reflow
Hydra
reflow | Hydra | |
---|---|---|
7 | 1 | |
953 | 30 | |
0.1% | - | |
6.2 | 0.0 | |
7 months ago | about 12 years ago | |
Go | Haskell | |
Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
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
-
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
-
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.
-
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.
Hydra
-
Why isn't differential dataflow more popular?
I think there are a lot of similarly interesting paradigms that goes mostly unnoticed because of a lack of explanation and simple to use api's.
My personal favorite is "Functional hybrid modelling" - https://github.com/giorgidze/Hydra
What are some alternatives?
differential-dataflow - An implementation of differential dataflow using timely dataflow on Rust.
blog - Some notes on things I find interesting and important.
rslint - A (WIP) Extremely fast JavaScript and TypeScript linter and Rust crate
timely-dataflow - A modular implementation of timely dataflow in Rust
ballista - Distributed compute platform implemented in Rust, and powered by Apache Arrow.
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
lambdo - Feature engineering and machine learning: together at last!
odict - A blazingly-fast, offline-first format and toolchain for lexical data 📖
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.