Pyspark 简明教程
PySpark Tutorial
What is PySpark?
Apache Spark 是用 Scala 编写的功能强大的开源数据处理引擎，专用于大规模数据处理。为了在 Spark 中支持 Python，Apache Spark 社区发布了一个工具 PySpark。通过使用 PySpark，您还可以使用 Python 编程语言处理 RDD。这是因为他们能够通过名为 Py4j 的库实现这项工作。这是一份入门教程，它介绍了数据驱动型文档的基础，并说明如何处理其各个组件和子组件。
Apache Spark is a powerful open-source data processing engine written in Scala, designed for large-scale data processing. To support Python with Spark, Apache Spark community released a tool, PySpark. Using PySpark, you can work with RDDs in Python programming language also. It is because of a library called Py4j that they are able to achieve this. This is an introductory tutorial, which covers the basics of Data-Driven Documents and explains how to deal with its various components and sub-components.
PySpark 是 Apache Spark 的 Python API。它允许您使用 Python 与 Spark 的分布式计算框架进行交互，从而更容易使用许多数据科学家和工程师熟悉的语言处理大数据。通过使用 PySpark，您可以创建和管理 Spark 作业，并执行复杂的数据转换和分析。
PySpark is the Python API for Apache Spark. It allows you to interface with Spark’s distributed computation framework using Python, making it easier to work with big data in a language many data scientists and engineers are familiar with. By using PySpark, you can create and manage Spark jobs, and perform complex data transformations and analyses.
Key Components of PySpark
以下是 PySpark 的关键组件：
Following are the key components of PySpark −
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RDDs (Resilient Distributed Datasets) − RDDs are the fundamental data structure in Spark. They are immutable distributed collections of objects that can be processed in parallel.
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DataFrames − DataFrames are similar to RDDs but with additional features like named columns, and support for a wide range of data sources. They are analogous to tables in a relational database and provide a higher-level abstraction for data manipulation.
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Spark SQL − This module allows you to execute SQL queries on DataFrames and RDDs. It provides a programming abstraction called DataFrame and can also act as a distributed SQL query engine.
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MLlib (Machine Learning Library) − MLlib is Spark’s scalable machine learning library, offering various algorithms and utilities for classification, regression, clustering, collaborative filtering, and more.
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Spark Streaming − Spark Streaming enables real-time data processing and stream processing. It allows you to process live data streams and update results in real-time.
Purpose of PySpark
PySpark 的主要目的是使用 Python 在分布式计算环境中实时处理大规模数据集。PySpark 提供了一个用于使用 Python 编程语言与 Spark 的核心功能交互的界面，例如使用弹性分布式数据集 (RDD) 和数据框。
The primary purpose of PySpark is to enable processing of large-scale datasets in real-time across a distributed computing environment using Python. PySpark provides an interface for interacting with Spark’s core functionalities, such as working with Resilient Distributed Datasets (RDDs) and DataFrames, using the Python programming language.
Features of PySpark
PySpark 具有以下特点：
PySpark has the following features −
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Integration with Spark − PySpark is tightly integrated with Apache Spark, allowing seamless data processing and analysis using Python Programming.
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Real-time Processing − It enables real-time processing of large-scale datasets.
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Ease of Use − PySpark simplifies complex data processing tasks using Python’s simple syntax and extensive libraries.
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Interactive Shell − PySpark offers an interactive shell for real-time data analysis and experimentation.
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Machine Learning − It includes MLlib, a scalable machine learning library.
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Data Sources − PySpark can read data from various sources, including HDFS, S3, HBase, and more.
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Partitioning − Efficiently partitions data to enhance processing speed and efficiency.
Applications of PySpark
PySpark 广泛应用于各种应用程序，包括 −
PySpark is widely used in various applications, including −
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Data Analysis − Analyzing large datasets to extract meaningful information.
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Machine Learning − Implementing machine learning algorithms for predictive analytics.
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Data Streaming − Processing streaming data in real-time.
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Data Engineering − Managing and transforming big data for various use cases.
Why to learn PySpark?
学习 PySpark 对任何对大数据和数据工程感兴趣的人来说都至关重要。它提供了多种好处 −
Learning PySpark is essential for anyone interested in big data and data engineering. It offers various benefits −
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Scalability − Efficiently handles large datasets across distributed systems.
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Performance − High-speed data processing and real-time analytics.
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Flexibility − PySpark supports integration with various data sources and tools.
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Comprehensive Toolset − Includes tools for data manipulation, machine learning, and graph processing.
Prerequisites to learn PySpark
在学习本教程中给出的各种概念之前，我们假定读者已经了解编程语言和框架。此外，如果读者具备 Apache Spark、Apache Hadoop、Scala 编程语言、Hadoop 分布式文件系统 (HDFS) 和 Python 方面的扎实知识，将非常有帮助。
Before proceeding with the various concepts given in this tutorial, it is being assumed that the readers are already aware about what a programming language and a framework is. In addition to this, it will be very helpful, if the readers have a sound knowledge of Apache Spark, Apache Hadoop, Scala Programming Language, Hadoop Distributed File System (HDFS) and Python.