[GitHub] [airflow] potiuk commented on a change in pull request #18356: Explain scheduler fine-tuning better

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

potiuk commented on a change in pull request #18356:
URL: https://github.com/apache/airflow/pull/18356#discussion_r712050719



##########
File path: docs/apache-airflow/concepts/scheduler.rst
##########
@@ -138,12 +141,101 @@ The following databases are fully supported and provide an "optimal" experience:
 
   Microsoft SQLServer has not been tested with HA.
 
+
+Fine-tuning your Scheduler
+--------------------------
+
+When you deploy Airflow in production you often would like to optimize its performance and
+fine-tune Scheduler behaviour. Firs of all you need to remember that Scheduler performs two
+operations:
+
+* continuously parses DAG files and updates their starts in ``Serialized DAG`` form in the database
+* continuously finds and schedules for execution the next tasks to run and sends those tasks for
+  execution to the executor you have configured
+
+Those two tasks are executed in parallel by scheduler, they are fairly independent from each other and
+they are run using different processes. You can fine tune the behaviour of both components, however
+in order to fine-tune your scheduler, you need to included a number of factors:
+
+* The kind of deployment you have
+    * what kind of filesystem you have to share the DAGS
+    * how fast the filesystem is (in many cases of distributed cloud filesystem you can pay extra to get
+      more throughput/faster filesystem
+    * how much memory you have for your processing
+    * how much CPU you have available
+    * how much networking throughput you have available
+
+* The logic and definition of your DAG structure:
+    * how many DAG files you have
+    * how many DAGs you have in your files
+    * how large the DAG files are (remember scheduler needs to read and parse the file every n seconds)
+    * how complex they are
+    * whether parsing your DAGs involves heavy processing (Hint! It should not. See:doc:`/best-practices`)
+
+* The scheduler configuration
+   * How many schedulers you have
+   * How many parsing processes you have in your scheduler
+   * How much time scheduler waits between re-parsing of the same DAG (it happens continuously)
+   * How many task instances scheduler processes in one loop
+   * How many new dag runs should be created/scheduled per loop
+   * Whether to execute "mini-scheduler" after completed task to speed up scheduling dependent tasks
+   * How often the scheduler should perform cleanup and check for orphaned tasks/adopting them
+   * Whether scheduler uses row-level locking
+
+
+Airflow gives you a lot of "knobs" to turn to fine tune the performance but it's a separate task,
+depending on your particular deployment, your DAG structure, hardware availability and expectations,
+to decide which knobs to turn to get best effect for you. Part of the job when managing the
+deployment is to decide what you are going to optimize for. Some users are ok with
+30 seconds delays of new DAG parsing, at the expense of lower CPU usage, where some other users
+expect the DAGs to be parsed almost instantly when they appear in the DAGs folder at the
+expense of higher CPU usage for example.
+
+Airflow gives you the flexibility to decide, but you should find out what aspect of performance is
+most important for you and decide which knobs you want to turn in which direction.
+
+Generally for fine-tuning, your approach should be the same as for any performance improvement and
+optimizations (we will not recommend any specific tools - just use the tools that you usually use
+to observe and monitor your systems):
+
+* decide which aspect of performance is most important for you (what you want to improve)
+* observe your system to see where your bottlenecks are: CPU, memory, I/O are the usual limiting factors
+* based on your expectations and observations - decide what is your next improvement and go back to
+  the observation of your performance, bottlenecks. Performance improvement is an iterative process
+
+The improvements that you can consider are:
+
+* improve utilization of your resources. This is when you have a free capacity in your system that
+  seems underutilized (again CPU, memory I/O, networking are the prime candidates) - you can take
+  actions like increasing number of schedulers, parsing processes or decreasing intervals for more
+  frequent actions might bring improvements in performance at the expense of higher utilization of those.
+* increase hardware capacity (for example if you see that CPU is limiting you or tha I/O you use for
+  DAG filesystem is at its limits). Often the problem with scheduler performance is
+  simply because your system is not "capable" enough and this might be the only way. For example if
+  you see that you are using all CPU you have on machine, you might want to add another scheduler on
+  a new machine - in most cases, when you add 2nd or 3rd scheduler, the capacity of scheduling grows
+  linearly (unless the shared database or filesystem is a bottleneck).
+* experiment with different values for the "scheduler tunables". Often you might get better effects by
+  simply exchanging one performance aspect for another. For example if you want to decrease the
+  cpu usage, you might increase file processing interval (but the result will be that new DAGs will
+  appear with bigger delay). Usually performance tuning is the art of balancing different aspects.
+* sometimes you change scheduler behaviour slightly (for example change parsing sort order)
+  in order to get better fine-tuned results for your particular deployment.
+
+In order to perform fine-tuning, it's good to understand how Scheduler works under-the-hood.
+You can take a look at the ``Airflow Summit 2021``
+`Deep Dive into the Airflow Scheduler talk <https://youtu.be/DYC4-xElccE>`_ to perform the fine-tuning.
+
+Here are the most important tunables you can use to impact various performance aspects of the Scheduler:
+
 .. _scheduler:ha:tunables:
 
-Scheduler Tuneables
-"""""""""""""""""""
+Scheduler Tunables

Review comment:
       Good for me . The "tunables" word sounds nice but is a bit strange indeed




-- 
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.

To unsubscribe, e-mail: commits-unsubscribe@airflow.apache.org

For queries about this service, please contact Infrastructure at:
users@infra.apache.org