Crates.io | arroyo-udf-macros |
lib.rs | arroyo-udf-macros |
version | 0.2.0 |
source | src |
created_at | 2024-04-20 03:06:48.239281 |
updated_at | 2024-07-02 18:29:58.182506 |
description | Macros for the Arroyo UDF interface |
homepage | https://arroyo.dev |
repository | https://github.com/ArroyoSystems/arroyo |
max_upload_size | |
id | 1214301 |
size | 20,312 |
Arroyo is a distributed stream processing engine written in Rust, designed to efficiently perform stateful computations on streams of data. Unlike traditional batch processing, streaming engines can operate on both bounded and unbounded sources, emitting results as soon as they are available.
In short: Arroyo lets you ask complex questions of high-volume real-time data with subsecond results.
π¦ SQL and Rust pipelines
π Scales up to millions of events per second
πͺ Stateful operations like windows and joins
π₯State checkpointing for fault-tolerance and recovery of pipelines
π Timely stream processing via the Dataflow model
Some example use cases include:
There are already a number of existing streaming engines out there, including Apache Flink, Spark Streaming, and Kafka Streams. Why create a new one?
You can get started with a single node Arroyo cluster by running the following docker command:
$ docker run -p 8000:8000 ghcr.io/arroyosystems/arroyo-single:latest
or if you have Cargo installed, you can use the arroyo
cli:
$ cargo install arroyo
$ arroyo start
Then, load the Web UI at http://localhost:8000.
For a more in-depth guide, see the getting started guide.
Once you have Arroyo running, follow the tutorial to create your first real-time pipeline.
We love contributions from the community! See the developer setup guide to get started, and reach out to the team on discord or create an issue.
Don't want to self-host? Arroyo Systems provides fully-managed cloud hosting for Arroyo. Sign up here.