"""
Tokenizer implementing normalisation as used before Nominatim 4.
"""
+from collections import OrderedDict
import logging
+import re
import shutil
+from textwrap import dedent
+from icu import Transliterator
import psycopg2
+import psycopg2.extras
from nominatim.db.connection import connect
from nominatim.db import properties
+from nominatim.db import utils as db_utils
+from nominatim.db.sql_preprocessor import SQLPreprocessor
from nominatim.errors import UsageError
DBCFG_NORMALIZATION = "tokenizer_normalization"
+DBCFG_MAXWORDFREQ = "tokenizer_maxwordfreq"
LOG = logging.getLogger()
return LegacyTokenizer(dsn, data_dir)
-def _install_module(src_dir, module_dir):
+def _install_module(config_module_path, src_dir, module_dir):
""" 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
The function detects when the installation is run from the
build directory. It doesn't touch the module in that case.
"""
+ # Custom module locations are simply used as is.
+ if config_module_path:
+ LOG.info("Using custom path for database module at '%s'", config_module_path)
+ return config_module_path
+
+ # 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
+ return module_dir
+ # In any other case install the module in the project directory.
if not module_dir.exists():
module_dir.mkdir()
LOG.info('Database module installed at %s', str(destfile))
+ return module_dir
+
def _check_module(module_dir, conn):
+ """ 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)
self.normalization = None
- def init_new_db(self, config):
+ def init_new_db(self, config, init_db=True):
""" 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.
"""
- # Find and optionally install the PsotgreSQL normalization module.
- if config.DATABASE_MODULE_PATH:
- LOG.info("Using custom path for database module at '%s'",
- config.DATABASE_MODULE_PATH)
- module_dir = config.DATABASE_MODULE_PATH
- else:
- _install_module(config.lib_dir.module, config.project_dir / 'module')
- module_dir = config.project_dir / 'module'
+ 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)
+
with connect(self.dsn) as conn:
_check_module(module_dir, conn)
-
- # Stable configuration is saved in the database.
- properties.set_property(conn, DBCFG_NORMALIZATION, self.normalization)
-
+ self._save_config(conn, config)
conn.commit()
+ if init_db:
+ self.update_sql_functions(config)
+ self._init_db_tables(config)
+
def init_from_project(self):
""" Initialise the tokenizer from the project directory.
"""
with connect(self.dsn) as conn:
self.normalization = properties.get_property(conn, DBCFG_NORMALIZATION)
+
+
+ def finalize_import(self, config):
+ """ Do any required postprocessing to make the tokenizer data ready
+ for use.
+ """
+ with connect(self.dsn) as conn:
+ sqlp = SQLPreprocessor(conn, config)
+ sqlp.run_sql_file(conn, 'tokenizer/legacy_tokenizer_indices.sql')
+
+
+ def update_sql_functions(self, config):
+ """ Reimport the SQL functions for this tokenizer.
+ """
+ with connect(self.dsn) as conn:
+ max_word_freq = properties.get_property(conn, DBCFG_MAXWORDFREQ)
+ modulepath = config.DATABASE_MODULE_PATH or \
+ str((config.project_dir / 'module').resolve())
+ sqlp = SQLPreprocessor(conn, config)
+ sqlp.run_sql_file(conn, 'tokenizer/legacy_tokenizer.sql',
+ max_word_freq=max_word_freq,
+ modulepath=modulepath)
+
+
+ def check_database(self):
+ """ Check that the tokenizer is set up correctly.
+ """
+ hint = """\
+ The Postgresql extension nominatim.so was not correctly loaded.
+
+ Error: {error}
+
+ Hints:
+ * Check the output of the CMmake/make installation step
+ * Does nominatim.so exist?
+ * Does nominatim.so exist on the database server?
+ * Can nominatim.so be accessed by the database user?
+ """
+ with connect(self.dsn) as conn:
+ with conn.cursor() as cur:
+ try:
+ out = cur.scalar("SELECT make_standard_name('a')")
+ except psycopg2.Error as err:
+ return hint.format(error=str(err))
+
+ if out != 'a':
+ return hint.format(error='Unexpected result for make_standard_name()')
+
+ return None
+
+
+ def migrate_database(self, config):
+ """ 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.
+ """
+ self.normalization = config.TERM_NORMALIZATION
+ module_dir = _install_module(config.DATABASE_MODULE_PATH,
+ config.lib_dir.module,
+ config.project_dir / 'module')
+
+ with connect(self.dsn) as conn:
+ _check_module(module_dir, conn)
+ self._save_config(conn, config)
+
+
+ def name_analyzer(self):
+ """ Create a new analyzer for tokenizing names and queries
+ using this tokinzer. Analyzers are context managers and should
+ be used accordingly:
+
+ ```
+ with tokenizer.name_analyzer() as analyzer:
+ analyser.tokenize()
+ ```
+
+ When used outside the with construct, the caller must ensure to
+ call the close() function before destructing the analyzer.
+
+ Analyzers are not thread-safe. You need to instantiate one per thread.
+ """
+ normalizer = Transliterator.createFromRules("phrase normalizer",
+ self.normalization)
+ return LegacyNameAnalyzer(self.dsn, normalizer)
+
+
+ def _install_php(self, config):
+ """ 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)))
+
+
+ def _init_db_tables(self, config):
+ """ Set up the word table and fill it with pre-computed word
+ frequencies.
+ """
+ with connect(self.dsn) as conn:
+ sqlp = SQLPreprocessor(conn, config)
+ sqlp.run_sql_file(conn, 'tokenizer/legacy_tokenizer_tables.sql')
+ conn.commit()
+
+ LOG.warning("Precomputing word tokens")
+ db_utils.execute_file(self.dsn, config.lib_dir.data / 'words.sql')
+
+
+ def _save_config(self, conn, config):
+ """ Save the configuration that needs to remain stable for the given
+ database as database properties.
+ """
+ properties.set_property(conn, DBCFG_NORMALIZATION, self.normalization)
+ properties.set_property(conn, DBCFG_MAXWORDFREQ, config.MAX_WORD_FREQUENCY)
+
+
+class LegacyNameAnalyzer:
+ """ The legacy analyzer uses the special Postgresql module for
+ splitting names.
+
+ Each instance opens a connection to the database to request the
+ normalization.
+ """
+
+ def __init__(self, dsn, normalizer):
+ self.conn = connect(dsn).connection
+ self.conn.autocommit = True
+ self.normalizer = normalizer
+ psycopg2.extras.register_hstore(self.conn)
+
+ self._cache = _TokenCache(self.conn)
+
+
+ def __enter__(self):
+ return self
+
+
+ def __exit__(self, exc_type, exc_value, traceback):
+ self.close()
+
+
+ def close(self):
+ """ Free all resources used by the analyzer.
+ """
+ if self.conn:
+ self.conn.close()
+ self.conn = None
+
+
+ def normalize(self, phrase):
+ """ Normalize the given phrase, i.e. remove all properties that
+ are irrelevant for search.
+ """
+ return self.normalizer.transliterate(phrase)
+
+
+ def add_postcodes_from_db(self):
+ """ Add postcodes from the location_postcode table to the word table.
+ """
+ with self.conn.cursor() as cur:
+ cur.execute("""SELECT count(create_postcode_id(pc))
+ FROM (SELECT distinct(postcode) as pc
+ FROM location_postcode) x""")
+
+
+ def update_special_phrases(self, phrases):
+ """ Replace the search index for special phrases with the new phrases.
+ """
+ norm_phrases = set(((self.normalize(p[0]), p[1], p[2], p[3])
+ for p in phrases))
+
+ with self.conn.cursor() as cur:
+ # Get the old phrases.
+ existing_phrases = set()
+ cur.execute("""SELECT word, class, type, operator FROM word
+ WHERE class != 'place'
+ OR (type != 'house' AND type != 'postcode')""")
+ for label, cls, typ, oper in cur:
+ existing_phrases.add((label, cls, typ, oper or '-'))
+
+ to_add = norm_phrases - existing_phrases
+ to_delete = existing_phrases - norm_phrases
+
+ if to_add:
+ psycopg2.extras.execute_values(
+ cur,
+ """ INSERT INTO word (word_id, word_token, word, class, type,
+ search_name_count, operator)
+ (SELECT nextval('seq_word'), make_standard_name(name), name,
+ class, type, 0,
+ CASE WHEN op in ('in', 'near') THEN op ELSE null END
+ FROM (VALUES %s) as v(name, class, type, op))""",
+ to_add)
+
+ if to_delete:
+ psycopg2.extras.execute_values(
+ cur,
+ """ DELETE FROM word USING (VALUES %s) as v(name, in_class, in_type, op)
+ WHERE word = name and class = in_class and type = in_type
+ and ((op = '-' and operator is null) or op = operator)""",
+ to_delete)
+
+ LOG.info("Total phrases: %s. Added: %s. Deleted: %s",
+ len(norm_phrases), len(to_add), len(to_delete))
+
+
+ def add_country_names(self, country_code, names):
+ """ Add names for the given country to the search index.
+ """
+ with self.conn.cursor() as cur:
+ cur.execute(
+ """INSERT INTO word (word_id, word_token, country_code)
+ (SELECT nextval('seq_word'), lookup_token, %s
+ FROM (SELECT ' ' || make_standard_name(n) as lookup_token
+ FROM unnest(%s)n) y
+ WHERE NOT EXISTS(SELECT * FROM word
+ WHERE word_token = lookup_token and country_code = %s))
+ """, (country_code, names, country_code))
+
+
+ def process_place(self, place):
+ """ Determine tokenizer information about the given place.
+
+ Returns a JSON-serialisable structure that will be handed into
+ the database via the token_info field.
+ """
+ token_info = _TokenInfo(self._cache)
+
+ names = place.get('name')
+
+ if names:
+ token_info.add_names(self.conn, names)
+
+ country_feature = place.get('country_feature')
+ if country_feature and re.fullmatch(r'[A-Za-z][A-Za-z]', country_feature):
+ self.add_country_names(country_feature.lower(), list(names.values()))
+
+ address = place.get('address')
+
+ if address:
+ hnrs = []
+ addr_terms = []
+ for key, value in address.items():
+ if key == 'postcode':
+ self._add_postcode(value)
+ 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'):
+ addr_terms.append((key, value))
+
+ if hnrs:
+ token_info.add_housenumbers(self.conn, hnrs)
+
+ if addr_terms:
+ token_info.add_address_terms(self.conn, addr_terms)
+
+ return token_info.data
+
+
+ def _add_postcode(self, postcode):
+ """ Make sure the normalized postcode is present in the word table.
+ """
+ def _create_postcode_from_db(pcode):
+ with self.conn.cursor() as cur:
+ cur.execute('SELECT create_postcode_id(%s)', (pcode, ))
+
+ if re.search(r'[:,;]', postcode) is None:
+ self._cache.postcodes.get(postcode.strip().upper(), _create_postcode_from_db)
+
+
+class _TokenInfo:
+ """ Collect token information to be sent back to the database.
+ """
+ def __init__(self, cache):
+ self.cache = cache
+ self.data = {}
+
+
+ def add_names(self, conn, names):
+ """ Add token information for the names of the place.
+ """
+ with conn.cursor() as cur:
+ # Create the token IDs for all names.
+ self.data['names'] = cur.scalar("SELECT make_keywords(%s)::text",
+ (names, ))
+
+
+ def add_housenumbers(self, conn, hnrs):
+ """ Extract housenumber information from the address.
+ """
+ if len(hnrs) == 1:
+ token = self.cache.get_housenumber(hnrs[0])
+ if token is not None:
+ self.data['hnr_tokens'] = token
+ self.data['hnr'] = hnrs[0]
+ return
+
+ # split numbers if necessary
+ simple_list = []
+ for hnr in hnrs:
+ simple_list.extend((x.strip() for x in re.split(r'[;,]', hnr)))
+
+ if len(simple_list) > 1:
+ 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()
+
+
+ def add_street(self, conn, street):
+ """ Add addr:street match terms.
+ """
+ def _get_street(name):
+ with conn.cursor() as cur:
+ return cur.scalar("SELECT word_ids_from_name(%s)::text", (name, ))
+
+ self.data['street'] = self.cache.streets.get(street, _get_street)
+
+
+ def add_place(self, conn, place):
+ """ Add addr:place search and match terms.
+ """
+ def _get_place(name):
+ with conn.cursor() as cur:
+ cur.execute("""SELECT (addr_ids_from_name(%s)
+ || getorcreate_name_id(make_standard_name(%s), ''))::text,
+ word_ids_from_name(%s)::text""",
+ (name, name, name))
+ return cur.fetchone()
+
+ self.data['place_search'], self.data['place_match'] = \
+ self.cache.places.get(place, _get_place)
+
+
+ def add_address_terms(self, conn, terms):
+ """ Add additional address terms.
+ """
+ def _get_address_term(name):
+ 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()
+
+ tokens = {}
+ for key, value in terms:
+ tokens[key] = self.cache.address_terms.get(value, _get_address_term)
+
+ self.data['addr'] = tokens
+
+
+class _LRU:
+ """ Least recently used cache that accepts a generator function to
+ produce the item when there is a cache miss.
+ """
+
+ def __init__(self, maxsize=128, init_data=None):
+ self.data = init_data or 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):
+ """ 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.
+ """
+ value = self.data.get(key)
+ if value is not None:
+ self.data.move_to_end(key)
+ else:
+ value = generator(key)
+ if len(self.data) >= self.maxsize:
+ self.data.popitem(last=False)
+ self.data[key] = value
+
+ return value
+
+
+class _TokenCache:
+ """ Cache for token information to avoid repeated database queries.
+
+ This cache is not thread-safe and needs to be instantiated per
+ analyzer.
+ """
+ def __init__(self, conn):
+ # various LRU caches
+ self.streets = _LRU(maxsize=256)
+ self.places = _LRU(maxsize=128)
+ self.address_terms = _LRU(maxsize=1024)
+
+ # Lookup houseunumbers up to 100 and cache them
+ 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}
+
+ # Get postcodes that are already saved
+ postcodes = OrderedDict()
+ with conn.cursor() as cur:
+ cur.execute("""SELECT word FROM word
+ WHERE class ='place' and type = 'postcode'""")
+ for row in cur:
+ postcodes[row[0]] = None
+ self.postcodes = _LRU(maxsize=32, init_data=postcodes)
+
+ def get_housenumber(self, number):
+ """ Get a housenumber token from the cache.
+ """
+ return self._cached_housenumbers.get(number)
projects / nominatim.git / blobdiff