The Oryx open source project provides infrastructure for lambda-architecture
applications on top of Spark, Spark Streaming and Kafka. On this, it provides further
support for real-time, large scale machine learning, and end-to-end applications of
this support for common machine learning use cases, like recommendations, clustering,
classification and regression.
The Oryx open source project provides infrastructure for lambda-architecture
applications on top of Spark, Spark Streaming and Kafka. On this, it provides further
support for real-time, large scale machine learning, and end-to-end applications of
this support for common machine learning use cases, like recommendations, clustering,
classification and regression.
Библиотека не имеет зависимостей. Это самодостаточное приложение, которое не зависит ни от каких других библиотек.
Модули Проекта
framework/oryx-api
framework/oryx-common
framework/kafka-util
framework/oryx-lambda
framework/oryx-lambda-serving
framework/oryx-ml
app/oryx-app-api
app/oryx-app-common
app/oryx-app-mllib
app/oryx-app
app/oryx-app-serving
app/example
deploy/oryx-batch
deploy/oryx-speed
deploy/oryx-serving
Oryx 2 is a realization of the lambda architecture built on Apache Spark and Apache Kafka, but with specialization for real-time large scale machine learning. It is a framework for building applications, but also includes packaged, end-to-end applications for collaborative filtering, classification, regression and clustering.
Proceed to the Oryx 2 site for full documentation.
Just looking to deploy a ready-made, end-to-end application for collaborative filtering, clustering or classification? Easy. Proceed directly to:
Developers can consume Oryx 2 as a framework for building custom applications as well. Following the architecture overview below, proceed to Making an Oryx App to learn how to create a new application. You can review a module diagram as well to understand the project structure.
Oryx Project
Realization of the lambda architecture on Spark and Kafka with specialization for real-time large scale machine learning