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Thoughts...

I‘m embedding my answer to this "Solr-vs-Elasticsearch" Quora question verbatim here:

1. Elasticsearch was born in the age of REST APIs. If you love REST APIs, you‘ll probably feel more at home with ES from the get-go. I don‘t actually think it‘s ‘cleaner‘ or ‘easier to use‘, but just that it is more aligned with web 2.0 developers‘ mindsets.

2. Elasticsearch‘s Query DSL syntax is really flexible and it‘s pretty easy to write complex queries with it, though it does border on being verbose. Solr doesn‘t have an equivalent, last I checked. Having said that, I‘ve never found Solr‘s query syntax wanting, and I‘ve always been able to easily write a custom SearchComponent if needed (more on this later).

3. I find Elasticsearch‘s documentation to be pretty awful. It doesn‘t help that some examples in the documentation are written in YAML and others in JSON. I wrote a ES code parser once to auto-generate documentation from Elasticsearch‘s source and found a number of discrepancies between code and what‘s documented on the website, not to mention a number of undocumented/alternative ways to specify the same config key.

By contrast, I‘ve found Solr to be consistent and really well-documented. I‘ve found pretty much everything I‘ve wanted to know about querying and updating indices without having to dig into code much. Solr‘s schema.xml and solrconfig.xml are *extensively* documented with most if not all commonly used configurations.

4. Whilst what Rick says about ES being mostly ready to go out-of-box is true, I think that is also a possible problem with ES. Many users don‘t take the time to do the most simple config (e.g. type mapping) of ES because it ‘just works‘ in dev, and end up running into issues in production.

And once you do have to do config, then I personally prefer Solr‘s config system over ES‘. Long JSON config files can get overwhelming because of the JSON‘s lack of support for comments. Yes you can use YAML, but it‘s annoying and confusing to go back and forth between YAML and JSON.

5. If your own app works/thinks in JSON, then without a doubt go for ES because ES thinks in JSON too. Solr merely supports it as an afterthought. ES has a number of nice JSON-related features such as parent-child and nested docs that makes it a very natural fit. Parent-child joins are awkward in Solr, and I don‘t think there‘s a Solr equivalent for ES Inner hits.

6. ES doesn‘t require ZooKeeper for it‘s ‘elastic‘ features which is nice coz I personally find ZK unpleasant, but as a result, ES does have issues with split-brain scenarios though (google ‘elasticsearch split-brain‘ or see this: Elasticsearch Resiliency Status).

7. Overall from working with clients as a Solr/Elasticsearch consultant, I‘ve found that developer preferences tend to end up along language party lines: if you‘re a Java/c# developer, you‘ll be pretty happy with Solr. If you live in Javascript or Ruby, you‘ll probably love Elasticsearch. If you‘re on Python or PHP, you‘ll probably be fine with either.

Something to add about this: ES doesn‘t have a very elegant Java API IMHO (you‘ll basically end up using REST because it‘s less painful), whereas Solrj is very satisfactory and more efficient than Solr‘s REST API. If you‘re primarily a Java dev team, do take this into consideration for your sanity. There‘s no scenario in which constructing JSON in Java is fun/simple, whereas in Python its absolutely pain-free, and believe me, if you have a non-trivial app, your ES json query strings will be works of art.

8. ES doesn‘t have in-built support for pluggable ‘SearchComponents‘, to use Solr‘s terminology. SearchComponents are (for me) a pretty indispensable part of Solr for anyone who needs to do anything customized and in-depth with search queries.

Yes of course, in ES you can just implement your own RestHandler, but that‘s just not the same as being able to plug-into and rewire the way search queries are handled and parsed.

9. Whichever way you go, I highly suggest you choose a client library which is as ‘close to the metal‘ as you can get. Both ES and Solr have *really* simple search and updating search APIs. If a client library introduces an additional DSL layer in attempt to ‘simplify‘, I suggest you think long and hard about using it, as it‘s likely to complicate matters in the long-run, and make debugging and asking for help on SO more problematic.

In particular, if you‘re using Rails + Solr, consider using rsolr/rsolr
instead of sunspot/sunspot if you can help it. ActiveRecord is complex code and sufficiently magical. The last thing you want is more magic on top of that.

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To conclude, ES and Solr have more or less feature-parity and from a feature standpoint, there‘s rarely one reason to go one way or the other (unless your app lives/breathes JSON). Performance-wise, they are also likely to be quite similar (I‘m sure there are exceptions to the rule. ES‘ relatively new autocomplete implementation, for example, is a pretty dramatic departure from previous Lucene/Solr implementations, and I suspect it produces faster responses at scale).

ES does offer less friction from the get-go and you feel like you have something working much quicker, but I find this to be illusory. Any time gained in this stage is lost when figuring out how to properly configure ES because of poor documentation - an inevitablity when you have a non-trivial application.

Solr encourages you to understand a little more about what you‘re doing, and the chance of you shooting yourself in the foot is somewhat lower, mainly because you‘re forced to read and modify the 2 well-documented XML config files in order to have a working search app.

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EDIT on Nov 2015:

ES has been gradually distinguishing itself from Solr when it comes to data analytics. I think it‘s fair to attribute this to the immense traction of the ELK stack in the logging, monitoring and analytic space. My guess is that this is where Elastic (the company) gets the majority of its revenue, so it makes perfect sense that ES (the product) reflects this.

We see this manifesting primarily in the form of aggregations, which is a more flexible and nuanced replacement for facets. Read more about aggregations here: Migrating to aggregations

Aggregations have been out for a while now (since 1.4), but with the recently released ES 2.0 comes pipeline aggregations, which let you compute aggregations such as derivatives, moving averages, and series arithmetic on the results of other aggregations. Very cool stuff, and Solr simply doesn‘t have an equivalent. More on pipeline aggregations here: Out of this world aggregations

If you‘re currently using or contemplating using Solr in an analytics app, it is worth your while to look into ES aggregation features to see if you need any of it.

Resources

• My other sites may be of interest if you‘re new to Lucene, Solr and Elasticsearch:
  • Lucene Tutorial
  • Elasticsearch Tutorial
  • Solr Tutorial
• TheSolr wikiand theElasticsearch Guideare your friends.

Contribute

If you see any mistakes, or would like to append to the information on this webpage, you can clone theGitHub repo for this sitewith:

git clone https://github.com/superkelvint/solr-vs-elasticsearch

and submit a pull request.

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