由于Apache Solr的悠久历史，我认为Solr的一个优势是它的生态系统。针对不同类型的数据和用途，有许多Solr插件。

搜索平台从下到上分为以下几层:

数据 目的:表示各种数据类型和来源 文档建立 目的:为索引建立文档信息 索引和搜索 目的:建立和查询文档索引 逻辑增强 用途:用于处理搜索查询和结果的附加逻辑 搜索平台服务 目的:增加搜索引擎核心的附加功能，提供服务平台。 UI应用程序 用途:用于终端用户搜索界面或应用程序

参考文章:企业搜索

如果您已经在使用SOLR，请坚持使用。如果你正在创业，使用弹性搜索。

最大的主要问题已经在SOLR中得到了解决，它是相当成熟的。

更新

既然问题的范围已经被纠正了，我也可以在这方面补充一些东西:

Apache Solr和ElasticSearch之间有很多比较，所以我将引用我自己认为最有用的，即涵盖最重要的方面:

Bob Yoplait already linked kimchy's answer to ElasticSearch, Sphinx, Lucene, Solr, Xapian. Which fits for which usage?, which summarizes the reasons why he went ahead and created ElasticSearch, which in his opinion provides a much superior distributed model and ease of use in comparison to Solr. Ryan Sonnek's Realtime Search: Solr vs Elasticsearch provides an insightful analysis/comparison and explains why he switched from Solr to ElasticSeach, despite being a happy Solr user already - he summarizes this as follows: Solr may be the weapon of choice when building standard search applications, but Elasticsearch takes it to the next level with an architecture for creating modern realtime search applications. Percolation is an exciting and innovative feature that singlehandedly blows Solr right out of the water. Elasticsearch is scalable, speedy and a dream to integrate with. Adios Solr, it was nice knowing you. [emphasis mine] The Wikipedia article on ElasticSearch quotes a comparison from the reputed German iX magazine, listing advantages and disadvantages, which pretty much summarize what has been said above already: Advantages: ElasticSearch is distributed. No separate project required. Replicas are near real-time too, which is called "Push replication". ElasticSearch fully supports the near real-time search of Apache Lucene. Handling multitenancy is not a special configuration, where with Solr a more advanced setup is necessary. ElasticSearch introduces the concept of the Gateway, which makes full backups easier. Disadvantages: Only one main developer [not applicable anymore according to the current elasticsearch GitHub organization, besides having a pretty active committer base in the first place] No autowarming feature [not applicable anymore according to the new Index Warmup API]

最初的回答

它们是针对完全不同用例的完全不同的技术，因此根本无法以任何有意义的方式进行比较:

Apache Solr - Apache Solr offers Lucene's capabilities in an easy to use, fast search server with additional features like faceting, scalability and much more Amazon ElastiCache - Amazon ElastiCache is a web service that makes it easy to deploy, operate, and scale an in-memory cache in the cloud. Please note that Amazon ElastiCache is protocol-compliant with Memcached, a widely adopted memory object caching system, so code, applications, and popular tools that you use today with existing Memcached environments will work seamlessly with the service (see Memcached for details).

(强调我的)

也许这已经与以下两种相关技术混淆了:

ElasticSearch -这是一个开源(Apache 2)、分布式、RESTful的搜索引擎，建立在Apache Lucene之上。 Amazon CloudSearch—Amazon CloudSearch是一个完全托管的云搜索服务，允许客户轻松地将快速和高度可扩展的搜索功能集成到他们的应用程序中。

Solr和ElasticSearch的产品乍听起来非常相似，并且都使用相同的后端搜索引擎，即Apache Lucene。

虽然Solr更老，功能更全面，更成熟，因此被广泛使用，但ElasticSearch是专门为解决Solr在现代云环境中可伸缩性需求方面的缺点而开发的，这些缺点很难用Solr解决。

因此，将ElasticSearch与最近推出的Amazon CloudSearch进行比较可能是最有用的(参见介绍性文章Start Searching in One Hour for Less Than 100 $ / Month)，因为两者都声称在原则上涵盖相同的用例。

在solr中添加嵌套文档非常复杂，嵌套数据搜索也非常复杂。但弹性搜索容易添加嵌套文档和搜索

我发现上面的一些答案现在有点过时了。从我的角度来看，我每天都在使用Solr(云和非云)和ElasticSearch，这里有一些有趣的区别:

Community: Solr has a bigger, more mature user, dev, and contributor community. ES has a smaller, but active community of users and a growing community of contributors Maturity: Solr is more mature, but ES has grown rapidly and I consider it stable Performance: hard to judge. I/we have not done direct performance benchmarks. A person at LinkedIn did compare Solr vs. ES vs. Sensei once, but the initial results should be ignored because they used non-expert setup for both Solr and ES. Design: People love Solr. The Java API is somewhat verbose, but people like how it's put together. Solr code is unfortunately not always very pretty. Also, ES has sharding, real-time replication, document and routing built-in. While some of this exists in Solr, too, it feels a bit like an after-thought. Support: there are companies providing tech and consulting support for both Solr and ElasticSearch. I think the only company that provides support for both is Sematext (disclosure: I'm Sematext founder) Scalability: both can be scaled to very large clusters. ES is easier to scale than pre-Solr 4.0 version of Solr, but with Solr 4.0 that's no longer the case.

有关Solr vs. ElasticSearch主题的更全面报道，请查看https://sematext.com/blog/solr-vs-elasticsearch-part-1-overview/。这是Sematext系列文章中的第一篇，对Solr和ElasticSearch进行了直接和中立的比较。披露:我在Sematext工作。

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)