{"id":65400,"date":"2026-01-24T22:49:03","date_gmt":"2026-01-24T14:49:03","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/65400.html"},"modified":"2026-01-24T22:49:03","modified_gmt":"2026-01-24T14:49:03","slug":"%e3%80%90%e8%ae%a1%e7%ae%97%e6%9c%ba%e6%af%95%e8%ae%be%e9%80%89%e9%a2%98%e3%80%91%e5%9f%ba%e4%ba%8ehadoopspark%e4%b8%8a%e6%b5%b7%e9%a4%90%e9%a5%ae%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90%e7%b3%bb","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/65400.html","title":{"rendered":"\u3010\u8ba1\u7b97\u673a\u6bd5\u8bbe\u9009\u9898\u3011\u57fa\u4e8eHadoop+Spark\u4e0a\u6d77\u9910\u996e\u6570\u636e\u5206\u6790\u7cfb\u7edf\u6e90\u7801 \u6bd5\u4e1a\u8bbe\u8ba1 \u9009\u9898\u63a8\u8350 \u6bd5\u8bbe\u9009\u9898 \u6570\u636e\u5206\u6790 \u673a\u5668\u5b66\u4e60 \u6570\u636e\u6316\u6398"},"content":{"rendered":"

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\u4e0a\u6d77\u9910\u996e\u6570\u636e\u5206\u6790\u4e0e\u53ef\u89c6\u5316\u7cfb\u7edf-\u4ee3\u7801\u5c55\u793a<\/h2>\n

from<\/span> pyspark.<\/span>sql import<\/span> SparkSession,<\/span> functions as<\/span> F
\nfrom<\/span> pyspark.<\/span>ml.<\/span>feature import<\/span> VectorAssembler
\nfrom<\/span> pyspark.<\/span>ml.<\/span>clustering import<\/span> KMeans
\nspark =<\/span> SparkSession.<\/span>builder.<\/span>appName(<\/span>"RestaurantAnalysis"<\/span>)<\/span>.<\/span>getOrCreate(<\/span>)<\/span>
\ndf =<\/span> spark.<\/span>read.<\/span>csv(<\/span>"hdfs:\/\/…\/shanghai_restaurants.csv"<\/span>,<\/span> header=<\/span>True<\/span>,<\/span> inferSchema=<\/span>True<\/span>)<\/span>
\n# \u529f\u80fd1: \u5404\u884c\u653f\u533a\u9910\u996e\u5e97\u94fa\u6570\u91cf\u4e0e\u5360\u6bd4\u5206\u6790<\/span>
\ndistrict_count =<\/span> df.<\/span>filter<\/span>(<\/span>F.<\/span>col(<\/span>"\u884c\u653f\u533a"<\/span>)<\/span>.<\/span>isNotNull(<\/span>)<\/span>)<\/span>.<\/span>groupBy(<\/span>"\u884c\u653f\u533a"<\/span>)<\/span>.<\/span>agg(<\/span>F.<\/span>count(<\/span>"*"<\/span>)<\/span>.<\/span>alias(<\/span>"\u5e97\u94fa\u6570\u91cf"<\/span>)<\/span>)<\/span>
\ntotal_count =<\/span> district_count.<\/span>agg(<\/span>F.<\/span>sum<\/span>(<\/span>"\u5e97\u94fa\u6570\u91cf"<\/span>)<\/span>.<\/span>alias(<\/span>"\u603b\u6570"<\/span>)<\/span>)<\/span>.<\/span>collect(<\/span>)<\/span>[<\/span>0<\/span>]<\/span>[<\/span>"\u603b\u6570"<\/span>]<\/span>
\ndistrict_analysis =<\/span> district_count.<\/span>withColumn(<\/span>"\u5360\u6bd4"<\/span>,<\/span> F.<\/span>format_number(<\/span>F.<\/span>col(<\/span>"\u5e97\u94fa\u6570\u91cf"<\/span>)<\/span> \/<\/span> total_count *<\/span> 100<\/span>,<\/span> 2<\/span>)<\/span>)<\/span>.<\/span>orderBy(<\/span>F.<\/span>desc(<\/span>"\u5e97\u94fa\u6570\u91cf"<\/span>)<\/span>)<\/span>
\ndistrict_analysis.<\/span>write.<\/span>csv(<\/span>"hdfs:\/\/…\/output\/district_analysis"<\/span>,<\/span> mode=<\/span>"overwrite"<\/span>,<\/span> header=<\/span>True<\/span>)<\/span>
\n# \u529f\u80fd2: \u5404\u884c\u653f\u533a\u9ad8\u53e3\u7891\u9910\u5385\u52a0\u6743\u6392\u540dTop10<\/span>
\nweighted_df =<\/span> df.<\/span>withColumn(<\/span>"\u70b9\u8bc4\u6570_log"<\/span>,<\/span> F.<\/span>log1p(<\/span>F.<\/span>col(<\/span>"\u70b9\u8bc4\u6570"<\/span>)<\/span>)<\/span>)<\/span>
\nweighted_df =<\/span> weighted_df.<\/span>withColumn(<\/span>"\u7efc\u5408\u5f97\u5206"<\/span>,<\/span> (<\/span>F.<\/span>col(<\/span>"\u53e3\u5473"<\/span>)<\/span> +<\/span> F.<\/span>col(<\/span>"\u73af\u5883"<\/span>)<\/span> +<\/span> F.<\/span>col(<\/span>"\u670d\u52a1"<\/span>)<\/span>)<\/span> \/<\/span> 3<\/span> *<\/span> F.<\/span>col(<\/span>"\u70b9\u8bc4\u6570_log"<\/span>)<\/span>)<\/span>
\nwindow_spec =<\/span> F.<\/span>Window.<\/span>partitionBy(<\/span>"\u884c\u653f\u533a"<\/span>)<\/span>.<\/span>orderBy(<\/span>F.<\/span>desc(<\/span>"\u7efc\u5408\u5f97\u5206"<\/span>)<\/span>)<\/span>
\ntop10_restaurants =<\/span> weighted_df.<\/span>withColumn(<\/span>"rank"<\/span>,<\/span> F.<\/span>rank(<\/span>)<\/span>.<\/span>over(<\/span>window_spec)<\/span>)<\/span>.<\/span>filter<\/span>(<\/span>F.<\/span>col(<\/span>"rank"<\/span>)<\/span> <=<\/span> 10<\/span>)<\/span>
\ntop10_restaurants.<\/span>select(<\/span>"\u884c\u653f\u533a"<\/span>,<\/span> "\u5e97\u540d"<\/span>,<\/span> "\u7efc\u5408\u5f97\u5206"<\/span>,<\/span> "\u53e3\u5473"<\/span>,<\/span> "\u73af\u5883"<\/span>,<\/span> "\u670d\u52a1"<\/span>)<\/span>.<\/span>write.<\/span>csv(<\/span>"hdfs:\/\/…\/output\/top10_restaurants"<\/span>,<\/span> mode=<\/span>"overwrite"<\/span>,<\/span> header=<\/span>True<\/span>)<\/span>
\n# \u529f\u80fd3: \u57fa\u4e8eK-Means\u7b97\u6cd5\u7684\u9910\u5385\u7efc\u5408\u753b\u50cf\u805a\u7c7b<\/span>
\nfeature_cols =<\/span> [<\/span>"\u53e3\u5473"<\/span>,<\/span> "\u73af\u5883"<\/span>,<\/span> "\u670d\u52a1"<\/span>,<\/span> "\u4eba\u5747\u6d88\u8d39"<\/span>,<\/span> "\u70b9\u8bc4\u6570"<\/span>]<\/span>
\nassembler =<\/span> VectorAssembler(<\/span>inputCols=<\/span>feature_cols,<\/span> outputCol=<\/span>"features"<\/span>)<\/span>
\ndata_for_clustering =<\/span> assembler.<\/span>transform(<\/span>df.<\/span>na.<\/span>drop(<\/span>subset=<\/span>feature_cols)<\/span>)<\/span>
\nkmeans =<\/span> KMeans(<\/span>featuresCol=<\/span>"features"<\/span>,<\/span> predictionCol=<\/span>"cluster"<\/span>,<\/span> k=<\/span>5<\/span>,<\/span> seed=<\/span>42<\/span>)<\/span>
\nmodel =<\/span> kmeans.<\/span>fit(<\/span>data_for_clustering)<\/span>
\nclustered_data =<\/span> model.<\/span>transform(<\/span>data_for_clustering)<\/span>
\nclustered_data.<\/span>select(<\/span>"\u5e97\u540d"<\/span>,<\/span> "\u884c\u653f\u533a"<\/span>,<\/span> "\u7c7b\u522b"<\/span>,<\/span> *<\/span>feature_cols,<\/span> "cluster"<\/span>)<\/span>.<\/span>write.<\/span>csv(<\/span>"hdfs:\/\/…\/output\/clustered_restaurants"<\/span>,<\/span> mode=<\/span>"overwrite"<\/span>,<\/span> header=<\/span>True<\/span>)<\/span><\/p>\n

\u4e0a\u6d77\u9910\u996e\u6570\u636e\u5206\u6790\u4e0e\u53ef\u89c6\u5316\u7cfb\u7edf-\u7ed3\u8bed<\/h2>\n

\u81f3\u6b64，\u57fa\u4e8eHadoop+Spark\u7684\u4e0a\u6d77\u9910\u996e\u6570\u636e\u5206\u6790\u4e0e\u53ef\u89c6\u5316\u7cfb\u7edf\u5df2\u57fa\u672c\u5b8c\u6210\u3002\u7cfb\u7edf\u5b9e\u73b0\u4e86\u4ece\u6570\u636e\u91c7\u96c6\u3001\u5904\u7406\u5230\u524d\u7aef\u5c55\u793a\u7684\u5b8c\u6574\u94fe\u8def，\u867d\u7136\u4ecd\u6709\u4f18\u5316\u7a7a\u95f4，\u4f46\u5e0c\u671b\u80fd\u4e3a\u6b63\u5728\u505a\u6bd5\u8bbe\u7684\u540c\u5b66\u63d0\u4f9b\u4e00\u4e2a\u6e05\u6670\u7684\u5927\u6570\u636e\u9879\u76ee\u601d\u8def，\u611f\u8c22\u5927\u5bb6\u7684\u89c2\u770b\u3002<\/p>\n

\u5982\u679c\u8fd9\u4e2a\u89c6\u9891\u5bf9\u4f60\u7684\u6bd5\u8bbe\u9009\u9898\u548c\u5f00\u53d1\u6709\u5e2e\u52a9，\u522b\u5fd8\u4e86\u7ed9\u6211\u4e00\u4e2a\u4e00\u952e\u4e09\u8fde\u652f\u6301\u4e00\u4e0b！\u5927\u5bb6\u6b63\u5728\u505a\u4ec0\u4e48\u65b9\u5411\u7684\u6bd5\u8bbe\u5462？\u6216\u8005\u5bf9\u5927\u6570\u636e\u5206\u6790\u6709\u4ec0\u4e48\u7591\u95ee，\u90fd\u6b22\u8fce\u5728\u8bc4\u8bba\u533a\u7559\u8a00\u4ea4\u6d41，\u6211\u4eec\u4e00\u8d77\u8fdb\u6b65！<\/p>\n

\u26a1\u26a1\u83b7\u53d6\u6e90\u7801\u4e3b\u9875\u2013> \u8ba1\u7b97\u673a\u7f16\u7a0b\u6307\u5bfc\u5e08 \u26a1\u26a1\u6709\u6280\u672f\u95ee\u9898\u6216\u8005\u83b7\u53d6\u6e90\u4ee3\u7801！\u6b22\u8fce\u5728\u8bc4\u8bba\u533a\u4e00\u8d77\u4ea4\u6d41！ \u26a1\u26a1\u5927\u5bb6\u70b9\u8d5e\u3001\u6536\u85cf\u3001\u5173\u6ce8\u3001\u6709\u95ee\u9898\u90fd\u53ef\u7559\u8a00\u8bc4\u8bba\u4ea4\u6d41！ \u26a1\u26a1\u5982\u679c\u4f60\u9047\u5230\u5177\u4f53\u7684\u6280\u672f\u95ee\u9898\u6216\u8ba1\u7b97\u673a\u6bd5\u8bbe\u65b9\u9762\u9700\u6c42\u53ef\u4ee5\u5728\u4e3b\u9875\u4e0a\u8be6\u7ec6\u8d44\u6599\u91cc\u2191\u2191\u8054\u7cfb\u6211~~<\/p>\n","protected":false},"excerpt":{"rendered":"

\u270d\u270d\u8ba1\u7b97\u673a\u7f16\u7a0b\u6307\u5bfc\u5e08 \u2b50\u2b50\u4e2a\u4eba\u4ecb\u7ecd：\u81ea\u5df1\u975e\u5e38\u559c\u6b22\u7814\u7a76\u6280\u672f\u95ee\u9898！\u4e13\u4e1a\u505aJava\u3001Python\u3001\u5c0f\u7a0b\u5e8f\u3001\u5b89\u5353\u3001\u5927\u6570\u636e\u3001\u722c\u866b\u3001Golang\u3001\u5927\u5c4f\u7b49\u5b9e\u6218\u9879\u76ee\u3002 \u26fd\u26fd\u5b9e\u6218\u9879\u76ee：\u6709\u6e90\u7801\u6216\u8005\u6280\u672f\u4e0a\u7684\u95ee\u9898\u6b22\u8fce\u5728\u8bc4\u8bba\u533a\u4e00\u8d77\u8ba8\u8bba\u4ea4\u6d41！ \u26a1\u26a1\u5982\u679c\u4f60\u9047\u5230\u5177\u4f53\u7684\u6280\u672f\u95ee\u9898\u6216\u8ba1\u7b97\u673a\u6bd5\u8bbe\u65b9\u9762\u9700\u6c42\u53ef\u4ee5\u5728\u4e3b\u9875\u4e0a\u8be6\u7ec6\u8d44\u6599\u91cc\u2191\u2191\u8054\u7cfb\u6211~~ Java\u5b9e\u6218 | SpringBoot\/SSM Python\u5b9e\u6218\u9879\u76ee | Django \u5fae\u4fe1\u5c0f\u7a0b\u5e8f\/\u5b89\u5353\u5b9e\u6218\u9879\u76ee \u5927\u6570\u636e\u5b9e<\/p>\n","protected":false},"author":2,"featured_media":65389,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[6903,779,81,365,323,1908,957],"topic":[],"class_list":["post-65400","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-server","tag-6903","tag-hadoop","tag-python","tag-spark","tag-323","tag-1908","tag-957"],"yoast_head":"\n\u3010\u8ba1\u7b97\u673a\u6bd5\u8bbe\u9009\u9898\u3011\u57fa\u4e8eHadoop+Spark\u4e0a\u6d77\u9910\u996e\u6570\u636e\u5206\u6790\u7cfb\u7edf\u6e90\u7801 \u6bd5\u4e1a\u8bbe\u8ba1 \u9009\u9898\u63a8\u8350 \u6bd5\u8bbe\u9009\u9898 \u6570\u636e\u5206\u6790 \u673a\u5668\u5b66\u4e60 \u6570\u636e\u6316\u6398 - 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