arroyo
cli
| arroyo | cli | |
|---|---|---|
| 13 | 1 | |
| 3,326 | 288 | |
| 3.2% | 1.7% | |
| 9.6 | 7.5 | |
| 7 days ago | 7 days ago | |
| Rust | Rust | |
| Apache License 2.0 | MIT License |
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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
cli
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Django + PostgreSQL Deployment on Railway App
We'll first install the CLI on our local system, for that the guide is quite limited in a way for a few options to choose from like npm, shell, and scoop. For me, the shell was the way to go, but it had a few issues with permission, so I made a few changes in the install.sh script ran on my machine and it worked fine.
What are some alternatives?
bytewax - Python Stream Processing
nickel - Better configuration for less
risingwave - SQL stream processing, analytics, and management. We decouple storage and compute to offer speedy bootstrapping, dynamic scaling, time-travel queries, and efficient joins.
django-blog - A Pure Django REST API for simple Blog
Benthos - Fancy stream processing made operationally mundane
feldera - Feldera Continuous Analytics Platform
timely-dataflow - A modular implementation of timely dataflow in Rust
sqlglot - Python SQL Parser and Transpiler
tensorbase - TensorBase is a new big data warehousing with modern efforts.
vector - A high-performance observability data pipeline.
LLM4Decompile - Reverse Engineering: Decompiling Binary Code with Large Language Models
sliding-window-aggregators - Reference implementations of sliding window aggregation algorithms