Why developers like Arroyo

What is Arroyo?

It is a distributed stream processing engine written in Rust, designed to efficiently perform stateful computations on streams of data.

Arroyo is a tool in the Stream Processing category of a tech stack.

Arroyo is an open source tool with 3.3K GitHub stars and 173 GitHub forks. Here’s a link to Arroyo's open source repository on GitHub

Arroyo Integrations

Docker, Kafka, Rust, SQL, and Linux are some of the popular tools that integrate with Arroyo. Here's a list of all 6 tools that integrate with Arroyo.

Docker

Kafka

Rust

SQL

Linux

macOS

Arroyo's Features

SQL and Rust pipelines
Scales up to millions of events per second
Stateful operations like windows and joins
State checkpointing for fault-tolerance and recovery of pipelines

Arroyo Alternatives & Comparisons

What are some alternatives to Arroyo?

Kafka Streams

It is a client library for building applications and microservices, where the input and output data are stored in Kafka clusters. It combines the simplicity of writing and deploying standard Java and Scala applications on the client side with the benefits of Kafka's server-side cluster technology.

Apache NiFi

An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.

Confluent

It is a data streaming platform based on Apache Kafka: a full-scale streaming platform, capable of not only publish-and-subscribe, but also the storage and processing of data within the stream

Apache Storm

Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate.

Kafka Manager

This interface makes it easier to identify topics which are unevenly distributed across the cluster or have partition leaders unevenly distributed across the cluster. It supports management of multiple clusters, preferred replica election, replica re-assignment, and topic creation. It is also great for getting a quick bird’s eye view of the cluster.

Arroyo