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。

想象一下用例:

大量(100+)小型(10Mb-100Mb, 1000-100000个文档)搜索索引。 它们被很多应用程序(微服务)使用 每个应用程序可以使用多个索引 小尺寸指数，是的。但是巨大的负载(每秒数百个搜索请求)和复杂的请求(多个聚合、条件等) 不允许停机 所有这些都是持续多年的工作，并不断增长。

每个索引都有单独的ES实例的想法在这种情况下是巨大的开销。

根据我的经验，用Elasticsearch来支持这种用例非常复杂。

Why?

第一。

主要问题是根本的向后兼容性忽略。

突破性的改变太酷了! (注意:想象一下SQL-server需要你在所有sql语句中做小的改变，当升级时…真不敢想象。但对于ES来说是正常的)

将在下一个主要版本中删除的弃用是如此性感! (注意:你知道，Java包含一些过时的东西，已经有20多年的历史了，但在实际的Java版本中仍然可以工作…)

不仅如此，有时你甚至有一些没有记录的东西(个人只遇到过一次，但是……)

所以。如果你想升级ES(因为你需要一些应用程序的新功能或者你想修复错误)-你就在地狱里。尤其是关于重大版本升级的时候。

客户端API将不向后兼容。索引设置将不向后兼容。 在升级ES的同时升级所有应用/服务是不现实的。

但你必须时不时地去做。没有别的办法。

现有索引是否自动升级?——是的。但是当您需要更改一些旧的索引设置时，它没有帮助。

为了适应这一点，你需要不断地在……您的应用程序/服务与ES未来版本的向前兼容性。 或者，您需要在应用程序/服务和ES之间构建某种中间件(无论如何都要不断地支持)，从而为您提供兼容的客户机API。 (而且，您不能使用Transport Client(因为每次小版本ES升级都需要升级jar)，这并没有使您的生活更轻松)

它看起来简单便宜吗?不，不是。远非如此。 对基于ES的复杂基础设施的持续维护，无论如何都是昂贵的。

第二。 简单的API ?嗯…没有真的。 当你真的在使用复杂的条件和聚合....带有5个嵌套级别的JSON-request是什么，但并不简单。

不幸的是，我对SOLR没有经验，不能说什么。

但是Sphinxsearch在这种情况下要好得多，因为SphinxQL完全向后兼容。

注意: Sphinxsearch/Manticore确实很有趣。它不是基于Lucine的，结果严重不同。包含几个独特的功能，从盒子，ES没有和疯狂的快速与小/中等大小的索引。

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。

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。

我已经创建了elasticsearch和Solr和splunk之间的主要差异表，您可以使用它作为2016年的更新:

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