Coverage for /pythoncovmergedfiles/medio/medio/src/airflow/build/lib/airflow/timetables/simple.py: 51%

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3# distributed with this work for additional information 

4# regarding copyright ownership. The ASF licenses this file 

5# to you under the Apache License, Version 2.0 (the 

6# "License"); you may not use this file except in compliance 

7# with the License. You may obtain a copy of the License at 

8# 

9# http://www.apache.org/licenses/LICENSE-2.0 

10# 

11# Unless required by applicable law or agreed to in writing, 

12# software distributed under the License is distributed on an 

13# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY 

14# KIND, either express or implied. See the License for the 

15# specific language governing permissions and limitations 

16# under the License. 

17from __future__ import annotations 

18 

19import operator 

20from typing import TYPE_CHECKING, Any, Collection 

21 

22from airflow.timetables.base import DagRunInfo, DataInterval, TimeRestriction, Timetable 

23 

24if TYPE_CHECKING: 

25 from pendulum import DateTime 

26 from sqlalchemy import Session 

27 

28 from airflow.models.dataset import DatasetEvent 

29 from airflow.utils.types import DagRunType 

30 

31 

32class _TrivialTimetable(Timetable): 

33 """Some code reuse for "trivial" timetables that has nothing complex.""" 

34 

35 periodic = False 

36 can_run = False 

37 run_ordering = ("execution_date",) 

38 

39 @classmethod 

40 def deserialize(cls, data: dict[str, Any]) -> Timetable: 

41 return cls() 

42 

43 def __eq__(self, other: Any) -> bool: 

44 """As long as *other* is of the same type. 

45 

46 This is only for testing purposes and should not be relied on otherwise. 

47 """ 

48 if not isinstance(other, type(self)): 

49 return NotImplemented 

50 return True 

51 

52 def serialize(self) -> dict[str, Any]: 

53 return {} 

54 

55 def infer_manual_data_interval(self, *, run_after: DateTime) -> DataInterval: 

56 return DataInterval.exact(run_after) 

57 

58 

59class NullTimetable(_TrivialTimetable): 

60 """Timetable that never schedules anything. 

61 

62 This corresponds to ``schedule=None``. 

63 """ 

64 

65 description: str = "Never, external triggers only" 

66 

67 @property 

68 def summary(self) -> str: 

69 return "None" 

70 

71 def next_dagrun_info( 

72 self, 

73 *, 

74 last_automated_data_interval: DataInterval | None, 

75 restriction: TimeRestriction, 

76 ) -> DagRunInfo | None: 

77 return None 

78 

79 

80class OnceTimetable(_TrivialTimetable): 

81 """Timetable that schedules the execution once as soon as possible. 

82 

83 This corresponds to ``schedule="@once"``. 

84 """ 

85 

86 description: str = "Once, as soon as possible" 

87 

88 @property 

89 def summary(self) -> str: 

90 return "@once" 

91 

92 def next_dagrun_info( 

93 self, 

94 *, 

95 last_automated_data_interval: DataInterval | None, 

96 restriction: TimeRestriction, 

97 ) -> DagRunInfo | None: 

98 if last_automated_data_interval is not None: 

99 return None # Already run, no more scheduling. 

100 if restriction.earliest is None: # No start date, won't run. 

101 return None 

102 # "@once" always schedule to the start_date determined by the DAG and 

103 # tasks, regardless of catchup or not. This has been the case since 1.10 

104 # and we're inheriting it. See AIRFLOW-1928. 

105 run_after = restriction.earliest 

106 if restriction.latest is not None and run_after > restriction.latest: 

107 return None 

108 return DagRunInfo.exact(run_after) 

109 

110 

111class DatasetTriggeredTimetable(NullTimetable): 

112 """Timetable that never schedules anything. 

113 

114 This should not be directly used anywhere, but only set if a DAG is triggered by datasets. 

115 

116 :meta private: 

117 """ 

118 

119 description: str = "Triggered by datasets" 

120 

121 @property 

122 def summary(self) -> str: 

123 return "Dataset" 

124 

125 def generate_run_id( 

126 self, 

127 *, 

128 run_type: DagRunType, 

129 logical_date: DateTime, 

130 data_interval: DataInterval | None, 

131 session: Session | None = None, 

132 events: Collection[DatasetEvent] | None = None, 

133 **extra, 

134 ) -> str: 

135 from airflow.models.dagrun import DagRun 

136 

137 return DagRun.generate_run_id(run_type, logical_date) 

138 

139 def data_interval_for_events( 

140 self, 

141 logical_date: DateTime, 

142 events: Collection[DatasetEvent], 

143 ) -> DataInterval: 

144 

145 if not events: 

146 return DataInterval(logical_date, logical_date) 

147 

148 start = min( 

149 events, key=operator.attrgetter("source_dag_run.data_interval_start") 

150 ).source_dag_run.data_interval_start 

151 end = max( 

152 events, key=operator.attrgetter("source_dag_run.data_interval_end") 

153 ).source_dag_run.data_interval_end 

154 return DataInterval(start, end)