Show HN: Slipstream – A Python library for stateful stream processing

This freedom let's you do things that other libraries may not offer within the bounds of their API. For instance, I do see that joins and windows are supported in Bytewax, but is it possible to do more complex stateful joins based timestamps (temporal joins) or other arbitrary conditions?

If it does, then that's great. But I've had experiences where limitations became apparent during an end-phase of a project. When the API starts to reach its limits, but you're already invested in it quite deeply.

I hope I get your question right, but if you're joining on a fixed key, the solution should be simpler. If the data is partitioned by id (partition 0 has id's 0 - 9, and 1 has 10 - 19), each message still passes through the handler that stores each message in the cache. So in the cache it will no longer be partitioned. Let's say each weather update has a fixed `id` we could join on, we'd simply use the following logic instead:

``` if w := weather_cache[id]: return f'The weather during {a["value"]} was {w["value"]}'

return a['value'], '?' ```

It may be the case that the weather updates come in late, in which case we may be joining with stale data. For that we can use Synchronization: https://slipstream.readthedocs.io/en/1.0.1/cookbook.html#syn...

Here's a full example that sends out corrections: https://gist.github.com/Menziess/22d8a511f61c04a8142d81510a0...

Instead, you could also wait by pausing the activity stream by setting the Checkpoints Dependeny downtime_threshold to 0. Perhaps negative values may also work, although I haven't tried yet.