{"id":476592,"date":"2023-08-09T07:31:20","date_gmt":"2023-08-09T07:31:20","guid":{"rendered":""},"modified":"2023-09-05T11:13:02","modified_gmt":"2023-09-05T11:13:02","slug":"dask","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/cn\/wiki\/dask\/","title":{"rendered":"\u8fbe\u65af\u514b"},"content":{"rendered":"<p>Dask \u662f\u4e00\u4e2a\u5f3a\u5927\u3001\u7075\u6d3b\u7684\u5f00\u6e90\u5e93\uff0c\u7528\u4e8e Python \u4e2d\u7684\u5e76\u884c\u8ba1\u7b97\u3002 Dask \u65e8\u5728\u4ece\u5355\u53f0\u8ba1\u7b97\u673a\u6269\u5c55\u5230\u670d\u52a1\u5668\u96c6\u7fa4\uff0c\u4e3a\u5206\u6790\u63d0\u4f9b\u9ad8\u7ea7\u5e76\u884c\u6027\uff0c\u5141\u8bb8\u7528\u6237\u8de8\u591a\u4e2a\u6838\u5fc3\u6267\u884c\u5927\u578b\u8ba1\u7b97\u3002 Dask \u662f\u5927\u6570\u636e\u5904\u7406\u7684\u70ed\u95e8\u9009\u62e9\uff0c\u4e3a\u9700\u8981 Python \u7684\u5e76\u884c\u8ba1\u7b97\u4efb\u52a1\u63d0\u4f9b\u4e86 Apache Spark \u7684\u66ff\u4ee3\u65b9\u6848\u3002<\/p>\n<h2>\u8fbe\u65af\u514b\u7684\u5386\u53f2<\/h2>\n<p>\u8be5\u9879\u76ee\u6700\u521d\u662f\u4e00\u9879\u5f00\u6e90\u8ba1\u5212\uff0c\u7531\u5176\u521b\u5efa\u8005 Matthew Rocklin \u4e8e 2014 \u5e74\u9996\u6b21\u5ba3\u5e03\u3002 Rocklin \u662f\u5f53\u65f6\u4e0e Anaconda Inc. \u5408\u4f5c\u7684\u5f00\u53d1\u4eba\u5458\uff0c\u81f4\u529b\u4e8e\u89e3\u51b3 Python \u5185\u5b58\u5904\u7406\u7684\u8ba1\u7b97\u9650\u5236\uff0c\u7279\u522b\u662f\u5728 NumPy \u548c Pandas \u7b49\u6d41\u884c\u5e93\u4e2d\u3002\u8fd9\u4e9b\u5de5\u5177\u5f88\u96be\u6709\u6548\u5730\u5904\u7406\u5927\u4e8e\u5185\u5b58\u7684\u6570\u636e\u96c6\uff0c\u8fd9\u662f Dask \u8bd5\u56fe\u514b\u670d\u7684\u9650\u5236\u3002<\/p>\n<h2>\u4e86\u89e3 Dask<\/h2>\n<p>Dask 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\u4f7f\u7528\u5206\u5e03\u5f0f\u8c03\u5ea6\u7a0b\u5e8f\u5e76\u884c\u6267\u884c\u4efb\u52a1\u56fe\u3002\u8be5\u8c03\u5ea6\u7a0b\u5e8f\u534f\u8c03\u4efb\u52a1\u7684\u6267\u884c\u5e76\u5904\u7406\u96c6\u7fa4\u4e2d\u5de5\u4f5c\u8282\u70b9\u4e4b\u95f4\u7684\u901a\u4fe1\u3002\u8c03\u5ea6\u7a0b\u5e8f\u548c\u5de5\u4f5c\u4eba\u5458\u901a\u8fc7\u4e2d\u592e\u201c\u5206\u5e03\u5f0f\u8c03\u5ea6\u7a0b\u5e8f\u201d\u8fdb\u884c\u901a\u4fe1\uff0c\u8be5\u8c03\u5ea6\u7a0b\u5e8f\u4f5c\u4e3a\u5355\u72ec\u7684 Python \u8fdb\u7a0b\u5b9e\u73b0\u3002<\/p>\n<p>\u5f53\u63d0\u4ea4\u8ba1\u7b97\u65f6\uff0cDask \u9996\u5148\u6784\u5efa\u4e00\u4e2a\u8868\u793a\u8ba1\u7b97\u7684\u4efb\u52a1\u56fe\u3002\u56fe\u4e2d\u7684\u6bcf\u4e2a\u8282\u70b9\u4ee3\u8868\u4e00\u4e2aPython\u51fd\u6570\uff0c\u800c\u6bcf\u6761\u8fb9\u4ee3\u8868\u5728\u51fd\u6570\u4e4b\u95f4\u4f20\u8f93\u7684\u6570\u636e\uff08\u901a\u5e38\u662fPython\u5bf9\u8c61\uff09\u3002<\/p>\n<p>\u7136\u540e\uff0cDask 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\u53ef\u4ee5\u5e76\u884c\u6267\u884c\u64cd\u4f5c\uff0c\u5229\u7528\u73b0\u4ee3\u591a\u6838\u5904\u7406\u5668\u548c\u5206\u5e03\u5f0f\u73af\u5883\u7684\u5f3a\u5927\u529f\u80fd\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u53ef\u6269\u5c55\u6027<\/strong>\uff1a\u5b83\u53ef\u4ee5\u4ece\u5355\u673a\u65e0\u7f1d\u6269\u5c55\u5230\u57fa\u4e8e\u96c6\u7fa4\u7684\u8ba1\u7b97\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u4e00\u4f53\u5316<\/strong>\uff1aDask \u4e0e Pandas\u3001NumPy \u548c Scikit-Learn \u7b49\u73b0\u6709 Python \u5e93\u5b8c\u7f8e\u96c6\u6210\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u7075\u6d3b\u6027<\/strong>\uff1a\u5b83\u53ef\u4ee5\u5904\u7406\u5e7f\u6cdb\u7684\u4efb\u52a1\uff0c\u4ece\u6570\u636e\u5206\u6790\u548c\u6570\u636e\u8f6c\u6362\u5230\u673a\u5668\u5b66\u4e60\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u5904\u7406\u5927\u4e8e\u5185\u5b58\u7684\u6570\u636e\u96c6<\/strong>\uff1a\u901a\u8fc7\u5c06\u6570\u636e\u5206\u89e3\u4e3a\u66f4\u5c0f\u7684\u5757\uff0cDask \u53ef\u4ee5\u5904\u7406\u65e0\u6cd5\u653e\u5165\u5185\u5b58\u7684\u6570\u636e\u96c6\u3002<\/p>\n<\/li>\n<\/ul>\n<h2>Dask \u7684\u7c7b\u578b<\/h2>\n<p>\u867d\u7136 Dask \u672c\u8d28\u4e0a\u662f\u4e00\u4e2a\u5355\u4e00\u7684\u5e93\uff0c\u4f46\u5b83\u63d0\u4f9b\u4e86\u591a\u79cd\u6570\u636e\u7ed3\u6784\u6216\u201c\u96c6\u5408\u201d\u6765\u6a21\u4eff\u548c\u6269\u5c55\u719f\u6089\u7684 Python \u6570\u636e\u7ed3\u6784\u3002\u8fd9\u4e9b\u5305\u62ec\uff1a<\/p>\n<ol>\n<li>\n<p><strong>\u8fbe\u65af\u514b\u9635\u5217<\/strong>\uff1a\u6a21\u4effNumPy\u7684ndarray\u63a5\u53e3\uff0c\u53ef\u4ee5\u652f\u6301\u5927\u90e8\u5206NumPy\u7684API\u3002\u5b83\u4e13\u4e3a\u65e0\u6cd5\u88c5\u5165\u5185\u5b58\u7684\u5927\u578b\u6570\u636e\u96c6\u800c\u8bbe\u8ba1\u3002<\/p>\n<\/li>\n<li>\n<p><strong>Dask\u6570\u636e\u6846<\/strong>\uff1a\u955c\u50cf Pandas DataFrame \u63a5\u53e3\u5e76\u652f\u6301 Pandas API \u7684\u5b50\u96c6\u3002\u5bf9\u4e8e\u5904\u7406\u5927\u4e8e\u5185\u5b58\u7684\u6570\u636e\u96c6\u975e\u5e38\u6709\u7528\uff0c\u5176\u63a5\u53e3\u4e0e Pandas \u7c7b\u4f3c\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u624b\u63d0\u5305<\/strong>: \u5b9e\u73b0\u7c7b\u4f3c\u7684\u64cd\u4f5c <code data-no-translation=\"\">map<\/code>, <code data-no-translation=\"\">filter<\/code>, <code data-no-translation=\"\">groupby<\/code> \u5728\u4e00\u822c\u7684 Python \u5bf9\u8c61\u4e0a\u3002\u5b83\u975e\u5e38\u9002\u5408\u5904\u7406\u534a\u7ed3\u6784\u5316\u6570\u636e\uff0c\u4f8b\u5982 JSON \u6216 XML\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u8fbe\u65af\u514bML<\/strong>\uff1a\u5b83\u63d0\u4f9b\u53ef\u6269\u5c55\u7684\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\uff0c\u53ef\u4ee5\u4e0e\u5176\u4ed6 Dask \u96c6\u5408\u5f88\u597d\u5730\u96c6\u6210\u3002<\/p>\n<\/li>\n<\/ol>\n<h2>Dask \u7684\u4f7f\u7528\u65b9\u6cd5<\/h2>\n<p>Dask \u7528\u9014\u5e7f\u6cdb\uff0c\u53ef\u7528\u4e8e\u5404\u79cd\u5e94\u7528\uff0c\u5305\u62ec\uff1a<\/p>\n<ul>\n<li>\n<p>\u6570\u636e\u8f6c\u6362\u548c\u9884\u5904\u7406\uff1aDask \u7684 DataFrame \u548c\u6570\u7ec4\u7ed3\u6784\u5141\u8bb8\u5e76\u884c\u9ad8\u6548\u5730\u8f6c\u6362\u5927\u578b\u6570\u636e\u96c6\u3002<\/p>\n<\/li>\n<li>\n<p>\u673a\u5668\u5b66\u4e60\uff1aDask-ML \u63d0\u4f9b\u4e86\u4e00\u5957\u53ef\u6269\u5c55\u7684\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\uff0c\u5728\u5904\u7406\u5927\u578b\u6570\u636e\u96c6\u65f6\u7279\u522b\u6709\u7528\u3002<\/p>\n<\/li>\n<li>\n<p>\u6a21\u62df\u548c\u590d\u6742\u8ba1\u7b97\uff1aDask \u5ef6\u8fdf\u63a5\u53e3\u53ef\u7528\u4e8e\u5e76\u884c\u6267\u884c\u4efb\u610f\u8ba1\u7b97\u3002<\/p>\n<\/li>\n<\/ul>\n<p>\u5c3d\u7ba1 Dask \u5177\u6709\u591a\u529f\u80fd\u6027\u548c\u5f3a\u5927\u529f\u80fd\uff0c\u4f46\u5b83\u4e5f\u5e26\u6765\u4e86\u6311\u6218\u3002\u4f8b\u5982\uff0c\u67d0\u4e9b\u7b97\u6cd5\u4e0d\u5bb9\u6613\u5e76\u884c\u5316\uff0c\u5e76\u4e14\u53ef\u80fd\u65e0\u6cd5\u4ece Dask \u7684\u5206\u5e03\u5f0f\u8ba1\u7b97\u529f\u80fd\u4e2d\u663e\u7740\u53d7\u76ca\u3002\u6b64\u5916\uff0c\u4e0e\u4efb\u4f55\u5206\u5e03\u5f0f\u8ba1\u7b97\u7cfb\u7edf\u4e00\u6837\uff0cDask \u8ba1\u7b97\u53ef\u80fd\u4f1a\u53d7\u5230\u7f51\u7edc\u5e26\u5bbd\u7684\u9650\u5236\uff0c\u7279\u522b\u662f\u5728\u96c6\u7fa4\u4e0a\u5de5\u4f5c\u65f6\u3002<\/p>\n<h2>\u4e0e\u7c7b\u4f3c\u5de5\u5177\u7684\u6bd4\u8f83<\/h2>\n<p>Dask \u7ecf\u5e38\u4e0e\u5176\u4ed6\u5206\u5e03\u5f0f\u8ba1\u7b97\u6846\u67b6\u8fdb\u884c\u6bd4\u8f83\uff0c\u7279\u522b\u662f Apache Spark\u3002\u8fd9\u662f\u4e00\u4e2a\u7b80\u77ed\u7684\u6bd4\u8f83\uff1a<\/p>\n<table>\n<thead>\n<tr>\n<th>\u7279\u5f81<\/th>\n<th>\u8fbe\u65af\u514b<\/th>\n<th>Apache Spark<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\u8bed\u8a00<\/td>\n<td>Python<\/td>\n<td>Scala\u3001Java\u3001Python\u3001R<\/td>\n<\/tr>\n<tr>\n<td>\u4f7f\u7528\u65b9\u4fbf<\/td>\n<td>\u9ad8\uff08\u7279\u522b\u662f\u5bf9\u4e8ePython\u7528\u6237\uff09<\/td>\n<td>\u7f13\u548c<\/td>\n<\/tr>\n<tr>\n<td>\u751f\u6001\u7cfb\u7edf<\/td>\n<td>\u4e0e Python \u6570\u636e\u5806\u6808\uff08Pandas\u3001NumPy\u3001Scikit-learn\uff09\u7684\u672c\u673a\u96c6\u6210<\/td>\n<td>\u5e7f\u6cdb\uff08Spark SQL\u3001MLLib\u3001GraphX\uff09<\/td>\n<\/tr>\n<tr>\n<td>\u53ef\u6269\u5c55\u6027<\/td>\n<td>\u597d\u7684<\/td>\n<td>\u51fa\u8272\u7684<\/td>\n<\/tr>\n<tr>\n<td>\u8868\u73b0<\/td>\n<td>\u5feb\u901f\uff0c\u9488\u5bf9\u590d\u6742\u8ba1\u7b97\u8fdb\u884c\u4e86\u4f18\u5316<\/td>\n<td>\u5feb\u901f\uff0c\u9488\u5bf9\u6570\u636e\u6d17\u724c\u64cd\u4f5c\u8fdb\u884c\u4e86\u4f18\u5316<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>\u4e0e Dask \u76f8\u5173\u7684\u672a\u6765\u524d\u666f\u548c\u6280\u672f<\/h2>\n<p>\u968f\u7740\u6570\u636e\u89c4\u6a21\u4e0d\u65ad\u589e\u957f\uff0cDask \u7b49\u5de5\u5177\u53d8\u5f97\u8d8a\u6765\u8d8a\u91cd\u8981\u3002 Dask \u6b63\u5728\u79ef\u6781\u5f00\u53d1\u4e2d\uff0c\u672a\u6765\u7684\u66f4\u65b0\u65e8\u5728\u63d0\u9ad8\u6027\u80fd\u3001\u7a33\u5b9a\u6027\u4ee5\u53ca\u4e0e PyData \u751f\u6001\u7cfb\u7edf\u4e2d\u5176\u4ed6\u5e93\u7684\u96c6\u6210\u3002<\/p>\n<p>\u5927\u6570\u636e\u673a\u5668\u5b66\u4e60\u662f Dask \u7684\u4e00\u4e2a\u6709\u524d\u9014\u7684\u9886\u57df\u3002 Dask \u80fd\u591f\u4e0e Scikit-Learn \u548c XGBoost \u7b49\u5e93\u65e0\u7f1d\u534f\u4f5c\uff0c\u4f7f\u5176\u6210\u4e3a\u5206\u5e03\u5f0f\u673a\u5668\u5b66\u4e60\u4efb\u52a1\u7684\u6709\u5438\u5f15\u529b\u7684\u5de5\u5177\u3002\u672a\u6765\u7684\u53d1\u5c55\u53ef\u80fd\u4f1a\u8fdb\u4e00\u6b65\u52a0\u5f3a\u8fd9\u4e9b\u80fd\u529b\u3002<\/p>\n<h2>\u4ee3\u7406\u670d\u52a1\u5668\u548c Dask<\/h2>\n<p>\u4ee3\u7406\u670d\u52a1\u5668\u53ef\u4ee5\u5728 Dask \u73af\u5883\u4e2d\u53d1\u6325\u4f5c\u7528\uff0c\u5728 Dask \u4e0e\u5916\u90e8\u8d44\u6e90\u4ea4\u4e92\u65f6\u63d0\u4f9b\u989d\u5916\u7684\u5b89\u5168\u548c\u63a7\u5236\u5c42\u3002\u4f8b\u5982\uff0c\u4ee3\u7406\u670d\u52a1\u5668\u53ef\u7528\u4e8e\u63a7\u5236\u548c\u76d1\u89c6 Dask \u5de5\u4f5c\u4eba\u5458\u4e0e\u4e92\u8054\u7f51\u4e0a\u7684\u6570\u636e\u6e90\u6216\u5b58\u50a8\u670d\u52a1\u4e4b\u95f4\u7684\u6d41\u91cf\u3002\u4f46\u662f\uff0c\u5fc5\u987b\u6ce8\u610f\u786e\u4fdd\u4ee3\u7406\u670d\u52a1\u5668\u4e0d\u4f1a\u6210\u4e3a\u9650\u5236Dask\u6027\u80fd\u7684\u74f6\u9888\u3002<\/p>\n<h2>\u76f8\u5173\u94fe\u63a5<\/h2>\n<ol>\n<li><a href=\"https:\/\/dask.org\/\" target=\"_new\" rel=\"noopener nofollow\">\u8fbe\u65af\u514b\u6587\u6863<\/a>\uff1a\u6db5\u76d6 Dask \u5404\u4e2a\u65b9\u9762\u7684\u5168\u9762\u5b98\u65b9\u6587\u6863\u3002<\/li>\n<li><a href=\"https:\/\/github.com\/dask\/dask\" target=\"_new\" rel=\"noopener nofollow\">Dask GitHub \u5b58\u50a8\u5e93<\/a>\uff1aDask \u7684\u6e90\u4ee3\u7801\uff0c\u4ee5\u53ca\u793a\u4f8b\u548c\u95ee\u9898\u8ddf\u8e2a\u3002<\/li>\n<li><a href=\"https:\/\/tutorial.dask.org\/\" target=\"_new\" rel=\"noopener nofollow\">\u8fbe\u65af\u514b\u6559\u7a0b<\/a>\uff1a\u4e3a\u65b0\u7528\u6237\u63d0\u4f9b Dask \u5165\u95e8\u7684\u8be6\u7ec6\u6559\u7a0b\u3002<\/li>\n<li><a href=\"https:\/\/blog.dask.org\/\" target=\"_new\" rel=\"noopener nofollow\">\u8fbe\u65af\u514b\u535a\u5ba2<\/a>\uff1a\u5b98\u65b9\u535a\u5ba2\uff0c\u5305\u542b\u4e0e Dask \u76f8\u5173\u7684\u66f4\u65b0\u548c\u7528\u4f8b\u3002<\/li>\n<li><a href=\"https:\/\/stories.dask.org\/en\/latest\/\" target=\"_new\" rel=\"noopener nofollow\">Dask \u7528\u4f8b<\/a>\uff1a\u5982\u4f55\u4f7f\u7528 Dask \u7684\u771f\u5b9e\u793a\u4f8b\u3002<\/li>\n<li><a href=\"https:\/\/docs.dask.org\/en\/latest\/api.html\" target=\"_new\" rel=\"noopener nofollow\">\u8fbe\u65af\u514bAPI<\/a>\uff1a\u6709\u5173 Dask API \u7684\u8be6\u7ec6\u4fe1\u606f\u3002<\/li>\n<\/ol>","protected":false},"featured_media":468085,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-476592","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Dask: An Overview<\/mark>","faq_items":[{"question":"What is Dask?","answer":"<p>Dask is an open-source library for parallel computing in Python. It is designed to scale from a single computer to a cluster of servers, allowing large computations to be performed across many cores. Dask is particularly useful for big data processing tasks.<\/p>"},{"question":"When was Dask first introduced and by whom?","answer":"<p>Dask was first announced in 2014 by Matthew Rocklin, a developer associated with Anaconda Inc. He created Dask to overcome the computational limitations of in-memory processing in Python, specifically for large datasets.<\/p>"},{"question":"How does Dask work?","answer":"<p>Dask works by breaking down computations into smaller tasks, executing these tasks in a parallel manner, and effectively managing memory resources. It creates a task scheduling graph, a directed acyclic graph (DAG), that describes the sequence of computations to be performed. The Dask distributed scheduler then assigns and executes these tasks across worker nodes in a cluster.<\/p>"},{"question":"What are the key features of Dask?","answer":"<p>The key features of Dask include its ability to perform parallel operations, scale seamlessly, integrate with existing Python libraries, handle a wide range of tasks, and manage datasets larger than memory by breaking them into smaller chunks.<\/p>"},{"question":"What types of Dask exist?","answer":"<p>Dask provides several data structures or 'collections' that mimic and extend familiar Python data structures, including Dask Array, Dask DataFrame, Dask Bag, and Dask ML.<\/p>"},{"question":"How can Dask be used and what challenges can arise?","answer":"<p>Dask can be used for various applications including data transformation, machine learning, and complex computations. Despite its versatility, Dask can present challenges. Some algorithms are not easily parallelizable and network bandwidth can limit Dask computations when working on a cluster.<\/p>"},{"question":"How does Dask compare to similar tools like Apache Spark?","answer":"<p>While both Dask and Apache Spark are distributed computing frameworks, Dask is built around Python and natively integrates with Python data stack. It is often considered easier to use for Python developers. Apache Spark, on the other hand, is built around Scala and Java, and while it supports Python, it is often considered more extensive in its ecosystem.<\/p>"},{"question":"What are the future perspectives and technologies related to Dask?","answer":"<p>As data sizes continue to grow, tools like Dask become increasingly important. Future developments aim to improve Dask's performance, stability, and integration with other libraries. Machine learning with big data is a promising area for Dask.<\/p>"},{"question":"How are proxy servers associated with Dask?","answer":"<p>Proxy servers can provide an additional layer of security and control when Dask interacts with external resources. A proxy server can control and monitor the traffic between Dask workers and data sources or storage services on the internet. However, it must be ensured that the proxy server does not limit Dask's performance.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki\/476592","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\/476592\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/media\/468085"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/media?parent=476592"}],"curies":[{"name":"\u53ef\u6e7f\u6027\u7c89\u5242","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}