Files
Nominatim/nominatim/indexer/indexer.py
Sarah Hoffmann fa2bc60468 introduce name analyzer
The name analyzer is the actual work horse of the tokenizer. It
is instantiated on a thread-base and provides all functions for
analysing names and queries.
2021-04-30 11:30:51 +02:00

208 lines
6.3 KiB
Python

"""
Main work horse for indexing (computing addresses) the database.
"""
import logging
import select
from nominatim.indexer.progress import ProgressLogger
from nominatim.indexer import runners
from nominatim.db.async_connection import DBConnection
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.
"""
REOPEN_CONNECTIONS_AFTER = 100000
def __init__(self, dsn, pool_size):
self.threads = [DBConnection(dsn) for _ in range(pool_size)]
self.free_workers = self._yield_free_worker()
def finish_all(self):
""" Wait for all connection to finish.
"""
for thread in self.threads:
while not thread.is_done():
thread.wait()
self.free_workers = self._yield_free_worker()
def close(self):
""" Close all connections and clear the pool.
"""
for thread in self.threads:
thread.close()
self.threads = []
self.free_workers = None
def next_free_worker(self):
""" Get the next free connection.
"""
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, [])
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, traceback):
self.close()
class Indexer:
""" Main indexing routine.
"""
def __init__(self, dsn, tokenizer, num_threads):
self.dsn = dsn
self.tokenizer = tokenizer
self.num_threads = num_threads
def index_full(self, analyse=True):
""" Index the complete database. This will first index boudnaries
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():
with conn.cursor() as cur:
cur.execute('ANALYZE')
else:
def _analyze():
pass
self.index_by_rank(0, 4)
_analyze()
self.index_boundaries(0, 30)
_analyze()
self.index_by_rank(5, 25)
_analyze()
self.index_by_rank(26, 30)
_analyze()
self.index_postcodes()
_analyze()
def index_boundaries(self, minrank, maxrank):
""" Index only administrative boundaries within the given rank range.
"""
LOG.warning("Starting indexing boundaries using %s threads",
self.num_threads)
with self.tokenizer.name_analyzer() as analyzer:
for rank in range(max(minrank, 4), min(maxrank, 26)):
self._index(runners.BoundaryRunner(rank, analyzer))
def index_by_rank(self, minrank, maxrank):
""" Index all entries of placex in the given rank range (inclusive)
in order of their address rank.
When rank 30 is requested then also interpolations and
places with address rank 0 will be indexed.
"""
maxrank = min(maxrank, 30)
LOG.warning("Starting indexing rank (%i to %i) using %i threads",
minrank, maxrank, self.num_threads)
with self.tokenizer.name_analyzer() as analyzer:
for rank in range(max(1, minrank), maxrank):
self._index(runners.RankRunner(rank, analyzer))
if maxrank == 30:
self._index(runners.RankRunner(0, analyzer))
self._index(runners.InterpolationRunner(), 20)
self._index(runners.RankRunner(30, analyzer), 20)
else:
self._index(runners.RankRunner(maxrank, analyzer))
def index_postcodes(self):
"""Index the entries ofthe location_postcode table.
"""
LOG.warning("Starting indexing postcodes using %s threads", self.num_threads)
self._index(runners.PostcodeRunner(), 20)
def update_status_table(self):
""" Update the status in the status table to 'indexed'.
"""
with connect(self.dsn) as conn:
with conn.cursor() as cur:
cur.execute('UPDATE import_status SET indexed = true')
conn.commit()
def _index(self, runner, batch=1):
""" 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
"""
LOG.warning("Starting %s (using batch size %s)", runner.name(), batch)
with connect(self.dsn) as conn:
with conn.cursor() as cur:
total_tuples = cur.scalar(runner.sql_count_objects())
LOG.debug("Total number of rows: %i", total_tuples)
conn.commit()
progress = ProgressLogger(runner.name(), total_tuples)
if total_tuples > 0:
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
LOG.debug("Processing places: %s", str(places))
worker = pool.next_free_worker()
runner.index_places(worker, places)
progress.add(len(places))
pool.finish_all()
conn.commit()
progress.done()