ArroyoSystems/arroyo
Distributed stream processing engine in Rust observed · 2026-08-28
Health v2 · maintenance only
78/100
- Activity 98
- Release rhythm 47
- Longevity 89
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: 60.0
- age_days: 1251
- days_rel: 275
- days_push: 13
- n_releases_24m: 7
Adoption not part of the score
5016 stars · 375 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
Arroyo is a distributed stream processing engine written in Rust that lets users run stateful computations on high-volume real-time data streams using SQL. It supports windows, joins, event-time processing, checkpointing, and connectors like Kafka and Iceberg, scaling to millions of events per second.
Use cases
- process kafka streams with sql
- real-time fraud detection
- stream data into a data lake or iceberg tables
- real-time product analytics dashboards
- generate ml features in real time
- run streaming pipelines on kubernetes
- replace flink with a simpler sql stream processor
When to choose
- you want to build streaming pipelines in SQL without hiring streaming experts
- you need sub-second results on high-volume event streams
- you want a lightweight single-binary engine that deploys easily to Docker or Kubernetes
- you need stateful operations like windows, joins, and checkpointing with fault tolerance
- you want to ingest real-time data into Iceberg, a data warehouse, or a data lake
When to avoid
- your workloads are purely batch and latency is not a concern
- you need the mature ecosystem and extensive connectors of Apache Flink or Spark Streaming
- you require complex custom pipeline logic beyond SQL and UDFs
- you need Windows support, which is not well supported
Facets
application · maturity active
streaming etl data-science cli web-framework big-data analytics databases developer-tools rust cloud self-hosted stream-processing sql-pipelines kafka iceberg event-time watermarks stateful-computation dataflow flink-alternative real-time-analytics sql data-engineering real-time linux macos docker kubernetes
9 sources
- readme: https://github.com/ArroyoSystems/arroyo · fetched 2026-08-28 · 77b98a3b8b47
- homepage: https://arroyo.dev · fetched 2026-08-29 · 8b529511c6ed
- site_page: https://doc.arroyo.dev/getting-started · fetched 2026-08-29 · b25a687ed45c
- site_page: https://www.arroyo.dev/about · fetched 2026-08-29 · e716daa66fb6
- registry_crates: https://crates.io/api/v1/crates/arroyo · fetched 2026-08-29 · 2cc07f67a201
- site_page: https://doc.arroyo.dev · fetched 2026-08-29 · 7a8a136ef628
- site_page: https://doc.arroyo.dev/sql · fetched 2026-08-29 · 9fedd3cfea00
- site_page: https://doc.arroyo.dev/deployment · fetched 2026-08-29 · 87f14da8c82d
- site_page: https://www.arroyo.dev/blog/arroyo-0-15-0 · fetched 2026-08-29 · fe613a992fd6
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| ArroyoSystems/arroyo | main | 78 |
For agents
markdown · JSON · MCP: product_card(name="ArroyoSystems/arroyo")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem