timely-dataflow
differential-dataflow
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timely-dataflow | differential-dataflow | |
---|---|---|
11 | 14 | |
3,141 | 2,466 | |
1.0% | 1.3% | |
7.2 | 8.3 | |
14 days ago | 5 days ago | |
Rust | Rust | |
MIT License | MIT 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.
timely-dataflow
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Readyset: A MySQL and Postgres wire-compatible caching layer
They have a bit about their technical foundation here[0].
Given that Readyset was co-founded by Jon Gjengset (but has apparently since departed the company), who authored the paper on Noria[1], I would assume that Readyset is the continuation of that research.
So it shares some roots with Materialize. They have a common conceptual ancestry in Naiad, where Materialize evolved out of timely-dataflow.
[0]: https://docs.readyset.io/concepts/streaming-dataflow
[1]: https://jon.thesquareplanet.com/papers/osdi18-noria.pdf
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Mandala: experiment data management as a built-in (Python) language feature
And systems like timely dataflow, https://github.com/TimelyDataflow/timely-dataflow
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Arroyo: A distributed stream processing engine written in Rust
Project looks cool! Glad you open sourced it. It could use some comments in the code base to help contributors ;). I also like the datafusion usage, that is awesome. BTW I work on github.com/bytewax/bytewax, which is based on https://github.com/TimelyDataflow/timely-dataflow another Rust dataflow computation engine.
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Rust MPI -- Will there ever be a fully oxidized implementation?
Just found this https://github.com/TimelyDataflow/timely-dataflow and my heart skipped a beat.
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Streaming processing in Python using Timely Dataflow with Bytewax
Bytewax is a Python native binding to the Timely Dataflow library (written in Rust) for building highly scalable streaming (and batch) processing pipelines.
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Alternative Kafka Integration Framework to Kafka Connect?
I am working on Bytewax, which is a Python stream processing framework built on Timely Dataflow. It is not exactly a Kafka integration framework because it is a more of a general stream processing framework, but might be interesting for you. We are focused on enabling people to more easily debug, containerize, parallelize and customize and less on enabling a declarative integration framework. It is still early days for us! And we are looking for feedback and ideas from the community.
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[AskJS] JavaScript for data processing
We used to use a library called Pond.js, https://github.com/esnet/pond, but the reliance on Immutable.JS caused some performance pitfalls, so we wrote a system from scratch that deals with data in a batched streaming fashion. A lot of the concepts were borrowed from a Rust library called timely-dataflow, https://github.com/TimelyDataflow/timely-dataflow.
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Dataflow: An Efficient Data Processing Library for Machine Learning
Though the name "Dataflow" might be an unfortunate name conflict with another Rust project: https://github.com/TimelyDataflow/timely-dataflow
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Ask HN: Is there a way to subscribe to an SQL query for changes?
> In the simplest case, I'm talking about regular SQL non-materialized views which are essentially inlined.
I see that now -- makes sense!
> Wish we had some better database primitives to assemble rather than building everything on Postgres - its not ideal for a lot of things.
I'm curious to hear more about this! We agree that better primitives are required and that's why Materialize is written in Rust using using TimelyDataflow[1] and DifferentialDataflow[2] (both developed by Materialize co-founder Frank McSherry). The only relationship between Materialize and Postgres is that we are wire-compatible with Postgres and we don't share any code with Postgres nor do we have a dependence on it.
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7 Real-Time Data Streaming Tools You Should Consider On Your Next Project
Under the hood, Materialize uses Timely Dataflow (TDF) as the stream-processing engine. This allows Materialize to take advantage of the distributed data-parallel compute engine. The great thing about using TDF is that it has been in open source development since 2014 and has since been battle-tested in production at large Fortune 1000-scale companies.
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.
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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.
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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?
noria - Fast web applications through dynamic, partially-stateful dataflow
ballista - Distributed compute platform implemented in Rust, and powered by Apache Arrow.
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.
materialize - The data warehouse for operational workloads.
reflow - A language and runtime for distributed, incremental data processing in the cloud
bytewax - Python Stream Processing
realtime - Broadcast, Presence, and Postgres Changes via WebSockets
clj-3df - Clojure(Script) client for Declarative Dataflow.
flow - 🌊 Continuously synchronize the systems where your data lives, to the systems where you _want_ it to live, with Estuary Flow. 🌊
rslint - A (WIP) Extremely fast JavaScript and TypeScript linter and Rust crate