reflow
differential-dataflow
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reflow | differential-dataflow | |
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7 | 14 | |
952 | 2,467 | |
-0.1% | 1.3% | |
6.2 | 8.3 | |
6 months ago | 7 days ago | |
Go | Rust | |
Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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reflow
- reflow - A language and runtime for distributed, incremental data processing in the cloud
- Reflow, a language for distributed, incremental data processing in the cloud
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Jolie, the service-oriented programming language
Reflow [1] is a similar attempt at a slightly different domain: bioinformatics and ETL pipelines. Reflow exposes a data model and programming model that reclaims programmability in these systems, and, by leaning on these abstractions, gives the runtime much more leeway to do interesting things. It unties the hands of the implementer.
[1] https://github.com/grailbio/reflow
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Data as a build system ?
https://github.com/grailbio/reflow is the closest that I know, as it has a design that resembles the Bazel build system.
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Why isn't differential dataflow more popular?
It seems Reflow falls in this category:
https://github.com/grailbio/reflow
> Reflow thus allows scientists and engineers to write straightforward programs and then have them transparently executed in a cloud environment. Programs are automatically parallelized and distributed across multiple machines, and redundant computations (even across runs and users) are eliminated by its memoization cache. Reflow evaluates its programs incrementally: whenever the input data or program changes, only those outputs that depend on the changed data or code are recomputed.
differential-dataflow
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We Built a Streaming SQL Engine
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/
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Hydroflow: Dataflow Runtime in Rust
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
- Using Rust to write a Data Pipeline. Thoughts. Musings.
- PlanetScale Boost
- Program Synthesis is Possible (2018)
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Convex vs. Firebase
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/
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Announcing avalanche 0.1, a React- and Svelte-inspired GUI library
differential dataflow which is used to power materialize db
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Differential Datalog
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...
- Dbt and Materialize
- Materialized view questions
What are some alternatives?
rslint - A (WIP) Extremely fast JavaScript and TypeScript linter and Rust crate
ballista - Distributed compute platform implemented in Rust, and powered by Apache Arrow.
materialize - The data warehouse for operational workloads.
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
differential-datalog - DDlog is a programming language for incremental computation. It is well suited for writing programs that continuously update their output in response to input changes. A DDlog programmer does not write incremental algorithms; instead they specify the desired input-output mapping in a declarative manner.
timely-dataflow - A modular implementation of timely dataflow in Rust
odict - A blazingly-fast, offline-first format and toolchain for lexical data 📖
clj-3df - Clojure(Script) client for Declarative Dataflow.
blog - Some notes on things I find interesting and important.