timely-dataflow
rethinkdb_rebirth
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timely-dataflow | rethinkdb_rebirth | |
---|---|---|
11 | 1 | |
3,141 | 1,016 | |
1.0% | - | |
7.2 | 0.0 | |
13 days ago | about 5 years ago | |
Rust | C++ | |
MIT License | GNU General Public License v3.0 or later |
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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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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.
- Why isn't differential dataflow more popular?
rethinkdb_rebirth
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Ask HN: Is there a way to subscribe to an SQL query for changes?
I know [RethinkDB][1] used to do this with their SQL-like ReQL language, but I looked around a bit and can't find much else about it - and I would have thought it would be more common.
If we think about modern frontends using SQL-based backends, essentially every time we render, its ultimately the result of a tree of SQL queries (queries depend on results of other queries) running in the backend. Our frontend app state is just a tree of materialized views of our database which depend on each other. We've got a bunch of state management libraries that deal with trees but they don't fit so well with relational/graph-like data.
I came across a Postgres proposal for [Incremental View Maintenance][2] which generates a diff against an existing query with the purpose of updating a materialized view. Oracle also has [`FAST REFRESH`](https://docs.oracle.com/database/121/DWHSG/refresh.htm#DWHSG8361) for materialized views.
I guess it's relatively easy to do until you start needing joins or traversing graphs/hierarchies - which is why its maybe avoided.
[1]: https://github.com/rethinkdb/rethinkdb_rebirth
What are some alternatives?
noria - Fast web applications through dynamic, partially-stateful dataflow
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.
bytewax - Python Stream Processing
realtime - Broadcast, Presence, and Postgres Changes via WebSockets
differential-dataflow - An implementation of differential dataflow using timely dataflow on Rust.
flow - 🌊 Continuously synchronize the systems where your data lives, to the systems where you _want_ it to live, with Estuary Flow. 🌊
Hasura - Blazing fast, instant realtime GraphQL APIs on your DB with fine grained access control, also trigger webhooks on database events.
db_watch
diagnostics - Diagnostic tools for timely dataflow computations