I got my first taste of task scheduling with plain old cron jobs. I used them to set up notifications with new and interesting words from a long text file on my Ubuntu machine. GitHub repo

At the time I didn’t ever think about how important and ridiculously difficult it is to make sure that a specific script runs at a specific time.

A few adventures in Israel, and Estonia later, and also completely oblivious to this problem I ended up working as a Data Engineer at STACC. That’s when I was reintroduced to the whole ecosystem of workflow orchestration.

Now, every platform has its own way of defining the problem that it tries to solve. However, at the heart of it, the problem that they solve is this:

1. Run a specific program at a specific time, in a specific environment
2. Based on the result of step 1, repeat step 1 for a possibly different program, different time and different environment

Did I mention I like loops?

Anyway, getting back to the problem at hand. There are a plethora of tools and platforms out there which have their own way of solving this problem. The biggest and the most established of which is Airflow.

Now for those who already know about Airflow and have worked with it a fair amount, I’ve gotta say Prefect feels so much better.

Now, I wasn’t born this morning, and I am fully aware that Prefect is just another tool. Having said that it comes with its own set of good, bad, and ugly.

The GoodPermalink

Plain Python*Permalink

With Prefect, you simply define the order in which you want to run your programs/functions the way you’d normally execute the code, and then sprinkle the necessary decorators. That’s it. You don’t have to really worry about a DAG as such.

With Airflow however, you’ve to define DAGs inorder to achieve the same result.

* Plain Decorated Python

Free scheduler in the cloudPermalink

When working with Airflow, you have worry about at least three things before you even begin running flows:

1. The scheduler
2. The worker
3. The webserver for the UI

With Prefect, you get the scheduler, and webserver for “free” in the cloud. This free tier comes with a generous 5000 flow runs a day.

The UI is also pretty slick compared to the prehistoric UI of Airflow.

Timezone awarePermalink

One of the things I remember worrying about the entire time when working with Airflow was that it wasn’t timezone aware. With Estonia still observing Daylight Savings Time (DST), it was always a mess trying to schedule even slightly complicated DAGs.

With Prefect however, you simply set the timezone and that’s it. The schedules work the way you’d expect in local time.

No dependency hellPermalink

A project that I got stuck on when working with Airflow was that of upgrading from Airflow 1.x to Airflow 2.x. I was stuck on that project for more than a month. Now of course, the project was a lot more complicated than simply upgrading Airflow. It also included upgrading the Python version from 3.6 to 3.9. Given that it was a fairly large legacy project, I still take pride in the fact that I kept chipping away at it little by little and eventually got everything working.

With Prefect, no such complications so far. It is one package, and a few more optional dependencies that you can list in one line of your requirements.txt file. No BS of listing constraints and then praying to the Airflow gods that it works.

The BadPermalink

Unclear pricingPermalink

The free tier of Prefect is quite generous, and allows 5000/flow runs per day. Once your scheduling needs go over the free tier, it isn’t very clear on what the move to a paid tier would cost.

The website simply asks you to contact the team. Of course, with a product like Prefect which can do a million things pricing it isn’t very straightforward. I get that, however it would be nice to have some indicative prices without having to reach out for a quote.

The UglyPermalink

deploy, serve, workers, agents, what?Permalink

With Prefect, there are some concepts that just end up confusing me a lot. You can use a function called .deploy to create a deployment, and you can also serve a flow with .serve, but then you actually create a deployment. I remember with one of these functions, Prefect also created a docker image for you and uploaded it to dockerhub.

Now that’s one function doing too many things.

Sketchy documentation at timesPermalink

As part of my take home task for my current position, I built an ETL pipeline on AWS, where the worker machine would be spawned on the trigger of a run. Although there were docs for something like this, I couldn’t build the full project without help from a Sales Engineer at Prefect. Eventually the problem simply was that I had to meddle with some settings from the Prefect UI.

This is where I believe Airflow shines. Sure, there is so much documentation that finding what you need might be a bit challenging, but at the very least you will find some help from the docs.

The verdictPermalink

The way I see it, Prefect is a great tool in a Data Engineer’s tool chest. It excels at certain things, and leaves you wanting on others. Need a quick way to orchestrate some Python, give Prefect a go, and you’ll be surprised at how quickly you’ll be up and running.