Spark 学习资源收集【Updating】

（一）spark 相关安装部署、开发环境

1、Spark 伪分布式 & 全分布式 安装指南

http://my.oschina.net/leejun2005/blog/394928

2、Apache Spark探秘：三种分布式部署方式比较

http://dongxicheng.org/framework-on-yarn/apache-spark-comparing-three-deploying-ways/

3、idea上运行local的spark sql hive

http://dataknocker.github.io/2014/10/11/idea%E4%B8%8A%E8%BF%90%E8%A1%8Clocal%E7%9A%84spark-sql-hive/

4、Apache Spark学习：利用Scala语言开发Spark应用程序

http://dongxicheng.org/framework-on-yarn/spark-scala-writing-application/

5、如何在CDH5上运行Spark应用（Scala、Java、Python）

http://blog.javachen.com/2015/02/04/how-to-run-a-simple-apache-spark-app-in-cdh-5/

6、Spark集群安装和使用

http://blog.javachen.com/2014/07/01/spark-install-and-usage/#

（二）spark 架构、原理与编码

1、理解Spark的核心RDD

http://www.infoq.com/cn/articles/spark-core-rdd

2、How-to: Translate from MapReduce to Apache Spark（怎样从 MapReduce 迁移到 Spark）

http://blog.cloudera.com/blog/2014/09/how-to-translate-from-mapreduce-to-apache-spark/

3、Spark SQL 源码分析之 In-Memory Columnar Storage 之 cache table

http://blog.csdn.net/oopsoom/article/details/39525483

4、Databricks Spark 知识库

• 最佳实践
  
  
  • 避免使用 GroupByKey
  • 不要将大型 RDD 的所有元素拷贝到请求驱动者
    
    
• 常规故障处理
  
  
  • Job aborted due to stage failure: Task not serializable
  • 缺失依赖
  • 执行 start-all.sh 错误 - Connection refused
  • Spark 组件之间的网络连接问题
    
    
• 性能 & 优化
  
  
  • 一个 RDD 有多少个分区
  • 数据本地性
    
    
• Spark Streaming
  
  
  • ERROR OneForOneStrategy
    
    

http://aiyanbo.gitbooks.io/databricks-spark-knowledge-base-zh-cn/content/

5、Spark1.0.0 编程模型

http://blog.csdn.net/book_mmicky/article/details/32096871

6、Spark技术内幕：Client，Master和Worker 通信源码解析

http://blog.csdn.net/anzhsoft/article/details/30802603

7、Spark Streaming编程指南

http://yangqijun.com/archives/200

8、Spark分布式计算执行模型

http://www.flickering.cn/%E5%88%86%E5%B8%83%E5%BC%8F%E8%AE%A1%E7%AE%97/2014/07/spark%E5%88%86%E5%B8%83%E5%BC%8F%E8%AE%A1%E7%AE%97%E6%89%A7%E8%A1%8C%E6%A8%A1%E5%9E%8B/

9、Top 3 Troubleshooting Tips To Keep You Sparking

http://engineering.sharethrough.com/blog/2013/09/13/top-3-troubleshooting-tips-to-keep-you-sparking/

10、Apache Spark 设计与实现（重点关注设计思想、运行原理、实现架构及性能调优，附带讨论与 MapReduce 在设计与实现上的区别。）

https://github.com/JerryLead/SparkInternals/tree/master/markdown

11、Spark Examples

http://spark.apache.org/examples.html

12、RDD操作详解

http://dataknocker.github.io/2014/07/20/RDD%E5%90%84%E6%93%8D%E4%BD%9C%E8%AF%A6%E8%A7%A3/

13、Spark编程指南笔记

http://blog.javachen.com/2015/02/03/spark-programming-guide/#

14、Spark Core Runtime分析: DAGScheduler, TaskScheduler, SchedulerBackend

http://blog.csdn.net/pelick/article/details/44495611

15、Getting Started with Spark (in Python)

https://districtdatalabs.silvrback.com/getting-started-with-spark-in-python

16、Spark编程指南笔记

http://blog.javachen.com/2015/02/03/spark-programming-guide/#

17、Spark SQL中的DataFrame

http://blog.javachen.com/2015/03/26/spark-sql-dataframe/#

18、Spark RDD API详解(一) Map和Reduce

https://www.zybuluo.com/jewes/note/35032

（三）spark 监控与管理

1、Common Spark Troubleshooting

http://www.datastax.com/dev/blog/common-spark-troubleshooting

2、

（四）YARN & spark

1、Apache Spark探秘：多进程模型还是多线程模型？

http://dongxicheng.org/framework-on-yarn/apache-spark-multi-threads-model/

（五）spark 数据平台架构

（六）spark 应用与实践

1、How-to: Do Near-Real Time Sessionization with Spark Streaming and Apache Hadoop

http://blog.cloudera.com/blog/2014/11/how-to-do-near-real-time-sessionization-with-spark-streaming-and-apache-hadoop/

2、Integrating Kafka and Spark Streaming: Code Examples and State of the Game

http://www.michael-noll.com/blog/2014/10/01/kafka-spark-streaming-integration-example-tutorial/

3、spark读取 kafka nginx网站日志消息 并写入HDFS中

http://yangqijun.com/archives/227

4、Flafka: Apache Flume Meets Apache Kafka for Event Processing

http://blog.cloudera.com/blog/2014/11/flafka-apache-flume-meets-apache-kafka-for-event-processing/

5、Log Analysis with Spark

http://databricks.gitbooks.io/databricks-spark-reference-applications/content/logs_analyzer/README.html

6、Spark将计算结果写入到Mysql中

http://www.iteblog.com/archives/1275

7、Spark Streaming 1.3对Kafka整合的提升详解

http://www.iteblog.com/archives/1307

8、Spark SQL中的数据源

http://blog.javachen.com/2015/04/03/spark-sql-datasource/#

（七）spark 机器学习实践

1、ML Pipelines: A New High-Level API for MLlib

http://databricks.com/blog/2015/01/07/ml-pipelines-a-new-high-level-api-for-mllib.html

2、Spark 0.9.1 MLLib 机器学习库简介

http://rdc.taobao.org/?p=2163

（八）Scala 学习指北

1、Spark开发指南（0.8.1中文版）

http://rdc.taobao.org/?p=2024

2、Swift和Scala语法上的诸多相似之处

http://segmentfault.com/a/1190000000575561

3、Awesome Scala

https://github.com/lauris/awesome-scala

4、scala（有关jvm,scala与后端架构，阿里工程师的博客，相当不错）

http://hongjiang.info/scala/

5、Scala极速入门

http://my.oschina.net/mup/blog/363436?from=20150111

6、An-Overview-of-the-Scala-Programming-Language

https://github.com/wecite/papers/tree/master/An-Overview-of-the-Scala-Programming-Language

7、Scala简明教程

http://colobu.com/2015/01/14/Scala-Quick-Start-for-Java-Programmers/

（九）Spark book

1、Spark Cook Book

http://www.infoobjects.com/spark-cookbook/

2、Fast Data Processing with Spark

http://it-ebooks.info/book/3185/

3、Scala语言概览

http://wecite.github.io/docs/ScalaOverview-20150226.pdf

4、Effective Scala

http://twitter.github.io/effectivescala/index-cn.html

5、有趣的 Scala 语言: 简洁的 Scala 语法

http://www.ibm.com/developerworks/cn/java/j-lo-funinscala2/