differential-datalog
streaming-consistency
differential-datalog | streaming-consistency | |
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22 | 3 | |
1,340 | 19 | |
0.5% | - | |
0.0 | 1.8 | |
10 months ago | about 3 years ago | |
Java | Java | |
MIT License | - |
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differential-datalog
- DDlog: A programming language for incremental computation
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Feldera – a more performant streaming database based on Z-sets
Hi,
> I wonder if it lives up to the hype.
We do think so! (disclaimer: I'm a co-founder at Feldera)
To give some more background: We are co-designing/trialing feldera with several industry/enterprise partners from different domains. Our core team also built differential datalog (https://github.com/vmware/differential-datalog) in the past. And while ddlog is used quite successfully in products today, we believe the many lessons we learned with ddlog will help us to build an even better continuous analytics platform. FYI our code is open-source at https://github.com/feldera/feldera if you'd like to try it out.
Also feel free to join our community slack channel (https://www.feldera.com/slack/) if you have more questions.
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Why Are There No Relational DBMSs? [pdf]
The relational model (and generally working at the level of sets/collections, instead of the level of individual values/objects) actually makes it easier to have this kind of incremental computation in a consistent way, I think.
There's a bunch of work being done on making relational systems work this way. Some interesting reading:
- https://www.scattered-thoughts.net/writing/an-opinionated-ma...
- https://materialize.com/ which is built on https://timelydataflow.github.io/differential-dataflow/, which has a lot of research behind it
- Which also can be a compilation target for Datalog: https://github.com/vmware/differential-datalog
- Some prototype work on building UI systems in exactly the way you describe using a relational approach: https://riffle.systems/essays/prelude/ (and HN discussion: https://news.ycombinator.com/item?id=30530120)
(There's a lot more too -- I have a hobby interest in this space, so I have a small collection of links)
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Differential Datalog: a programming language for incremental computation
Tutorial which I didn’t see linked in the README: https://github.com/vmware/differential-datalog/blob/master/d...
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Show HN: Cozo – new Graph DB with Datalog, embedded like SQLite, written in Rust
This is amazing!
Have you looked at differential-datalog? It's rust-based, maintained by VMWare, and has a very rich, well-typed Datalog language. differential-datalog is in-memory only right now, but could be ideal to integrate your graph as a datastore or disk spill cache.
https://github.com/vmware/differential-datalog
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Help wanted!
Sort of related, in my mind at least, is differential dataflow, e.g. https://github.com/vmware/differential-datalog
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Datalog in JavaScript
It’s fascinating to see so many different parties converging on Datalog for reactive apps & UI.
- There are several such talks at https://www.hytradboi.com/ (happening this Friday)
- Roam Research and its clones Athens, Logseq, use Datascript / ClojureScript https://github.com/tonsky/datascript
- differential-datalog isn’t an end-to-end system, but is highly optimized for quick reactivity https://github.com/vmware/differential-datalog
- Datalog UI is a Typescript port of some of differential-datalog’s ideas https://datalogui.dev/
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Call for Help - Open Source Datom/EAV/Fact database in Rust.
Rust related https://github.com/vmware/differential-datalog
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Anything like Svelte/Jetpack Compose for Haskell?
Actually, that makes me wonder whether or not differential datalog falls under that umbrella, and if it could be applied in the same way Compose is.
streaming-consistency
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The Query Your Database Can’t Answer
Anyone thinking about using Confluent as some kind of alternative to a database should read this blog post outlining the myriad correctness problems with ksqlDB: https://scattered-thoughts.net/writing/internal-consistency-...
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An opinionated map of incremental and streaming systems (2018)
Spark structured streaming is in there under structured, high temporal locality.
It didn't make it into https://scattered-thoughts.net/writing/internal-consistency-... because it has severe limitations for low temporal locality operations:
> * As of Spark 2.4, you can use joins only when the query is in Append output mode. Other output modes are not yet supported.
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Internal Consistency in Streaming Systems
> And then try to join credits and debits together by updating_tx.
You can't join on updating_tx because the credits and debits per account are disjoint sets of transactions - that join will never produce output.
I did try something similar with timestamps - https://github.com/jamii/streaming-consistency/blob/main/fli.... This is also wrong (because the timestamps don't have to match between credits and debits) but it at least produces output. It had a very similar error distribution to the original.
What are some alternatives?
scryer-prolog - A modern Prolog implementation written mostly in Rust.
lasp - Prototype implementation of Lasp in Erlang.
timely-dataflow - A modular implementation of timely dataflow in Rust
Pravega - Pravega - Streaming as a new software defined storage primitive
materialize - The data warehouse for operational workloads.
flow - Computational parallel flows on top of GenStage
differential-dataflow - An implementation of differential dataflow using timely dataflow on Rust.
datalevin - A simple, fast and versatile Datalog database
logica - Logica is a logic programming language that compiles to SQL. It runs on Google BigQuery, PostgreSQL and SQLite.
diagnostics - Diagnostic tools for timely dataflow computations
souffle - Soufflé is a variant of Datalog for tool designers crafting analyses in Horn clauses. Soufflé synthesizes a native parallel C++ program from a logic specification.
datascript - Immutable database and Datalog query engine for Clojure, ClojureScript and JS