New Apache Airflow Operators for Google Generative AI
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It doesn't scale for teams because the package is incredibly bloated, so once you need to run multiple images via K8s operators, you lose out on a lot of other Airflow functionality because it all assumes you have your biz logic embedded within DAGs, but at the same time I don't know why anyone would develop this way.
Note that any feedforward neural network (e.g. anything using attention) is also a DAG.
You can always encapsulate more complex logic in a single subroutine or even choose a hex or onion pattern for biz logic.
But what do you suggest besides DAGs + saga patterns that doesn't result in a ball of mud over time for distributed systems?
Why do you think you define DAGs in Python? The point is to be dynamic, exactly to do things like switching between operator types based on things like the environment. Sorry you really don't seem to know a lot about airflow for your strong opinions against it, I'm out, no offense intended at all.
As an old Airflow user, I can relate with that.
The contributors are great and the speed of the new features and fixes are great also.
I see two main issues with Airflow: (i) the lack of good interoperability with a cloud native stack, specifically with the k8s operators, that sounds quite hackish and (ii) lack of a better version control in the DAGs in way that we can have actually real lineage.
The point is that for most of the veterans in ~ETL~ Data Engineering in Cloud Native stacks, we just want to send a cli command or a Make command to a container to execute the thing that we want.
Apache Airflow does that, but as most of the things in the Python ecosystem at the time that you have a great UI (not confuse with UX) become way easier to embed business logic in the DAGs and have your application embedded with Airflow logic.
I will never bash Airflow since I got a lot of money in consulting migrating from and to it, but the general feeling of working on a daily basis for almost 7 years made me realize the sad reality around orquestrators. In the past, those used to be kind of the bedrock technology that you would never want to migrate, but today, due to this feature bloat and lack of vision, I started to see orquestrators as disposable as Java Script frameworks.
We looked at Dagster as well as Airflow. I really, really liked Dagster but the BI team didn't.
I cannot imagine using Airflow for anything meaningful and respecting myself at the end of a work day. The local development experience was abysmal. Deployments sucked.
That being said, if you're not using anything except maybe cron right now, and if you don't care about the solution being a proper data pipelines orchestration platform trademark symbol, I'd recommend starting with Windmill.
Might as well go with the devil everyone knows.
I wouldn't, that's just describing a custom codebase. Airflow comes with bells and whistles that can be taken advantage of if you fit your execution in their DAG model, that's all. Those bells and whistles can be an excellent pattern to work with. You can go all in on operators, or you can just have an operator call out to an external task that does all the work and only rely on Airflow for its retry/alerting mechanism while keeping the external task in language of your choice.
You just need a well defined interface on how to call your biz tasks, and then you can use any of ExternalPythonOperator, DockerOperator, DockerSwarmOperator, KubernetesPodOperator, etc. etc. or write your own to pass in values or data to your task however you want.
Airflow is quite complex, and I don't recommend it as people's go to, but IMO that's in large part because it is so unopinionated about how you call and run your tasks and leaves the configuration up to you. But this also means it ends up being a lot of people's choice because they are able to get it to fit what they need.
GCP Dataflow is based on Airflow. I imagine a lot of GCP customers would default to this for their data workflow
But isn't that the point? I never got the impression that you were supposed to build everything with the built-in operators, they are just the "batteries included" part you can wrap or extend. I really don't understand the criticism.
My criticism was always xcom, but that's a moot point now that we have TaskFlow. Airflow is awesome and very flexible you just have to adapt to how it's supposed to be used instead of fighting against it I find.
Airflow, aws step functions, etc are complex event processors and shouldn't typically be used as wide as you are suggesting.
It is a common pitfall to accidentally build distributed monoliths, and in fact often requires active architectural governance to avoid it.
Airflow isn't perfect, but I highly recommend considering a review on why modernization efforts fail.
The what and the how shouldn't be tightly coupled, especially across capabilities.
But yes. Airflow makes a poor ESB/DTC, but that is not the target for the project.
Airflow has sensors and triggers. https://airflow.apache.org/docs/apache-airflow/stable/author...
But in the core it is built around data pipeline concept, event driven pipeline will much more fragile. Airflow intentionally doesn't manage business logic, it works with "tasks".
In a modern even based SOA, products like airflow are a sometimes food while pub/sub is the default.
Perhaps a search for images of the zachman framework would help conceptualize how you are tightly coupling to the implementation.
But also research SOA 2.0, or event based SOA, the Enterprise Service Bus concept of the original SOA is as dead as COBRA.
ETA: the minimal package load for airflow isn't bad, are you installing all of the plugins and their dependencies?
There are a number of blog posts echoing a similar critique[1].
Using KubernetesOperators creates a lot of wrong abstractions, impedes testability, and makes Airflow as a whole a pretty overkill system just to monitor external tasks. At that point, you should have just had your orchestration in client code to begin with, and many other frameworks made this correct division between client and server. That would also make it easier to support multiple languages.
According to their README: https://github.com/apache/airflow#approach-to-dependencies-o...
> Airflow has a lot of dependencies - direct and transitive > The important dependencies are: SQLAlchemy, Alembic, Flask, werkzeug, celery, kubernetes
Why should biz logic that just needs to run Spark and interact with S3 now need to run a web server?
[1] Anecdotes from various posts - https://medium.com/bluecore-engineering/were-all-using-airfl... - https://eng.lyft.com/orchestrating-data-pipelines-at-lyft-co... - https://dagster.io/blog/dagster-airflow
> Airflow, in its design, made the incorrect abstraction by having Operators actually implement functional work instead of spinning up developer work.
> By simply moving to using a Kubernetes Operator, Airflow developers can develop more quickly, debug more confidently, and not worry about conflicting package requirements.
> Airflow lacks proper library isolation. It becomes hard or impossible to do if any team requires a specific library version for a given workflow
> There is no way to separate DAGs to development, staging, and production using out-of-the-box Airflow features. That makes Airflow harder to use for mission-critical applications that require proper testing and the ability to roll back
> Data pipelines written for Airflow are typically bound to a particular environment. To avoid dependency hell, most guides recommend defining Airflow tasks with operators like the KubernetesPodOperator, which dictates that the task gets executed in Kubernetes. When a DAG is written in this way, it’s nigh-impossible to run it locally or as part of CI. And it requires opting out of all of the integrations that come out-of-the-box with Airflow.
> Why should biz logic that just needs to run Spark and interact with S3 now need to run a web server?
Webserver is mostly UI. Scheduler service triggers the jobs.
We have groups which run everything as Bash Operator, no dependency issues that way.
You maybe have a very specific use case in mind, the main points of using Airflow for me
* Single orchestration center: manual job control (stop, pause, rerun), backfill; automated scheduler/retry; built-in notification
* Framework built around "reporting period" - it enforces correct abstraction, if a data batch is broken, I can rerun it and rerun all dependent downstream. How do you fix data in event driven workflow?
* managing dependencies
In most cases all Airflow does is running your job with passing it "date" parameter. You can test your code without Airflow - just pass it a date and run from command line.