uranusjr commented on a change in pull request #17552:
URL: https://github.com/apache/airflow/pull/17552#discussion_r689285513
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File path: docs/apache-airflow/dag-run.rst
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@@ -54,17 +54,31 @@ Cron Presets
Your DAG will be instantiated for each schedule along with a corresponding
DAG Run entry in the database backend.
-.. note::
+Data Interval
+-------------
+
+Each DAG run in Airflow has an assigned "data interval" that represents the
time
+range it operates in. For a DAG scheduled with ``@daily``, for example, each of
+its data interval would start at midnight of each day, and end at midnight of
+the next day.
+
+A DAG run happens *after* its associated data interval has ended, to ensure the
+run is able to collect all the actual data within the time period. Therefore, a
+run covering the data period of 2020-01-01 will not start to run until
+2020-01-01 has ended, i.e. 2020-01-02 onwards.
+
+All dates in Airflow are tied to the data interval concept in some way. The
+"logical date" (also called ``execution_date`` from previous Airflow version)
+of a DAG run, for example, usually denotes the start of the data interval, not
+when the DAG is actually executed. Similarly, since the ``start_date`` argument
+for the DAG and its tasks points to the same logical date, a run will only
+be created after that data interval ends. So a DAG with ``@daily`` schedule and
+``start_date`` of 2020-01-01, for example, will not be created until
2020-01-02.
Review comment:
The previous paragraph tries to establish the idea of data intervals in
general, and this paragraph explains the concept underlies almost all of
Airflow’s date-related configurations, including (the most commonly
misunderstood) `start_date`. The intuitive interpretation from the argument
name is my DAG should *start running* after this date, while in fact only the
DAG’s *first data interval* starts after it. I’ve reworded both paragraphs a
bit to make them read more different.
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