My name is Ask and I'm the co-creator of this project, along with Vineet.
Yes, we're definitely interested in supporting Redis Streams!
Faust is designed to support different brokers, but Kafka is the only implementation as of now.
Yes, we're definitely interested in supporting Redis Streams!
Faust is designed to support different brokers, but Kafka is the only implementation as of now.
What are the advantages of using Faust feature-wise or otherwise? Are you guys planning on having feature parity with Flink (triggers, evictors, process function equivalent, etc.)? I can definitely see its use in having better compatibility with ML/AI env and the other tools in python toolkit. But specifically regarding ML/AI, I can export those things into the JVM usually. And with regards with Flask/Django, I can use scala http4s.
Sorry if that seemed like a lot! Trust me very excited about this project!
But when it comes to streaming, I don't think Spark Structured Streaming was very close, although Spark 2.3.0 might be sufficient for your use cases, and I use spark structured streaming only when I have to deploy spark pipeline/ml.
But, besides the features I listed, I think Flink is just better written for streaming.
For example, defining checkpoints and state management in Flink is so much more expressive and easier to write (and perhaps more performant from what I saw at Flink Forward).
Flink Async is amazing!!!!
The Flink table/sql API is not as nice as Spark SQL for batch right now, but it is getting there.
Flink also seems to perform better than Spark Structured Streaming when you turn on object reusability, at least according to Alibaba and Netflix.
And then, the features I listed are amazing. Evictors and triggers are super helpful. Process Functions let me write custom aggregations without a lot of mental overhead.
Time Characteristics are really nice in Flink.
Flink has a lot of nice connectors and stuff written already (although Parquet files are troublesome though if they come from Spark because of the way that Spark handles it read and write support classes). For example, you have to write your own kinesis connector in spark structured streaming (or use the one put out by databricks on their blogs).
Sorry if this seems all over the place. I just posted what came to mind. And I should add, Databricks is doing a lot of work to make Spark Structured Streaming really comparable, so who knows what stuff looks like in 2-3 years. Like I said above, I use Spark Structured Streaming (2.3.0+) for some very specific ML stuff that my Flink environment cannot handle nicely yet.
My only complaint is that there isn't a lot of community written code out there, but the Data Artisans team is super active, helpful, and nice.
- A Stream iterates over a channel
- A Channel implements: `channel.send` and `channel.__aiter__`.
- A topic is a "named" channel backed by a Kafka topic
- Further the topic is backed by a Transport
- Transport is very Kafka specific
To implement support for AMQP the idea is you only need to implement a custom channel.
If you open an issue we can consider how to best implement it.
Simplicity is of course a goal, but this may mean we have to sacrifice some features when Redis Streams is used as a backend.
I'm glad you like Celery, this project in many ways realize what I wanted it to be.
https://github.com/closeio/tasktiger (supports subqueues to prevent users bottlenecking each other)
https://github.com/Bogdanp/dramatiq (high message throughput)
If you have a Faust app that depends on a particular feature we strongly suggest you submit it as an integration test for us to run.
Hopefully some day Celery will be able to take the same approach, but running cloud servers cost money that the project does not have.