collecting pyspark retrying (retry(total=4, connect=none, read=none, redirec

时间: 2023-09-07 11:04:00 浏览: 119

Learning pyspark

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About This Book, Learn why and how you can efficiently use Python to process data and build machine learning models in Apache Spark 2.0Develop and deploy efficient, scalable real-time Spark solutionsTake your understanding of using Spark with Python to the next level with this jump start guide, Who This Book Is For, If you are a Python developer who wants to learn about the Apache Spark 2.0 ecosystem, this book is for you. A firm understanding of Python is expected to get the best out of the book. Familiarity with Spark would be useful, but is not mandatory., What You Will Learn, Learn about Apache Spark and the Spark 2.0 architectureBuild and interact with Spark DataFrames using Spark SQLLearn how to solve graph and deep learning problems using GraphFrames and TensorFrames respectivelyRead, transform, and understand data and use it to train machine learning modelsBuild machine learning models with MLlib and MLLearn how to submit your applications programmatically using spark-submitDeploy locally built applications to a cluster, In Detail, Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. This book will show you how to leverage the power of Python and put it to use in the Spark ecosystem. You will start by getting a firm understanding of the Spark 2.0 architecture and how to set up a Python environment for Spark., You will get familiar with the modules available in PySpark. You will learn how to abstract data with RDDs and DataFrames and understand the streaming capabilities of PySpark. Also, you will get a thorough overview of machine learning capabilities of PySpark using ML and MLlib, graph processing using GraphFrames, and polyglot persistence using Blaze. Finally, you will learn how to deploy your applications to the cloud using the spark-submit command., By the end of this book, you will have established a firm understanding of the Spark Python API and how it can be used to build data-intensive applications., Style and approach, This book takes a very comprehensive, step-by-step approach so you understand how the Spark ecosystem can be used with Python to develop efficient, scalable solutions. Every chapter is standalone and written in a very easy-to-understand manner, with a focus on both the hows and the whys of each concept.

"Collecting PySpark" 是指在 PySpark 中收集数据的过程。而 "retrying (retry(total=4, connect=none, read=none, redirect=none)" 则是指在执行此过程时发生了错误，系统正在尝试重新执行的次数和阶段。在 PySpark 中，"collect" 是一个动作操作，用于将分布式数据集（也称为 RDD）的所有元素返回到驱动程序。然而，由于网络延迟、资源不足或其他可能导致错误的原因，可能会导致 collect 操作失败。当失败发生时，PySpark 会尝试多次重新执行该操作，以提高成功的机会。在这个错误信息中，有几个参数被提及。"retry(total=4)" 表示总共尝试的次数是 4 次。"connect=none" 表示此次重试是因为连接错误而发生的，"read=none" 表示此次重试是由于读取错误而发生的，"redirect=none" 表示此次重试是由于重定向错误而发生的。这个错误消息的目的是向用户提供有关收集操作的执行情况的信息，并告知系统正在尝试重新执行收集操作的次数和重试的阶段。用户可以根据此信息进行故障排查，例如查看网络连接、资源配额等是否正常，并可以根据需要调整重试次数和阶段。总而言之，这段错误消息提供了有关 PySpark 中收集数据过程中出现问题的重试信息，以帮助用户理解和解决相关问题。

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collecting pyspark retrying (retry(total=4, connect=none, read=none, redirec

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