OryxProject/oryx

Health v2 · maintenance only

Flags: archived

Adoption not part of the score

1782 stars · 400 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Oryx 2 is a Java framework implementing the lambda architecture on Apache Spark and Apache Kafka, specialized for real-time large-scale machine learning. It ships with end-to-end packaged applications for collaborative filtering (ALS), classification, regression, and clustering, plus batch, speed, and serving layers that can be reused independently.

Use cases

• build a real-time recommendation system with ALS on Spark
• serve continuously updated ML models over a REST API
• process streaming data with Kafka and update models incrementally
• run batch and speed layers for large-scale machine learning
• deploy collaborative filtering, classification, or clustering as ready-made apps
• build a custom lambda architecture ML application in Java

When to choose

• you need real-time model updates on large-scale streaming data
• you already run a Hadoop/Spark/Kafka cluster
• you want packaged recommendation, classification, or clustering pipelines
• you want a reusable lambda architecture framework in the JVM ecosystem

When to avoid

• you need a lightweight single-node ML setup without Hadoop/Kafka
• you want modern actively maintained tooling (last release 2021)
• you prefer Python-based ML stacks like Spark MLlib pipelines or cloud ML services
• your use case is simple batch training without streaming updates

Facets

framework · maturity maintenance

machine-learning streaming message-queue api-framework data-science machine-learning big-data microservices jvm cloud lambda-architecture apache-spark apache-kafka collaborative-filtering real-time-ml hadoop als k-means random-forests data-engineering recommendation linux docker

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