A Mental Model for Distributed Compute: Kubernetes, Slurm, Ray, and Spark
一位开发者正在尝试为分布式计算系统建立一个更清晰的思维模型，而不是独立学习每个框架。他们指出，Kubernetes、Slurm、Ray 和 Spark 等系统虽然使用不同的抽象，但许多底层问题是相同的，例如调度、资源管理、工作执行、状态、通信、内存和故障恢复。一篇 Substack 文章深入探讨了现代分布式系统背后的架构。
- 发布
- 09/07 20:33 UTC+0
- 收录
- 09/08 00:00 UTC+0
- 来源类型
- 开发者社区
- 档位
- 社区
- 信源状态
- 正常
档位是按信源手工设定的编辑判断，不是逐条打分。
I’ve been trying to build a cleaner mental model for distributed compute systems instead of learning each framework independently.
Kubernetes, Slurm, Ray, and Spark all use different abstractions, but many of the underlying problems are the same: scheduling, resource management, worker execution, state, communication, memory, and failure recovery.
I wrote up the framework-independent model first, then mapped each system onto it.
Would be interested in how others think about the boundaries between cluster scheduler, runtime, and application-level scheduler.
Article: https://pawankjha.substack.com/p/the-architecture-behind-modern-distributed