"""
Main work horse for indexing (computing addresses) the database.
"""
+from typing import Optional, Any, cast
import logging
import time
import psycopg2.extras
+from nominatim.tokenizer.base import AbstractTokenizer
from nominatim.indexer.progress import ProgressLogger
from nominatim.indexer import runners
from nominatim.db.async_connection import DBConnection, WorkerPool
-from nominatim.db.connection import connect
+from nominatim.db.connection import connect, Connection, Cursor
+from nominatim.typing import DictCursorResults
LOG = logging.getLogger()
class PlaceFetcher:
""" Asynchronous connection that fetches place details for processing.
"""
- def __init__(self, dsn, setup_conn):
- self.wait_time = 0
- self.current_ids = None
- self.conn = DBConnection(dsn, cursor_factory=psycopg2.extras.DictCursor)
+ def __init__(self, dsn: str, setup_conn: Connection) -> None:
+ self.wait_time = 0.0
+ self.current_ids: Optional[DictCursorResults] = None
+ self.conn: Optional[DBConnection] = DBConnection(dsn,
+ cursor_factory=psycopg2.extras.DictCursor)
with setup_conn.cursor() as cur:
# need to fetch those manually because register_hstore cannot
psycopg2.extras.register_hstore(self.conn.conn, oid=hstore_oid,
array_oid=hstore_array_oid)
- def close(self):
+ def close(self) -> None:
""" Close the underlying asynchronous connection.
"""
if self.conn:
self.conn = None
- def fetch_next_batch(self, cur, runner):
+ def fetch_next_batch(self, cur: Cursor, runner: runners.Runner) -> bool:
""" Send a request for the next batch of places.
If details for the places are required, they will be fetched
asynchronously.
Returns true if there is still data available.
"""
- ids = cur.fetchmany(100)
+ ids = cast(Optional[DictCursorResults], cur.fetchmany(100))
if not ids:
self.current_ids = None
return False
- if hasattr(runner, 'get_place_details'):
- runner.get_place_details(self.conn, ids)
- self.current_ids = []
- else:
- self.current_ids = ids
+ assert self.conn is not None
+ self.current_ids = runner.get_place_details(self.conn, ids)
return True
- def get_batch(self):
+ def get_batch(self) -> DictCursorResults:
""" Get the next batch of data, previously requested with
`fetch_next_batch`.
"""
+ assert self.conn is not None
+ assert self.conn.cursor is not None
+
if self.current_ids is not None and not self.current_ids:
tstart = time.time()
self.conn.wait()
self.wait_time += time.time() - tstart
- self.current_ids = self.conn.cursor.fetchall()
+ self.current_ids = cast(Optional[DictCursorResults],
+ self.conn.cursor.fetchall())
- return self.current_ids
+ return self.current_ids if self.current_ids is not None else []
- def __enter__(self):
+ def __enter__(self) -> 'PlaceFetcher':
return self
- def __exit__(self, exc_type, exc_value, traceback):
+ def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
+ assert self.conn is not None
self.conn.wait()
self.close()
""" Main indexing routine.
"""
- def __init__(self, dsn, tokenizer, num_threads):
+ def __init__(self, dsn: str, tokenizer: AbstractTokenizer, num_threads: int):
self.dsn = dsn
self.tokenizer = tokenizer
self.num_threads = num_threads
- def has_pending(self):
+ def has_pending(self) -> bool:
""" Check if any data still needs indexing.
This function must only be used after the import has finished.
Otherwise it will be very expensive.
return cur.rowcount > 0
- def index_full(self, analyse=True):
+ def index_full(self, analyse: bool = True) -> None:
""" Index the complete database. This will first index boundaries
followed by all other objects. When `analyse` is True, then the
database will be analysed at the appropriate places to
with connect(self.dsn) as conn:
conn.autocommit = True
- def _analyze():
+ def _analyze() -> None:
if analyse:
with conn.cursor() as cur:
cur.execute('ANALYZE')
_analyze()
- def index_boundaries(self, minrank, maxrank):
+ def index_boundaries(self, minrank: int, maxrank: int) -> None:
""" Index only administrative boundaries within the given rank range.
"""
LOG.warning("Starting indexing boundaries using %s threads",
for rank in range(max(minrank, 4), min(maxrank, 26)):
self._index(runners.BoundaryRunner(rank, analyzer))
- def index_by_rank(self, minrank, maxrank):
+ def index_by_rank(self, minrank: int, maxrank: int) -> None:
""" Index all entries of placex in the given rank range (inclusive)
in order of their address rank.
self._index(runners.InterpolationRunner(analyzer), 20)
- def index_postcodes(self):
- """Index the entries ofthe location_postcode table.
+ def index_postcodes(self) -> None:
+ """Index the entries of the location_postcode table.
"""
LOG.warning("Starting indexing postcodes using %s threads", self.num_threads)
self._index(runners.PostcodeRunner(), 20)
- def update_status_table(self):
+ def update_status_table(self) -> None:
""" Update the status in the status table to 'indexed'.
"""
with connect(self.dsn) as conn:
conn.commit()
- def _index(self, runner, batch=1):
+ def _index(self, runner: runners.Runner, batch: int = 1) -> None:
""" Index a single rank or table. `runner` describes the SQL to use
for indexing. `batch` describes the number of objects that
should be processed with a single SQL statement
# asynchronously get the next batch
has_more = fetcher.fetch_next_batch(cur, runner)
- # And insert the curent batch
+ # And insert the current batch
for idx in range(0, len(places), batch):
part = places[idx:idx + batch]
LOG.debug("Processing places: %s", str(part))
projects / nominatim.git / blobdiff