4 ms·

What does spark win at exactly? Dask+Perfect is a much better experience all round including perf, with virtually none of the cluster management hell involved.

ai_ja_nai 7y ago
Very interesting. Can't find references to "Perfect", though; could you please point to a link?
- missosoup 7y ago
  https://www.prefect.io https://www.prefect.io Not the most SEO-friendly choice of name. Great product though.
  - vamega 7y ago
    Are you using their cloud product? The core/open source product doesn't have a way to persist schedule data.
sandGorgon 7y ago
Could you talk about Prefect ? we are in the process of moving from Spark to Dask. I have never heard of prefect. what do you use it for ?
- missosoup 7y ago
  tl;dr we use it for a similar set of tasks that one would use Airflow for. Unlike Airflow, this lends itself to microbatching and streaming. Plus a bunch of housekeeping items ticked off that Airflow never got around to. With a bit of devops engineering time, you can have perfect manage the size of your worker cluster on k8s and scale it up/down with ingest demand, etc. I'll say one thing though. The Perfect website used to be a lot more technical and explicit about what it is and isn't. Now it's mostly sales gobbledegook. Maybe not a good sign. I've seen this happen before with dremio.
  - sandGorgon 7y ago
    This is super interesting! Do you run dask on k8s ? I have been concerned that dask does not leverage kubernetes HPA for autoscaling...but instead chooses to run an external scheduler. How has your experience been ?
maximente 7y ago
looks like dask is python-only, so it's a nonstarter (loser) for already existing JVM code that runs on spark
- missosoup 7y ago
  Spark stacks inevitably end up with PySpark though. It's rework for people who already committed to Spark, sure. And for bigger projects that committed to Spark this change isn't justifiable. But for a greenfield project, choosing Spark is just silly today.

ai_ja_nai 7y ago

Very interesting. Can't find references to "Perfect", though; could you please point to a link?

missosoup 7y ago
https://www.prefect.io https://www.prefect.io Not the most SEO-friendly choice of name. Great product though.
- vamega 7y ago
  Are you using their cloud product? The core/open source product doesn't have a way to persist schedule data.

sandGorgon 7y ago

Could you talk about Prefect ? we are in the process of moving from Spark to Dask. I have never heard of prefect. what do you use it for ?

missosoup 7y ago
tl;dr we use it for a similar set of tasks that one would use Airflow for. Unlike Airflow, this lends itself to microbatching and streaming. Plus a bunch of housekeeping items ticked off that Airflow never got around to. With a bit of devops engineering time, you can have perfect manage the size of your worker cluster on k8s and scale it up/down with ingest demand, etc. I'll say one thing though. The Perfect website used to be a lot more technical and explicit about what it is and isn't. Now it's mostly sales gobbledegook. Maybe not a good sign. I've seen this happen before with dremio.
- sandGorgon 7y ago
  This is super interesting! Do you run dask on k8s ? I have been concerned that dask does not leverage kubernetes HPA for autoscaling...but instead chooses to run an external scheduler. How has your experience been ?

maximente 7y ago

looks like dask is python-only, so it's a nonstarter (loser) for already existing JVM code that runs on spark

missosoup 7y ago
Spark stacks inevitably end up with PySpark though. It's rework for people who already committed to Spark, sure. And for bigger projects that committed to Spark this change isn't justifiable. But for a greenfield project, choosing Spark is just silly today.