Graphene
differential-dataflow
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Graphene | differential-dataflow | |
---|---|---|
19 | 14 | |
7,973 | 2,467 | |
0.6% | 1.3% | |
4.3 | 8.3 | |
about 1 month ago | 6 days ago | |
Python | 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.
Graphene
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Who moved my error codes? Adding error types to your GoLang GraphQL Server
And gqlgen is not alone in this. We found several more GraphQL frameworks that don’t take it upon themselves to address this problem. Widely used GraphQL server implementations, such as graphql-go/graphql and Python’s graphene, have the exact same gap of exposing messages of unexpected errors by default.
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Using GraphQL with Strawberry, FastAPI, and Next.js
There are multiple Python-based GraphQL libraries and they all vary slightly from each other. For the longest time, Graphene was a natural choice as it was the oldest and was used in production at different companies, but now other newer libraries have also started gaining some traction.
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Wasmer and Trademarks
> But you need to know that Wasmer and its sibling projects will stay free forever. We are open source lovers, all of us, and we have a strong background on working on open source projects before joining Wasmer too.
That's great. I'm sure the closed source paid 10x faster editions of the graphene Python GraphQL server written by Syrus "CEO of Graphene" and CEO of wasmer will be open sourced in this spirit.
https://github.com/graphql-python/graphene/issues/268#issuec...
http://graphql-quiver.com/
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graphene django with cloudinary model field
CloudinaryField is a custom model field not a django built in one and graphene-python doesn't know what do with it. See the list of types it does https://github.com/graphql-python/graphene/tree/master/graphene/types
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[Python] What minimal application server do you run your Python x GraphQL services with? Django, Flask....
Thanks for the reply, I was looking at Strawberry a bit but not enough documentation for a graphql noob like me... Hate to turn this into tech support but could you potentially answer a question for me? I'm having a really hard time figuring out how to do this. Given this example: https://github.com/graphql-python/graphene/blob/master/examples/simple_example.py and a slight modification:
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Strawberry Django Plus: Enchanted Strawberry GraphQL integration with Django
Graphene was one of the first (if not the first) lib built on top of graphql-core to provide an easy to use api to build graphql applications. It's been around since 2015 and has a lot of integrations built for it (e.g. Django, SQL Alchemy, etc).
- Graphene – Python GraphQL Library
- Graphene 3.0 is released
- Graphene 3.0 Is Released
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?
strawberry - A GraphQL library for Python that leverages type annotations 🍓
ballista - Distributed compute platform implemented in Rust, and powered by Apache Arrow.
ariadne - Python library for implementing GraphQL servers using schema-first approach.
materialize - The data warehouse for operational workloads.
tartiflette-aiohttp - tartiflette-aiohttp is a wrapper of aiohttp which includes the Tartiflette GraphQL Engine, do not hesitate to take a look of the Tartiflette project.
reflow - A language and runtime for distributed, incremental data processing in the cloud
AIOHTTP - Asynchronous HTTP client/server framework for asyncio and Python
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.
CherryPy - CherryPy is a pythonic, object-oriented HTTP framework. https://cherrypy.dev
timely-dataflow - A modular implementation of timely dataflow in Rust
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
clj-3df - Clojure(Script) client for Declarative Dataflow.