[GitHub] [airflow] csp98 commented on a diff in pull request #25121: Add "Optimizing" chapter to dynamic-dags section

[GitBox <gi...@apache.org>]

csp98 commented on code in PR #25121:
URL: https://github.com/apache/airflow/pull/25121#discussion_r924208929


##########
docs/apache-airflow/howto/dynamic-dag-generation.rst:
##########
@@ -140,3 +140,74 @@ Each of them can run separately with related configuration
 
 .. warning::
   Using this practice, pay attention to "late binding" behaviour in Python loops. See `that GitHub discussion <https://github.com/apache/airflow/discussions/21278#discussioncomment-2103559>`_ for more details
+
+
+Optimizing DAG parsing delays during execution
+----------------------------------------------
+
+Sometimes when you generate a lot of Dynamic DAGs from a single DAG file, it might cause unnecessary delays
+when the DAG file is parsed during task execution. The impact is a delay before a task starts.
+
+Why is this happening? You might not be aware but when your task is executed just before execution,
+Airflow parses the Python file the DAG comes from.
+
+The Airflow Scheduler (or DAG Processor) requires loading of a complete DAG file to process all metadata.
+However, task execution requires only a single DAG object to execute a task. Knowing this, we can
+skip the generation of unnecessary DAG objects when task is executed, shortening the parsing time.
+Upon evaluation of a DAG file, command line arguments are supplied which we can use to determine which
+Airflow component performs parsing:
+
+* Scheduler/DAG Processor args: ``["airflow", "scheduler"]`` or ``["airflow", "dag-processor"]``
+* Task execution args: ``["airflow", "tasks", "run", "dag_id", "task_id", ...]``
+
+
+When tasks are executed, parsing time of dynamically generated DAGs when a task is executed can be optimized.
+
+However, depending on the executor used and forking model, those args might be available via ``sys.args``
+or via name of the process running. Airflow either executes tasks via running a new Python interpreter or
+sets the name of the process as "airflow task supervisor: {ARGS}".
+
+This optimization is most effective when the number of generated DAGs is high.
+
+Upon iterating over the collection of things to generate DAGs for, you can use these arguments to determine
+whether you need to generate all DAG objects (when parsing in the DAG File processor), or to generate only
+a single DAG object (when executing the task)"
+
+.. code-block:: python
+  :emphasize-lines: 6,7,15,16,22,23
+
+  import sys
+  import ast
+  import setproctitle
+  from airflow.models import DAG
+
+  current_dag = None
+  if len(sys.argv) > 3 and sys.argv[1] == "tasks":
+      # task executed by starting a new Python interpreter
+      current_dag = sys.argv[3]
+  else:
+      try:
+          PROCTITLE_PREFIX = "airflow task supervisor: "
+          proctitle = str(setproctitle.getproctitle())
+          if proctitle.startswith(PROCTITLE_PREFIX):
+              # task executed via forked process
+              args_string = proctitle[len(PROCTITLE_PREFIX) :]
+              args = ast.literal_eval(args_string)
+              if len(args) > 3 and args[1] == "tasks":
+                  current_dag = args[3]
+      except:

Review Comment:
   ```suggestion
         except Exception:
   ```



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