Coverage for python/lsst/daf/butler/registry/bridge/ephemeral.py: 32%

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1# This file is part of daf_butler. 

2# 

3# Developed for the LSST Data Management System. 

4# This product includes software developed by the LSST Project 

5# (http://www.lsst.org). 

6# See the COPYRIGHT file at the top-level directory of this distribution 

7# for details of code ownership. 

8# 

9# This program is free software: you can redistribute it and/or modify 

10# it under the terms of the GNU General Public License as published by 

11# the Free Software Foundation, either version 3 of the License, or 

12# (at your option) any later version. 

13# 

14# This program is distributed in the hope that it will be useful, 

15# but WITHOUT ANY WARRANTY; without even the implied warranty of 

16# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 

17# GNU General Public License for more details. 

18# 

19# You should have received a copy of the GNU General Public License 

20# along with this program. If not, see <http://www.gnu.org/licenses/>. 

21from __future__ import annotations 

22 

23__all__ = ("EphemeralDatastoreRegistryBridge",) 

24 

25from contextlib import contextmanager 

26from typing import Iterable, Iterator, Set, Tuple, TYPE_CHECKING, Optional, Type 

27 

28from lsst.daf.butler import DatasetId 

29from lsst.daf.butler.registry.interfaces import ( 

30 DatasetIdRef, 

31 DatastoreRegistryBridge, 

32 FakeDatasetRef, 

33 OpaqueTableStorage, 

34) 

35 

36if TYPE_CHECKING: 36 ↛ 37line 36 didn't jump to line 37, because the condition on line 36 was never true

37 from lsst.daf.butler import StoredDatastoreItemInfo 

38 

39 

40class EphemeralDatastoreRegistryBridge(DatastoreRegistryBridge): 

41 """An implementation of `DatastoreRegistryBridge` for ephemeral datastores 

42 - those whose artifacts never outlive the current process. 

43 

44 Parameters 

45 ---------- 

46 datastoreName : `str` 

47 Name of the `Datastore` as it should appear in `Registry` tables 

48 referencing it. 

49 

50 Notes 

51 ----- 

52 The current implementation just uses a Python set to remember the dataset 

53 IDs associated with the datastore. This will probably need to be converted 

54 to use in-database temporary tables instead in the future to support 

55 "in-datastore" constraints in `Registry.queryDatasets`. 

56 """ 

57 def __init__(self, datastoreName: str): 

58 super().__init__(datastoreName) 

59 self._datasetIds: Set[DatasetId] = set() 

60 self._trashedIds: Set[DatasetId] = set() 

61 

62 def insert(self, refs: Iterable[DatasetIdRef]) -> None: 

63 # Docstring inherited from DatastoreRegistryBridge 

64 self._datasetIds.update(ref.getCheckedId() for ref in refs) 

65 

66 def forget(self, refs: Iterable[DatasetIdRef]) -> None: 

67 self._datasetIds.difference_update(ref.id for ref in refs) 

68 

69 def moveToTrash(self, refs: Iterable[DatasetIdRef]) -> None: 

70 # Docstring inherited from DatastoreRegistryBridge 

71 self._trashedIds.update(ref.getCheckedId() for ref in refs) 

72 

73 def check(self, refs: Iterable[DatasetIdRef]) -> Iterable[DatasetIdRef]: 

74 # Docstring inherited from DatastoreRegistryBridge 

75 yield from (ref for ref in refs if ref in self) 

76 

77 def __contains__(self, ref: DatasetIdRef) -> bool: 

78 return ref.getCheckedId() in self._datasetIds and ref.getCheckedId() not in self._trashedIds 

79 

80 @contextmanager 

81 def emptyTrash(self, records_table: Optional[OpaqueTableStorage] = None, 

82 record_class: Optional[Type[StoredDatastoreItemInfo]] = None, 

83 record_column: Optional[str] = None, 

84 ) -> Iterator[Tuple[Iterable[Tuple[DatasetIdRef, 

85 Optional[StoredDatastoreItemInfo]]], 

86 Optional[Set[str]]]]: 

87 # Docstring inherited from DatastoreRegistryBridge 

88 matches: Iterable[Tuple[FakeDatasetRef, Optional[StoredDatastoreItemInfo]]] = () 

89 if isinstance(records_table, OpaqueTableStorage): 

90 if record_class is None: 

91 raise ValueError("Record class must be provided if records table is given.") 

92 matches = ((FakeDatasetRef(id), record_class.from_record(record)) 

93 for id in self._trashedIds for record in records_table.fetch(dataset_id=id)) 

94 else: 

95 matches = ((FakeDatasetRef(id), None) for id in self._trashedIds) 

96 

97 # Indicate to caller that we do not know about artifacts that 

98 # should be retained. 

99 yield ((matches, None)) 

100 

101 if isinstance(records_table, OpaqueTableStorage): 

102 # Remove the records entries 

103 records_table.delete(["dataset_id"], *[{"dataset_id": id} for id in self._trashedIds]) 

104 

105 # Empty the trash table 

106 self._datasetIds.difference_update(self._trashedIds) 

107 self._trashedIds = set()