Integrate with River

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Assessment

Difficulty
5/5
Estimated time
Over a week
Newbie friendliness
30/100
Issue type
Feature
Clarity
Mostly clear
Activity status
Stale
Tech stack
python

Research direction

No files or tests are named. Start by reviewing existing streamz node APIs and River's online model and pipeline interfaces, then determine how RiverLearn and RiverPredict should share a model and how condition-based emissions should work. Done means the integration supports the proposed node pair and relevant River event types with tests.

Written by the indexing model from the issue text.

Description

https://riverml.xyz/latest/ provides online, per-event machine learning. Whilst we have tried to approach them to accept streamz as a known type of input, it makes sense to just do it here.

The idea would be to have RiverLearn streamz node (either a sink, or a normal node returning a metric) and a RiverPredict node, the pair sharing the same River model or pipeline. The River calls are blocking, but fast - so this can be part of a fast async events system. There are other specific types in River that might emit on a condition, e.g., the drift detector's .change_detected, so we might have a few classes to make, but all of them pretty simple.

Dominant language
Python
Stars
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Forks
149
Avg merge
17h 39m
Merged PRs (30d)
1

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