Spark的RDD原理以及2.0特性的介绍(4)

最后 2.0 版本还有一些其他的特性,如：

1. 用 SparkSession 替换掉原来的 SQLContext and HiveContext.
2. mllib 里的计算用 DataFrame-based API 代替以前的 RDD 计算逻辑.
3. 提供更多的 R 语言算法.
4. 默认使用 Scala 2.11 编译与运行.

参考资料

1. http://spark.apache.org/
2. https://databricks.com/blog/2016/05/11/spark-2-0-technical-preview-easier-faster-and-smarter.html
3. http://www.eecs.berkeley.edu/Pubs/TechRpts/2014/EECS-2014-12.pdf
4. http://www.infoq.com/cn/articles/spark-core-rdd
5. https://databricks.com/blog/2015/02/17/introducing-dataframes-in-spark-for-large-scale-data-science.html
6. http://www.slideshare.net/databricks/spark-summit-eu-2015-spark-dataframes-simple-and-fast-analysis-of-structured-data
7. https://databricks.com/blog/2015/04/28/project-tungsten-bringing-spark-closer-to-bare-metal.html
8. https://databricks.com/blog/2016/01/04/introducing-spark-datasets.html
9. https://databricks-prod-cloudfront.cloud.databricks.com/public/4027ec902e239c93eaaa8714f173bcfc/6122906529858466/293651311471490/5382278320999420/latest.html
10. http://www.csdn.net/article/2014-01-28/2818282-Spark-Streaming-big-data
11. http://www.slideshare.net/rxin/the-future-of-realtime-in-spark

文/王联辉

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