Hive 与 HBase 相比如何? [英] How does Hive compare to HBase?

问题描述

我有兴趣了解最近发布的 (http://mirror.facebook.com/facebook/hive/hadoop-0.17/) Hive 在性能方面与 HBase 相比.Hive 使用的类 SQL 接口比我们实现的 HBase API 更可取.

I'm interested in finding out how the recently-released (http://mirror.facebook.com/facebook/hive/hadoop-0.17/) Hive compares to HBase in terms of performance. The SQL-like interface used by Hive is very much preferable to the HBase API we have implemented.

推荐答案

很难找到很多关于 Hive 的信息，但我找到了这个 代码片段，严重支持 HBase(粗体添加):

It's hard to find much about Hive, but I found this snippet on the Hive site that leans heavily in favor of HBase (bold added):

Hive 基于 Hadoop，它是一个批处理系统.因此，该系统不会也不能保证查询的低延迟.这里的范式严格来说是提交作业并在作业完成时收到通知，而不是实时查询.因此，不应将其与 Oracle 等系统进行比较，后者分析的数据量要少得多，但分析会以更多的迭代方式进行，迭代之间的响应时间不到几分钟.对于 Hive 查询，即使是最小的作业的响应时间也可能在 5 到 10 分钟的数量级，而对于较大的作业，这甚至可能达到数小时.

Hive is based on Hadoop which is a batch processing system. Accordingly, this system does not and cannot promise low latencies on queries. The paradigm here is strictly of submitting jobs and being notified when the jobs are completed as opposed to real time queries. As a result it should not be compared with systems like Oracle where analysis is done on a significantly smaller amount of data but the analysis proceeds much more iteratively with the response times between iterations being less than a few minutes. For Hive queries response times for even the smallest jobs can be of the order of 5-10 minutes and for larger jobs this may even run into hours.

由于 HBase 和 HyperTable 都是关于性能的(以 Google 的 BigTable 为模型)，因此它们听起来肯定比 Hive 快得多，但代价是功能性和更高的学习曲线(例如，它们没有联接或类似 SQL 的语法).

Since HBase and HyperTable are all about performance (being modeled on Google's BigTable), they sound like they would certainly be much faster than Hive, at the cost of functionality and a higher learning curve (e.g., they don't have joins or the SQL-like syntax).

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