arroyo
plotnine
arroyo | plotnine | |
---|---|---|
13 | 36 | |
3,326 | 3,841 | |
3.2% | - | |
9.6 | 9.6 | |
7 days ago | 6 days ago | |
Rust | Python | |
Apache License 2.0 | 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.
arroyo
- FLaNK AI Weekly 18 March 2024
- Arryo 0.8 released — streaming SQL engine
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Query Engines: Push vs. Pull
Interesting - I looked into your code a bit. I found your window aggregation library [1]. You may be interested in looking into the Rust implementation of some of the research work I've been a part of [2].
In Flink, I believe the reason they need to implement their own backpressure system is that they multiplex TCP connections. That is, they have multiple logical streams flowing through a single TCP connection. If that's the case, you need to do some work to 1) detect which logical stream is the one that's blocking, and 2) don't block because other logical streams may be able to use the active TCP connection.
Thinking it through, I think what Flink's approach buys is not necessarily better performance, but better just a manageable number of connections. That is, imagine you have a process P1 with operators A, B and C. And then P2 has D, E, F. Now imagine that this is a shuffle, where A, B and C are fully connected to D, E and F. In my old system, you would have 9 TCP connections. In Flink, you will have 1.
[1] https://github.com/ArroyoSystems/arroyo/blob/master/arroyo-w...
- Arroyo
- Show HN: Arroyo – Write SQL on streaming data
- Release v0.3.0 · ArroyoSystems/arroyo - Stream Processing Engine
- Arroyo 0.2 released - Rust stream processing engine, now on Kubernetes
- Distributed stream processing engine written in Rust
- ArroyoSystems/arroyo: Arroyo is a distributed stream processing engine written in Rust
- Arroyo, a new open-source SQL stream processing engine written in Rust
plotnine
- FLaNK AI Weekly 18 March 2024
-
A look at the Mojo language for bioinformatics
To your last point, have you tried plotnine? It's meant to be ggplot2 for python.
https://github.com/has2k1/plotnine
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Mastering Matplotlib: A Step-by-Step Tutorial for Beginners
plotnine - A grammar of graphics for Python based on ggplot2.
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Top 10 growing data visualization libraries in Python in 2023
Github: https://github.com/has2k1/plotnine
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Lets-Plot: An open-source plotting library by JetBrains
This seems quite similar to plotnine [0], which also provides a grammar of graphics interface for Python. That said, I love ggplot and I can't wait to use this in my research! I hope we can port/re-implement ggthemes, scientificplots [1], and other ggplot libraries for lets-plot.
0: https://plotnine.readthedocs.io/en/stable/
1: https://github.com/garrettj403/SciencePlots
- When would you use R instead of Python?
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[P] Easily make complex plots using ChatGPT [open source]
There is [plotnine](https://plotnine.readthedocs.io/en/stable/) which tries to implement ggplot in Python.
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Is R or Python an EASIER option for non-CS/SE grads?
You could use plotnine if you like the grammar of graphics concept: https://plotnine.readthedocs.io/en/stable/
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Every modeler is supposed to be a great Python programmer
> Python doesn’t yet have anything remotely close to ggplot for rapidly making exploratory graphics, for example.
Plug for plotnine (https://plotnine.readthedocs.io/en/stable/). I don't know R but use ggplot indirectly through this library for exploratory data analysis, and comparing the experience to any other python plotting library, I understand why R folks are usually so sad to be using Python.
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Why has nobody ported ggplot to Python?
They have, https://plotnine.readthedocs.io/en/stable/
What are some alternatives?
bytewax - Python Stream Processing
seaborn - Statistical data visualization in Python
risingwave - SQL stream processing, analytics, and management. We decouple storage and compute to offer speedy bootstrapping, dynamic scaling, time-travel queries, and efficient joins.
matplotlib - matplotlib: plotting with Python
Benthos - Fancy stream processing made operationally mundane
Altair - Declarative statistical visualization library for Python
cli - Railway CLI
plotly - The interactive graphing library for Python :sparkles: This project now includes Plotly Express!
feldera - Feldera Continuous Analytics Platform
ggplot - ggplot port for python
timely-dataflow - A modular implementation of timely dataflow in Rust
bokeh - Interactive Data Visualization in the browser, from Python