{"id":478586,"date":"2023-08-09T09:35:14","date_gmt":"2023-08-09T09:35:14","guid":{"rendered":""},"modified":"2023-09-05T11:17:08","modified_gmt":"2023-09-05T11:17:08","slug":"pyspark","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/cn\/wiki\/pyspark\/","title":{"rendered":"pySpark"},"content":{"rendered":"<p>PySpark \u662f\u201cPython\u201d\u548c\u201cSpark\u201d\u7684\u7ec4\u5408\uff0c\u662f\u4e00\u4e2a\u5f00\u6e90 Python \u5e93\uff0c\u4e3a Apache Spark \u63d0\u4f9b Python API\uff0cApache Spark \u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u96c6\u7fa4\u8ba1\u7b97\u6846\u67b6\uff0c\u65e8\u5728\u4ee5\u5206\u5e03\u5f0f\u65b9\u5f0f\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u96c6\u3002 PySpark \u5c06 Python \u7f16\u7a0b\u7684\u7b80\u4fbf\u6027\u4e0e Spark \u7684\u9ad8\u6027\u80fd\u529f\u80fd\u65e0\u7f1d\u96c6\u6210\uff0c\u4f7f\u5176\u6210\u4e3a\u5904\u7406\u5927\u6570\u636e\u7684\u6570\u636e\u5de5\u7a0b\u5e08\u548c\u79d1\u5b66\u5bb6\u7684\u70ed\u95e8\u9009\u62e9\u3002<\/p>\n<h2>PySpark \u7684\u8d77\u6e90\u5386\u53f2<\/h2>\n<p>PySpark \u8d77\u6e90\u4e8e 2009 \u5e74\u52a0\u5dde\u5927\u5b66\u4f2f\u514b\u5229\u5206\u6821 AMPLab \u7684\u4e00\u4e2a\u9879\u76ee\uff0c\u76ee\u6807\u662f\u89e3\u51b3\u73b0\u6709\u6570\u636e\u5904\u7406\u5de5\u5177\u5728\u9ad8\u6548\u5904\u7406\u6d77\u91cf\u6570\u636e\u96c6\u65b9\u9762\u7684\u5c40\u9650\u6027\u3002 PySpark \u7b2c\u4e00\u6b21\u88ab\u63d0\u53ca\u662f\u5728 2012 \u5e74\u5de6\u53f3\uff0c\u5f53\u65f6 Spark \u9879\u76ee\u5728\u5927\u6570\u636e\u793e\u533a\u4e2d\u83b7\u5f97\u4e86\u5173\u6ce8\u3002\u7531\u4e8e\u5b83\u80fd\u591f\u63d0\u4f9b Spark \u5206\u5e03\u5f0f\u5904\u7406\u7684\u5f3a\u5927\u529f\u80fd\uff0c\u540c\u65f6\u5229\u7528 Python \u7684\u7b80\u5355\u6027\u548c\u6613\u7528\u6027\uff0c\u5b83\u5f88\u5feb\u5c31\u53d7\u5230\u4e86\u6b22\u8fce\u3002<\/p>\n<h2>\u6709\u5173 PySpark \u7684\u8be6\u7ec6\u4fe1\u606f<\/h2>\n<p>PySpark \u901a\u8fc7\u4f7f\u5f00\u53d1\u4eba\u5458\u80fd\u591f\u4e0e Spark \u7684\u5e76\u884c\u5904\u7406\u548c\u5206\u5e03\u5f0f\u8ba1\u7b97\u529f\u80fd\u8fdb\u884c\u4ea4\u4e92\uff0c\u6269\u5c55\u4e86 Python \u7684\u529f\u80fd\u3002\u8fd9\u5141\u8bb8\u7528\u6237\u65e0\u7f1d\u5730\u5206\u6790\u3001\u8f6c\u6362\u548c\u64cd\u4f5c\u5927\u578b\u6570\u636e\u96c6\u3002 PySpark \u63d0\u4f9b\u4e86\u4e00\u5957\u5168\u9762\u7684\u5e93\u548c API\uff0c\u4e3a\u6570\u636e\u64cd\u4f5c\u3001\u673a\u5668\u5b66\u4e60\u3001\u56fe\u5f62\u5904\u7406\u3001\u6d41\u5a92\u4f53\u7b49\u63d0\u4f9b\u4e86\u5de5\u5177\u3002<\/p>\n<h2>PySpark\u7684\u5185\u90e8\u7ed3\u6784<\/h2>\n<p>PySpark \u57fa\u4e8e\u5f39\u6027\u5206\u5e03\u5f0f\u6570\u636e\u96c6 (RDD) \u7684\u6982\u5ff5\u8fd0\u884c\uff0cRDD \u662f\u53ef\u5e76\u884c\u5904\u7406\u7684\u5bb9\u9519\u3001\u5206\u5e03\u5f0f\u6570\u636e\u96c6\u5408\u3002 RDD \u5141\u8bb8\u5c06\u6570\u636e\u8de8\u96c6\u7fa4\u4e2d\u7684\u591a\u4e2a\u8282\u70b9\u8fdb\u884c\u5206\u533a\uff0c\u5373\u4f7f\u5728\u5927\u91cf\u6570\u636e\u96c6\u4e0a\u4e5f\u80fd\u5b9e\u73b0\u9ad8\u6548\u5904\u7406\u3002\u5728\u5e95\u5c42\uff0cPySpark \u4f7f\u7528 Spark Core\uff0c\u5b83\u5904\u7406\u4efb\u52a1\u8c03\u5ea6\u3001\u5185\u5b58\u7ba1\u7406\u548c\u6545\u969c\u6062\u590d\u3002\u901a\u8fc7Py4J\u5b9e\u73b0\u4e0ePython\u7684\u96c6\u6210\uff0c\u5b9e\u73b0Python\u4e0e\u57fa\u4e8eJava\u7684Spark Core\u4e4b\u95f4\u7684\u65e0\u7f1d\u901a\u4fe1\u3002<\/p>\n<h2>PySpark\u5173\u952e\u7279\u6027\u5206\u6790<\/h2>\n<p>PySpark \u63d0\u4f9b\u4e86\u51e0\u4e2a\u6709\u52a9\u4e8e\u5176\u53d7\u6b22\u8fce\u7684\u5173\u952e\u529f\u80fd\uff1a<\/p>\n<ol>\n<li>\n<p><strong>\u4f7f\u7528\u65b9\u4fbf<\/strong>\uff1aPython \u7b80\u5355\u7684\u8bed\u6cd5\u548c\u52a8\u6001\u7c7b\u578b\u4f7f\u6570\u636e\u79d1\u5b66\u5bb6\u548c\u5de5\u7a0b\u5e08\u53ef\u4ee5\u8f7b\u677e\u4f7f\u7528 PySpark\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u5927\u6570\u636e\u5904\u7406<\/strong>\uff1aPySpark \u5229\u7528 Spark \u7684\u5206\u5e03\u5f0f\u8ba1\u7b97\u80fd\u529b\u6765\u5904\u7406\u6d77\u91cf\u6570\u636e\u96c6\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u4e30\u5bcc\u7684\u751f\u6001\u7cfb\u7edf<\/strong>\uff1aPySpark \u63d0\u4f9b\u7528\u4e8e\u673a\u5668\u5b66\u4e60 (MLlib)\u3001\u56fe\u5f62\u5904\u7406 (GraphX)\u3001SQL \u67e5\u8be2 (Spark SQL) \u548c\u5b9e\u65f6\u6570\u636e\u6d41 (Structured Streaming) \u7684\u5e93\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u517c\u5bb9\u6027<\/strong>\uff1aPySpark\u53ef\u4ee5\u4e0eNumPy\u3001pandas\u3001scikit-learn\u7b49\u5176\u4ed6\u6d41\u884c\u7684Python\u5e93\u96c6\u6210\uff0c\u589e\u5f3a\u5176\u6570\u636e\u5904\u7406\u80fd\u529b\u3002<\/p>\n<\/li>\n<\/ol>\n<h2>PySpark \u7684\u7c7b\u578b<\/h2>\n<p>PySpark \u63d0\u4f9b\u5404\u79cd\u7ec4\u4ef6\u6765\u6ee1\u8db3\u4e0d\u540c\u7684\u6570\u636e\u5904\u7406\u9700\u6c42\uff1a<\/p>\n<ul>\n<li>\n<p><strong>\u661f\u706bSQL<\/strong>\uff1a\u652f\u6301\u5bf9\u7ed3\u6784\u5316\u6570\u636e\u8fdb\u884cSQL\u67e5\u8be2\uff0c\u4e0ePython\u7684DataFrame API\u65e0\u7f1d\u96c6\u6210\u3002<\/p>\n<\/li>\n<li>\n<p><strong>MLlib<\/strong>\uff1a\u7528\u4e8e\u6784\u5efa\u53ef\u6269\u5c55\u7684\u673a\u5668\u5b66\u4e60\u7ba1\u9053\u548c\u6a21\u578b\u7684\u673a\u5668\u5b66\u4e60\u5e93\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u56feX<\/strong>\uff1a\u63d0\u4f9b\u56fe\u5f62\u5904\u7406\u529f\u80fd\uff0c\u5bf9\u4e8e\u5206\u6790\u5927\u578b\u6570\u636e\u96c6\u4e2d\u7684\u5173\u7cfb\u81f3\u5173\u91cd\u8981\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u6d41\u5a92\u4f53<\/strong>\uff1a\u901a\u8fc7\u7ed3\u6784\u5316\u6d41\uff0cPySpark \u53ef\u4ee5\u9ad8\u6548\u5730\u5904\u7406\u5b9e\u65f6\u6570\u636e\u6d41\u3002<\/p>\n<\/li>\n<\/ul>\n<h2>PySpark \u7684\u4f7f\u7528\u65b9\u6cd5\u3001\u95ee\u9898\u548c\u89e3\u51b3\u65b9\u6848<\/h2>\n<p>PySpark \u5e7f\u6cdb\u5e94\u7528\u4e8e\u5404\u4e2a\u884c\u4e1a\uff0c\u5305\u62ec\u91d1\u878d\u3001\u533b\u7597\u4fdd\u5065\u3001\u7535\u5b50\u5546\u52a1\u7b49\u3002\u7136\u800c\uff0c\u4f7f\u7528 PySpark \u53ef\u80fd\u4f1a\u5e26\u6765\u4e0e\u96c6\u7fa4\u8bbe\u7f6e\u3001\u5185\u5b58\u7ba1\u7406\u548c\u8c03\u8bd5\u5206\u5e03\u5f0f\u4ee3\u7801\u76f8\u5173\u7684\u6311\u6218\u3002\u8fd9\u4e9b\u6311\u6218\u53ef\u4ee5\u901a\u8fc7\u5168\u9762\u7684\u6587\u6863\u3001\u5728\u7ebf\u793e\u533a\u4ee5\u53ca Spark \u751f\u6001\u7cfb\u7edf\u7684\u5f3a\u5927\u652f\u6301\u6765\u89e3\u51b3\u3002<\/p>\n<h2>\u4e3b\u8981\u7279\u70b9\u53ca\u6bd4\u8f83<\/h2>\n<table>\n<thead>\n<tr>\n<th>\u7279\u5f81<\/th>\n<th>pySpark<\/th>\n<th>\u7c7b\u4f3c\u6761\u6b3e<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\u8bed\u8a00<\/td>\n<td>Python<\/td>\n<td>Hadoop MapReduce<\/td>\n<\/tr>\n<tr>\n<td>\u5904\u7406\u8303\u5f0f<\/td>\n<td>\u5206\u5e03\u5f0f\u8ba1\u7b97<\/td>\n<td>\u5206\u5e03\u5f0f\u8ba1\u7b97<\/td>\n<\/tr>\n<tr>\n<td>\u4f7f\u7528\u65b9\u4fbf<\/td>\n<td>\u9ad8\u7684<\/td>\n<td>\u7f13\u548c<\/td>\n<\/tr>\n<tr>\n<td>\u751f\u6001\u7cfb\u7edf<\/td>\n<td>\u4e30\u5bcc\uff08ML\u3001SQL\u3001\u56fe\u5f62\uff09<\/td>\n<td>\u6709\u9650\u7684<\/td>\n<\/tr>\n<tr>\n<td>\u5b9e\u65f6\u5904\u7406<\/td>\n<td>\u662f\uff08\u7ed3\u6784\u5316\u6d41\uff09<\/td>\n<td>\u662f\uff08\u963f\u5e15\u5947\u5f17\u6797\u514b\uff09<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>\u524d\u666f\u548c\u672a\u6765\u6280\u672f<\/h2>\n<p>PySpark \u7684\u672a\u6765\u770b\u8d77\u6765\u5145\u6ee1\u5e0c\u671b\uff0c\u56e0\u4e3a\u5b83\u968f\u7740\u5927\u6570\u636e\u9886\u57df\u7684\u8fdb\u6b65\u800c\u4e0d\u65ad\u53d1\u5c55\u3002\u4e00\u4e9b\u65b0\u5174\u8d8b\u52bf\u548c\u6280\u672f\u5305\u62ec\uff1a<\/p>\n<ul>\n<li>\n<p><strong>\u589e\u5f3a\u6027\u80fd<\/strong>\uff1a\u6301\u7eed\u4f18\u5316 Spark \u7684\u6267\u884c\u5f15\u64ce\uff0c\u4ee5\u5728\u73b0\u4ee3\u786c\u4ef6\u4e0a\u83b7\u5f97\u66f4\u597d\u7684\u6027\u80fd\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u6df1\u5ea6\u5b66\u4e60\u96c6\u6210<\/strong>\uff1a\u6539\u8fdb\u4e86\u4e0e\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u7684\u96c6\u6210\uff0c\u4ee5\u5b9e\u73b0\u66f4\u5f3a\u5927\u7684\u673a\u5668\u5b66\u4e60\u7ba1\u9053\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u65e0\u670d\u52a1\u5668 Spark<\/strong>\uff1a\u5f00\u53d1Spark\u7684Serverless\u6846\u67b6\uff0c\u964d\u4f4e\u96c6\u7fa4\u7ba1\u7406\u7684\u590d\u6742\u5ea6\u3002<\/p>\n<\/li>\n<\/ul>\n<h2>\u4ee3\u7406\u670d\u52a1\u5668\u548c PySpark<\/h2>\n<p>\u5728\u5404\u79cd\u573a\u666f\u4e2d\u4f7f\u7528 PySpark \u65f6\uff0c\u4ee3\u7406\u670d\u52a1\u5668\u53ef\u4ee5\u53d1\u6325\u81f3\u5173\u91cd\u8981\u7684\u4f5c\u7528\uff1a<\/p>\n<ul>\n<li>\n<p><strong>\u6570\u636e\u9690\u79c1<\/strong>\uff1a\u4ee3\u7406\u670d\u52a1\u5668\u53ef\u4ee5\u5e2e\u52a9\u533f\u540d\u6570\u636e\u4f20\u8f93\uff0c\u786e\u4fdd\u5904\u7406\u654f\u611f\u4fe1\u606f\u65f6\u7684\u9690\u79c1\u5408\u89c4\u6027\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u8d1f\u8f7d\u5747\u8861<\/strong>\uff1a\u4ee3\u7406\u670d\u52a1\u5668\u53ef\u4ee5\u8de8\u96c6\u7fa4\u5206\u53d1\u8bf7\u6c42\uff0c\u4f18\u5316\u8d44\u6e90\u5229\u7528\u7387\u548c\u6027\u80fd\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u9632\u706b\u5899\u7ed5\u8fc7<\/strong>\uff1a\u5728\u53d7\u9650\u7f51\u7edc\u73af\u5883\u4e2d\uff0c\u4ee3\u7406\u670d\u52a1\u5668\u53ef\u4ee5\u4f7fPySpark\u8bbf\u95ee\u5916\u90e8\u8d44\u6e90\u3002<\/p>\n<\/li>\n<\/ul>\n<h2>\u76f8\u5173\u94fe\u63a5<\/h2>\n<p>\u6709\u5173 PySpark \u53ca\u5176\u5e94\u7528\u7a0b\u5e8f\u7684\u66f4\u591a\u4fe1\u606f\uff0c\u60a8\u53ef\u4ee5\u6d4f\u89c8\u4ee5\u4e0b\u8d44\u6e90\uff1a<\/p>\n<ul>\n<li><a href=\"https:\/\/spark.apache.org\/\" target=\"_new\" rel=\"noopener nofollow\">Apache Spark \u5b98\u65b9\u7f51\u7ad9<\/a><\/li>\n<li><a href=\"https:\/\/spark.apache.org\/docs\/latest\/api\/python\/index.html\" target=\"_new\" rel=\"noopener nofollow\">PySpark \u6587\u6863<\/a><\/li>\n<li><a href=\"https:\/\/github.com\/apache\/spark\/tree\/master\/python\" target=\"_new\" rel=\"noopener nofollow\">PySpark GitHub \u5b58\u50a8\u5e93<\/a><\/li>\n<li><a href=\"https:\/\/community.cloud.databricks.com\/\" target=\"_new\" rel=\"noopener nofollow\">Databricks \u793e\u533a\u7248<\/a> \uff08\u7528\u4e8e\u5b66\u4e60\u548c\u5b9e\u9a8c Spark \u548c PySpark \u7684\u57fa\u4e8e\u4e91\u7684\u5e73\u53f0\uff09<\/li>\n<\/ul>","protected":false},"featured_media":469278,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-478586","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>PySpark: Empowering Big Data Processing with Simplicity and Efficiency<\/mark>","faq_items":[{"question":"What is PySpark and how does it relate to Apache Spark?","answer":"<p>PySpark is an open-source Python library that provides a Python API for Apache Spark, a powerful cluster-computing framework designed for processing large-scale data sets in a distributed manner. It allows Python developers to harness the capabilities of Spark's distributed computing while utilizing Python's simplicity and ease of use.<\/p>"},{"question":"How did PySpark originate and when was it first mentioned?","answer":"<p>PySpark originated as a project at the University of California, Berkeley's AMPLab in 2009. The first mention of PySpark emerged around 2012 as the Spark project gained traction within the big data community. It quickly gained popularity due to its ability to provide distributed processing power while leveraging Python's programming simplicity.<\/p>"},{"question":"What are the key features of PySpark?","answer":"<p>PySpark offers several key features, including:<\/p><ul><li><strong>Ease of Use<\/strong>: Python's simplicity and dynamic typing make it easy for data scientists and engineers to work with PySpark.<\/li><li><strong>Big Data Processing<\/strong>: PySpark allows processing of massive datasets by leveraging Spark's distributed computing capabilities.<\/li><li><strong>Rich Ecosystem<\/strong>: PySpark provides libraries for machine learning (MLlib), graph processing (GraphX), SQL querying (Spark SQL), and real-time data streaming (Structured Streaming).<\/li><li><strong>Compatibility<\/strong>: PySpark can integrate with other popular Python libraries like NumPy, pandas, and scikit-learn.<\/li><\/ul>"},{"question":"How does PySpark work internally?","answer":"<p>PySpark operates on the concept of Resilient Distributed Datasets (RDDs), which are fault-tolerant, distributed collections of data that can be processed in parallel. PySpark uses the Spark Core, which handles task scheduling, memory management, and fault recovery. The integration with Python is achieved through Py4J, allowing seamless communication between Python and the Java-based Spark Core.<\/p>"},{"question":"What are the different components of PySpark?","answer":"<p>PySpark offers various components, including:<\/p><ul><li><strong>Spark SQL<\/strong>: Allows SQL queries on structured data, integrating seamlessly with Python's DataFrame API.<\/li><li><strong>MLlib<\/strong>: A machine learning library for building scalable machine learning pipelines and models.<\/li><li><strong>GraphX<\/strong>: Provides graph processing capabilities essential for analyzing relationships in large datasets.<\/li><li><strong>Streaming<\/strong>: With Structured Streaming, PySpark can process real-time data streams efficiently.<\/li><\/ul>"},{"question":"What are the applications and challenges of using PySpark?","answer":"<p>PySpark finds applications in finance, healthcare, e-commerce, and more. Challenges when using PySpark can include cluster setup, memory management, and debugging distributed code. These challenges can be addressed through comprehensive documentation, online communities, and robust support from the Spark ecosystem.<\/p>"},{"question":"How does PySpark compare to other distributed computing frameworks?","answer":"<p>PySpark offers a simplified programming experience compared to Hadoop MapReduce. It also boasts a richer ecosystem with components like MLlib, Spark SQL, and GraphX, which some other frameworks lack. PySpark's real-time processing capabilities through Structured Streaming make it comparable to frameworks like Apache Flink.<\/p>"},{"question":"How does the future look for PySpark?","answer":"<p>The future of PySpark is promising, with advancements like enhanced performance optimizations, deeper integration with deep learning frameworks, and the development of serverless Spark frameworks. These trends will further solidify PySpark's role in the evolving big data landscape.<\/p>"},{"question":"How are proxy servers used with PySpark?","answer":"<p>Proxy servers can serve multiple purposes with PySpark, including data privacy, load balancing, and firewall bypassing. They can help anonymize data transfers, optimize resource utilization, and enable PySpark to access external resources in restricted network environments.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki\/478586","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki\/478586\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/media\/469278"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/media?parent=478586"}],"curies":[{"name":"\u53ef\u6e7f\u6027\u7c89\u5242","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}