[GitHub] [airflow] uranusjr commented on a change in pull request #16352: DagRun.date_interval_start and date_interval_start

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uranusjr commented on a change in pull request #16352:
URL: https://github.com/apache/airflow/pull/16352#discussion_r649340278



##########
File path: airflow/migrations/versions/142555e44c17_add_data_interval_start_end_to_dagrun.py
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@@ -0,0 +1,100 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Add data_interval_[start|end] to DagRun.
+
+Revision ID: 142555e44c17
+Revises: e9304a3141f0
+Create Date: 2021-06-09 08:28:02.089817
+
+"""
+
+import alembic
+import sqlalchemy
+
+from airflow.models import DagModel, DagRun
+from airflow.models.serialized_dag import SerializedDagModel
+from airflow.utils.session import create_session
+from airflow.utils.sqlalchemy import UtcDateTime
+from airflow.utils.timezone import utcnow
+
+# revision identifiers, used by Alembic.
+revision = "142555e44c17"
+down_revision = "e9304a3141f0"
+branch_labels = None
+depends_on = None
+
+
+# None and "@once" schedule intervals don't have a data interval and can be
+# bulk-updated with one SQL call, so we do them separately. Other "real"
+# schedule intervals are too complicated and need to be populated manually.
+NO_SCHEDULE_FILTER = DagModel.schedule_interval.in_([None, "@once"])
+
+
+def _populate_simple_dagrun_intervals(session):
+    """Handle DAG runs with simple schedule intervals."""
+    updates = {
+        DagRun.data_interval_start: DagRun.execution_date,
+        DagRun.data_interval_end: DagRun.execution_date,
+    }
+    joined = session.query(DagRun).join(DagModel, DagModel.dag_id == DagRun.dag_id)
+    joined.filter(NO_SCHEDULE_FILTER).update(updates)
+
+
+def _populate_complex_dagrun_intervals(session):
+    """Handle DAG runs with "real" schedule intervals."""
+    joined = session.query(DagRun, SerializedDagModel).join(
+        SerializedDagModel,
+        DagRun.dag_id == SerializedDagModel.dag_id,
+    )
+    for dag_run, serialized in joined.filter(~NO_SCHEDULE_FILTER):
+        dag = serialized.dag
+        data_interval_start = dag_run.execution_date
+        dag_run.data_interval_start = data_interval_start
+        dag_run.data_interval_end = dag.following_schedule(data_interval_start)

Review comment:
       Hmm, I just tried that, but… 
   
   https://github.com/apache/airflow/blob/6e9e56246b216a43eabb050c5b220f3665de6305/airflow/models/dag.py#L481-L491
   
   When calculating the data interval’s end (i.e. `next_execution_date`), we need to “fix” DST, which needs to know what timezone the DAG is in. But that information is only available in `DAG` (by inspecting the original `DAG.start_date` input) 😢




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