[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-5734":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":16,"stars7d":17,"stars30d":18,"stars90d":16,"forks30d":16,"starsTrendScore":19,"compositeScore":20,"rankGlobal":10,"rankLanguage":10,"license":21,"archived":22,"fork":22,"defaultBranch":23,"hasWiki":22,"hasPages":22,"topics":24,"createdAt":10,"pushedAt":10,"updatedAt":34,"readmeContent":35,"aiSummary":36,"trendingCount":16,"starSnapshotCount":16,"syncStatus":37,"lastSyncTime":38,"discoverSource":39},5734,"arroyo","ArroyoSystems\u002Farroyo","ArroyoSystems","Distributed stream processing engine in Rust","https:\u002F\u002Farroyo.dev",null,"Rust",4933,362,48,96,0,3,34,1,29.68,"Apache License 2.0",false,"master",[25,26,27,28,29,30,31,32,33],"data","data-stream-processing","dev-tools","infrastructure","kafka","rust","sql","stream-processing","stream-processing-engine","2026-06-12 02:01:14","\n\u003Ch1 align=\"center\">\n    \u003Cimg src=\"https:\u002F\u002Fraw.githubusercontent.com\u002FArroyoSystems\u002Farroyo\u002F760aabdbdb019d95f0c5ebb60933233aa735f830\u002Fimages\u002Farroyo_logo.png\" width=\"400px\" alt=\"Arroyo\" \u002F>\n\u003C\u002Fh1>\n\n\n\u003Ch4 align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fdoc.arroyo.dev\u002Fgetting-started\">Getting started\u003C\u002Fa> |\n  \u003Ca href=\"https:\u002F\u002Fdoc.arroyo.dev\">Docs\u003C\u002Fa> |\n  \u003Ca href=\"https:\u002F\u002Fdiscord.gg\u002FcjCr5rVmyR\">Discord\u003C\u002Fa> |\n  \u003Ca href=\"https:\u002F\u002Fwww.arroyo.dev\">Website\u003C\u002Fa>\n\u003C\u002Fh4>\n\n\u003Ch4 align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002FArroyoSystems\u002Farroyo\u002Fblob\u002Fmaster\u002FLICENSE-APACHE\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Flicense-MIT%2FApache--2.0-orange\" alt=\"Arroyo is dual-licensed under Apache 2 and MIT licenses.\" \u002F>\n  \u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002FArroyoSystems\u002Farroyo\u002Fblob\u002Fmaster\u002FCONTRIBUTING.md\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FPRs-Welcome-brightgreen\" alt=\"PRs welcome!\" \u002F>\n  \u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002FArroyoSystems\u002Farroyo\u002Fcommits\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fcommit-activity\u002Fm\u002FArroyoSystems\u002Farroyo\" alt=\"git commit activity\" \u002F>\n  \u003C\u002Fa>\n  \u003Cimg alt=\"CI\" src=\"https:\u002F\u002Fgithub.com\u002FArroyoSystems\u002Farroyo\u002Factions\u002Fworkflows\u002Fci.yml\u002Fbadge.svg\">\n\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002FArroyoSystems\u002Farroyo\u002Freleases\">\n    \u003Cimg alt=\"GitHub release (latest by date)\" src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fv\u002Frelease\u002FArroyoSystems\u002Farroyo?display_name=release\">\n  \u003C\u002Fa>\n\u003C\u002Fh4>\n\n\n[Arroyo](https:\u002F\u002Farroyo.dev) is a distributed stream processing engine written in Rust, designed to efficiently\nperform stateful computations on streams of data. Unlike traditional batch processing, streaming engines can operate\non both bounded and unbounded sources, emitting results as soon as they are available.\n\nIn short: Arroyo lets you ask complex questions of high-volume real-time data with subsecond results.\n\n![running job](https:\u002F\u002Fraw.githubusercontent.com\u002FArroyoSystems\u002Farroyo\u002F760aabdbdb019d95f0c5ebb60933233aa735f830\u002Fimages\u002Fheader_image.png)\n\n## Features\n\n🦀 SQL streaming pipelines\n\n🚀 Scales up to millions of events per second\n\n🪟 Stateful operations including windows and joins\n\n🔥State checkpointing for fault-tolerance and recovery of pipelines\n\n🕒 Time-oriented stream processing via the [Dataflow model](https:\u002F\u002Fwww.oreilly.com\u002Fradar\u002Fthe-world-beyond-batch-streaming-101\u002F)\n\n🔌 A wide variety of [connectors](https:\u002F\u002Fdoc.arroyo.dev\u002Fconnectors), including Kafka and Iceberg\n\n## Use cases\n\nSome example use cases include:\n\n* Detecting fraud and security incidents\n* Real-time product and business analytics\n* Real-time ingestion into your data warehouse or data lake\n* Real-time ML feature generation\n\n## Why Arroyo\n\nThere are already a number of existing streaming engines out there, including [Apache Flink](https:\u002F\u002Fflink.apache.org),\n[Spark Streaming](https:\u002F\u002Fspark.apache.org\u002Fdocs\u002Flatest\u002Fstreaming-programming-guide.html), and\n[Kafka Streams](https:\u002F\u002Fkafka.apache.org\u002Fdocumentation\u002Fstreams\u002F). Why create a new one?\n\n* _Serverless operations_: Arroyo pipelines are designed to run in modern cloud environments, supporting seamless scaling,\n    recovery, and rescheduling\n* _High performance SQL_: SQL is a first-class concern, with consistently excellent performance\n* _Designed for non-experts_: Arroyo cleanly separates the pipeline APIs from its internal implementation. You don't\n    need to be a streaming expert to build real-time data pipelines.\n\n## Installing\n\nArroyo ships as a single binary. You can install it locally on MacOS using Homebrew\n\n```shellsession\nbrew install arroyosystems\u002Ftap\u002Farroyo\n```\n\nor on MacOS or Linux with this script:\n\n```shellsession\ncurl -LsSf https:\u002F\u002Farroyo.dev\u002Finstall.sh | sh\n```\n\nor you can download a binary for your platform from the [releases page](https:\u002F\u002Fgithub.com\u002FArroyoSystems\u002Farroyo\u002Freleases).\n\nOnce you have Arroyo installed, start a cluster with\n\n```shellsession\n$ arroyo cluster\n```\n\nYou can also run a cluster in Docker, with\n\n```shellsession\ndocker run -p 5115:5115 \\\n      ghcr.io\u002Farroyosystems\u002Farroyo:latest\n```\n\nThen, load the Web UI at http:\u002F\u002Flocalhost:5115.\n\nFor a more in-depth guide, see the [getting started guide](https:\u002F\u002Fdoc.arroyo.dev\u002Fgetting-started).\n\nOnce you have Arroyo running, follow the [tutorial](https:\u002F\u002Fdoc.arroyo.dev\u002Ftutorial\u002Ffirst-pipeline\u002F) to create your first real-time\npipeline.\n\n\n## Cloudflare Pipelines\n\nIf you don't want to self-host, Arroyo is available as a fully-managed solution on the \nCloudflare Developer Platform: [Cloudflare Pipelines](https:\u002F\u002Fdevelopers.cloudflare.com\u002Fpipelines\u002F),\nnow available in beta. Currently, stateless pipelines ingesting into R2 are supported, and we'll\nbe expanding to stateful pipelines in the near future.\n\n## Developing Arroyo\n\nWe love contributions from the community! See the [developer setup](https:\u002F\u002Fdoc.arroyo.dev\u002Fdeveloping\u002Fdev-setup) guide\nto get started, and reach out to the team on [discord](https:\u002F\u002Fdiscord.gg\u002FcjCr5rVmyR) or create an issue.\n\n## Community\n\n* [Discord](https:\u002F\u002Fdiscord.gg\u002FcjCr5rVmyR) — support and project discussion\n* [GitHub issues](https:\u002F\u002Fgithub.com\u002FArroyoSystems\u002Farroyo\u002Fissues) — bugs and feature requests\n* [Arroyo Blog](https:\u002F\u002Farroyo.dev\u002Fblog) — updates from the Arroyo team\n","Arroyo是一个用Rust编写的分布式流处理引擎，专为高效执行数据流上的状态计算而设计。其核心功能包括支持SQL流处理管道、每秒可处理数百万事件的高扩展性、提供窗口和连接等状态操作以及通过状态检查点实现容错和恢复。此外，Arroyo采用Dataflow模型进行时间导向的流处理，并支持Kafka和Iceberg等多种连接器。适用于需要实时分析大量数据的场景，如欺诈检测、实时业务分析、数据仓库或数据湖的实时摄入以及机器学习特征生成等。",2,"2026-06-11 03:04:54","top_language"]