<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[RiverCode的博客]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/RiverCode</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; RiverCode]]></copyright><item><title><![CDATA[小问题可能存在大问题，希望大神帮忙解答。Spark本地运行模式中单线程与多线程问题之setMaster("local")可以运行，但是设置成setMaster("local[3]")或setMaste]]></title><link>https://blog.youkuaiyun.com/RiverCode/article/details/76434089</link><guid>https://blog.youkuaiyun.com/RiverCode/article/details/76434089</guid><author>RiverCode</author><pubDate>Mon, 31 Jul 2017 14:14:32 +0800</pubDate><description><![CDATA[小问题可能存在大问题，希望大神帮忙解答。Spark本地运行模式中单线程与多线程问题之setMaster("local")可以运行，但是设置成setMaster("local[3]")或setMaster("local[*]")则报错]]></description><category></category></item><item><title><![CDATA[求助KMeans算法关于转换矩阵Vectors问题]]></title><link>https://blog.youkuaiyun.com/RiverCode/article/details/72830307</link><guid>https://blog.youkuaiyun.com/RiverCode/article/details/72830307</guid><author>RiverCode</author><pubDate>Thu, 01 Jun 2017 13:53:44 +0800</pubDate><description><![CDATA[spark Machine-Learning中KMeans算法，转成矩阵时处理方式不同结果不一样，为什么]]></description><category></category></item><item><title><![CDATA[spark-submit local本地运行问题]]></title><link>https://blog.youkuaiyun.com/RiverCode/article/details/71542169</link><guid>https://blog.youkuaiyun.com/RiverCode/article/details/71542169</guid><author>RiverCode</author><pubDate>Wed, 10 May 2017 16:29:17 +0800</pubDate><description><![CDATA[spark-submit本地运行模式遇到的问题，第一次运行本地模式成功，再次运行数据却写到了HDFS目录下]]></description><category></category></item><item><title><![CDATA[hive-2.1.1报错Failed to start database 'metastore_db' with class loader sun.misc.Launcher$AppClassLoad]]></title><link>https://blog.youkuaiyun.com/RiverCode/article/details/71168743</link><guid>https://blog.youkuaiyun.com/RiverCode/article/details/71168743</guid><author>RiverCode</author><pubDate>Thu, 04 May 2017 13:12:30 +0800</pubDate><description><![CDATA[求帮忙：hive-2.1.1安装报错如下：
[root@master ~]# schematool -dbType mysql -initSchema
which: no hbase in (/root/anaconda2/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/root/bin:/root/package/jdk1.8.0_]]></description><category></category></item><item><title><![CDATA[zookeeper-3.4.9搭建]]></title><link>https://blog.youkuaiyun.com/RiverCode/article/details/70861097</link><guid>https://blog.youkuaiyun.com/RiverCode/article/details/70861097</guid><author>RiverCode</author><pubDate>Thu, 27 Apr 2017 17:50:13 +0800</pubDate><description><![CDATA[图文+代码讲解zookeeper-3.4.9搭建]]></description><category></category></item><item><title><![CDATA[图文+代码讲解spark-2.1.0集群搭建]]></title><link>https://blog.youkuaiyun.com/RiverCode/article/details/70801045</link><guid>https://blog.youkuaiyun.com/RiverCode/article/details/70801045</guid><author>RiverCode</author><pubDate>Wed, 26 Apr 2017 15:44:40 +0800</pubDate><description><![CDATA[图文+代码讲解spark-2.1.0集群搭建]]></description><category></category></item><item><title><![CDATA[Hadoop2.7.3完全分布式集群搭建]]></title><link>https://blog.youkuaiyun.com/RiverCode/article/details/70738132</link><guid>https://blog.youkuaiyun.com/RiverCode/article/details/70738132</guid><author>RiverCode</author><pubDate>Tue, 25 Apr 2017 13:55:49 +0800</pubDate><description><![CDATA[hadoop-2.7.3完全分布式集群搭建]]></description><category></category></item><item><title><![CDATA[mac配置Scala环境变量]]></title><link>https://blog.youkuaiyun.com/RiverCode/article/details/70234269</link><guid>https://blog.youkuaiyun.com/RiverCode/article/details/70234269</guid><author>RiverCode</author><pubDate>Wed, 19 Apr 2017 00:17:16 +0800</pubDate><description><![CDATA[Mac配置Scala环境变量]]></description><category></category></item><item><title><![CDATA[Spark机器学习算法之KMeans代码+图文讲解（入门）]]></title><link>https://blog.youkuaiyun.com/RiverCode/article/details/68065831</link><guid>https://blog.youkuaiyun.com/RiverCode/article/details/68065831</guid><author>RiverCode</author><pubDate>Wed, 29 Mar 2017 16:40:06 +0800</pubDate><description><![CDATA[图文+代码讲解Spark中机器学习算法之KMeans算法]]></description><category></category></item><item><title><![CDATA[spark搜狗日志数据分析实战]]></title><link>https://blog.youkuaiyun.com/RiverCode/article/details/66973199</link><guid>https://blog.youkuaiyun.com/RiverCode/article/details/66973199</guid><author>RiverCode</author><pubDate>Mon, 27 Mar 2017 17:29:32 +0800</pubDate><description><![CDATA[spark搜狗日志数据分析实战（搜索结果排名和点击结果排名都是第一的情况下排序）]]></description><category></category></item><item><title><![CDATA[scala命令行编译运行]]></title><link>https://blog.youkuaiyun.com/RiverCode/article/details/62039602</link><guid>https://blog.youkuaiyun.com/RiverCode/article/details/62039602</guid><author>RiverCode</author><pubDate>Tue, 14 Mar 2017 11:19:48 +0800</pubDate><description><![CDATA[代码+图文讲解scala命令行编译运行]]></description><category></category></item><item><title><![CDATA[scala中yield关键字讲解]]></title><link>https://blog.youkuaiyun.com/RiverCode/article/details/62037525</link><guid>https://blog.youkuaiyun.com/RiverCode/article/details/62037525</guid><author>RiverCode</author><pubDate>Tue, 14 Mar 2017 10:02:27 +0800</pubDate><description><![CDATA[代码+图文讲解scala中yield关键字]]></description><category></category></item><item><title><![CDATA[scala中优先级（:)与(.)讲解]]></title><link>https://blog.youkuaiyun.com/RiverCode/article/details/61922640</link><guid>https://blog.youkuaiyun.com/RiverCode/article/details/61922640</guid><author>RiverCode</author><pubDate>Mon, 13 Mar 2017 17:31:34 +0800</pubDate><description><![CDATA[代码+图文讲解scala优先级（:）与（.）]]></description><category></category></item><item><title><![CDATA[spark算子cogroup讲解]]></title><link>https://blog.youkuaiyun.com/RiverCode/article/details/60955386</link><guid>https://blog.youkuaiyun.com/RiverCode/article/details/60955386</guid><author>RiverCode</author><pubDate>Thu, 09 Mar 2017 10:12:40 +0800</pubDate><description><![CDATA[代码+图文讲解spark算子cogroup讲解]]></description><category></category></item><item><title><![CDATA[spark算子join讲解]]></title><link>https://blog.youkuaiyun.com/RiverCode/article/details/60879454</link><guid>https://blog.youkuaiyun.com/RiverCode/article/details/60879454</guid><author>RiverCode</author><pubDate>Wed, 08 Mar 2017 19:17:57 +0800</pubDate><description><![CDATA[代码+图文讲解spark算子union操作]]></description><category></category></item><item><title><![CDATA[SparkSQL中DataFrame Operations操作（select、filter、groupBy、count）]]></title><link>https://blog.youkuaiyun.com/RiverCode/article/details/60764116</link><guid>https://blog.youkuaiyun.com/RiverCode/article/details/60764116</guid><author>RiverCode</author><pubDate>Tue, 07 Mar 2017 14:38:06 +0800</pubDate><description><![CDATA[代码+图文讲解SparkSQL DataFrame操作，select、filter、groupBy、count操作]]></description><category></category></item><item><title><![CDATA[Spark中DataFrame的schema讲解]]></title><link>https://blog.youkuaiyun.com/RiverCode/article/details/60604327</link><guid>https://blog.youkuaiyun.com/RiverCode/article/details/60604327</guid><author>RiverCode</author><pubDate>Mon, 06 Mar 2017 22:20:51 +0800</pubDate><description><![CDATA[代码+图文讲解Spark中DataFrame的schema]]></description><category></category></item><item><title><![CDATA[spark创建DataFrames]]></title><link>https://blog.youkuaiyun.com/RiverCode/article/details/60596079</link><guid>https://blog.youkuaiyun.com/RiverCode/article/details/60596079</guid><author>RiverCode</author><pubDate>Mon, 06 Mar 2017 20:57:50 +0800</pubDate><description><![CDATA[代码+图文讲解SparkSQL、DataFrames]]></description><category></category></item><item><title><![CDATA[spark累加器介绍-案例通过累加器统计文章中空白行数量]]></title><link>https://blog.youkuaiyun.com/RiverCode/article/details/60574856</link><guid>https://blog.youkuaiyun.com/RiverCode/article/details/60574856</guid><author>RiverCode</author><pubDate>Mon, 06 Mar 2017 13:44:16 +0800</pubDate><description><![CDATA[代码+图文讲解spark累加器Accumulators]]></description><category></category></item><item><title><![CDATA[代码+图文讲解spark中scala常用正则匹配]]></title><link>https://blog.youkuaiyun.com/RiverCode/article/details/60570620</link><guid>https://blog.youkuaiyun.com/RiverCode/article/details/60570620</guid><author>RiverCode</author><pubDate>Mon, 06 Mar 2017 09:41:00 +0800</pubDate><description><![CDATA[代码+图文讲解spark中scala常用正则匹配]]></description><category></category></item></channel></rss>