Why JCR search is not suited for site search

Many, especially larger websites have an integrated search functionality, which let’s users directly find content of the site without using external search engines like Google or Bing. If properly implemented and used, it can be a tremendous help to get visitors directly to the information they need and want.

I’ve get questions in the past how one can implement such a site search using JCR queries. And at least in the last years my answer always was: don’t use JCR search for that. Let me elaborate on that.

JCR queries are querying the repository, but not the website

With JCR query you are querying the repository, but you don’t query the website. That sounds a bit strange, because the website lives in the repository. This is true, but in reality the website is much more. A rendered page consists of data stored below a cq:Page node. And more data from other parts of the repository. For example you pull in assets to a page, and you also add some metadata from the assets into the rendered page. You add references to other pages and include data from there.

This means, that the rendered page contains much meaningful and relevant information which can and should be leveraged from a search function to deliver the best results. And this data is not part of the cq:page structure in the repository.

Or to put in other words: You do SEO optimization for your page to deliver the most relevant results hoping that its rank on Google gets higher and more relevant for users searching for specific terms. Do you really think that your own integrated site search should deliver less relevant data for the same search?

As a site owner I do not expect that Google delivers for a certain search keyword combination a page A as highest ranked page on my site, but my internal search a different page B which is clearly less relevant for that keywords.

That means, that you should provide your site search the same information and metadata as to Google. And for JCR queries you only have the repository structure and the information stored there, and you should not optimize this as well for relevant search results, but the JCR repository structure aims for different goals (like performance, maintainability, evolvability and others).

JCR queries implement functionality not needed for site search

The JCR query implementation needs to take some concepts into account, which are often not relevant for site search, but which are quite expensive. Just to name a few:

  • Nodetype inheritance and Mixins:  On every search there are checks for nodetypes, sometimes with the need to traverse the hierarchy and check the mixin relationships. That’s overhead.
  • ACL checks: Every search result needs to be acl checked before returning it, which can be huge overhead, especially if in the most simple case all (relevant) content is public and there should be no need to do such checks at all.
  • And probably much more.

JCR is not good at features which I would expect from a site search

  • Performance is not always what I expect from a site search.
  • With the current Oak implementation you should test every query if it’s covered by indexes; as site search queries are often unpredictable (especially if you do not only allow for a single search term, but also want to include wild cards etc) you’ll always have the risk that something unexpected happens. And then it’s not only about bad performance if your query is not covered by a matching index. It can also be that you deliver the wrong search results (or no search results at all).
  • Changing index definitions (even adding synonyms or stopwords) are necessarily an admin task, and if improperly done, they impact the overall system. Not to mention the need of reindexing 😦

From my point of view if you cannot solely rely on external search engines (Google, Bing, DuckDuckGo, …) you should implement your site search not on JCR queries. It often causes more trouble than adding a dedicated Solr instance which crawling your site and which is embedded into your site to deliver the search results.  You can take this HelpX article as starting point how to integrate Solr into your site. But of course any other search engine is possible as well.