_info.add_names(self.conn, place.get('name'), place.get('country_feature'))
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address = place.get('address')
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if address:
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hnrs = []
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addr_terms = []
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for key, value in address.items():
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if key == 'postcode':
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self._add_postcode(value)
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elif key in ('housenumber', 'streetnumber', 'conscriptionnumber'):
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hnrs.append(value)
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elif key == 'street':
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token_info.add_street(self.conn, value)
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elif key == 'place':
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token_info.add_place(self.conn, value)
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elif not key.startswith('_') and \
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key not in ('country', 'full'):
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addr_terms.append((key, value))
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if hnrs:
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token_info.add_housenumbers(self.conn, hnrs)
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if addr_terms:
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token_info.add_address_terms(self.conn, addr_terms)
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return token_info.data
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def _add_postcode(self, postcode):
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""" Make sure the normalized postcode is present in the word table.
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"""
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def _create_postcode_from_db(pcode):
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with self.conn.cursor() as cur:
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cur.execute('SELECT create_postcode_id(%s)', (pcode, ))
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if re.search(r'[:,;]', postcode) is None:
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self._cache.postcodes.get(postcode.strip().upper(), _create_postcode_from_db)
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class _TokenInfo:
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""" Collect token information to be sent back to the database.
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"""
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def __init__(self, cache):
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self.cache = cache
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self.data = {}
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def add_names(self, conn, names, country_feature):
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""" Add token information for the names of the place.
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"""
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if not names:
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return
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with conn.cursor() as cur:
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# Create the token IDs for all names.
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self.data['names'] = cur.scalar("SELECT make_keywords(%s)::text",
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(names, ))
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# Add country tokens to word table if necessary.
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if country_feature and re.fullmatch(r'[A-Za-z][A-Za-z]', country_feature):
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cur.execute("SELECT create_country(%s, %s)",
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(names, country_feature.lower()))
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def add_housenumbers(self, conn, hnrs):
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""" Extract housenumber information from the address.
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"""
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if len(hnrs) == 1:
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token = self.cache.get_housenumber(hnrs[0])
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if token is not None:
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self.data['hnr_tokens'] = token
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self.data['hnr'] = hnrs[0]
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return
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# split numbers if necessary
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simple_list = []
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for hnr in hnrs:
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simple_list.extend((x.strip() for x in re.split(r'[;,]', hnr)))
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if len(simple_list) > 1:
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simple_list = list(set(simple_list))
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with conn.cursor() as cur:
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cur.execute("SELECT (create_housenumbers(%s)).* ", (simple_list, ))
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self.data['hnr_tokens'], self.data['hnr'] = cur.fetchone()
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def add_street(self, conn, street):
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""" Add addr:street match terms.
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"""
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def _get_street(name):
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with conn.cursor() as cur:
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return cur.scalar("SELECT word_ids_from_name(%s)::text", (name, ))
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self.data['street'] = self.cache.streets.get(street, _get_street)
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def add_place(self, conn, place):
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""" Add addr:place search and match terms.
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"""
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def _get_place(name):
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with conn.cursor() as cur:
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cur.execute("""SELECT (addr_ids_from_name(%s) || getorcreate_name_id(make_standard_name(%s), ''))::text,
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word_ids_from_name(%s)::text""",
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(name, name, name))
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return cur.fetchone()
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self.data['place_search'], self.data['place_match'] = \
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self.cache.places.get(place, _get_place)
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def add_address_terms(self, conn, terms):
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""" Add additional address terms.
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"""
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def _get_address_term(name):
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with conn.cursor() as cur:
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cur.execute("""SELECT addr_ids_from_name(%s)::text,
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word_ids_from_name(%s)::text""",
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(name, name))
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return cur.fetchone()
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tokens = {}
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for key, value in terms:
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tokens[key] = self.cache.address_terms.get(value, _get_address_term)
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self.data['addr'] = tokens
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class _LRU:
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""" Least recently used cache that accepts a generator function to
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produce the item when there is a cache miss.
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"""
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def __init__(self, maxsize=128, init_data=None):
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self.data = init_data or OrderedDict()
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self.maxsize = maxsize
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if init_data is not None and len(init_data) > maxsize:
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self.maxsize = len(init_data)
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def get(self, key, generator):
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""" Get the item with the given key from the cache. If nothing
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is found in the cache, generate the value through the
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generator function and store it in the cache.
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"""
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value = self.data.get(key)
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if value is not None:
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self.data.move_to_end(key)
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else:
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value = generator(key)
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if len(self.data) >= self.maxsize:
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self.data.popitem(last=False)
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self.data[key] = value
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return value
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class _TokenCache:
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""" Cache for token information to avoid repeated database queries.
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This cache is not thread-safe and needs to be instantiated per
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analyzer.
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"""
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def __init__(self, conn):
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# various LRU caches
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self.streets = _LRU(maxsize=256)
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self.places = _LRU(maxsize=128)
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self.address_terms = _LRU(maxsize=1024)
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# Lookup houseunumbers up to 100 and cache them
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with conn.cursor() as cur:
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cur.execute("""SELECT i, ARRAY[getorcreate_housenumber_id(i::text)]::text
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FROM generate_series(1, 100) as i""")
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self._cached_housenumbers = {str(r[0]) : r[1] for r in cur}
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# Get postcodes that are already saved
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postcodes = OrderedDict()
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with conn.cursor() as cur:
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cur.execute("""SELECT word FROM word
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WHERE class ='place' and type = 'postcode'""")
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for row in cur:
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postcodes[row[0]] = None
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self.postcodes = _LRU(maxsize=32, init_data=postcodes)
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def get_housenumber(self, number):
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""" Get a housenumber token from the cache.
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"""
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return self._cached_housenumbers.get(number)
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