Distributed Computing with Mars for Python (eBook, ePUB)
The Complete Guide for Developers and Engineers
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"Distributed Computing with Mars for Python" "Distributed Computing with Mars for Python" is a comprehensive guide for engineers, data scientists, and architects seeking to harness the power of large-scale distributed computation using the Mars framework in Python. The book begins with a clear exposition of distributed system principles, tracing the evolution of Python-centric frameworks and highlighting where Mars stands in comparison to established solutions like Dask, Ray, and Spark. It delves into the history, architecture, and ecosystem of Mars, equipping readers with a solid understandin...
"Distributed Computing with Mars for Python" "Distributed Computing with Mars for Python" is a comprehensive guide for engineers, data scientists, and architects seeking to harness the power of large-scale distributed computation using the Mars framework in Python. The book begins with a clear exposition of distributed system principles, tracing the evolution of Python-centric frameworks and highlighting where Mars stands in comparison to established solutions like Dask, Ray, and Spark. It delves into the history, architecture, and ecosystem of Mars, equipping readers with a solid understanding of its modular design, extensibility, and advanced features uniquely tailored for Python developers. Readers will benefit from an in-depth exploration of Mars' core abstractions, including distributed tensors and DataFrames, with practical strategies for chunking, sharding, and optimizing data locality. The book details Mars' cluster architecture and scheduling algorithms, task execution models, and robust failure recovery mechanisms, all illustrated with real-world ETL pipelines, storage integrations, and high-performance I/O optimizations. Additionally, it provides expert guidance for executing and scaling workloads, profiling and diagnostics, multi-tenant deployments, and geo-distributed computation, ensuring practitioners are prepared to operate Mars clusters at enterprise scale. Beyond foundational coverage, the book ventures into advanced programming with Mars APIs, including custom operators, plugin development, and orchestration of machine learning workflows. Chapters on security, compliance, and performance tuning provide actionable techniques for securing, optimizing, and monitoring distributed deployments at scale. To round out its practical focus, the book presents real-world integration patterns with popular Python ecosystem libraries and cloud-native infrastructure, culminating in a collection of case studies from diverse domains such as finance, healthcare, and IoT. This makes "Distributed Computing with Mars for Python" an authoritative and indispensable resource for anyone building scalable, distributed applications in Python.
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