Main work horse for indexing (computing addresses) the database.
"""
import logging
-import select
+import time
+
+import psycopg2.extras
from nominatim.indexer.progress import ProgressLogger
from nominatim.indexer import runners
-from nominatim.db.async_connection import DBConnection
+from nominatim.db.async_connection import DBConnection, WorkerPool
from nominatim.db.connection import connect
LOG = logging.getLogger()
-class WorkerPool:
- """ A pool of asynchronous database connections.
- The pool may be used as a context manager.
+class PlaceFetcher:
+ """ Asynchronous connection that fetches place details for processing.
"""
- REOPEN_CONNECTIONS_AFTER = 100000
+ 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, pool_size):
- self.threads = [DBConnection(dsn) for _ in range(pool_size)]
- self.free_workers = self._yield_free_worker()
+ with setup_conn.cursor() as cur:
+ # need to fetch those manually because register_hstore cannot
+ # fetch them on an asynchronous connection below.
+ hstore_oid = cur.scalar("SELECT 'hstore'::regtype::oid")
+ hstore_array_oid = cur.scalar("SELECT 'hstore[]'::regtype::oid")
+ psycopg2.extras.register_hstore(self.conn.conn, oid=hstore_oid,
+ array_oid=hstore_array_oid)
- def finish_all(self):
- """ Wait for all connection to finish.
+ def close(self):
+ """ Close the underlying asynchronous connection.
"""
- for thread in self.threads:
- while not thread.is_done():
- thread.wait()
+ if self.conn:
+ self.conn.close()
+ self.conn = None
- self.free_workers = self._yield_free_worker()
- def close(self):
- """ Close all connections and clear the pool.
+ def fetch_next_batch(self, cur, runner):
+ """ 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.
"""
- for thread in self.threads:
- thread.close()
- self.threads = []
- self.free_workers = None
+ ids = cur.fetchmany(100)
+ if not ids:
+ self.current_ids = None
+ return False
- def next_free_worker(self):
- """ Get the next free connection.
+ if hasattr(runner, 'get_place_details'):
+ runner.get_place_details(self.conn, ids)
+ self.current_ids = []
+ else:
+ self.current_ids = ids
+
+ return True
+
+ def get_batch(self):
+ """ Get the next batch of data, previously requested with
+ `fetch_next_batch`.
"""
- return next(self.free_workers)
-
-
- def _yield_free_worker(self):
- ready = self.threads
- command_stat = 0
- while True:
- for thread in ready:
- if thread.is_done():
- command_stat += 1
- yield thread
-
- if command_stat > self.REOPEN_CONNECTIONS_AFTER:
- for thread in self.threads:
- while not thread.is_done():
- thread.wait()
- thread.connect()
- ready = self.threads
- command_stat = 0
- else:
- _, ready, _ = select.select([], self.threads, [])
+ 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()
+ return self.current_ids
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, traceback):
+ self.conn.wait()
self.close()
def index_full(self, analyse=True):
- """ Index the complete database. This will first index boudnaries
+ """ 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
ensure that database statistics are updated.
with connect(self.dsn) as conn:
conn.autocommit = True
- if analyse:
- def _analyze():
+ def _analyze():
+ if analyse:
with conn.cursor() as cur:
cur.execute('ANALYZE')
- else:
- def _analyze():
- pass
self.index_by_rank(0, 4)
_analyze()
if maxrank == 30:
self._index(runners.RankRunner(0, analyzer))
- self._index(runners.InterpolationRunner(), 20)
+ self._index(runners.InterpolationRunner(analyzer), 20)
self._index(runners.RankRunner(30, analyzer), 20)
else:
self._index(runners.RankRunner(maxrank, analyzer))
LOG.warning("Starting %s (using batch size %s)", runner.name(), batch)
with connect(self.dsn) as conn:
+ psycopg2.extras.register_hstore(conn)
with conn.cursor() as cur:
total_tuples = cur.scalar(runner.sql_count_objects())
LOG.debug("Total number of rows: %i", total_tuples)
with conn.cursor(name='places') as cur:
cur.execute(runner.sql_get_objects())
- with WorkerPool(self.dsn, self.num_threads) as pool:
- while True:
- places = [p for p in cur.fetchmany(batch)]
- if not places:
- break
+ with PlaceFetcher(self.dsn, conn) as fetcher:
+ with WorkerPool(self.dsn, self.num_threads) as pool:
+ has_more = fetcher.fetch_next_batch(cur, runner)
+ while has_more:
+ places = fetcher.get_batch()
- LOG.debug("Processing places: %s", str(places))
- worker = pool.next_free_worker()
+ # asynchronously get the next batch
+ has_more = fetcher.fetch_next_batch(cur, runner)
- runner.index_places(worker, places)
- progress.add(len(places))
+ # And insert the curent batch
+ for idx in range(0, len(places), batch):
+ part = places[idx:idx + batch]
+ LOG.debug("Processing places: %s", str(part))
+ runner.index_places(pool.next_free_worker(), part)
+ progress.add(len(part))
- pool.finish_all()
+ LOG.info("Wait time: fetcher: %.2fs, pool: %.2fs",
+ fetcher.wait_time, pool.wait_time)
conn.commit()
projects / nominatim.git / blobdiff