streamparse VS luigi

Wonder why this is getting posted today in particular?

The quick summary here is that this was a clean-house rewrite of Apache Storm done by an internal team at Twitter. As an open source project history refresher, Apache Storm was originally built by a startup called Backtype, and the project was led by Nathan Marz, the technical founder of Backtype. Then, Backtype was acquired by Twitter, and Storm became a major component for large-scale stream processing (of tweets, tweet analytics, and other things) at Twitter.

I wrote a summary of the "interesting bits" of Apache Storm here:

https://blog.parse.ly/storm/

However, at a certain point, Nathan Marz left Twitter, and a different group of engineers tried to rethink Storm inside Twitter. There was also a lot of work going on around Apache Mesos at the time. Heron is kind of a merger of their "rethinking" of Storm while also making it possible to manage Storm-like Heron clusters using Mesos.

But, I don't think Heron really took off. Meanwhile, Storm got very, very stable in the 1.x series, and then had a clean-house rewrite from Clojure to Java in the 2.x series. The last stable/major Storm release was in 2020.

Storm provides a stream processing programming API, a multi-lang wire protocol, and a cluster management approach. But certain cluster computing problems can probably be better solved at the infrastructure layer today. That said, it's still a very powerful system; on my team, we process 75K+ events per second across hundreds of vCPU cores and thousands of Python processes by combining Storm and Kafka with our open source project, streamparse.

https://github.com/Parsely/streamparse

(Also, I'd be remiss if I didn't mention -- if you're interested in stream processing and distributed computing, we are hiring Python Data Engineers to work on a stack involving Storm, Spark, Kafka, Cassandra, etc.) -- https://www.parse.ly/careers/python_data_engineer

I agree there are many options in this space. Two others to consider:

- https://airflow.apache.org/

- https://github.com/spotify/luigi

There are also many Kubernetes based options out there. For the specific use case you specified, you might even consider a plain old Makefile and incrond if you expect these all to run on a single host and be triggered by a new file showing up in a directory…

Maybe if your use case is “smallish” and doesn’t require the whole studio suite you could check out apscheduler for doing python “tasks” on a schedule and luigi to build pipelines.

What are you trying to do? Distributed scheduler with a single instance? No database? Are you sure you don't just mean "a scheduler" ala Luigi? https://github.com/spotify/luigi

It's good to know what Airflow is not the only one on the market. There are Dagster and Spotify Luigi and others. But they have different pros and cons, be sure that you did a good investigation on the market to choose the best suitable tool for your tasks.

MLOps is a HUGE area to explore, and not surprisingly, there are many startups showing up in this space. If you want to get it on the latest trends, then I would look at workflow orchestration frameworks such as Metaflow (started off at Netflix, is now spinning off into its own enterprise business, https://metaflow.org/), Kubeflow (used at Google, https://www.kubeflow.org/), Airflow (used at Airbnb, https://airflow.apache.org/), and Luigi (used at Spotify, https://github.com/spotify/luigi). Then you have the model serving itself, so there is Seldon (https://www.seldon.io/), Torchserve (https://pytorch.org/serve/), and TensorFlow Serving (https://www.tensorflow.org/tfx/guide/serving). You also have the actual export and transfer of DL models, and ONNX is the most popular here (https://onnx.ai/). Spark (https://spark.apache.org/) still holds up nicely after all these years, especially if you are doing batch predictions on massive amount of data. There is also the GitFlow way of doing things and Data Version Control (DVC, https://dvc.org/) is taken a pole position there.

At Wonderflow we're doing a lot of ML / NLP using Python and recently we are enjoying writing data pipelines using Spotify's Luigi.

💡【About Luigi】 https://github.com/spotify/luigi Luigi was built at Spotify since 2012, it's open source and mainly used for getting data insights by showing recommendations, toplists, A/B test analysis, external reports, internal dashboards, etc.

So to sum that up, I went from having nothing for my side-project set up in AWS to having a Kubernetes cluster with the basic metrics and dashboard, a proper IAM-linked ServiceAccount support for a smooth IAM experience in K8s, and Luigi deployed so that I could then run a Luigi workflow using an ad-hoc run of a CronJob. That's quite remarkable to me. All that took hours to figure out and define when I first did it, over six months ago.