星火是有利还是对非迭代分析？ [英] Is Spark still advantageous for non-iterative analytics?

问题描述

星火用于内存计算和缓存减少对复杂分析的延迟，但是这主要是针对迭代algorythms
如果我需要执行一个更基本的分析，说也许每个元素是一组数字，我想寻找的元素与标准差小于X会引发仍然缩短延迟时间比一般集群计算（无内存计算）？假设我用同样的商品硬件在每种情况下。

Spark uses in memory computing and caching to decrease latency on complex analytics, however this is mainly for "iterative algorythms", If I needed to perform a more basic analytic, say perhaps each element was a group of numbers and I wanted to look for elements with a standard deviation less than 'x' would Spark still decrease latency compared to regular cluster computing (without in memory computing)? Assuming I used that same commodity hardware in each case.

推荐答案

据并列最高使用没有这些额外的机制排序框架，因此，我认为足够的理由。但是，你也可以无需切换齿轮运行流，图形或机器学习。然后，添加在您应尽可能使用DataFrames，你会得到超越其他任何框架，我所知道的查询优化。所以，是的，Spark是在几乎每一个实例中明确的选择。

It tied for the top sorting framework using none of those extra mechanisms, so I would argue that is reason enough. But, you can also run streaming, graphing, or machine learning without having to switch gears. Then, you add in that you should use DataFrames wherever possible and you get query optimizations beyond any other framework that I know of. So, yes, Spark is the clear choice in almost every instance.

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