forked from hans/Nominatim
port code to psycopg3
This commit is contained in:
Sarah Hoffmann
@@ -7,92 +7,20 @@
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"""
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Main work horse for indexing (computing addresses) the database.
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"""
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from typing import Optional, Any, cast
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from typing import cast, List, Any
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import logging
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import time
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import psycopg2.extras
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import psycopg
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from ..typing import DictCursorResults
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from ..db.async_connection import DBConnection, WorkerPool
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from ..db.connection import connect, Connection, Cursor, execute_scalar, register_hstore
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from ..db.connection import connect, execute_scalar
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from ..db.query_pool import QueryPool
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from ..tokenizer.base import AbstractTokenizer
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from .progress import ProgressLogger
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from . import runners
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LOG = logging.getLogger()
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class PlaceFetcher:
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""" Asynchronous connection that fetches place details for processing.
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"""
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def __init__(self, dsn: str, setup_conn: Connection) -> None:
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self.wait_time = 0.0
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self.current_ids: Optional[DictCursorResults] = None
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self.conn: Optional[DBConnection] = DBConnection(dsn,
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cursor_factory=psycopg2.extras.DictCursor)
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# need to fetch those manually because register_hstore cannot
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# fetch them on an asynchronous connection below.
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hstore_oid = execute_scalar(setup_conn, "SELECT 'hstore'::regtype::oid")
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hstore_array_oid = execute_scalar(setup_conn, "SELECT 'hstore[]'::regtype::oid")
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psycopg2.extras.register_hstore(self.conn.conn, oid=hstore_oid,
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array_oid=hstore_array_oid)
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def close(self) -> None:
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""" Close the underlying asynchronous connection.
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"""
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if self.conn:
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self.conn.close()
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self.conn = None
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def fetch_next_batch(self, cur: Cursor, runner: runners.Runner) -> bool:
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""" Send a request for the next batch of places.
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If details for the places are required, they will be fetched
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asynchronously.
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Returns true if there is still data available.
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"""
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ids = cast(Optional[DictCursorResults], cur.fetchmany(100))
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if not ids:
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self.current_ids = None
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return False
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assert self.conn is not None
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self.current_ids = runner.get_place_details(self.conn, ids)
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return True
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def get_batch(self) -> DictCursorResults:
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""" Get the next batch of data, previously requested with
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`fetch_next_batch`.
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"""
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assert self.conn is not None
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assert self.conn.cursor is not None
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if self.current_ids is not None and not self.current_ids:
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tstart = time.time()
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self.conn.wait()
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self.wait_time += time.time() - tstart
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self.current_ids = cast(Optional[DictCursorResults],
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self.conn.cursor.fetchall())
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return self.current_ids if self.current_ids is not None else []
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def __enter__(self) -> 'PlaceFetcher':
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return self
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def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
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assert self.conn is not None
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self.conn.wait()
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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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@@ -114,7 +42,7 @@ class Indexer:
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return cur.rowcount > 0
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def index_full(self, analyse: bool = True) -> None:
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async def index_full(self, analyse: bool = True) -> None:
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""" Index the complete database. This will first index boundaries
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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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@@ -128,23 +56,27 @@ class Indexer:
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with conn.cursor() as cur:
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cur.execute('ANALYZE')
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if self.index_by_rank(0, 4) > 0:
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_analyze()
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while True:
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if await self.index_by_rank(0, 4) > 0:
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_analyze()
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if self.index_boundaries(0, 30) > 100:
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_analyze()
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if await self.index_boundaries(0, 30) > 100:
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_analyze()
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if self.index_by_rank(5, 25) > 100:
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_analyze()
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if await self.index_by_rank(5, 25) > 100:
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_analyze()
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if self.index_by_rank(26, 30) > 1000:
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_analyze()
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if await self.index_by_rank(26, 30) > 1000:
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_analyze()
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if self.index_postcodes() > 100:
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_analyze()
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if await self.index_postcodes() > 100:
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_analyze()
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if not self.has_pending():
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break
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def index_boundaries(self, minrank: int, maxrank: int) -> int:
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async def index_boundaries(self, minrank: int, maxrank: int) -> int:
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""" Index only administrative boundaries within the given rank range.
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"""
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total = 0
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@@ -153,11 +85,11 @@ class Indexer:
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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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total += self._index(runners.BoundaryRunner(rank, analyzer))
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total += await self._index(runners.BoundaryRunner(rank, analyzer))
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return total
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def index_by_rank(self, minrank: int, maxrank: int) -> int:
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async def index_by_rank(self, minrank: int, maxrank: int) -> int:
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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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@@ -171,21 +103,27 @@ class Indexer:
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with self.tokenizer.name_analyzer() as analyzer:
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for rank in range(max(1, minrank), maxrank + 1):
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total += self._index(runners.RankRunner(rank, analyzer), 20 if rank == 30 else 1)
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if rank >= 30:
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batch = 20
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elif rank >= 26:
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batch = 5
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else:
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batch = 1
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total += await self._index(runners.RankRunner(rank, analyzer), batch)
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if maxrank == 30:
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total += self._index(runners.RankRunner(0, analyzer))
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total += self._index(runners.InterpolationRunner(analyzer), 20)
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total += await self._index(runners.RankRunner(0, analyzer))
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total += await self._index(runners.InterpolationRunner(analyzer), 20)
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return total
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def index_postcodes(self) -> int:
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async def index_postcodes(self) -> int:
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"""Index the entries of the 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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return self._index(runners.PostcodeRunner(), 20)
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return await self._index(runners.PostcodeRunner(), 20)
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def update_status_table(self) -> None:
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@@ -197,45 +135,58 @@ class Indexer:
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conn.commit()
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def _index(self, runner: runners.Runner, batch: int = 1) -> int:
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async def _index(self, runner: runners.Runner, batch: int = 1) -> int:
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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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register_hstore(conn)
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total_tuples = execute_scalar(conn, runner.sql_count_objects())
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LOG.debug("Total number of rows: %i", total_tuples)
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total_tuples = self._prepare_indexing(runner)
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conn.commit()
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progress = ProgressLogger(runner.name(), total_tuples)
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progress = ProgressLogger(runner.name(), total_tuples)
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if total_tuples > 0:
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async with await psycopg.AsyncConnection.connect(
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self.dsn, row_factory=psycopg.rows.dict_row) as aconn,\
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QueryPool(self.dsn, self.num_threads, autocommit=True) as pool:
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fetcher_time = 0.0
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tstart = time.time()
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async with aconn.cursor(name='places') as cur:
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query = runner.index_places_query(batch)
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params: List[Any] = []
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num_places = 0
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async for place in cur.stream(runner.sql_get_objects()):
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fetcher_time += time.time() - tstart
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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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params.extend(runner.index_places_params(place))
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num_places += 1
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with PlaceFetcher(self.dsn, conn) as fetcher:
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with WorkerPool(self.dsn, self.num_threads) as pool:
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has_more = fetcher.fetch_next_batch(cur, runner)
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while has_more:
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places = fetcher.get_batch()
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if num_places >= batch:
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LOG.debug("Processing places: %s", str(params))
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await pool.put_query(query, params)
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progress.add(num_places)
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params = []
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num_places = 0
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# asynchronously get the next batch
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has_more = fetcher.fetch_next_batch(cur, runner)
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tstart = time.time()
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# And insert the current batch
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for idx in range(0, len(places), batch):
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part = places[idx:idx + batch]
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LOG.debug("Processing places: %s", str(part))
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runner.index_places(pool.next_free_worker(), part)
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progress.add(len(part))
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if num_places > 0:
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await pool.put_query(runner.index_places_query(num_places), params)
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LOG.info("Wait time: fetcher: %.2fs, pool: %.2fs",
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fetcher.wait_time, pool.wait_time)
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conn.commit()
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LOG.info("Wait time: fetcher: %.2fs, pool: %.2fs",
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fetcher_time, pool.wait_time)
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return progress.done()
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def _prepare_indexing(self, runner: runners.Runner) -> int:
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with connect(self.dsn) as conn:
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hstore_info = psycopg.types.TypeInfo.fetch(conn, "hstore")
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if hstore_info is None:
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raise RuntimeError('Hstore extension is requested but not installed.')
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psycopg.types.hstore.register_hstore(hstore_info)
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total_tuples = execute_scalar(conn, runner.sql_count_objects())
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LOG.debug("Total number of rows: %i", total_tuples)
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return cast(int, total_tuples)
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