我一直致力于。net应用程序的solr和弹性搜索。 我所面临的主要不同是

弹性搜索:

更多的代码和更少的配置，但有api的改变 但仍然是一个代码更改 对于复杂类型，类型中类型即嵌套类型(在solr中无法实现)

Solr:

代码更少，配置更多，因此维护更少 用于在查询期间对结果进行分组(在 弹性搜索，简而言之，没有直接的方法)

我一直致力于。net应用程序的solr和弹性搜索。 我所面临的主要不同是

弹性搜索:

更多的代码和更少的配置，但有api的改变 但仍然是一个代码更改 对于复杂类型，类型中类型即嵌套类型(在solr中无法实现)

Solr:

代码更少，配置更多，因此维护更少 用于在查询期间对结果进行分组(在 弹性搜索，简而言之，没有直接的方法)

我看到这里有很多人回答了ElasticSearch和Solr在特性和功能方面的问题，但我在这里(或其他地方)没有看到太多关于它们在性能方面如何比较的讨论。

这就是我决定自己进行调查的原因。我采用了一个已经编码的异构数据源微服务，该服务已经使用Solr进行术语搜索。我把Solr换成了ElasticSearch，然后在AWS上用一个已经编码的负载测试应用程序运行了两个版本，并捕获了用于后续分析的性能指标。

以下是我的发现。在索引文档时，ElasticSearch的吞吐量要高13%，而Solr的速度要快10倍。在文档查询方面，Solr的吞吐量是ElasticSearch的5倍，速度也是ElasticSearch的5倍。

我使用Elasticsearch 3年了，使用Solr大约一个月，我觉得与Solr安装相比，Elasticsearch集群非常容易安装。Elasticsearch有一个帮助文档池，其中有很好的解释。其中一个用例是直方图聚合，它在ES中可用，但在Solr中找不到。

我只使用弹性搜索。因为我发现solr很难开始。 Elastic-search的特点:

启动方便，设置少。即使是新手也可以一步一步地设置集群。 简单的Restful API，使用NoSQL查询。以及许多易于访问的语言库。 好的文件，你可以读这本书:。官方网站上有网络版。

While all of the above links have merit, and have benefited me greatly in the past, as a linguist "exposed" to various Lucene search engines for the last 15 years, I have to say that elastic-search development is very fast in Python. That being said, some of the code felt non-intuitive to me. So, I reached out to one component of the ELK stack, Kibana, from an open source perspective, and found that I could generate the somewhat cryptic code of elasticsearch very easily in Kibana. Also, I could pull Chrome Sense es queries into Kibana as well. If you use Kibana to evaluate es, it will further speed up your evaluation. What took hours to run on other platforms was up and running in JSON in Sense on top of elasticsearch (RESTful interface) in a few minutes at worst (largest data sets); in seconds at best. The documentation for elasticsearch, while 700+ pages, didn't answer questions I had that normally would be resolved in SOLR or other Lucene documentation, which obviously took more time to analyze. Also, you may want to take a look at Aggregates in elastic-search, which have taken Faceting to a new level.

Bigger picture: if you're doing data science, text analytics, or computational linguistics, elasticsearch has some ranking algorithms that seem to innovate well in the information retrieval area. If you're using any TF/IDF algorithms, Text Frequency/Inverse Document Frequency, elasticsearch extends this 1960's algorithm to a new level, even using BM25, Best Match 25, and other Relevancy Ranking algorithms. So, if you are scoring or ranking words, phrases or sentences, elasticsearch does this scoring on the fly, without the large overhead of other data analytics approaches that take hours--another elasticsearch time savings. With es, combining some of the strengths of bucketing from aggregations with the real-time JSON data relevancy scoring and ranking, you could find a winning combination, depending on either your agile (stories) or architectural(use cases) approach.

注意:上面确实有关于聚合的类似讨论，但没有关于聚合和相关性评分的讨论——我为任何重叠道歉。 披露:我不为elastic工作，而且由于不同的架构路径，在不久的将来也无法从他们的出色工作中受益，除非我用elasticsearch做一些慈善工作，这也不是一个坏主意