Late data is very deliberately not handled. The reasoning for that is best available at [0].
Now, there are ways [1] to handle bitemporal data, but they have fairly significant issues in ergonomics and performance, due to the additional work needed to allow the bitemporal aggregations.
As for the data persistence, that's something the underlying approach for the aggregations could handle relatively well with LSM trees [2] (back then, `Aggregation` was called `ValueHistory`).
Along with syncing that state to replicated storage, it should not be a big problem to make it recover quickly from a dead node.
As for the data persistence, that's something the underlying approach for the aggregations could handle relatively well with LSM trees [2] (back then, `Aggregation` was called `ValueHistory`).
Along with syncing that state to replicated storage, it should not be a big problem to make it recover quickly from a dead node.
[0]: https://github.com/frankmcsherry/blog/blob/master/posts/2020... [1]: https://github.com/frankmcsherry/blog/blob/master/posts/2018... [2]: https://github.com/TimelyDataflow/differential-dataflow/issu...