Coverage for python / lsst / daf / butler / registry / databases / postgresql.py: 24%
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2#
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5# (http://www.lsst.org).
6# See the COPYRIGHT file at the top-level directory of this distribution
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9# This software is dual licensed under the GNU General Public License and also
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14#
15# This program is free software: you can redistribute it and/or modify
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27from __future__ import annotations
29from sqlalchemy.sql.expression import ColumnElement as ColumnElement
31from ... import ddl, time_utils
33__all__ = ["PostgresqlDatabase"]
35import re
36from collections.abc import Callable, Iterable, Iterator, Mapping
37from contextlib import closing, contextmanager
38from typing import Any
40import psycopg2
41import sqlalchemy
42import sqlalchemy.dialects.postgresql
43from sqlalchemy import sql
45from ..._named import NamedValueAbstractSet
46from ..._timespan import Timespan
47from ...timespan_database_representation import TimespanDatabaseRepresentation
48from ..interfaces import Database, DatabaseMetadata
51class PostgresqlDatabase(Database):
52 """An implementation of the `Database` interface for PostgreSQL.
54 Parameters
55 ----------
56 engine : `sqlalchemy.engine.Engine`
57 Engine to use for this connection.
58 origin : `int`
59 An integer ID that should be used as the default for any datasets,
60 quanta, or other entities that use a (autoincrement, origin) compound
61 primary key.
62 namespace : `str`, optional
63 The namespace (schema) this database is associated with. If `None`,
64 the default schema for the connection is used (which may be `None`).
65 writeable : `bool`, optional
66 If `True`, allow write operations on the database, including
67 ``CREATE TABLE``.
68 allow_temporary_tables : `bool`, optional
69 If `True`, database operations will be allowed to use temporary tables.
70 If `False`, other SQL constructs will be used instead of temporary
71 tables when possible.
73 Notes
74 -----
75 This currently requires the psycopg2 driver to be used as the backend for
76 SQLAlchemy. Running the tests for this class requires the
77 ``testing.postgresql`` be installed, which we assume indicates that a
78 PostgreSQL server is installed and can be run locally in userspace.
80 Some functionality provided by this class (and used by `Registry`) requires
81 the ``btree_gist`` PostgreSQL server extension to be installed an enabled
82 on the database being connected to; this is checked at connection time.
83 """
85 def __init__(
86 self,
87 *,
88 engine: sqlalchemy.engine.Engine,
89 origin: int,
90 namespace: str | None = None,
91 writeable: bool = True,
92 allow_temporary_tables: bool = True,
93 ):
94 with engine.connect() as connection:
95 # `Any` to make mypy ignore the line below, can't use type: ignore
96 dbapi: Any = connection.connection
97 try:
98 dsn = dbapi.get_dsn_parameters()
99 except (AttributeError, KeyError) as err:
100 raise RuntimeError("Only the psycopg2 driver for PostgreSQL is supported.") from err
101 if namespace is None:
102 query = sql.select(sql.func.current_schema())
103 namespace = connection.execute(query).scalar()
104 query_text = "SELECT COUNT(*) FROM pg_extension WHERE extname='btree_gist';"
105 if not connection.execute(sqlalchemy.text(query_text)).scalar():
106 raise RuntimeError(
107 "The Butler PostgreSQL backend requires the btree_gist extension. "
108 "As extensions are enabled per-database, this may require an administrator to run "
109 "`CREATE EXTENSION btree_gist;` in a database before a butler client for it is "
110 " initialized."
111 )
113 pg_version = get_postgres_server_version(connection)
114 self._init(
115 engine=engine,
116 origin=origin,
117 namespace=namespace,
118 writeable=writeable,
119 dbname=dsn.get("dbname"),
120 metadata=None,
121 pg_version=pg_version,
122 allow_temporary_tables=allow_temporary_tables,
123 )
125 def _init(
126 self,
127 *,
128 engine: sqlalchemy.engine.Engine,
129 origin: int,
130 namespace: str | None = None,
131 writeable: bool = True,
132 dbname: str,
133 metadata: DatabaseMetadata | None,
134 pg_version: tuple[int, int],
135 allow_temporary_tables: bool = True,
136 ) -> None:
137 # Initialization logic shared between ``__init__`` and ``clone``.
138 super().__init__(
139 origin=origin,
140 engine=engine,
141 namespace=namespace,
142 metadata=metadata,
143 allow_temporary_tables=allow_temporary_tables,
144 )
145 self._writeable = writeable
146 self.dbname = dbname
147 self._pg_version = pg_version
149 def clone(self) -> PostgresqlDatabase:
150 clone = self.__new__(type(self))
151 clone._init(
152 origin=self.origin,
153 engine=self._engine,
154 namespace=self.namespace,
155 writeable=self._writeable,
156 dbname=self.dbname,
157 metadata=self._metadata,
158 pg_version=self._pg_version,
159 allow_temporary_tables=self._allow_temporary_tables,
160 )
161 return clone
163 @classmethod
164 def makeEngine(
165 cls, uri: str | sqlalchemy.engine.URL, *, writeable: bool = True
166 ) -> sqlalchemy.engine.Engine:
167 return sqlalchemy.engine.create_engine(
168 uri,
169 # Prevent stale database connections from throwing exeptions, at
170 # the expense of a round trip to the database server each time we
171 # check out a session. Many services using the Butler operate in
172 # networks where connections are dropped when idle for some time.
173 pool_pre_ping=True,
174 # This engine and database connection pool can be shared between
175 # multiple Butler instances created via Butler.clone() or
176 # LabeledButlerFactory, and typically these will be used from
177 # multiple threads simultaneously. So we need to configure
178 # SQLAlchemy to pool connections for multi-threaded usage.
179 #
180 # This pool size was chosen to work well for services using
181 # FastAPI. FastAPI uses a thread pool of 40 by default, so this
182 # gives us a connection for each thread in the pool. Because Butler
183 # is currently sync-only, we won't ever be executing more queries
184 # than we have threads.
185 #
186 # Connections are only created as they are needed, so in typical
187 # single-threaded Butler use only one connection will ever be
188 # created. Services with low peak concurrency may never create this
189 # many connections.
190 #
191 # The main Butler databases at SLAC are run behind pgbouncer, so we
192 # can support a larger number of simultaneous connections than if
193 # we were connecting directly to Postgres.
194 #
195 # See
196 # https://docs.sqlalchemy.org/en/20/core/pooling.html#sqlalchemy.pool.QueuePool.__init__
197 # for more information on the behavior of this parameter.
198 pool_size=40,
199 # If we are experiencing heavy enough load that we overflow the
200 # connection pool, it will be harmful to start creating extra
201 # connections that we disconnect immediately after use.
202 # Connecting from scratch is fairly expensive, which is why we have
203 # a pool in the first place.
204 max_overflow=0,
205 # If the pool is full, this is the maximum number of seconds we
206 # will wait for a connection to become available before giving up.
207 pool_timeout=60,
208 # In combination with pool_pre_ping, prevent SQLAlchemy from
209 # unnecessarily reviving pooled connections that have gone stale.
210 # Setting this to true makes it always re-use the most recent
211 # known-good connection when possible, instead of cycling to other
212 # connections in the pool that we may no longer need.
213 pool_use_lifo=True,
214 )
216 @classmethod
217 def fromEngine(
218 cls,
219 engine: sqlalchemy.engine.Engine,
220 *,
221 origin: int,
222 namespace: str | None = None,
223 writeable: bool = True,
224 ) -> Database:
225 return cls(engine=engine, origin=origin, namespace=namespace, writeable=writeable)
227 @contextmanager
228 def _transaction(
229 self,
230 *,
231 interrupting: bool = False,
232 savepoint: bool = False,
233 lock: Iterable[sqlalchemy.schema.Table] = (),
234 for_temp_tables: bool = False,
235 ) -> Iterator[tuple[bool, sqlalchemy.engine.Connection]]:
236 with super()._transaction(interrupting=interrupting, savepoint=savepoint, lock=lock) as (
237 is_new,
238 connection,
239 ):
240 if is_new:
241 # pgbouncer with transaction-level pooling (which we aim to
242 # support) says that SET cannot be used, except for a list of
243 # "Startup parameters" that includes "timezone" (see
244 # https://www.pgbouncer.org/features.html#fnref:0). But I
245 # don't see "timezone" in PostgreSQL's list of parameters
246 # passed when creating a new connection
247 # (https://www.postgresql.org/docs/current/libpq-connect.html#LIBPQ-PARAMKEYWORDS).
248 # Given that the pgbouncer docs say, "PgBouncer detects their
249 # changes and so it can guarantee they remain consistent for
250 # the client", I assume we can use "SET TIMESPAN" and pgbouncer
251 # will take care of clients that share connections being set
252 # consistently. And if that assumption is wrong, we should
253 # still probably be okay, since all clients should be Butler
254 # clients, and they'll all be setting the same thing.
255 #
256 # The "SET TRANSACTION READ ONLY" should also be safe, because
257 # it only ever acts on the current transaction; I think it's
258 # not included in pgbouncer's declaration that SET is
259 # incompatible with transaction-level pooling because
260 # PostgreSQL actually considers SET TRANSACTION to be a
261 # fundamentally different statement from SET (they have their
262 # own distinct doc pages, at least).
263 with closing(connection.connection.cursor()) as cursor:
264 # PostgreSQL permits writing to temporary tables inside
265 # read-only transactions, but it doesn't permit creating
266 # them.
267 if not (self.isWriteable() or for_temp_tables):
268 cursor.execute("SET TRANSACTION READ ONLY")
269 # Make timestamps UTC, because we didn't use TIMESTAMPZ
270 # for the column type. When we can tolerate a schema
271 # change, we should change that type and remove this
272 # line.
273 cursor.execute("SET TIME ZONE 0")
274 # Using server-side cursors with complex queries frequently
275 # generates suboptimal query plan, setting
276 # cursor_tuple_fraction=1 helps for those cases.
277 cursor.execute("SET cursor_tuple_fraction = 1")
278 yield is_new, connection
280 @contextmanager
281 def temporary_table(
282 self, spec: ddl.TableSpec, name: str | None = None
283 ) -> Iterator[sqlalchemy.schema.Table]:
284 # Docstring inherited.
285 with self.transaction(for_temp_tables=True), super().temporary_table(spec, name) as table:
286 yield table
288 def _lockTables(
289 self, connection: sqlalchemy.engine.Connection, tables: Iterable[sqlalchemy.schema.Table] = ()
290 ) -> None:
291 # Docstring inherited.
292 for table in tables:
293 connection.execute(sqlalchemy.text(f"LOCK TABLE {table.key} IN EXCLUSIVE MODE"))
295 def isWriteable(self) -> bool:
296 return self._writeable
298 def __str__(self) -> str:
299 return f"PostgreSQL@{self.dbname}:{self.namespace}"
301 def shrinkDatabaseEntityName(self, original: str) -> str:
302 return self.name_shrinker.shrink(original)
304 def expandDatabaseEntityName(self, shrunk: str) -> str:
305 return self.name_shrinker.expand(shrunk)
307 def _convertExclusionConstraintSpec(
308 self,
309 table: str,
310 spec: tuple[str | type[TimespanDatabaseRepresentation], ...],
311 metadata: sqlalchemy.MetaData,
312 ) -> sqlalchemy.schema.Constraint:
313 # Docstring inherited.
314 args: list[tuple[sqlalchemy.schema.Column, str]] = []
315 names = ["excl"]
316 for item in spec:
317 if isinstance(item, str):
318 args.append((sqlalchemy.schema.Column(item), "="))
319 names.append(item)
320 elif issubclass(item, TimespanDatabaseRepresentation):
321 assert item is self.getTimespanRepresentation()
322 args.append((sqlalchemy.schema.Column(TimespanDatabaseRepresentation.NAME), "&&"))
323 names.append(TimespanDatabaseRepresentation.NAME)
324 return sqlalchemy.dialects.postgresql.ExcludeConstraint(
325 *args,
326 name=self.shrinkDatabaseEntityName("_".join(names)),
327 )
329 def _make_temporary_table(
330 self,
331 connection: sqlalchemy.engine.Connection,
332 spec: ddl.TableSpec,
333 name: str | None = None,
334 **kwargs: Any,
335 ) -> sqlalchemy.schema.Table:
336 # Docstring inherited
337 # Adding ON COMMIT DROP here is really quite defensive: we already
338 # manually drop the table at the end of the temporary_table context
339 # manager, and that will usually happen first. But this will guarantee
340 # that we drop the table at the end of the transaction even if the
341 # connection lasts longer, and that's good citizenship when connections
342 # may be multiplexed by e.g. pgbouncer.
343 return super()._make_temporary_table(connection, spec, name, postgresql_on_commit="DROP", **kwargs)
345 @classmethod
346 def getTimespanRepresentation(cls) -> type[TimespanDatabaseRepresentation]:
347 # Docstring inherited.
348 return _RangeTimespanRepresentation
350 def replace(self, table: sqlalchemy.schema.Table, *rows: dict) -> None:
351 self.assertTableWriteable(table, f"Cannot replace into read-only table {table}.")
352 if not rows:
353 return
354 # This uses special support for UPSERT in PostgreSQL backend:
355 # https://docs.sqlalchemy.org/en/13/dialects/postgresql.html#insert-on-conflict-upsert
356 query = sqlalchemy.dialects.postgresql.dml.insert(table)
357 # In the SET clause assign all columns using special `excluded`
358 # pseudo-table. If some column in the table does not appear in the
359 # INSERT list this will set it to NULL.
360 excluded = query.excluded
361 data = {
362 column.name: getattr(excluded, column.name)
363 for column in table.columns
364 if column.name not in table.primary_key
365 }
366 if not data:
367 self.ensure(table, *rows)
368 return
369 query = query.on_conflict_do_update(constraint=table.primary_key, set_=data)
370 with self._transaction() as (_, connection):
371 connection.execute(query, rows)
373 def ensure(self, table: sqlalchemy.schema.Table, *rows: dict, primary_key_only: bool = False) -> int:
374 # Docstring inherited.
375 self.assertTableWriteable(table, f"Cannot ensure into read-only table {table}.")
376 if not rows:
377 return 0
378 # Like `replace`, this uses UPSERT.
379 base_insert = sqlalchemy.dialects.postgresql.dml.insert(table)
380 if primary_key_only:
381 query = base_insert.on_conflict_do_nothing(constraint=table.primary_key)
382 else:
383 query = base_insert.on_conflict_do_nothing()
384 with self._transaction() as (_, connection):
385 return connection.execute(query, rows).rowcount
387 def constant_rows(
388 self,
389 fields: NamedValueAbstractSet[ddl.FieldSpec],
390 *rows: dict,
391 name: str | None = None,
392 ) -> sqlalchemy.sql.FromClause:
393 # Docstring inherited.
394 return super().constant_rows(fields, *rows, name=name)
396 @property
397 def has_distinct_on(self) -> bool:
398 # Docstring inherited.
399 return True
401 @property
402 def has_any_aggregate(self) -> bool:
403 # Docstring inherited.
404 return self._pg_version >= (16, 0)
406 def apply_any_aggregate(self, column: sqlalchemy.ColumnElement[Any]) -> sqlalchemy.ColumnElement[Any]:
407 # Docstring inherited
409 # The cast is required to prevent sqlalchemy from forgetting the type
410 # of the initial column. Without the cast, for example, Base64Region
411 # would become a String column in the output.
412 return sqlalchemy.cast(sqlalchemy.func.any_value(column), column.type)
414 def glob_expression(
415 self, expression: sqlalchemy.ColumnElement[Any], pattern: str
416 ) -> sqlalchemy.ColumnElement[bool]:
417 # Docstring inherited.
418 # With Postgres we can use LIKE as it is case-sensitive, we only need
419 # to translate our simple glob language to LIKE patterns, escaping few
420 # things. Also we can try to optimize special cases when we can use
421 # ``starts_with()`` function or "*" pattern. ``starts_with()`` in
422 # Postgres14 does not use btree index, and LIKE with a constant prefix
423 # is using index, so we may not need ``starts_with()`` after all.
424 if pattern == "*":
425 # Simple case.
426 return sqlalchemy.literal(True)
428 def _escape(pattern: str) -> str:
429 """Transform glob pattern string into LIKE pattern."""
430 tokens = []
431 escape = ""
432 for char in pattern:
433 if not escape:
434 if char == "\\":
435 escape = char
436 elif char in "_%":
437 # Escape special SQL characters.
438 tokens.append(f"\\{char}")
439 elif char == "*":
440 tokens.append("%")
441 elif char == "?":
442 tokens.append("_")
443 else:
444 tokens.append(char)
445 else:
446 if char == "\\":
447 # Two backslashes become one.
448 tokens.append(char)
449 elif char in "*?":
450 # Handle escaped wildcards.
451 tokens.append(f"{char}")
452 else:
453 # Copy both backslash and character.
454 tokens.append(escape)
455 tokens.append(char)
456 escape = ""
457 # Trailing backslash added to pattern.
458 tokens.append(escape)
459 return "".join(tokens)
461 pattern = _escape(pattern)
462 return expression.op("LIKE")(sqlalchemy.literal(pattern))
465class _RangeTimespanType(sqlalchemy.TypeDecorator):
466 """A single-column `Timespan` representation usable only with
467 PostgreSQL.
469 This type should be able to take advantage of PostgreSQL's built-in
470 range operators, and the indexing and EXCLUSION table constraints built
471 off of them.
472 """
474 impl = sqlalchemy.dialects.postgresql.INT8RANGE
476 cache_ok = True
478 def process_bind_param(
479 self, value: Timespan | None, dialect: sqlalchemy.engine.Dialect
480 ) -> psycopg2.extras.NumericRange | None:
481 if value is None:
482 return None
483 if not isinstance(value, Timespan):
484 raise TypeError(f"Unsupported type: {type(value)}, expected Timespan.")
485 if value.isEmpty():
486 return psycopg2.extras.NumericRange(empty=True)
487 else:
488 converter = time_utils.TimeConverter()
489 assert value.nsec[0] >= converter.min_nsec, "Guaranteed by Timespan.__init__."
490 assert value.nsec[1] <= converter.max_nsec, "Guaranteed by Timespan.__init__."
491 lower = None if value.nsec[0] == converter.min_nsec else value.nsec[0]
492 upper = None if value.nsec[1] == converter.max_nsec else value.nsec[1]
493 return psycopg2.extras.NumericRange(lower=lower, upper=upper)
495 def process_result_value(
496 self, value: psycopg2.extras.NumericRange | None, dialect: sqlalchemy.engine.Dialect
497 ) -> Timespan | None:
498 if value is None:
499 return None
500 if value.isempty:
501 return Timespan.makeEmpty()
502 converter = time_utils.TimeConverter()
503 begin_nsec = converter.min_nsec if value.lower is None else value.lower
504 end_nsec = converter.max_nsec if value.upper is None else value.upper
505 return Timespan(begin=None, end=None, _nsec=(begin_nsec, end_nsec))
508class _RangeTimespanRepresentation(TimespanDatabaseRepresentation):
509 """An implementation of `TimespanDatabaseRepresentation` that uses
510 `_RangeTimespanType` to store a timespan in a single
511 PostgreSQL-specific field.
513 Parameters
514 ----------
515 column : `sqlalchemy.sql.ColumnElement`
516 SQLAlchemy object representing the column.
517 """
519 def __init__(self, column: sqlalchemy.sql.ColumnElement, name: str):
520 self.column = column
521 self._name = name
523 __slots__ = ("column", "_name")
525 @classmethod
526 def makeFieldSpecs(
527 cls, nullable: bool, name: str | None = None, **kwargs: Any
528 ) -> tuple[ddl.FieldSpec, ...]:
529 # Docstring inherited.
530 if name is None:
531 name = cls.NAME
532 return (
533 ddl.FieldSpec(
534 name,
535 dtype=_RangeTimespanType,
536 nullable=nullable,
537 default=(None if nullable else sqlalchemy.sql.text("'(,)'::int8range")),
538 **kwargs,
539 ),
540 )
542 @classmethod
543 def getFieldNames(cls, name: str | None = None) -> tuple[str, ...]:
544 # Docstring inherited.
545 if name is None:
546 name = cls.NAME
547 return (name,)
549 @classmethod
550 def update(
551 cls, extent: Timespan | None, name: str | None = None, result: dict[str, Any] | None = None
552 ) -> dict[str, Any]:
553 # Docstring inherited.
554 if name is None:
555 name = cls.NAME
556 if result is None:
557 result = {}
558 result[name] = extent
559 return result
561 @classmethod
562 def extract(cls, mapping: Mapping[str, Any], name: str | None = None) -> Timespan | None:
563 # Docstring inherited.
564 if name is None:
565 name = cls.NAME
566 return mapping[name]
568 @classmethod
569 def fromLiteral(cls, timespan: Timespan | None) -> _RangeTimespanRepresentation:
570 # Docstring inherited.
571 if timespan is None:
572 # Cast NULL to an expected type, helps Postgres to figure out
573 # column type when doing UNION.
574 return cls(
575 column=sqlalchemy.func.cast(sqlalchemy.sql.null(), type_=_RangeTimespanType), name=cls.NAME
576 )
577 return cls(
578 column=sqlalchemy.sql.cast(
579 sqlalchemy.sql.literal(timespan, type_=_RangeTimespanType), type_=_RangeTimespanType
580 ),
581 name=cls.NAME,
582 )
584 @classmethod
585 def from_columns(
586 cls, columns: sqlalchemy.sql.ColumnCollection, name: str | None = None
587 ) -> _RangeTimespanRepresentation:
588 # Docstring inherited.
589 if name is None:
590 name = cls.NAME
591 return cls(columns[name], name)
593 @property
594 def name(self) -> str:
595 # Docstring inherited.
596 return self._name
598 def isNull(self) -> sqlalchemy.sql.ColumnElement:
599 # Docstring inherited.
600 return self.column.is_(None)
602 def isEmpty(self) -> sqlalchemy.sql.ColumnElement:
603 # Docstring inherited
604 return sqlalchemy.sql.func.isempty(self.column)
606 def __lt__(
607 self, other: _RangeTimespanRepresentation | sqlalchemy.sql.ColumnElement
608 ) -> sqlalchemy.sql.ColumnElement:
609 # Docstring inherited.
610 if isinstance(other, sqlalchemy.sql.ColumnElement):
611 return sqlalchemy.sql.and_(
612 sqlalchemy.sql.not_(sqlalchemy.sql.func.upper_inf(self.column)),
613 sqlalchemy.sql.not_(sqlalchemy.sql.func.isempty(self.column)),
614 sqlalchemy.sql.func.upper(self.column) <= other,
615 )
616 else:
617 return self.column << other.column
619 def __gt__(
620 self, other: _RangeTimespanRepresentation | sqlalchemy.sql.ColumnElement
621 ) -> sqlalchemy.sql.ColumnElement:
622 # Docstring inherited.
623 if isinstance(other, sqlalchemy.sql.ColumnElement):
624 return sqlalchemy.sql.and_(
625 sqlalchemy.sql.not_(sqlalchemy.sql.func.lower_inf(self.column)),
626 sqlalchemy.sql.not_(sqlalchemy.sql.func.isempty(self.column)),
627 sqlalchemy.sql.func.lower(self.column) > other,
628 )
629 else:
630 return self.column >> other.column
632 def overlaps(
633 self, other: _RangeTimespanRepresentation | sqlalchemy.sql.ColumnElement
634 ) -> sqlalchemy.sql.ColumnElement:
635 # Docstring inherited.
636 if not isinstance(other, _RangeTimespanRepresentation):
637 return self.contains(other)
638 return self.column.overlaps(other.column)
640 def contains(
641 self, other: _RangeTimespanRepresentation | sqlalchemy.sql.ColumnElement
642 ) -> sqlalchemy.sql.ColumnElement:
643 # Docstring inherited
644 if isinstance(other, _RangeTimespanRepresentation):
645 return self.column.contains(other.column)
646 else:
647 return self.column.contains(other)
649 def lower(self) -> sqlalchemy.sql.ColumnElement:
650 # Docstring inherited.
651 return sqlalchemy.sql.functions.coalesce(
652 sqlalchemy.sql.func.lower(self.column), sqlalchemy.sql.literal(0)
653 )
655 def upper(self) -> sqlalchemy.sql.ColumnElement:
656 # Docstring inherited.
657 return sqlalchemy.sql.functions.coalesce(
658 sqlalchemy.sql.func.upper(self.column), sqlalchemy.sql.literal(0)
659 )
661 def flatten(self, name: str | None = None) -> tuple[sqlalchemy.sql.ColumnElement]:
662 # Docstring inherited.
663 if name is None:
664 return (self.column,)
665 else:
666 return (self.column.label(name),)
668 def apply_any_aggregate(
669 self, func: Callable[[ColumnElement[Any]], ColumnElement[Any]]
670 ) -> TimespanDatabaseRepresentation:
671 # Docstring inherited.
672 return _RangeTimespanRepresentation(func(self.column), self.name)
675def get_postgres_server_version(connection: sqlalchemy.Connection) -> tuple[int, int]: # numpydoc ignore=PR01
676 """Return the postgres DB server version as a tuple
677 (major version, minor version).
678 """
679 raw_pg_version = connection.execute(sqlalchemy.text("SHOW server_version")).scalar()
680 _SERVER_VERSION_REGEX = re.compile(r"(?P<major>\d+)\.(?P<minor>\d+)")
681 if raw_pg_version is not None and (m := _SERVER_VERSION_REGEX.search(raw_pg_version)):
682 return (int(m.group("major")), int(m.group("minor")))
683 else:
684 raise RuntimeError("Failed to get PostgreSQL server version.")