I am trying to diagnose an under-performing airflow pipeline and am wondering what kind of performance I should expect out of the airflow scheduler in terms similar to "tasks scheduled per second".
I have few queued jobs and many of my tasks finish in seconds so I suspect the scheduler is the limiting component and it is my fault for having many quick tasks. Still, I would rather not rewrite my DAGs if it can be avoided.
What can I do to increase the rate at which the scheduler queues tasks?
Pipeline Details
Here is what my current airflow.cfg looks like.
I only have two dags running. One is scheduled every 5 min and the other is rarely triggered by the first. I am currently trying to backfill several years at this frequency, but may need to change my approach:
As for worker nodes: I currently have 4 fairly powerful servers running at less than 10% resource usage in disk, network, cpu, RAM, swap. Toggling 3 of the workers off has no impact on my task throughput and the server left on barely even registers the change in workload.
There are a number of config values in your airflow.cfg that could be related to this.
Under [core]:
parallelism: Total number of task instances that can run at once.
dag_concurrency: Limit of task instances that can run per DAG run, may need to bump if you have many parallel tasks. Can override when defining a DAG.
non_pooled_task_slot_count: Limit of tasks without a pool configured that can run at once.
max_active_runs_per_dag: The maximum number of active DAG runs per DAG. If you're triggering runs manually or there's a backup of DAG runs scheduled with a short interval. Can override when defining a DAG.
Under [scheduler]:
schedule_heartbeat_sec: Defines how often the scheduler runs, try it out with lower values.
min_file_process_interval: Process each file at most once every N seconds. Set to 0 to never limit how often you process a file.
Under [worker]:
celeryd_concurrency: Number of workers celery will run with, so essentially number of task instances a worker can take at once. Matching the number of CPUs is a popular starting point, but can definitely go higher.
Last one is only if you're using the CeleryExecutor, which I'd definitely recommend if you're looking to increase your task throughput.
Related
I'm using Airflow through Cloud Composer (Image: composer-2.0.29-airflow-2.3.3). I have defined 5 DAGS that run concurrently with 22 tasks run concurrently (max) distributed among the 5 DAGS. These DAGS are in the default-pool with default number of slots set to 128.
My composer instance has:
1 Scheduler: 0.5 vCPUs, 1.875 GB memory, 1 GB storage
Worker: 0.5 vCPUs, 1.875 GB memory, 1 GB storage
Autoscaling worker: from 1 to 3.
I would like to create different pools to separate my 5 systems. How do I define the number of slots in each pool? Suppose a pool has 1 DAG with 10 tasks (with 5/10 concurrent tasks). How many slots should I assign to each task?
DAG example:
task1.x is ingestion of JDBC table; while task2.x is update of the corresponding BigQuery table.
Thank you all!
Airflow pools are designed to avoid overwhelmed on external systems used by a group of tasks. For example, if you have some tasks in different dags which use a machine learning model API, a RDBMS, an API with quotas or any other system with limited scaling, you can use an Airflow pool to limit the number of parallel tasks which interact with this system.
In your case, you have two systems, JDBC database and BigQuery. You need to create just two pools, jdbc_pool and bigquery_pool, and assign all the tasks (form all the dags) which interact with the jdbc table to the first one and assign all the tasks which interact with biquery to the second one. For the slots, you can define them based on the performance of each system, and the computational weight of each task.
If you have a monitoring tool (prometheus, datadog, ...), you can run one of the tasks and watch the resources usage on your db, lets assume that it uses 10% of the resources, in this case you can create a pool with 8 slots to attend 80% of resources usage (you should avoid using 100% of the resources to avoid the problems when there is unexpected load). Then for the pool slots of each task:
if all the tasks are similar, you can use pool_slots=1 for all the tasks: max 8 parallel tasks with 80% of resources usage
if you have some tasks which are more complicated than the task you have tested (they use more than 10% of the db resources), you can use a higher value for pool_slots for these tasks based on the resources usage: assume there is a task which consumes 20% of the resources, you can use pool_slots=2 only for this tasks and keep 1 for the others, in this case you can have 8 parallel simple tasks or 6 parallel simple tasks with this heavy task with 80% of resources usage in the two cases.
For bigquery_pool, you need to check what are the quotas, but I think you can use a high value without any problem where it is a very scalable serverless DWH.
If you just want to limit the number of executed tasks in each worker to avoid OOM problem for ex, you can set the worker concurrency conf.
And if you want to limit the number of executed tasks in the whole Airflow server, you can set the parallelism conf.
I ran the following test command:
airflow test events {task_name_redacted} 2018-12-12
...and got the following output:
Dependencies not met for <TaskInstance: events.{redacted} 2018-12-12T00:00:00+00:00 [None]>, dependency 'Task Instance Slots Available' FAILED: The maximum number of running tasks (16) for this task's DAG 'events' has been reached.
[2019-01-17 19:47:48,978] {models.py:1556} WARNING -
--------------------------------------------------------------------------------
FIXME: Rescheduling due to concurrency limits reached at task runtime. Attempt 1 of 6. State set to NONE.
--------------------------------------------------------------------------------
[2019-01-17 19:47:48,978] {models.py:1559} INFO - Queuing into pool None
My Airflow is configured with a maximum concurrency of 16. Does this mean that I cannot test a task when the DAG is currently running, and has used all of it's task slots?
Also, it was a little unclear from the docs, but does the airflow test actually execute the task, as in if it was a SparkSubmitOperator, it would actually submit the job?
While I am yet to reach that phase of deployment where concurrency will matter, the docs do give a fairly good indication of problem at hand
Since at any point of time just one scheduler is running (and you shouldn't be running multiple anyways), indeed it appears that irrespective of whether the DAG-runs are live-runs or test-runs, this limit will apply on them collectively. So that is certainly a hurdle.
# The number of task instances allowed to run concurrently by the scheduler
dag_concurrency = 16
But beware that merely increasing this number (assuming you have big-enough boxes for hefty workers / multiple workers), several other configurations will have to be tweaked as well to achieve the kind of parallelism I sense you want.
They are all listed under [core] section
# The amount of parallelism as a setting to the executor. This
defines the max number of task instances that should run
simultaneously on this airflow installation
parallelism = 32
# When not using pools, tasks are run in the "default pool", whose
size is guided by this config element
non_pooled_task_slot_count = 128
# The maximum number of active DAG runs per DAG
max_active_runs_per_dag = 16
But we are still not there, because once you spawn so many tasks simultaneously, the backend metadata-db will start choking. While this is likely a minor problem (and might not be affecting unless you have some real huge DAGs / very large no of Variable interactions in your tasks), its still worth noting as a potential roadblock
# The SqlAlchemy pool size is the maximum number of database
connections in the pool. 0 indicates no limit.
sql_alchemy_pool_size = 5
# The SqlAlchemy pool recycle is the number of seconds a connection
can be idle in the pool before it is invalidated. This config does not
apply to sqlite. If the number of DB connections is ever exceeded, a
lower config value will allow the system to recover faster.
sql_alchemy_pool_recycle = 1800
# How many seconds to retry re-establishing a DB connection after
disconnects. Setting this to 0 disables retries.
sql_alchemy_reconnect_timeout = 300
Needless to say, all this is pretty much futile unless you pick the right executor; SequentialExecutor, in particular is only intended for testing
# The executor class that airflow should use. Choices include SequentialExecutor, LocalExecutor, CeleryExecutor, DaskExecutor,
KubernetesExecutor
executor = SequentialExecutor
But then params to BaseOperator like depends_on_past, wait_for_downstream are there to spoil the party as well
Finally I leave you with this link related to Airflow + Spark combination: How to submit Spark jobs to EMR cluster from Airflow?
(Pardon me if the answer confused you more than you already were, but..)
What is the practical limit on the number of DAGS in an airflow system?
We are seeing severe delays after a couple of hundred DAGS are created.
Is anyone running 1000's of DAGS?
It depends on how you run Airflow and how much resources you provide it. You can run Airflow in a distributed mode with Celery and Master and Worker having high memory and vCores, if you have huge number of tasks.
I recently upgraded from v1.7.1.2 to v1.9.0 and after the upgrade I noticed that the CPU usage increased significantly. After doing some digging, I tracked it down to these two scheduler config options: min_file_process_interval (defaults to 0) and max_threads (defaults to 2).
As expected, increasing min_file_process_interval avoids the tight loop and drops cpu usage when it goes idle. But what I don't understand is why min_file_process_interval affects tasks execution?
If I set min_file_process_interval to 60s, it now waits no less than 60s between executing each task in my DAG, so if my dag has 4 sequential tasks it has now added 4 minutes to my execution time. For example:
start -> [task1] -> [task2] -> [task3] -> [task4]
^ ^ ^ ^
60s 60s 60s 60s
I have Airflow setup in my test env and prod env. This is less of an issue in my prod env (although still concerning), but a big issue for my test env. After the upgrade the CPU usage is significantly higher so either I accept higher CPU usage or try to decrease it with a higher config value. However, this adds significant time to my test dags execution time.
Why does min_file_process_interval affect time between tasks after the DAG has been scheduled? Are there other config options that could solve my issue?
Another option you might want to look into is
SCHEDULER_HEARTBEAT_SEC
This setting is usually also set to a very tight interval but could loosened up a bit. This setting in combination with
MAX_THREADS
did the trick for us. The dev machines are fast enough for re-deployment but without a hot, glowing CPU which is good.
The most likely cause is that there are too many python files in the dags folder, and the airflow scheduler scans the instantiated DAG too much.
It is recommended to reduce the number of dag files under scheduler and worker first. At the same time, the SCHEDULER_HEARTBEAT_SEC and MAX_THREADS values are set as large as possible.
the Local Executor spawns new processes while scheduling tasks. Is there a limit to the number of processes it creates. I needed to change it. I need to know what is the difference between scheduler's "max_threads" and
"parallelism" in airflow.cfg ?
parallelism: not a very descriptive name. The description says it sets the maximum task instances for the airflow installation, which is a bit ambiguous — if I have two hosts running airflow workers, I'd have airflow installed on two hosts, so that should be two installations, but based on context 'per installation' here means 'per Airflow state database'. I'd name this max_active_tasks.
dag_concurrency: Despite the name based on the comment this is actually the task concurrency, and it's per worker. I'd name this max_active_tasks_for_worker (per_worker would suggest that it's a global setting for workers, but I think you can have workers with different values set for this).
max_active_runs_per_dag: This one's kinda alright, but since it seems to be just a default value for the matching DAG kwarg, it might be nice to reflect that in the name, something like default_max_active_runs_for_dags
So let's move on to the DAG kwargs:
concurrency: Again, having a general name like this, coupled with the fact that concurrency is used for something different elsewhere makes this pretty confusing. I'd call this max_active_tasks.
max_active_runs: This one sounds alright to me.
source: https://issues.apache.org/jira/browse/AIRFLOW-57
max_threads gives the user some control over cpu usage. It specifies scheduler parallelism.
It's 2019 and more updated docs have come out. In short:
AIRFLOW__CORE__PARALLELISM is the max number of task instances that can run concurrently across ALL of Airflow (all tasks across all dags)
AIRFLOW__CORE__DAG_CONCURRENCY is the max number of task instances allowed to run concurrently FOR A SINGLE SPECIFIC DAG
These docs describe it in more detail:
According to https://www.astronomer.io/guides/airflow-scaling-workers/:
parallelism is the max number of task instances that can run
concurrently on airflow. This means that across all running DAGs, no
more than 32 tasks will run at one time.
And
dag_concurrency is the number of task instances allowed to run
concurrently within a specific dag. In other words, you could have 2
DAGs running 16 tasks each in parallel, but a single DAG with 50 tasks
would also only run 16 tasks - not 32
And, according to https://airflow.apache.org/faq.html#how-to-reduce-airflow-dag-scheduling-latency-in-production:
max_threads: Scheduler will spawn multiple threads in parallel to
schedule dags. This is controlled by max_threads with default value of
2. User should increase this value to a larger value(e.g numbers of cpus where scheduler runs - 1) in production.
But it seems like this last piece shouldn't take up too much time, because it's just the "scheduling" portion. Not the actual running portion. Therefore we didn't see the need to tweak max_threads much, but AIRFLOW__CORE__PARALLELISM and AIRFLOW__CORE__DAG_CONCURRENCY did affect us.
The scheduler's max_threads is the number of processes to parallelize the scheduler over. The max_threads cannot exceed the cpu count. The LocalExecutor's parallelism is the number of concurrent tasks the LocalExecutor should run. Both the scheduler and the LocalExecutor use python's multiprocessing library for parallelism.