flink-statefun VS faust

When low latency matters you should always consider an ETL approach rather than ELT, e.g. collect data in Kafka and process using Kafka Streams/Flink in Java or Quix Streams/Bytewax in Python, then sink it to Snowflake where you can handle non-critical workloads (as is the case for 99% of BI/analytics). This way you can choose the right path for your data depending on how quickly it needs to be served.

Sometimes we may need to generate random data of type 2 in different streams, so the "coherency" must also spread across different entities, think for example to referential integrity in databases. If I am generating users, products and orders to three different Kafka topics and I want to create a streaming application with Apache Flink, I definitely need data to be coherent across topics.

The Treatment and Control audiences need to be stored for future low-latency, high-reliability retrieval. Retrieval happens when we are delivering the survey, and informs the system which users to send surveys to. How is this achieved at Reddit’s scale? Users interact with ads, which generate events that are sent to our downstream systems for processing. At the output, these interactions are stored in DynamoDB as engagement records for easy access. Records are indexed on user ID and ad campaign ID to allow for efficient retrieval. The use of stream processing (Apache Flink) ensures this whole process happens within minutes, and keeps audiences up to date in real-time. The following high-level diagram summarizes the process:

Most streaming database technologies use SQL for these reasons: RisingWave, Materialize, KsqlDB, Apache Flink, and so on offering SQL interfaces. This post explains how to choose the right streaming database.

Apache Flink.

There are different ways to implement parallel dataflows, such as using parallel data processing frameworks like Apache Hadoop, Apache Spark, and Apache Flink, or using cloud-based services like Amazon EMR and Google Cloud Dataflow. It is also possible to use parallel dataflow frameworks to handle big data and distributed computing, like Apache Nifi and Apache Kafka.

We’re not discussing the technical details behind the deduplication process. It could be Apache Flink, Apache Spark, or Kafka Streams. Anyway, it’s out of the scope of this article.

One can also consider https://flink.apache.org/ instead of Kafka for connecting a large number of devices.

Both Kafka and Pulsar provide some kind of stream processing capability, but Kafka is much further along in that regard. Pulsar stream processing relies on the Pulsar Functions interface which is only suited for simple callbacks. On the other hand, Kafka Streams and ksqlDB are more complete solutions that could be considered replacements for Apache Spark or Apache Flink, state-of-the-art stream-processing frameworks. You could use them to build streaming applications with stateful information, sliding windows, etc.

We have many similar ideas and paradigms, as one would expect from modern frameworks tackling the same problem. However, Faust has been deprecated and no longer managed or supported (there is a fork being maintained by the community at https://github.com/faust-streaming/faust). I guess Robinhood at some point decided not to spend any more resources on it, an often destiny of such side projects by large corporations. On the other hand, we hope to stay around for a long time and build to a framework and a community that loves it. This initial version was built using many libraries in order to have a working system and to gather feedback from the large community. We plan to replace eventually all, or at least most of dependancies, and replace then with Rust lib with Python bindings.

Just wanted to add that there is an actively maintained fork called faust-streaming, you can find it here: https://github.com/faust-streaming/faust

That's the basics yes. You have a pletora of things coming next. One is "Windowing" mentioned in the article, it's well explained and maybe it looks simple, but when you start with it, takes some time to wrap your mind around it.

The other things in kafka world are stateful transformations, which you would normally do using Java's Flink. The closest in python is Faust (the fork) [0]. What are stateful aggregations? something like doing SQL on top of a topic: group_by, count, reduce, and joins. So similar to SQL that you have kSQL [1].

Consumer groups IMO falls under basic usage, if you need to scale, take a look at it, and what are partitions and replicas, with that in mind, you'll be ok.

[0]: https://github.com/faust-streaming/faust

[1]: https://www.confluent.io/blog/ksql-streaming-sql-for-apache-...

The forked one, https://github.com/faust-streaming/faust, has been updated but still doesn’t seem to support joins.

Faust: Python framework

I’d like to use Python for this so I came across Faust but I’m not sure if it’s possible to create HTTP requests through Faust or if there are better alternatives.

There is a community fork which was stale for a while but got a new commit a couple days ago, this one might be usable, but is still quite risky (70 open issues etc.): https://github.com/faust-streaming/faust

There is a community which forked and actively develops faust, here’s the https://github.com/faust-streaming/faust

Hi, thanks for the answer, I'm actually using virtualenv with the requirements of the file associated with the example. However I wanted to tell you that due to covid the project is suspended but only temporarily, but there is an active fork https://github.com/faust-streaming/faust. But the example in the fork doesn't work either.