+# SPDX-License-Identifier: GPL-2.0-only
+#
+# This file is part of Nominatim. (https://nominatim.org)
+#
+# Copyright (C) 2022 by the Nominatim developer community.
+# For a full list of authors see the git log.
"""
Tokenizer implementing normalisation as used before Nominatim 4.
"""
+from typing import Optional, Sequence, List, Tuple, Mapping, Any, Callable, \
+ cast, Dict, Set, Iterable
from collections import OrderedDict
import logging
+from pathlib import Path
import re
import shutil
from textwrap import dedent
import psycopg2
import psycopg2.extras
-from nominatim.db.connection import connect
+from nominatim.db.connection import connect, Connection
+from nominatim.config import Configuration
from nominatim.db import properties
from nominatim.db import utils as db_utils
from nominatim.db.sql_preprocessor import SQLPreprocessor
+from nominatim.data.place_info import PlaceInfo
from nominatim.errors import UsageError
from nominatim.tokenizer.base import AbstractAnalyzer, AbstractTokenizer
LOG = logging.getLogger()
-def create(dsn, data_dir):
+def create(dsn: str, data_dir: Path) -> 'LegacyTokenizer':
""" Create a new instance of the tokenizer provided by this module.
"""
return LegacyTokenizer(dsn, data_dir)
-def _install_module(config_module_path, src_dir, module_dir):
+def _install_module(config_module_path: str, src_dir: Path, module_dir: Path) -> str:
""" Copies the PostgreSQL normalisation module into the project
directory if necessary. For historical reasons the module is
saved in the '/module' subdirectory and not with the other tokenizer
# Compatibility mode for builddir installations.
if module_dir.exists() and src_dir.samefile(module_dir):
LOG.info('Running from build directory. Leaving database module as is.')
- return module_dir
+ return str(module_dir)
# In any other case install the module in the project directory.
if not module_dir.exists():
LOG.info('Database module installed at %s', str(destfile))
- return module_dir
+ return str(module_dir)
-def _check_module(module_dir, conn):
+def _check_module(module_dir: str, conn: Connection) -> None:
""" Try to use the PostgreSQL module to confirm that it is correctly
installed and accessible from PostgreSQL.
"""
with conn.cursor() as cur:
try:
cur.execute("""CREATE FUNCTION nominatim_test_import_func(text)
- RETURNS text AS '{}/nominatim.so', 'transliteration'
+ RETURNS text AS %s, 'transliteration'
LANGUAGE c IMMUTABLE STRICT;
DROP FUNCTION nominatim_test_import_func(text)
- """.format(module_dir))
+ """, (f'{module_dir}/nominatim.so', ))
except psycopg2.DatabaseError as err:
LOG.fatal("Error accessing database module: %s", err)
raise UsageError("Database module cannot be accessed.") from err
calls to the database.
"""
- def __init__(self, dsn, data_dir):
+ def __init__(self, dsn: str, data_dir: Path) -> None:
self.dsn = dsn
self.data_dir = data_dir
- self.normalization = None
+ self.normalization: Optional[str] = None
- def init_new_db(self, config, init_db=True):
+ def init_new_db(self, config: Configuration, init_db: bool = True) -> None:
""" Set up a new tokenizer for the database.
This copies all necessary data in the project directory to make
sure the tokenizer remains stable even over updates.
"""
+ assert config.project_dir is not None
module_dir = _install_module(config.DATABASE_MODULE_PATH,
config.lib_dir.module,
config.project_dir / 'module')
self.normalization = config.TERM_NORMALIZATION
- self._install_php(config)
+ self._install_php(config, overwrite=True)
with connect(self.dsn) as conn:
_check_module(module_dir, conn)
self._init_db_tables(config)
- def init_from_project(self):
+ def init_from_project(self, config: Configuration) -> None:
""" Initialise the tokenizer from the project directory.
"""
+ assert config.project_dir is not None
+
with connect(self.dsn) as conn:
self.normalization = properties.get_property(conn, DBCFG_NORMALIZATION)
+ if not (config.project_dir / 'module' / 'nominatim.so').exists():
+ _install_module(config.DATABASE_MODULE_PATH,
+ config.lib_dir.module,
+ config.project_dir / 'module')
+
+ self._install_php(config, overwrite=False)
- def finalize_import(self, config):
+ def finalize_import(self, config: Configuration) -> None:
""" Do any required postprocessing to make the tokenizer data ready
for use.
"""
sqlp.run_sql_file(conn, 'tokenizer/legacy_tokenizer_indices.sql')
- def update_sql_functions(self, config):
+ def update_sql_functions(self, config: Configuration) -> None:
""" Reimport the SQL functions for this tokenizer.
"""
+ assert config.project_dir is not None
+
with connect(self.dsn) as conn:
max_word_freq = properties.get_property(conn, DBCFG_MAXWORDFREQ)
modulepath = config.DATABASE_MODULE_PATH or \
modulepath=modulepath)
- def check_database(self):
+ def check_database(self, _: Configuration) -> Optional[str]:
""" Check that the tokenizer is set up correctly.
"""
hint = """\
return None
- def migrate_database(self, config):
+ def migrate_database(self, config: Configuration) -> None:
""" Initialise the project directory of an existing database for
use with this tokenizer.
This is a special migration function for updating existing databases
to new software versions.
"""
+ assert config.project_dir is not None
+
self.normalization = config.TERM_NORMALIZATION
module_dir = _install_module(config.DATABASE_MODULE_PATH,
config.lib_dir.module,
self._save_config(conn, config)
- def name_analyzer(self):
+ def update_statistics(self) -> None:
+ """ Recompute the frequency of full words.
+ """
+ with connect(self.dsn) as conn:
+ if conn.table_exists('search_name'):
+ with conn.cursor() as cur:
+ cur.drop_table("word_frequencies")
+ LOG.info("Computing word frequencies")
+ cur.execute("""CREATE TEMP TABLE word_frequencies AS
+ SELECT unnest(name_vector) as id, count(*)
+ FROM search_name GROUP BY id""")
+ cur.execute("CREATE INDEX ON word_frequencies(id)")
+ LOG.info("Update word table with recomputed frequencies")
+ cur.execute("""UPDATE word SET search_name_count = count
+ FROM word_frequencies
+ WHERE word_token like ' %' and word_id = id""")
+ cur.drop_table("word_frequencies")
+ conn.commit()
+
+
+ def update_word_tokens(self) -> None:
+ """ No house-keeping implemented for the legacy tokenizer.
+ """
+ LOG.info("No tokenizer clean-up available.")
+
+
+ def name_analyzer(self) -> 'LegacyNameAnalyzer':
""" Create a new analyzer for tokenizing names and queries
using this tokinzer. Analyzers are context managers and should
be used accordingly:
return LegacyNameAnalyzer(self.dsn, normalizer)
- def _install_php(self, config):
+ def most_frequent_words(self, conn: Connection, num: int) -> List[str]:
+ """ Return a list of the `num` most frequent full words
+ in the database.
+ """
+ with conn.cursor() as cur:
+ cur.execute(""" SELECT word FROM word WHERE word is not null
+ ORDER BY search_name_count DESC LIMIT %s""", (num,))
+ return list(s[0] for s in cur)
+
+
+ def _install_php(self, config: Configuration, overwrite: bool = True) -> None:
""" Install the php script for the tokenizer.
"""
php_file = self.data_dir / "tokenizer.php"
- php_file.write_text(dedent("""\
- <?php
- @define('CONST_Max_Word_Frequency', {0.MAX_WORD_FREQUENCY});
- @define('CONST_Term_Normalization_Rules', "{0.TERM_NORMALIZATION}");
- require_once('{0.lib_dir.php}/tokenizer/legacy_tokenizer.php');
- """.format(config)))
+ if not php_file.exists() or overwrite:
+ php_file.write_text(dedent(f"""\
+ <?php
+ @define('CONST_Max_Word_Frequency', {config.MAX_WORD_FREQUENCY});
+ @define('CONST_Term_Normalization_Rules', "{config.TERM_NORMALIZATION}");
+ require_once('{config.lib_dir.php}/tokenizer/legacy_tokenizer.php');
+ """), encoding='utf-8')
- def _init_db_tables(self, config):
+
+ def _init_db_tables(self, config: Configuration) -> None:
""" Set up the word table and fill it with pre-computed word
frequencies.
"""
db_utils.execute_file(self.dsn, config.lib_dir.data / 'words.sql')
- def _save_config(self, conn, config):
+ def _save_config(self, conn: Connection, config: Configuration) -> None:
""" Save the configuration that needs to remain stable for the given
database as database properties.
"""
+ assert self.normalization is not None
+
properties.set_property(conn, DBCFG_NORMALIZATION, self.normalization)
properties.set_property(conn, DBCFG_MAXWORDFREQ, config.MAX_WORD_FREQUENCY)
normalization.
"""
- def __init__(self, dsn, normalizer):
- self.conn = connect(dsn).connection
+ def __init__(self, dsn: str, normalizer: Any):
+ self.conn: Optional[Connection] = connect(dsn).connection
self.conn.autocommit = True
self.normalizer = normalizer
psycopg2.extras.register_hstore(self.conn)
self._cache = _TokenCache(self.conn)
- def close(self):
+ def close(self) -> None:
""" Free all resources used by the analyzer.
"""
if self.conn:
self.conn = None
- def get_word_token_info(self, words):
+ def get_word_token_info(self, words: Sequence[str]) -> List[Tuple[str, str, int]]:
""" Return token information for the given list of words.
If a word starts with # it is assumed to be a full name
otherwise is a partial name.
The function is used for testing and debugging only
and not necessarily efficient.
"""
+ assert self.conn is not None
with self.conn.cursor() as cur:
cur.execute("""SELECT t.term, word_token, word_id
FROM word, (SELECT unnest(%s::TEXT[]) as term) t
return [(r[0], r[1], r[2]) for r in cur]
- def normalize(self, phrase):
+ def normalize(self, phrase: str) -> str:
""" Normalize the given phrase, i.e. remove all properties that
are irrelevant for search.
"""
- return self.normalizer.transliterate(phrase)
+ return cast(str, self.normalizer.transliterate(phrase))
- @staticmethod
- def normalize_postcode(postcode):
+ def normalize_postcode(self, postcode: str) -> str:
""" Convert the postcode to a standardized form.
This function must yield exactly the same result as the SQL function
return postcode.strip().upper()
- def update_postcodes_from_db(self):
+ def update_postcodes_from_db(self) -> None:
""" Update postcode tokens in the word table from the location_postcode
table.
"""
+ assert self.conn is not None
+
with self.conn.cursor() as cur:
# This finds us the rows in location_postcode and word that are
# missing in the other table.
- def update_special_phrases(self, phrases, should_replace):
+ def update_special_phrases(self, phrases: Iterable[Tuple[str, str, str, str]],
+ should_replace: bool) -> None:
""" Replace the search index for special phrases with the new phrases.
"""
+ assert self.conn is not None
+
norm_phrases = set(((self.normalize(p[0]), p[1], p[2], p[3])
for p in phrases))
len(norm_phrases), len(to_add), len(to_delete))
- def add_country_names(self, country_code, names):
+ def add_country_names(self, country_code: str, names: Mapping[str, str]) -> None:
""" Add names for the given country to the search index.
"""
+ assert self.conn is not None
+
with self.conn.cursor() as cur:
cur.execute(
"""INSERT INTO word (word_id, word_token, country_code)
""", (country_code, list(names.values()), country_code))
- def process_place(self, place):
+ def process_place(self, place: PlaceInfo) -> Mapping[str, Any]:
""" Determine tokenizer information about the given place.
Returns a JSON-serialisable structure that will be handed into
the database via the token_info field.
"""
+ assert self.conn is not None
+
token_info = _TokenInfo(self._cache)
names = place.name
if names:
token_info.add_names(self.conn, names)
- country_feature = place.country_feature
- if country_feature and re.fullmatch(r'[A-Za-z][A-Za-z]', country_feature):
- self.add_country_names(country_feature.lower(), names)
+ if place.is_country():
+ assert place.country_code is not None
+ self.add_country_names(place.country_code, names)
address = place.address
if address:
return token_info.data
- def _process_place_address(self, token_info, address):
+ def _process_place_address(self, token_info: '_TokenInfo', address: Mapping[str, str]) -> None:
+ assert self.conn is not None
hnrs = []
addr_terms = []
if key == 'postcode':
# Make sure the normalized postcode is present in the word table.
if re.search(r'[:,;]', value) is None:
- self._cache.add_postcode(self.conn,
- self.normalize_postcode(value))
+ norm_pc = self.normalize_postcode(value)
+ token_info.set_postcode(norm_pc)
+ self._cache.add_postcode(self.conn, norm_pc)
elif key in ('housenumber', 'streetnumber', 'conscriptionnumber'):
hnrs.append(value)
elif key == 'street':
token_info.add_street(self.conn, value)
elif key == 'place':
token_info.add_place(self.conn, value)
- elif not key.startswith('_') and key not in ('country', 'full'):
+ elif not key.startswith('_') \
+ and key not in ('country', 'full', 'inclusion'):
addr_terms.append((key, value))
if hnrs:
class _TokenInfo:
""" Collect token information to be sent back to the database.
"""
- def __init__(self, cache):
+ def __init__(self, cache: '_TokenCache') -> None:
self.cache = cache
- self.data = {}
+ self.data: Dict[str, Any] = {}
- def add_names(self, conn, names):
+ def add_names(self, conn: Connection, names: Mapping[str, str]) -> None:
""" Add token information for the names of the place.
"""
with conn.cursor() as cur:
(names, ))
- def add_housenumbers(self, conn, hnrs):
+ def add_housenumbers(self, conn: Connection, hnrs: Sequence[str]) -> None:
""" Extract housenumber information from the address.
"""
if len(hnrs) == 1:
return
# split numbers if necessary
- simple_list = []
+ simple_list: List[str] = []
for hnr in hnrs:
simple_list.extend((x.strip() for x in re.split(r'[;,]', hnr)))
simple_list = list(set(simple_list))
with conn.cursor() as cur:
- cur.execute("SELECT (create_housenumbers(%s)).* ", (simple_list, ))
- self.data['hnr_tokens'], self.data['hnr'] = cur.fetchone()
+ cur.execute("SELECT * FROM create_housenumbers(%s)", (simple_list, ))
+ result = cur.fetchone()
+ assert result is not None
+ self.data['hnr_tokens'], self.data['hnr'] = result
- def add_street(self, conn, street):
+ def set_postcode(self, postcode: str) -> None:
+ """ Set or replace the postcode token with the given value.
+ """
+ self.data['postcode'] = postcode
+
+ def add_street(self, conn: Connection, street: str) -> None:
""" Add addr:street match terms.
"""
- def _get_street(name):
+ def _get_street(name: str) -> Optional[str]:
with conn.cursor() as cur:
- return cur.scalar("SELECT word_ids_from_name(%s)::text", (name, ))
+ return cast(Optional[str],
+ cur.scalar("SELECT word_ids_from_name(%s)::text", (name, )))
- self.data['street'] = self.cache.streets.get(street, _get_street)
+ tokens = self.cache.streets.get(street, _get_street)
+ self.data['street'] = tokens or '{}'
- def add_place(self, conn, place):
+ def add_place(self, conn: Connection, place: str) -> None:
""" Add addr:place search and match terms.
"""
- def _get_place(name):
+ def _get_place(name: str) -> Tuple[List[int], List[int]]:
with conn.cursor() as cur:
cur.execute("""SELECT make_keywords(hstore('name' , %s))::text,
word_ids_from_name(%s)::text""",
(name, name))
- return cur.fetchone()
+ return cast(Tuple[List[int], List[int]], cur.fetchone())
self.data['place_search'], self.data['place_match'] = \
self.cache.places.get(place, _get_place)
- def add_address_terms(self, conn, terms):
+ def add_address_terms(self, conn: Connection, terms: Sequence[Tuple[str, str]]) -> None:
""" Add additional address terms.
"""
- def _get_address_term(name):
+ def _get_address_term(name: str) -> Tuple[List[int], List[int]]:
with conn.cursor() as cur:
cur.execute("""SELECT addr_ids_from_name(%s)::text,
word_ids_from_name(%s)::text""",
(name, name))
- return cur.fetchone()
+ return cast(Tuple[List[int], List[int]], cur.fetchone())
tokens = {}
for key, value in terms:
- tokens[key] = self.cache.address_terms.get(value, _get_address_term)
+ items = self.cache.address_terms.get(value, _get_address_term)
+ if items[0] or items[1]:
+ tokens[key] = items
- self.data['addr'] = tokens
+ if tokens:
+ self.data['addr'] = tokens
class _LRU:
produce the item when there is a cache miss.
"""
- def __init__(self, maxsize=128, init_data=None):
- self.data = init_data or OrderedDict()
+ def __init__(self, maxsize: int = 128):
+ self.data: 'OrderedDict[str, Any]' = OrderedDict()
self.maxsize = maxsize
- if init_data is not None and len(init_data) > maxsize:
- self.maxsize = len(init_data)
- def get(self, key, generator):
+
+ def get(self, key: str, generator: Callable[[str], Any]) -> Any:
""" Get the item with the given key from the cache. If nothing
is found in the cache, generate the value through the
generator function and store it in the cache.
This cache is not thread-safe and needs to be instantiated per
analyzer.
"""
- def __init__(self, conn):
+ def __init__(self, conn: Connection):
# various LRU caches
self.streets = _LRU(maxsize=256)
self.places = _LRU(maxsize=128)
with conn.cursor() as cur:
cur.execute("""SELECT i, ARRAY[getorcreate_housenumber_id(i::text)]::text
FROM generate_series(1, 100) as i""")
- self._cached_housenumbers = {str(r[0]): r[1] for r in cur}
+ self._cached_housenumbers: Dict[str, str] = {str(r[0]): r[1] for r in cur}
# For postcodes remember the ones that have already been added
- self.postcodes = set()
+ self.postcodes: Set[str] = set()
- def get_housenumber(self, number):
+ def get_housenumber(self, number: str) -> Optional[str]:
""" Get a housenumber token from the cache.
"""
return self._cached_housenumbers.get(number)
- def add_postcode(self, conn, postcode):
+ def add_postcode(self, conn: Connection, postcode: str) -> None:
""" Make sure the given postcode is in the database.
"""
if postcode not in self.postcodes:
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