Apache Airflow Solutions Performance Issues!

Apache airflow

How to Deal With Apache Airflow Solutions Performance Issues? 

When it comes to orchestration of data pipelines for data engineering purposes and management of workflows, you can’t miss out on Apache Airflow. The solution is highly popular among developers for use in data engineering and data analytics. Additionally, with the automation of workflows by defining them as codes, Apache Airflow Solutions make the workflows much more manageable and maintainable thereby speeding them up and driving operational efficiency.

However, when you try running tens of workflows with hundreds of tasks in your Apache Airflow Scheduler, the solution starts making the tasks messier thereby creating performance issues. This is what makes it difficult to leverage Apache Airflow Solution for more complicated, complex, and bigger use cases.

Clearly, there is a lot of room for improvement with the solution and if you want to leverage it for the best of its capabilities, you need to tackle the performance issues coming forth. But how do you possibly deal with them?

Well, the first step in dealing with the performance issues is knowing where they are coming from. This requires you to get an understanding of your DAG schedule.

What is DAG Schedule?


DAG or Directed Acrylic Graph is the collection of tasks that you want to run on your Airflow Scheduler. You can easily organize all your tasks in a manner to create relationships and dependencies between them so that they can run smoothly and cater to automated and fast-paced workflows.

However, the way DAG works is very tricky. When you create a DAG schedule in Airflow, it runs periodically on the basis of start_date and schedule_interval that are specified in the DAG file. However, when DAG is triggered in Apache Airflow Scheduler, it does not run in the beginning of the schedule period. Instead, it’s triggered to run at the end of the period that is scheduled. This can easily confuse the users and cause performance issues in working with Airflow.

So, it’s important that you understand the scheduling mechanism of DAG.

When you create your schedule, it’s triggered to run on the basis of start_date and schedule_time. Now with different Airflow schedules for different tasks, jobs, and workflows, each DAG run gets triggered when it meets a specified time dependency and this dependency is based on the end of the schedule period rather than the start of it. So, your tasks will start to run at the end of the period you have scheduled them.

So, basically, the problem is with the execution time of the tasks in Apache Airflow DAG Schedule. It does not depend on the actual run time that you have specified. Instead, it works on the basis of the timestamp that is set within the schedule period.

Due to this complicated scheduling mechanism of Apache Airflow Scheduler, the users need to use a static start_date, because that’s not when the DAG run will actually be triggered. With a static start_date, you can be sure that the DAG run will be triggered just when you want them to be triggered and your tasks will be performed as per your expectations thereby eliminating the performance issues in Apache Airflow.

Another aspect that comes with DAG is Catchup and Idempotent DAG.

What is Catchup and Idempotent DAG in Apache Airflow?

Catchup is an important functionality in Airflow. The functionality is used to backfill the previously executed DAG schedules. In case this functionality is turned off, you will have no records of any earlier DAG entry and the Airflow Scheduler will show only the current and running DAGs. So, it’s important to configure this setting in your Airflow solution.

There are two ways for DAG configuration.

1. Airflow Cluster Level
This is a default setting that is applied to all the DAGs unless you configure them through a DAG level catchup.

2. DAG level Catchup
To configure this, you simply have to run the below command in your DAG file.
dag = DAG(‘sample_dag’, catchup=False, default_args=default_args)

This is how you configure the DAG catchup to make sure that you have a record of all your schedules so that you can go back and check on the tasks performed and their performance levels at any point in time. However, since all DAGs can be backfilled through catchup, you also want to make sure that different schedules do not get mixed up. This is why you want to keep all your DAG schedules independent from each other and that’s where idempotent DAG comes in.

Idempotent DAG means that a particular DAG will render the same results irrespective on the number of times it has been run. So, even when your DAGs are getting backfilled due to catchup, they will give the same performance without creating any performance issues.

Finally, you need to keep up with the Metadata on your Airflow Solution to make sure that you are able to tackle the performance issues over it.

What is Airflow Metadata?


There are two parts of the Airflow Metadata.

1. Metadata Database
This is the database that carries and manages all the information about your DAGs, their execution, and task status.

2. Scheduler
This is where the entire working takes place. It processes and manages the DAG files. The scheduler accesses the metadata database to read the scheduled tasks and decides when they must be run.

As the tasks are triggered and run with the scheduler constantly processing the DAG files, performance issues occur in case the size of these files is too much. The best way to tackle this issue is keeping your DAG files light so that the scheduler can quickly work on them and run the DAG schedulers at their best performance.

Basically, the scheduler must not need to actually process the files but simply be able to run them quickly in a heartbeat. That’s what will make up for good and efficient performance in your Airflow solutions.

Besides these DAG scheduling, functionalities, and metadata, you must also make sure that you never rename the DAG files unless the need is inevitable. Renaming a DAG files creates a new DAG altogether which will result in the deletion of previous DAG history and the catchup trying to backfill the same DAG file all over again. This will lead to the same tasks being performed again which does not really work for a good performance.

Conclusion

So, these are some ways you can use to tackle the performance issues in your Airflow Scheduler and make sure that you are able to achieve seamless workflows. The bottom line is that Apache Airflow Solution is orchestrated to manage your workflows and data pipelines, however, it’s tricky to use, so you need to be aware of the basics and work your way through them to leverage the best capabilities of this robust workflow automation solution.

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