csvsource
arroyo
| csvsource | arroyo | |
|---|---|---|
| 3 | 13 | |
| 8 | 3,293 | |
| - | 2.3% | |
| 6.8 | 9.6 | |
| 5 months ago | 5 days ago | |
| Rust | Rust | |
| Apache License 2.0 | Apache License 2.0 |
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.
csvsource
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Rust newbie developed a tool to convert csv to sql
Thank you so much! Huge improvements to the code: https://github.com/htmfilho/roma/commit/c0ff64e9ff2d85f7f2bee37aef9956bb72c23e15
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
What are some alternatives?
obake - Versioned data-structures for Rust
bytewax - Python Stream Processing
c2rust - Migrate C code to Rust
risingwave - SQL stream processing, analytics, and management. PostgreSQL simplicity, unrivaled performance, and seamless elasticity. 🚀 10x more productive. 🚀 10x more cost-efficient.
undermoon - Mordern Redis Cluster solution for easy operation.
Benthos - Fancy stream processing made operationally mundane
cli - Railway CLI
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.