differential-dataflow VS lambdo

Some recent solutions to this problem include Differential Dataflow and Materialize. It would be neat if postgres adopted something similar for live-updating materialized views.

https://github.com/timelydataflow/differential-dataflow

https://materialize.com/

I'm looking for this but can't find it, how does this project compare to differential dataflow?

As a sibling commenter mentioned, it's built on timely dataflow (which is lower-level), but that already has differential dataflow[0] built on top of it by the same authors.

How do they differ?

[0]: https://github.com/TimelyDataflow/differential-dataflow

hi! sujay from convex here. I remember reading about your "reverse query engine" when we were getting started last year and really liking that framing of the broadcast problem here.

as james mentions, we entirely re-run the javascript function whenever we detect any of its inputs change. incrementality at this layer would be very difficult, since we're dealing with a general purpose programming language. also, since we fully sandbox and determinize these javascript "queries," the majority of the cost is in accessing the database.

eventually, I'd like to explore "reverse query execution" on the boundary between javascript and the underlying data using an approach like differential dataflow [1]. the materialize folks [2] have made a lot of progress applying it for OLAP and readyset [3] is using similar techniques for OLTP.

[1] https://github.com/TimelyDataflow/differential-dataflow

[2] https://materialize.com/

[3] https://readyset.io/

differential dataflow which is used to power materialize db

It's partially inspired by Linq, so the similarity you see is expected.

It's not really arbitrary structures so much, though you're mostly free in what record type you use in a relation (structs and tagged enums are typical, though).

The incremental part is that you can feed it changes to the input (additions/retractions of facts) and get changes to the outputs back with low latency (you can alternatively just use it to keep an index up-to-date, where you can quickly look up based on a key (like a materialized view in SQL)).

This [0] section in the readme of the underlying incremental dataflow framework may help get the concept across, but feel free to follow up if you're still not seeing the incrementality.

[0]: https://github.com/TimelyDataflow/differential-dataflow#an-e...

It will return the sum of all values in column A. For large tables it will take some time to compute the result. Now assume we append a new record and want to get the new result. The traditional approach is execute this query again. A better approach is to process this new record only by adding its value in A to the result of the previous query. It is important in (stateful) stream processing.

Something similar is implemented in these libraries which however rely on a different data processing conception (alternative to map-reduce):

https://github.com/asavinov/prosto - Functions matter! No join-groupby, No map-reduce.

https://github.com/asavinov/lambdo - Feature engineering and machine learning: together at last!

I find this project quite interesting because sklearn has a good general design including data transformations and it does make sense to provide compatible functionality for Go.

Feature engineering in general is a hot topic and especially if features are not simple hard-coded transformations but rather can be learned from data. For example, I developed a toolkit intended for combining feature engineering and ML:

    https://github.com/asavinov/lambdo - Feature engineering and machine learning: together at last!