make DB helper functions free functions

[nominatim.git] / src / nominatim_db / tokenizer / legacy_tokenizer.py

diff --git a/src/nominatim_db/tokenizer/legacy_tokenizer.py b/src/nominatim_db/tokenizer/legacy_tokenizer.py
index 136a733159cd3180e2e0558e48b97141887165c1..0e8dfcf97fc07b51216e88862b13f6018043f082 100644 (file)
--- a/src/nominatim_db/tokenizer/legacy_tokenizer.py
+++ b/src/nominatim_db/tokenizer/legacy_tokenizer.py
@@ -18,10 +18,10 @@ from textwrap import dedent
 
 from icu import Transliterator
 import psycopg2
-import psycopg2.extras
 
 from ..errors import UsageError
-from ..db.connection import connect, Connection
+from ..db.connection import connect, Connection, drop_tables, table_exists,\
+                            execute_scalar, register_hstore
 from ..config import Configuration
 from ..db import properties
 from ..db import utils as db_utils
@@ -179,11 +179,10 @@ class LegacyTokenizer(AbstractTokenizer):
              * 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))
+            try:
+                out = execute_scalar(conn, "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()')
@@ -214,9 +213,9 @@ class LegacyTokenizer(AbstractTokenizer):
         """ Recompute the frequency of full words.
         """
         with connect(self.dsn) as conn:
-            if conn.table_exists('search_name'):
+            if table_exists(conn, 'search_name'):
+                drop_tables(conn, "word_frequencies")
                 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(*)
@@ -226,7 +225,7 @@ class LegacyTokenizer(AbstractTokenizer):
                     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")
+                drop_tables(conn, "word_frequencies")
             conn.commit()
 
 
@@ -316,7 +315,7 @@ class LegacyNameAnalyzer(AbstractAnalyzer):
         self.conn: Optional[Connection] = connect(dsn).connection
         self.conn.autocommit = True
         self.normalizer = normalizer
-        psycopg2.extras.register_hstore(self.conn)
+        register_hstore(self.conn)
 
         self._cache = _TokenCache(self.conn)
 
@@ -536,9 +535,8 @@ class _TokenInfo:
     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:
-            # Create the token IDs for all names.
-            self.data['names'] = cur.scalar("SELECT make_keywords(%s)::text",
+        # Create the token IDs for all names.
+        self.data['names'] = execute_scalar(conn, "SELECT make_keywords(%s)::text",
                                             (names, ))
 
 
@@ -576,9 +574,8 @@ class _TokenInfo:
         """ Add addr:street match terms.
         """
         def _get_street(name: str) -> Optional[str]:
-            with conn.cursor() as cur:
-                return cast(Optional[str],
-                            cur.scalar("SELECT word_ids_from_name(%s)::text", (name, )))
+            return cast(Optional[str],
+                        execute_scalar(conn, "SELECT word_ids_from_name(%s)::text", (name, )))
 
         tokens = self.cache.streets.get(street, _get_street)
         self.data['street'] = tokens or '{}'