Scaling Python with Ray: Adventures in Cloud and Serverless Patterns

Serverless computing enables developers to concentrate solely on their applications rather than worry about where they've been deployed. With the Ray general-purpose serverless implementation in Python, programmers and data scientists can hide servers, implement stateful applications, support direct communication between tasks, and access hardware accelerators.

In this book, experienced software architecture practitioners Holden Karau and Boris Lublinsky show you how to scale existing Python applications and pipelines, allowing you to stay in the Python ecosystem while reducing single points of failure and manual scheduling. Scaling Python with Ray is ideal for software architects and developers eager to explore successful case studies and learn more about decision and measurement effectiveness.

If your data processing or server application has grown beyond what a single computer can handle, this book is for you. You'll explore distributed processing (the pure Python implementation of serverless) and learn how to:

• Implement stateful applications with Ray actors
• Build workflow management in Ray
• Use Ray as a unified system for batch and stream processing
• Apply advanced data processing with Ray
• Build microservices with Ray
• Implement reliable Ray applications

無伺服器運算使開發人員能夠專注於應用程式，而不必擔心它們被部署在哪裡。使用Python的Ray通用無伺服器實現，程式設計師和資料科學家可以隱藏伺服器，實現有狀態的應用程式，支援任務之間的直接通訊，並存取硬體加速器。

在這本書中，經驗豐富的軟體架構師Holden Karau和Boris Lublinsky向您展示如何擴展現有的Python應用程式和流程，讓您能夠留在Python生態系統中，同時減少單一故障點和手動排程。《使用Ray擴展Python》非常適合軟體架構師和開發人員，他們渴望探索成功案例並更深入地了解決策和效果測量。

如果您的資料處理或伺服器應用程式已經超出單台電腦的處理能力，這本書就是為您而寫的。您將探索分散處理（無伺服器的純Python實現）並學習如何：

- 使用Ray actors實現有狀態的應用程式
- 在Ray中建立工作流管理
- 使用Ray作為批次和流程處理的統一系統
- 應用高級資料處理技術使用Ray
- 使用Ray建立微服務
- 實現可靠的Ray應用程式