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