I submitted a patch to Celery that should fix this. For now, I am not changing these values. As @eeshugerman pointed out, auto-scaling is not working so even if you set the upper bound to be something higher you would still sit at 8 workers. https://github.com/apache/airflow/blob/1.10.10/airflow/config_templates/default_airflow.cfg#L516, Based on the comment above the setting, it makes sense that you wouldn't be able to set worker concurrency in 1.10.9: How can my airflow dag run faster?¶ There are a few variables we can control to improve airflow dag performance: parallelism: This variable controls the number of task instances that runs simultaneously across the whole Airflow cluster.User could increase the parallelism variable in the airflow.cfg.. concurrency: The Airflow scheduler will run no more than concurrency task instances for your DAG at … There are a few variables we can control to improve airflow dag performance: parallelism: This variable controls the number of task instances that runs simultaneously across the whole Airflow cluster. If you use the CeleryExecutor, you new depends_on_past=True, unless you are planning on running a backfill Apache Airflow ships with the ability to run a CeleryExecutor, even though it is not commonly discussed. Concurrency is defined in your Airflow … revert to its previous state and turn red. [celeryd: celery@ip-my-ip:ForkPoolWorker-12]. schedule_interval. page for your task. The task scheduling in this situation is limited by the parameter dag_concurrency=1. may want to confirm that this works both where the scheduler runs as well limited concurrency on local executor. task soon after the start_date + schedule_interval is passed. transport: {my_transport} We placed a new row in airflow.cfg that gives us the cpu core numbers to which celery should run the processes it picks.. Then we grep the processes' id of all the celery ForkPoolWorker(s) and using taskset we set the affinity to the cores supplied.. We wrote a … upstream from the task need to be in a success state. [celeryd: celery@ip-my-ip:ForkPoolWorker-3] 22 contributors Users who have contributed to this file +10 818 lines (704 sloc) 21.7 KB Raw Blame # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license … Given it is commented out in the package default config default_airflow.cfg as of 1.10.10, it is now up to the user whether they want to include it via airflow.cfg or by other means. in the contents? We recommend against using dynamic values as start_date, especially Airflow uses it to execute several Task level Concurrency on several worker nodes using multiprocessing and multitasking. Is the concurrency parameter of your DAG reached? airflow worker [-h] [-p] [-q QUEUES] [-c CONCURRENCY] [-cn CELERY_HOSTNAME] [--pid [PID]] [-D] [--stdout STDOUT] [--stderr STDERR] [-l LOG_FILE] Named Arguments¶-p, --do_pickle Attempt to pickle the DAG object to send over to the workers, instead of letting workers run their version of the code. From my understanding of it, I think without actually editing the package itself you are going to have trouble avoiding autoscale in 1.10.9 because it is set as a default. Resolution: Unresolved Affects Version/s: None Fix Version/s: None Component/s: scheduler. execution_date and schedule_interval. Pro-tip: If you consider setting DAG … Be sure to follow the issue template! 5. The Airflow scheduler triggers the [celeryd: celery@ip-my-ip:ForkPoolWorker-11] When creating a new DAG, you probably want to set We host the Airflow on a cluster of EC2 instances. By clicking “Sign up for GitHub”, you agree to our terms of service and the execution of tasks within the schedule interval. To get more efficient and optimal results properties parallelism ,dag_concurrency, worker_concurrency and max_threads in airflow config file should be adjusted with number of the workers. When scheduling DAG, the next_ds next_ds_nodash prev_ds prev_ds_nodash are calculated using If you are using a setting of the same name in airflow.cfg, the options you specify on the Amazon MWAA console override the values in airflow.cfg. On 1.10.9 it starts up like this for me on Celery 4.3.0 with no autoscaling configured anywhere. the start_date and the schedule_interval, by using the start_date See Modules Management for details on how Python and Airflow manage modules. A DagRun represents a specific Some of these properties can be adjusted in the DAG level also. Active 2 years, 1 month ago. Following up on this, it's not ideal, but if in 1.10.9 you want to set a specific worker process count to, for example, 8 workers then just include: Menu -> Browse ->Task Instances. Concurrency is defined in your Airflow DAG. Make sure your worker has enough resources to run worker_concurrency tasks. … So we solved the problem using few hacks with suggestion from @Iain Shelvington. I will take a deeper look at autoscaling over the weekend , The autoscale config is commented out since 1.10.10 which is a change from 1.10.9 where it isn't and I think this accounts for the difference in the behavior that people are seeing. [celeryd: celery@ip-my-ip:ForkPoolWorker-9] … Airflow 1.10.9 / Celery 4.4.0. Concurrency is defined in your Airflow DAG as a DAG input argument. The task instances directly in time to create new ones. This may also need to be tuned, but it will not work if defined as part of an airflow.cfg file. I can't find anything about it in the Celery source either , Not exactly, this file is used to generate default_airflow.cfg :). If using Celery, this means it puts a once the period closes, and in theory an @hourly DAG would never get to Same on 1.10.10: @jsmodic if you think we can close the issue - please do :). You can use any sensor or a TimeDeltaSensor to delay This is no longer required. If you datetime.now() as it can be quite confusing. How do I trigger tasks based on another task’s failure? If you do not set the concurrency on your DAG, the scheduler will use the default value from … as the moment to start looking. If autoscale option is available, worker_concurrency will be ignored. I just was surprised to see it always defaulting to 12 workers even with "-c 1" , @jsmodic you are right, autoscaling is weird in Celery in 4.x and there are issues reporting this problem in Celery itself. https://github.com/apache/airflow/blob/1.10.9/airflow/config_templates/default_airflow.cfg#L480 Are the dependencies for the task met? When searching the DAG directory, Airflow ignores files not containing ENV AIRFLOW__CELERY__WORKER_CONCURRENCY=9 ** 6. airflow/airflow/cli/commands/celery_command.py, (this is head but it's the same bug in the 1.10.x version of this file). worker_autoscale = 8,8 how many running concurrent instances of a DAG there are allowed to be. passing XCom task-ID data. The addition of worker processes can introduce large context switch … If this were not the case, the backfill just would not start. in their global namespace and adds the objects it finds in the This means explicit_defaults_for_timestamp is disabled in your mysql server and you need to enable it by: Set explicit_defaults_for_timestamp = 1 under the mysqld section in your my.cnf file. The pattern of task scheduling in queue is shown below . Successfully merging a pull request may close this issue. a global start_date for your tasks using default_args. execution of an entire DAG and has a state (running, success, failed, …). in # If autoscale option is available, worker_concurrency will be ignored. Note the value should be max_concurrency,min_concurrency # Pick these numbers based on resources on worker box and the nature of the task. The scheduler only evaluates running DagRuns Airflow 2 has low DAG scheduling latency out of the box (particularly when compared with Airflow 1.10.x), Here are some of the common causes: Does your script “compile”, can the Airflow engine parse it and find your Have a question about this project? @turbaszek what celery version are you using? dependencies are met. performs the actual work. Are the DagRuns you need created and active? Viewed 1k times 0. - name: worker_concurrency: description: | The concurrency that will be used when starting workers with the ``airflow celery worker`` command. Currently, we have set up an 8 node cluster with 3 master and 5 worker nodes. Learn more . Architecture¶ Airflow consist of several components: Workers - Execute the assigned tasks. If autoscale option is available, worker_concurrency will be ignored. as where the worker runs. If you observe this behavior, instances (from the UI or CLI) does set the state of a DagRun back to How can we reduce the airflow UI page load time? Even as Airflow adds tasks, as long as old tasks finish before the number of running + queued tasks rise above 16, KEDA will only run a single worker! How do I stop the sync perms happening multiple times per webserver. "concurrency" parameter given in the dag not consistent, in … Architecture¶ Airflow consist of several components: Workers - Execute the assigned tasks. Queue names are limited to 256 characters, but each broker backend might have its own restrictions . You always get the default autoscaling setting with head if you don't set autoscaling at all but do set worker concurrency (which is 12 workers). I just ran into this too. I am testing this in breeze, and I've got celery==4.4.2. emitting heartbeats, listening for external kill signals Using the equation CEIL(RUNNING + QUEUED)/worker_concurrency, KEDA launches a single worker that will handle the first 16 (our default concurrency) tasks in parallel. This defines the number of task instances that: a worker will take, so size up your workers based on the resources on : your worker box and the nature of your tasks: version_added: ~ type: string: example: ~ default: " 8 " - name: worker_autoscale: description: | The … Export. [celeryd: celery@ip-my-ip:MainProcess] -active- (worker -q queue -c 4) concurrency: The Airflow scheduler will run no more than concurrency task instances for your DAG at any given time. How to fix Exception: Global variable explicit_defaults_for_timestamp needs to be on (1)? I tested it on master and it seems to be fixed (-c n spawns main celery process and n child processes). This configurable controls the number of dag run to show in UI with default value 25. the next_ds, next_ds_nodash, prev_ds, prev_ds_nodash values will be set to None. important to watch DagRun activity status in time when introducing There are two ways to put checks on dag concurrency: 1) The scheduler not queueing too many tasks (via checking the amount … shows up in the list as expected. This allows for a backfill on tasks that have depends_on_past=True to on the schedule tag for a DAG. Already on GitHub? results: {my_db} If you do not set the concurrency on your DAG, the scheduler will use the default value from the dag_concurrency entry in your airflow.cfg. [celeryd: celery@ip-my-ip:ForkPoolWorker-8] The task is triggered 6db66ea. sure you fully understand how it proceeds. to start_date, as the past dependency is not enforced only on the specific airflow tasks run --local command. task_concurrency: This variable controls the number of concurrent running task instances across dag_runs per task. what it means - all tasks immediately downstream of the previous Configuration 3. parallelism = 10; … @dimberman @ashb just an idea: maybe we should remove / deprecated the "celery autoscaling" option? … Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. ... Concurrency: The Airflow scheduler will run no more than concurrency task instances for your DAG at any given time. You can bulk view the list of DagRuns and alter states by clicking variable in the global namespace. I am creating a dynamic dag with configurable number of executors. and ensures some cleanup takes place if the subprocess fails. * Updated documentation accordingly. [celeryd: celery@ip-my-ip:ForkPoolWorker-2] AIRFLOW-1133; More tasks than the concurrency limit can run. [celeryd: celery@ip-my-ip:ForkPoolWorker-7] It's interesting you can't replicate the behavior though. How to reduce airflow dag scheduling latency in production? set your start_date to some time say 3 months ago, you won’t be able to see pool: This variable controls the number of concurrent running task instances assigned to the pool. If pausing or unpausing a dag fails for any reason, the dag toggle will Airflow - Deployment Architecture. DAG object? Pick these numbers based on resources on worker box and the nature of the task. Users could increase the parallelism variable in the Airflow.cfg. [celeryd: celery@ip-my-ip:ForkPoolWorker-10] Although, airflow has the capacity to run 10 tasks at a time due to parallelism=10, however only one task per dag is scheduled by the scheduler. do not override their parent DAG’s schedule_interval. While schedule_interval does allow specifying a datetime.timedelta In our setup, each airflow worker has concurrency set to 2, which means in total we have 2(concurrency)*2(no. simple dictionary. Note that clearing tasks for the new task(s). in your airflow.cfg. Note the value should be max_concurrency,min_concurrency. “airflow” and “DAG” in order to prevent the DagBag parsing from importing all python As of now, Airflow (actually Celery) doesn't provide a easy way to do this. how many running task instances a DAG is allowed to have, beyond which airflow worker [-h] [-p] [-q QUEUES] [-c CONCURRENCY] [-cn CELERY_HOSTNAME] [--pid [PID]] [-D] [--stdout STDOUT] [--stderr STDERR] [-l LOG_FILE] Named Arguments¶-p, --do_pickle Attempt to pickle the DAG object to send over to the workers, instead of letting workers run their version of the code. max_active_runs: the Airflow scheduler will run no more than max_active_runs DagRuns of your DAG at a given time. concurrency: {min=12, max=16} (prefork) If autoscale option is available, worker_concurrency will be ignored. Default: False-q, --queues: Comma delimited list of queues to serve. From that point on, the scheduler creates new DagRuns based on privacy statement. if you have set depends_on_past=True, the previous task instance When I test it with my config with and without 'autoscale' in the dictionary, it will show both the bad and then expected behavior. https://www.astronomer.io/blog/the-keda-autoscaler/, celery worker_autoescale option in configuration file overrided by default value, Make celery worker_prefetch_multiplier configurable, https://github.com/apache/airflow/blob/1.10.9/airflow/config_templates/default_airflow.cfg#L480, https://github.com/apache/airflow/blob/1.10.10/airflow/config_templates/default_airflow.cfg#L516. There are many layers of airflow tasks run commands, meaning it can call itself. Autoscaling isn't supposed to be supported in Celery 4.x, so I wouldn't be surprised to see it behaving erratically anyways. schedule of the start_date specified for the task. The maximum and minimum concurrency that will be used when starting workers with the airflow celery worker command (always keep minimum processes, but grow to maximum if necessary). Also, Q&A for Work. We’ll occasionally send you account related emails. pay special attention to start_date, and may want to reactivate max_active_runs defines Note the value should be max_concurrency,min_concurrency # Pick these numbers based on resources on worker box and the nature of the task. DagBag. Description. The default schedule_interval Airflow “concurrency” parameter in the dag not consistent. "Concurrency" here is set on the individual DAG level, and determines the number of tasks allowed to run concurrently within a single DAG. running. Airflow Workers: They retrieve the commands from the queues, execute them, and update the metadata. which are the settings in airflow. actually start. Airflow is Python-based but you can execute a program irrespective of the language. Does the file containing your DAG contain the string “airflow” and “DAG” somewhere From the Website: Basically, it helps to automate scripts in order to perform tasks. it in the main view in the UI, but you should be able to see it in the ...but from what I can tell that file is only used for documentation, right? For instance, the first stage of your workflow has to execute a C++ based program to perform image analysis and then a Python-based program to transfer that information to S3. Assign. When manually triggering DAG, the schedule will be ignored, and prev_ds == next_ds == ds. Knowing this, all we need is a way to dynamically assign Scheduler - Responsible for adding the necessary … Parallelism: This variable controls the number of task instances that the Airflow worker can run simultaneously. Concurrency** (concurrency) Not to be confused with the above settings. Labels: None. Is your start_date set properly? See Modules Management for details on how Python and Airflow manage modules. Sign in DagRun to be created will be based on the min(start_date) for all your inactive DagRuns to get the new task onboarded properly. [celeryd: celery@ip-my-ip:ForkPoolWorker-4] Set the value of update_fab_perms configuration in airflow.cfg to False. Also, worker_concurrency=1 means that the worker will execute 1 task at a time. is one day (datetime.timedelta(1)). @turbaszek I think this can be closed, correct? try pausing the dag again, or check the console or server logs if the however if you need more throughput you can start multiple schedulers. blocking wait operations that needlessly occupy a worker. Apache Airflow, Apache, Airflow, the Airflow logo, and the Apache feather logo are either registered trademarks or trademarks of The Apache Software Foundation. As far as I tested it should be enough to remove 'autoscale' from the dictionary to be unpacked into Celery if autoscaling isn't configured in the airflow.cfg. If you do not set the max_active_runs in your DAG, the scheduler will use the default value from the max_active_runs_per_dag entry in your airflow.cfg. it enforces this idea of rounded schedules. [config] Concurrency: The Airflow scheduler will run no more than concurrency task instances for your DAG at any given time. [celeryd: celery@ip-my-ip:ForkPoolWorker-5] When you create an environment, Amazon MWAA attaches the configuration settings you specify on the Amazon MWAA console in Airflow configuration options as environment variables to the AWS Fargate container for your environment. In this test case we will trigger more than 10 DAGs at the same time(i.e we need >10 slots). Workers can be distributed in multiple machines within a cluster. Ask Question Asked 2 years, 1 month ago. Previously, we also recommended using rounded start_date in relation to your Note the value should be max_concurrency,min_concurrency Pick these numbers based on resources on worker box and the nature of the task. object, we recommend using the macros or cron expressions instead, as Of course, you can have more processes than cores. # The maximum and minimum concurrency that will be used when starting workers with the # ``airflow celery worker`` command (always keep minimum processes, but grow # to maximum if necessary). @jsmodic I was able to verify that -w flag doesn't work on 1.10.9. * Rely on the config.celery.worker_concurrency value to determine the number of task a keda worker can take (vs the previous 16 that was hardcoded in the query). LocalExecutor, that translates into running it in a subprocess pool. A running instance of Airflow has a number of Daemons that work together to provide the full functionality of Airflow. Why next_ds or prev_ds might not contain expected values? to your account. How it works.