什么是谷歌的 Dremel?它与 Mapreduce 有何不同? [英] What is Google's Dremel? How is it different from Mapreduce?

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问题描述

Google 的 Dremel 此处描述.Dremel 和 Mapreduce 有什么区别?

Google's Dremel is described here. What's the difference between Dremel and Mapreduce?

推荐答案

检查这个 文章.Dremel 是 Hive 的未来应该(并将)成为的样子.

Check this article out. Dremel is the what the future of hive should (and will) be.

MapReduce 及其之上的解决方案(如 Pig、Hive 等)的主要问题是它们在运行作业和获得答案之间存在固有的延迟.Dremel 使用了一种全新的方法(于 2010 年在 google 的那篇论文中提出),它......

The major issue of MapReduce and solutions on top of it, like Pig, Hive etc, is that they have an inherent latency between running the job and getting the answer. Dremel uses a totally novel approach (came out in 2010 in that paper by google) which...

...使用基于聚合树的新型查询执行引擎...

...uses a novel query execution engine based on aggregator trees...

...运行几乎实时交互式adhoc查询,而MapReduce则不能.Pig 和 Hive 不是实时

...to run almost realtime , interactive AND adhoc queries both of which MapReduce cannot. And Pig and Hive aren't real time

您应该密切关注由此产生的项目.这对我来说也很新……所以欢迎任何其他专家评论!

You should keep an eye on projects coming out of this. Is is pretty new for me too... so any other expert comments are welcome!

编辑:HIVE(而不是我之前提到的 MapReduce)的未来应该是 Dremel.Hive 现在提供了一个类似于 SQL 的接口来运行 MapReduce 作业.Hive 具有非常高的延迟,因此在临时数据分析中不实用.Dremel 使用与 MapReduce 不同的技术为数据提供了一个非常快速的类似 SQL 的接口.

Dremel is what the future of HIVE (and not MapReduce as I mentioned before) should be. Hive right now provides a SQL like interface to run MapReduce jobs. Hive has very high latency, and so is not practical in ad-hoc data analysis. Dremel provides a very fast SQL like interface to the data by using a different technique than MapReduce.

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