potiuk commented on a change in pull request #18356: URL: https://github.com/apache/airflow/pull/18356#discussion_r712384933
########## File path: docs/apache-airflow/concepts/scheduler.rst ########## @@ -138,18 +141,172 @@ The following databases are fully supported and provide an "optimal" experience: Microsoft SQLServer has not been tested with HA. + +Fine-tuning your Scheduler performance +-------------------------------------- + +What impacts scheduler's performance +"""""""""""""""""""""""""""""""""""" + +The Scheduler is responsible for two operations: + +* continuously parsing DAG files and synchronizing with the DAG in the database +* continuously scheduling tasks for execution + +Those two tasks are executed in parallel by the scheduler and run independently of each other in +different processes. In order to fine-tune your scheduler, you need to include a number of factors: + +* The kind of deployment you have + * what kind of filesystem you have to share the DAGs (impacts performance of continuous reading 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 + +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. + +How to approach Scheduler's fine-tuning +""""""""""""""""""""""""""""""""""""""" + +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, whereas 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): + +* its extremely important to monitor your system with the right set of tools that you usually use to + monitor your system. This document does not go into details of particular metrics and tools that you + can use, it just describes what kind of resources you should monitor, but you should follow your best + practices for monitoring to grab the right data. +* 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. + +What resources might limit Scheduler's performance +"""""""""""""""""""""""""""""""""""""""""""""""""" + +There are several areas of resource usage that you should pay attention to: + +* FileSystem performance. Airflow Scheduler relies heavily on parsing (sometimes a lot) of Python + files, which are often located on a shared filesystem. Airflow Scheduler continuously reads and + re-parses those files. The same files have to be made available to workers, so often they are + stored in a distributed filesystem. You can use various filesystems for that purpose (NFS, CIFS, EFS, + GCS fuse, Azure File System are good examples). There are various parameters you can control for those + filesystems and fine-tune their performance, but this is beyond the scope of this document. You should + observe statistics and usage of your filesystem to determine if problems come from the filesystem + performance. For example there are anecdotal evidences that increasing IOPS (and paying more) for the + EFS performance, dramatically improves stability and speed of parsing Airflow DAGs when EFS is used. +* Another solution to FileSystem performance, if it becomes your bottleneck, is to turn to alternative + mechanisms of distributing your DAGs. Embedding DAGs in your image and GitSync distribution have both + the property that the files are available locally for Scheduler and it does not have to use a + distributed filesystem to read the files, the files are available locally for the Scheduler and it is + usually as fast as it can be, especially if your machines use fast SSD disks for local storage. Those + distribution mechanisms have other characteristics that might make them not the best choice for you, + but if your problems with performance come from distributed filesystem performance, they might be the + best approach to follow. +* Database connections and Database usage might become a problem as you want to increase performance and + process more things in parallel. Airflow is known from being "database-connection hungry" - the more DAGs + you have and the more you want to process in parallel, the more database connections will be opened. + This is generally not a problem for MySQL as its model of handling connections is thread-based, but this + might be a problem for Postgres, where connection handling is process-based. It is a general consensus + that if you have even medium size Postgres-based Airflow installation, the best solution is to use + `PGBouncer <https://www.pgbouncer.org/>`_ as a proxy to your database. The :doc:`helm-chart:index` + supports PGBouncer out-of-the-box. For MsSQL we have not yet worked out the best practices as support + for MsSQL is still experimental. Review comment: When we release 2.2 it will not be. But I plan to merge this into v2-1 branch and publish it for 2.1.4 -- This is an automated message from the Apache Git Service. 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