<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[oblesslyy的专栏]]></title><description><![CDATA[赞美者]]></description><link>https://blog.youkuaiyun.com/oblesslyy</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; oblesslyy]]></copyright><item><title><![CDATA[Spark-streaming-杂]]></title><link>https://blog.youkuaiyun.com/oblesslyy/article/details/46364587</link><guid>https://blog.youkuaiyun.com/oblesslyy/article/details/46364587</guid><author>oblesslyy</author><pubDate>Thu, 04 Jun 2015 17:17:47 +0800</pubDate><description><![CDATA[Spark-streaming-杂]]></description><category></category></item><item><title><![CDATA[Spark-streaming-scheduler]]></title><link>https://blog.youkuaiyun.com/oblesslyy/article/details/46364617</link><guid>https://blog.youkuaiyun.com/oblesslyy/article/details/46364617</guid><author>oblesslyy</author><pubDate>Thu, 04 Jun 2015 17:17:14 +0800</pubDate><description><![CDATA[Spark-streaming-scheduler]]></description><category></category></item><item><title><![CDATA[Spark-streaming-summary]]></title><link>https://blog.youkuaiyun.com/oblesslyy/article/details/46364565</link><guid>https://blog.youkuaiyun.com/oblesslyy/article/details/46364565</guid><author>oblesslyy</author><pubDate>Thu, 04 Jun 2015 17:15:25 +0800</pubDate><description><![CDATA[Spark Streaming functionality.]]></description><category></category></item><item><title><![CDATA[scala]]></title><link>https://blog.youkuaiyun.com/oblesslyy/article/details/45898549</link><guid>https://blog.youkuaiyun.com/oblesslyy/article/details/45898549</guid><author>oblesslyy</author><pubDate>Thu, 21 May 2015 22:57:08 +0800</pubDate><description><![CDATA[Scala@(scala)Why ScalaFirst Question: Why scala个人理解的Scala优势
Functional Programming 在多核/云计算上的优势
运行在JVM上，可以有效利用Java现有的各种资源
用更少的代码干更多的事儿，和Java相比同一件事情可以省很多代码
同时结合了FP和OO，不是纯的语言。实际上Java也在试图结合FP。
推荐
[入门视频]
快学]]></description><category></category></item><item><title><![CDATA[Akka]]></title><link>https://blog.youkuaiyun.com/oblesslyy/article/details/45898517</link><guid>https://blog.youkuaiyun.com/oblesslyy/article/details/45898517</guid><author>oblesslyy</author><pubDate>Thu, 21 May 2015 22:56:38 +0800</pubDate><description><![CDATA[akka@(scala)基本介绍Akka 是一个用 Scala 编写的库，用于简化编写容错的、高可伸缩性的 Java 和 Scala 的 Actor 模型应用。 
官方网站 （http://akka.io/）的介绍是：
  Akka is a toolkit and runtime for building highly concurrent, distributed, and fault tole]]></description><category></category></item><item><title><![CDATA[Spark-SQL-core]]></title><link>https://blog.youkuaiyun.com/oblesslyy/article/details/45898405</link><guid>https://blog.youkuaiyun.com/oblesslyy/article/details/45898405</guid><author>oblesslyy</author><pubDate>Thu, 21 May 2015 22:49:50 +0800</pubDate><description><![CDATA[整个spark-sql的作用就是完成SQL语句到spark api的转换过程。]]></description><category></category></item><item><title><![CDATA[spark-sql-catalyst]]></title><link>https://blog.youkuaiyun.com/oblesslyy/article/details/45898391</link><guid>https://blog.youkuaiyun.com/oblesslyy/article/details/45898391</guid><author>oblesslyy</author><pubDate>Thu, 21 May 2015 22:49:32 +0800</pubDate><description><![CDATA[简单说这部分就是做optimizer的工作的，关于这部分是有一篇论文，写的很清楚，可以当作high leve design来看。还有一篇blog，内容差不多。总的来说，在catalyst这部分做的事情基本上是传统关系数据库的： 
1. parse（让sql语句变成合法的语法树） 
2. resolve（验证olum]]></description><category></category></item><item><title><![CDATA[Spark-sql-row]]></title><link>https://blog.youkuaiyun.com/oblesslyy/article/details/45898397</link><guid>https://blog.youkuaiyun.com/oblesslyy/article/details/45898397</guid><author>oblesslyy</author><pubDate>Thu, 21 May 2015 22:49:23 +0800</pubDate><description><![CDATA[Spark-sql-row@(spark)[sql|row]Rowrow就是关系数据库中的一行。/**]]></description><category></category></item><item><title><![CDATA[spark-sql-readme]]></title><link>https://blog.youkuaiyun.com/oblesslyy/article/details/45898381</link><guid>https://blog.youkuaiyun.com/oblesslyy/article/details/45898381</guid><author>oblesslyy</author><pubDate>Thu, 21 May 2015 22:48:28 +0800</pubDate><description><![CDATA[spark-sql-readme@(spark)[sql][readme]This module provides support for executing relational queries expressed in either SQL or a LINQ-like Scala DSL.]]></description><category></category></item><item><title><![CDATA[Spark-deploy]]></title><link>https://blog.youkuaiyun.com/oblesslyy/article/details/45898377</link><guid>https://blog.youkuaiyun.com/oblesslyy/article/details/45898377</guid><author>oblesslyy</author><pubDate>Thu, 21 May 2015 22:47:04 +0800</pubDate><description><![CDATA[Spark-deploy@(spark)[deploy|yarn]]]></description><category></category></item><item><title><![CDATA[Spark-scheduler]]></title><link>https://blog.youkuaiyun.com/oblesslyy/article/details/45898371</link><guid>https://blog.youkuaiyun.com/oblesslyy/article/details/45898371</guid><author>oblesslyy</author><pubDate>Thu, 21 May 2015 22:47:01 +0800</pubDate><description><![CDATA[Spark-scheduler@(spark)[scheduler]Task/**]]></description><category></category></item><item><title><![CDATA[Spark-executor]]></title><link>https://blog.youkuaiyun.com/oblesslyy/article/details/45898357</link><guid>https://blog.youkuaiyun.com/oblesslyy/article/details/45898357</guid><author>oblesslyy</author><pubDate>Thu, 21 May 2015 22:45:38 +0800</pubDate><description><![CDATA[Spark-executor@(spark)[executor]]]></description><category></category></item><item><title><![CDATA[Spark-shuffle]]></title><link>https://blog.youkuaiyun.com/oblesslyy/article/details/45898349</link><guid>https://blog.youkuaiyun.com/oblesslyy/article/details/45898349</guid><author>oblesslyy</author><pubDate>Thu, 21 May 2015 22:45:06 +0800</pubDate><description><![CDATA[Spark-shuffle@(spark)[shuffle]ShuffleHandle/BaseShuffleHandeAn opaque handle to a shuffle, used by a ShuffleManager to pass information about it to tasksShuffleMemoryManager控制shuffle的memory使用的/**]]></description><category></category></item><item><title><![CDATA[Spark-storage]]></title><link>https://blog.youkuaiyun.com/oblesslyy/article/details/45898335</link><guid>https://blog.youkuaiyun.com/oblesslyy/article/details/45898335</guid><author>oblesslyy</author><pubDate>Thu, 21 May 2015 22:44:26 +0800</pubDate><description><![CDATA[Spark-storage@(spark)[storage]]]></description><category></category></item><item><title><![CDATA[Spark-杂项]]></title><link>https://blog.youkuaiyun.com/oblesslyy/article/details/45898317</link><guid>https://blog.youkuaiyun.com/oblesslyy/article/details/45898317</guid><author>oblesslyy</author><pubDate>Thu, 21 May 2015 22:44:16 +0800</pubDate><description><![CDATA[Spark-杂项@(spark)[input|serializer|partial]]]></description><category></category></item><item><title><![CDATA[spark-broadcast]]></title><link>https://blog.youkuaiyun.com/oblesslyy/article/details/45898301</link><guid>https://blog.youkuaiyun.com/oblesslyy/article/details/45898301</guid><author>oblesslyy</author><pubDate>Thu, 21 May 2015 22:42:43 +0800</pubDate><description><![CDATA[Spark’s broadcast variables, used to broadcast immutable datasets to all nodeBroadcast/**]]></description><category></category></item><item><title><![CDATA[Spark-futureAction]]></title><link>https://blog.youkuaiyun.com/oblesslyy/article/details/45898259</link><guid>https://blog.youkuaiyun.com/oblesslyy/article/details/45898259</guid><author>oblesslyy</author><pubDate>Thu, 21 May 2015 22:40:50 +0800</pubDate><description><![CDATA[@(spark)[FutureAction]FutureAction]]></description><category></category></item><item><title><![CDATA[Spark-Dependency/Aggregator]]></title><link>https://blog.youkuaiyun.com/oblesslyy/article/details/45898231</link><guid>https://blog.youkuaiyun.com/oblesslyy/article/details/45898231</guid><author>oblesslyy</author><pubDate>Thu, 21 May 2015 22:38:49 +0800</pubDate><description><![CDATA[@(spark)[Dependency|Aggregator] 
RDD的核心之一：依赖关系]]></description><category></category></item><item><title><![CDATA[Spark-partitioner]]></title><link>https://blog.youkuaiyun.com/oblesslyy/article/details/45898243</link><guid>https://blog.youkuaiyun.com/oblesslyy/article/details/45898243</guid><author>oblesslyy</author><pubDate>Thu, 21 May 2015 22:38:42 +0800</pubDate><description><![CDATA[Spark-partitioner@(spark)[partitioner]Partitioner/**]]></description><category></category></item><item><title><![CDATA[Spark-rdd]]></title><link>https://blog.youkuaiyun.com/oblesslyy/article/details/45898209</link><guid>https://blog.youkuaiyun.com/oblesslyy/article/details/45898209</guid><author>oblesslyy</author><pubDate>Thu, 21 May 2015 22:37:20 +0800</pubDate><description><![CDATA[Spark-rdd@(spark)[rdd] 
首先介绍一下rdd，然后按字母字母顺序逐个描述各个rddRDD基类就叫RDD，这个文件非常长，有非常多的函数： 
1. 省略比较直观的函数的说明 
2. 有大量的功能函数distinct之类 
3. 再次重申sc.runJob是所有实质性函数的入口 
4. 在object RDD中含有大量的隐式转化  
5. 这中间最重要的一个函数就是override]]></description><category></category></item></channel></rss>