{"id":475880,"date":"2023-08-09T07:24:43","date_gmt":"2023-08-09T07:24:43","guid":{"rendered":""},"modified":"2023-09-05T11:11:30","modified_gmt":"2023-09-05T11:11:30","slug":"apache-spark","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/cn\/wiki\/apache-spark\/","title":{"rendered":"Apache Spark"},"content":{"rendered":"<p>Apache Spark \u662f\u4e00\u4e2a\u5f00\u6e90\u5206\u5e03\u5f0f\u8ba1\u7b97\u7cfb\u7edf\uff0c\u4e13\u4e3a\u5927\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u800c\u8bbe\u8ba1\u3002\u5b83\u6700\u521d\u4e8e 2009 \u5e74\u5728\u52a0\u5dde\u5927\u5b66\u4f2f\u514b\u5229\u5206\u6821\u7684 AMPLab \u5f00\u53d1\uff0c\u540e\u6765\u6350\u8d60\u7ed9 Apache \u8f6f\u4ef6\u57fa\u91d1\u4f1a\uff0c\u4e8e 2010 \u5e74\u6210\u4e3a Apache \u9879\u76ee\u3002\u6b64\u540e\uff0cApache Spark \u56e0\u5176\u901f\u5ea6\u3001\u6613\u7528\u6027\u548c\u591a\u529f\u80fd\u6027\u3002<\/p>\n<h2>Apache Spark \u7684\u8d77\u6e90\u5386\u53f2\u548c\u9996\u6b21\u63d0\u53ca<\/h2>\n<p>Apache Spark \u8bde\u751f\u4e8e AMPLab \u7684\u7814\u7a76\u5de5\u4f5c\uff0c\u5f53\u65f6\u5f00\u53d1\u4eba\u5458\u9762\u4e34 Hadoop MapReduce \u7684\u6027\u80fd\u548c\u6613\u7528\u6027\u65b9\u9762\u7684\u9650\u5236\u3002\u9996\u6b21\u63d0\u53ca Apache Spark \u662f\u5728 Matei Zaharia \u7b49\u4eba\u4e8e 2012 \u5e74\u53d1\u8868\u7684\u4e00\u7bc7\u9898\u4e3a\u201c\u5f39\u6027\u5206\u5e03\u5f0f\u6570\u636e\u96c6\uff1a\u5185\u5b58\u96c6\u7fa4\u8ba1\u7b97\u7684\u5bb9\u9519\u62bd\u8c61\u201d\u7684\u7814\u7a76\u8bba\u6587\u4e2d\u3002\u8be5\u8bba\u6587\u5f15\u5165\u4e86\u5f39\u6027\u5206\u5e03\u5f0f\u6570\u636e\u96c6\uff08RDD\uff09\u7684\u6982\u5ff5\uff09\uff0cSpark \u4e2d\u7684\u57fa\u672c\u6570\u636e\u7ed3\u6784\u3002<\/p>\n<h2>\u6709\u5173 Apache Spark \u7684\u8be6\u7ec6\u4fe1\u606f\uff1a\u6269\u5c55\u4e3b\u9898<\/h2>\n<p>Apache Spark \u63d0\u4f9b\u4e86\u4e00\u79cd\u9ad8\u6548\u7075\u6d3b\u7684\u65b9\u5f0f\u6765\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u3002\u5b83\u63d0\u4f9b\u5185\u5b58\u5904\u7406\uff0c\u4e0e Hadoop MapReduce \u7b49\u4f20\u7edf\u7684\u57fa\u4e8e\u78c1\u76d8\u7684\u5904\u7406\u7cfb\u7edf\u76f8\u6bd4\uff0c\u53ef\u663e\u7740\u52a0\u901f\u6570\u636e\u5904\u7406\u4efb\u52a1\u3002 Spark \u5141\u8bb8\u5f00\u53d1\u4eba\u5458\u4f7f\u7528\u5404\u79cd\u8bed\u8a00\uff08\u5305\u62ec Scala\u3001Java\u3001Python \u548c R\uff09\u7f16\u5199\u6570\u636e\u5904\u7406\u5e94\u7528\u7a0b\u5e8f\uff0c\u4ece\u800c\u4f7f\u5176\u53ef\u4f9b\u66f4\u5e7f\u6cdb\u7684\u53d7\u4f17\u4f7f\u7528\u3002<\/p>\n<h2>Apache Spark \u7684\u5185\u90e8\u7ed3\u6784\uff1aApache Spark \u7684\u5de5\u4f5c\u539f\u7406<\/h2>\n<p>Apache Spark \u7684\u6838\u5fc3\u662f\u5f39\u6027\u5206\u5e03\u5f0f\u6570\u636e\u96c6 (RDD)\uff0c\u8fd9\u662f\u4e00\u4e2a\u53ef\u4ee5\u5e76\u884c\u5904\u7406\u7684\u4e0d\u53ef\u53d8\u5206\u5e03\u5f0f\u5bf9\u8c61\u96c6\u5408\u3002 RDD \u5177\u6709\u5bb9\u9519\u80fd\u529b\uff0c\u8fd9\u610f\u5473\u7740\u5b83\u4eec\u53ef\u4ee5\u5728\u8282\u70b9\u53d1\u751f\u6545\u969c\u65f6\u6062\u590d\u4e22\u5931\u7684\u6570\u636e\u3002 Spark\u7684DAG\uff08\u6709\u5411\u65e0\u73af\u56fe\uff09\u5f15\u64ce\u4f18\u5316\u548c\u8c03\u5ea6RDD\u64cd\u4f5c\u4ee5\u5b9e\u73b0\u6700\u5927\u6027\u80fd\u3002<\/p>\n<p>Spark \u751f\u6001\u7cfb\u7edf\u7531\u51e0\u4e2a\u9ad8\u7ea7\u7ec4\u4ef6\u7ec4\u6210\uff1a<\/p>\n<ol>\n<li>Spark Core\uff1a\u63d0\u4f9b\u57fa\u672c\u529f\u80fd\u548c RDD \u62bd\u8c61\u3002<\/li>\n<li>Spark SQL\uff1a\u652f\u6301\u7c7b\u4f3c SQL \u7684\u67e5\u8be2\u4ee5\u8fdb\u884c\u7ed3\u6784\u5316\u6570\u636e\u5904\u7406\u3002<\/li>\n<li>Spark Streaming\uff1a\u5b9e\u73b0\u5b9e\u65f6\u6570\u636e\u5904\u7406\u3002<\/li>\n<li>MLlib\uff08\u673a\u5668\u5b66\u4e60\u5e93\uff09\uff1a\u63d0\u4f9b\u5e7f\u6cdb\u7684\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u3002<\/li>\n<li>GraphX\uff1a\u5141\u8bb8\u56fe\u5f62\u5904\u7406\u548c\u5206\u6790\u3002<\/li>\n<\/ol>\n<h2>Apache Spark \u4e3b\u8981\u7279\u6027\u5206\u6790<\/h2>\n<p>Apache Spark \u7684\u4e3b\u8981\u529f\u80fd\u4f7f\u5176\u6210\u4e3a\u5927\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u7684\u70ed\u95e8\u9009\u62e9\uff1a<\/p>\n<ol>\n<li>\u5185\u5b58\u4e2d\u5904\u7406\uff1aSpark \u5728\u5185\u5b58\u4e2d\u5b58\u50a8\u6570\u636e\u7684\u80fd\u529b\u663e\u7740\u63d0\u9ad8\u4e86\u6027\u80fd\uff0c\u51cf\u5c11\u4e86\u91cd\u590d\u78c1\u76d8\u8bfb\/\u5199\u64cd\u4f5c\u7684\u9700\u8981\u3002<\/li>\n<li>\u5bb9\u9519\uff1aRDD \u63d0\u4f9b\u5bb9\u9519\u80fd\u529b\uff0c\u5373\u4f7f\u5728\u8282\u70b9\u53d1\u751f\u6545\u969c\u65f6\u4e5f\u80fd\u786e\u4fdd\u6570\u636e\u7684\u4e00\u81f4\u6027\u3002<\/li>\n<li>\u6613\u7528\u6027\uff1aSpark\u7684API\u7528\u6237\u53cb\u597d\uff0c\u652f\u6301\u591a\u79cd\u7f16\u7a0b\u8bed\u8a00\uff0c\u7b80\u5316\u4e86\u5f00\u53d1\u6d41\u7a0b\u3002<\/li>\n<li>\u591a\u529f\u80fd\u6027\uff1aSpark \u63d0\u4f9b\u4e86\u5e7f\u6cdb\u7684\u6279\u5904\u7406\u3001\u6d41\u5904\u7406\u3001\u673a\u5668\u5b66\u4e60\u548c\u56fe\u5f62\u5904\u7406\u5e93\uff0c\u4f7f\u5176\u6210\u4e3a\u4e00\u4e2a\u591a\u529f\u80fd\u5e73\u53f0\u3002<\/li>\n<li>\u901f\u5ea6\uff1aSpark \u7684\u5185\u5b58\u5904\u7406\u548c\u4f18\u5316\u7684\u6267\u884c\u5f15\u64ce\u6709\u52a9\u4e8e\u5176\u5353\u8d8a\u7684\u901f\u5ea6\u3002<\/li>\n<\/ol>\n<h2>Apache Spark \u7684\u7c7b\u578b<\/h2>\n<p>Apache Spark \u6839\u636e\u5176\u7528\u9014\u548c\u529f\u80fd\u53ef\u4ee5\u5206\u4e3a\u4e0d\u540c\u7684\u7c7b\u578b\uff1a<\/p>\n<table>\n<thead>\n<tr>\n<th>\u7c7b\u578b<\/th>\n<th>\u63cf\u8ff0<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\u6279\u91cf\u5904\u7406<\/td>\n<td>\u4e00\u6b21\u5206\u6790\u548c\u5904\u7406\u5927\u91cf\u6570\u636e\u3002<\/td>\n<\/tr>\n<tr>\n<td>\u6d41\u5904\u7406<\/td>\n<td>\u6570\u636e\u6d41\u5230\u8fbe\u65f6\u8fdb\u884c\u5b9e\u65f6\u5904\u7406\u3002<\/td>\n<\/tr>\n<tr>\n<td>\u673a\u5668\u5b66\u4e60<\/td>\n<td>\u5229\u7528 Spark \u7684 MLlib \u6765\u5b9e\u73b0\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u3002<\/td>\n<\/tr>\n<tr>\n<td>\u56fe\u5f62\u5904\u7406<\/td>\n<td>\u5206\u6790\u548c\u5904\u7406\u56fe\u5f62\u548c\u590d\u6742\u7684\u6570\u636e\u7ed3\u6784\u3002<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Apache Spark\u7684\u4f7f\u7528\u65b9\u6cd5\uff1a\u4f7f\u7528\u76f8\u5173\u95ee\u9898\u53ca\u89e3\u51b3\u65b9\u6848<\/h2>\n<p>Apache Spark \u5728\u5404\u4e2a\u9886\u57df\u90fd\u6709\u5e94\u7528\uff0c\u5305\u62ec\u6570\u636e\u5206\u6790\u3001\u673a\u5668\u5b66\u4e60\u3001\u63a8\u8350\u7cfb\u7edf\u548c\u5b9e\u65f6\u4e8b\u4ef6\u5904\u7406\u3002\u7136\u800c\uff0c\u5728\u4f7f\u7528 Apache Spark \u65f6\uff0c\u53ef\u80fd\u4f1a\u51fa\u73b0\u4e00\u4e9b\u5e38\u89c1\u7684\u6311\u6218\uff1a<\/p>\n<ol>\n<li>\n<p><strong>\u5185\u5b58\u7ba1\u7406<\/strong>\uff1a\u7531\u4e8e Spark \u4e25\u91cd\u4f9d\u8d56\u5185\u5b58\u4e2d\u5904\u7406\uff0c\u56e0\u6b64\u9ad8\u6548\u7684\u5185\u5b58\u7ba1\u7406\u5bf9\u4e8e\u907f\u514d\u5185\u5b58\u4e0d\u8db3\u9519\u8bef\u81f3\u5173\u91cd\u8981\u3002<\/p>\n<ul>\n<li>\u89e3\u51b3\u65b9\u6848\uff1a\u4f18\u5316\u6570\u636e\u5b58\u50a8\u3001\u660e\u667a\u5730\u4f7f\u7528\u7f13\u5b58\u5e76\u76d1\u63a7\u5185\u5b58\u4f7f\u7528\u60c5\u51b5\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\n<p><strong>\u6570\u636e\u504f\u5dee<\/strong>\uff1a\u8de8\u5206\u533a\u7684\u6570\u636e\u5206\u5e03\u4e0d\u5747\u5300\u53ef\u80fd\u4f1a\u5bfc\u81f4\u6027\u80fd\u74f6\u9888\u3002<\/p>\n<ul>\n<li>\u89e3\u51b3\u65b9\u6848\uff1a\u4f7f\u7528\u6570\u636e\u91cd\u65b0\u5206\u533a\u6280\u672f\u6765\u5747\u5300\u5206\u5e03\u6570\u636e\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\n<p><strong>\u96c6\u7fa4\u89c4\u6a21\u8c03\u6574<\/strong>\uff1a\u4e0d\u6b63\u786e\u7684\u96c6\u7fa4\u5927\u5c0f\u53ef\u80fd\u4f1a\u5bfc\u81f4\u8d44\u6e90\u5229\u7528\u4e0d\u8db3\u6216\u8fc7\u8f7d\u3002<\/p>\n<ul>\n<li>\u89e3\u51b3\u65b9\u6848\uff1a\u5b9a\u671f\u76d1\u63a7\u96c6\u7fa4\u6027\u80fd\u5e76\u76f8\u5e94\u8c03\u6574\u8d44\u6e90\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\n<p><strong>\u6570\u636e\u5e8f\u5217\u5316<\/strong>\uff1a\u4f4e\u6548\u7684\u6570\u636e\u5e8f\u5217\u5316\u4f1a\u5f71\u54cd\u6570\u636e\u4f20\u8f93\u671f\u95f4\u7684\u6027\u80fd\u3002<\/p>\n<ul>\n<li>\u89e3\u51b3\u65b9\u6848\uff1a\u9009\u62e9\u5408\u9002\u7684\u5e8f\u5217\u5316\u683c\u5f0f\u5e76\u5728\u9700\u8981\u65f6\u538b\u7f29\u6570\u636e\u3002<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<h2>\u4e3b\u8981\u7279\u70b9\u53ca\u5176\u4ed6\u4e0e\u540c\u7c7b\u4ea7\u54c1\u7684\u6bd4\u8f83<\/h2>\n<table>\n<thead>\n<tr>\n<th>\u7279\u5f81<\/th>\n<th>Apache Spark<\/th>\n<th>Hadoop MapReduce<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\u5904\u7406\u8303\u5f0f<\/td>\n<td>\u5185\u5b58\u4e2d\u548c\u8fed\u4ee3\u5904\u7406<\/td>\n<td>\u57fa\u4e8e\u78c1\u76d8\u7684\u6279\u5904\u7406<\/td>\n<\/tr>\n<tr>\n<td>\u6570\u636e\u5904\u7406<\/td>\n<td>\u6279\u91cf\u548c\u5b9e\u65f6\u5904\u7406<\/td>\n<td>\u4ec5\u6279\u5904\u7406<\/td>\n<\/tr>\n<tr>\n<td>\u5bb9\u9519\u80fd\u529b<\/td>\n<td>\u662f\uff08\u901a\u8fc7 RDD\uff09<\/td>\n<td>\u662f\uff08\u901a\u8fc7\u590d\u5236\uff09<\/td>\n<\/tr>\n<tr>\n<td>\u6570\u636e\u5b58\u50a8<\/td>\n<td>\u57fa\u4e8e\u5185\u5b58\u548c\u57fa\u4e8e\u78c1\u76d8<\/td>\n<td>\u57fa\u4e8e\u78c1\u76d8<\/td>\n<\/tr>\n<tr>\n<td>\u751f\u6001\u7cfb\u7edf<\/td>\n<td>\u591a\u6837\u5316\u7684\u5e93\u96c6\uff08Spark SQL\u3001Spark Streaming\u3001MLlib\u3001GraphX \u7b49\uff09<\/td>\n<td>\u6709\u9650\u7684\u751f\u6001\u7cfb\u7edf<\/td>\n<\/tr>\n<tr>\n<td>\u8868\u73b0<\/td>\n<td>\u7531\u4e8e\u5185\u5b58\u4e2d\u5904\u7406\uff0c\u901f\u5ea6\u66f4\u5feb<\/td>\n<td>\u7531\u4e8e\u78c1\u76d8\u8bfb\/\u5199\u901f\u5ea6\u8f83\u6162<\/td>\n<\/tr>\n<tr>\n<td>\u4f7f\u7528\u65b9\u4fbf<\/td>\n<td>\u7528\u6237\u53cb\u597d\u7684 API \u548c\u591a\u8bed\u8a00\u652f\u6301<\/td>\n<td>\u66f4\u9661\u5ced\u7684\u5b66\u4e60\u66f2\u7ebf\u5e76\u4e14\u57fa\u4e8eJava<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>\u4e0e Apache Spark \u76f8\u5173\u7684\u672a\u6765\u524d\u666f\u548c\u6280\u672f<\/h2>\n<p>Apache Spark \u7684\u672a\u6765\u770b\u8d77\u6765\u5145\u6ee1\u5e0c\u671b\uff0c\u56e0\u4e3a\u5927\u6570\u636e\u4ecd\u7136\u662f\u5404\u4e2a\u884c\u4e1a\u7684\u91cd\u8981\u65b9\u9762\u3002\u4e0e Apache Spark \u7684\u672a\u6765\u76f8\u5173\u7684\u4e00\u4e9b\u5173\u952e\u89c2\u70b9\u548c\u6280\u672f\u5305\u62ec\uff1a<\/p>\n<ol>\n<li><strong>\u4f18\u5316<\/strong>\uff1a\u6301\u7eed\u52aa\u529b\u589e\u5f3a Spark \u7684\u6027\u80fd\u548c\u8d44\u6e90\u5229\u7528\u7387\u53ef\u80fd\u4f1a\u5e26\u6765\u66f4\u5feb\u7684\u5904\u7406\u901f\u5ea6\u5e76\u51cf\u5c11\u5185\u5b58\u5f00\u9500\u3002<\/li>\n<li><strong>\u4e0e\u4eba\u5de5\u667a\u80fd\u6574\u5408<\/strong>\uff1aApache Spark \u53ef\u80fd\u4f1a\u4e0e\u4eba\u5de5\u667a\u80fd\u548c\u673a\u5668\u5b66\u4e60\u6846\u67b6\u66f4\u6df1\u5165\u5730\u96c6\u6210\uff0c\u4f7f\u5176\u6210\u4e3a\u4eba\u5de5\u667a\u80fd\u9a71\u52a8\u7684\u5e94\u7528\u7a0b\u5e8f\u7684\u9996\u9009\u3002<\/li>\n<li><strong>\u5b9e\u65f6\u5206\u6790<\/strong>\uff1aSpark \u7684\u6d41\u5904\u7406\u529f\u80fd\u53ef\u80fd\u4f1a\u8fdb\u6b65\uff0c\u4ece\u800c\u5b9e\u73b0\u66f4\u65e0\u7f1d\u7684\u5b9e\u65f6\u5206\u6790\uff0c\u4ee5\u5b9e\u73b0\u5373\u65f6\u6d1e\u5bdf\u548c\u51b3\u7b56\u3002<\/li>\n<\/ol>\n<h2>\u5982\u4f55\u4f7f\u7528\u4ee3\u7406\u670d\u52a1\u5668\u6216\u5c06\u5176\u4e0e Apache Spark \u5173\u8054<\/h2>\n<p>\u4ee3\u7406\u670d\u52a1\u5668\u5728\u589e\u5f3a Apache Spark \u90e8\u7f72\u7684\u5b89\u5168\u6027\u548c\u6027\u80fd\u65b9\u9762\u53ef\u4ee5\u53d1\u6325\u91cd\u8981\u4f5c\u7528\u3002\u4f7f\u7528\u4ee3\u7406\u670d\u52a1\u5668\u6216\u4e0e Apache Spark \u5173\u8054\u7684\u4e00\u4e9b\u65b9\u5f0f\u5305\u62ec\uff1a<\/p>\n<ol>\n<li><strong>\u8d1f\u8f7d\u5747\u8861<\/strong>\uff1a\u4ee3\u7406\u670d\u52a1\u5668\u53ef\u4ee5\u5c06\u4f20\u5165\u8bf7\u6c42\u5206\u53d1\u5230\u591a\u4e2a Spark \u8282\u70b9\uff0c\u786e\u4fdd\u8d44\u6e90\u5229\u7528\u7387\u5747\u5300\u548c\u66f4\u597d\u7684\u6027\u80fd\u3002<\/li>\n<li><strong>\u5b89\u5168<\/strong>\uff1a\u4ee3\u7406\u670d\u52a1\u5668\u5145\u5f53\u7528\u6237\u548c Spark \u96c6\u7fa4\u4e4b\u95f4\u7684\u4e2d\u4ecb\uff0c\u63d0\u4f9b\u989d\u5916\u7684\u5b89\u5168\u5c42\u5e76\u5e2e\u52a9\u9632\u6b62\u6f5c\u5728\u7684\u653b\u51fb\u3002<\/li>\n<li><strong>\u7f13\u5b58<\/strong>\uff1a\u4ee3\u7406\u670d\u52a1\u5668\u53ef\u4ee5\u7f13\u5b58\u9891\u7e41\u8bf7\u6c42\u7684\u6570\u636e\uff0c\u51cf\u5c11 Spark \u96c6\u7fa4\u7684\u8d1f\u8f7d\u5e76\u63d0\u9ad8\u54cd\u5e94\u65f6\u95f4\u3002<\/li>\n<\/ol>\n<h2>\u76f8\u5173\u94fe\u63a5<\/h2>\n<p>\u6709\u5173 Apache Spark \u7684\u66f4\u591a\u4fe1\u606f\uff0c\u60a8\u53ef\u4ee5\u6d4f\u89c8\u4ee5\u4e0b\u8d44\u6e90\uff1a<\/p>\n<ol>\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\/documentation.html\" target=\"_new\" rel=\"noopener nofollow\">Apache Spark \u6587\u6863<\/a><\/li>\n<li><a href=\"https:\/\/github.com\/apache\/spark\" target=\"_new\" rel=\"noopener nofollow\">Apache Spark GitHub \u5b58\u50a8\u5e93<\/a><\/li>\n<li><a href=\"https:\/\/databricks.com\/spark\/about\" target=\"_new\" rel=\"noopener nofollow\">Databricks\u2014\u2014Apache Spark<\/a><\/li>\n<\/ol>\n<p>Apache Spark \u4e0d\u65ad\u53d1\u5c55\u5e76\u9769\u65b0\u5927\u6570\u636e\u683c\u5c40\uff0c\u4f7f\u7ec4\u7ec7\u80fd\u591f\u5feb\u901f\u9ad8\u6548\u5730\u4ece\u6570\u636e\u4e2d\u83b7\u53d6\u6709\u4ef7\u503c\u7684\u89c1\u89e3\u3002\u65e0\u8bba\u60a8\u662f\u6570\u636e\u79d1\u5b66\u5bb6\u3001\u5de5\u7a0b\u5e08\u8fd8\u662f\u4e1a\u52a1\u5206\u6790\u5e08\uff0cApache Spark \u90fd\u80fd\u63d0\u4f9b\u5f3a\u5927\u800c\u7075\u6d3b\u7684\u5927\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u5e73\u53f0\u3002<\/p>","protected":false},"featured_media":467620,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-475880","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Apache Spark: A Comprehensive Guide<\/mark>","faq_items":[{"question":"What is Apache Spark?","answer":"<p>Apache Spark is an open-source distributed computing system designed for big data processing and analytics. It provides fast in-memory processing, fault tolerance, and supports multiple programming languages for data processing applications.<\/p>"},{"question":"How did Apache Spark originate?","answer":"<p>Apache Spark originated from research efforts at the AMPLab, University of California, Berkeley, and was first mentioned in a research paper titled \"Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing\" in 2012.<\/p>"},{"question":"What is the internal structure of Apache Spark?","answer":"<p>At the core of Apache Spark is the concept of Resilient Distributed Datasets (RDDs), which are immutable distributed collections of objects processed in parallel. Spark's ecosystem includes Spark Core, Spark SQL, Spark Streaming, MLlib, and GraphX.<\/p>"},{"question":"What are the key features of Apache Spark?","answer":"<p>The key features of Apache Spark include in-memory processing, fault tolerance, ease of use with various APIs, versatility with multiple libraries, and superior processing speed.<\/p>"},{"question":"What are the types of Apache Spark?","answer":"<p>Apache Spark can be categorized into batch processing, stream processing, machine learning, and graph processing.<\/p>"},{"question":"What are the ways to use Apache Spark?","answer":"<p>Apache Spark finds applications in data analytics, machine learning, recommendation systems, and real-time event processing. Some common challenges include memory management, data skew, and cluster sizing.<\/p>"},{"question":"How does Apache Spark compare to Hadoop MapReduce?","answer":"<p>Apache Spark excels in in-memory and iterative processing, supports real-time analytics, offers a more diverse ecosystem, and is user-friendly compared to Hadoop MapReduce's disk-based batch processing and limited ecosystem.<\/p>"},{"question":"What are the future perspectives for Apache Spark?","answer":"<p>The future of Apache Spark looks promising with ongoing optimizations, deeper integration with AI, and advancements in real-time analytics.<\/p>"},{"question":"How can proxy servers be associated with Apache Spark?","answer":"<p>Proxy servers can enhance Apache Spark's security and performance by providing load balancing, caching, and acting as intermediaries between users and Spark clusters.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki\/475880","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\/475880\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/media\/467620"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/media?parent=475880"}],"curies":[{"name":"\u53ef\u6e7f\u6027\u7c89\u5242","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}