Is it appropriate to use a search engine as a caching layer?

These guys have done this...

http://www.artirix.com/elasticsearch-as-a-smart-cache/

The problem I see is not in the read speed, but in the write speed. You are incurring a pretty hefty cost for adding things to the cache (forcing spool to disk and index merge).

Things like memcached or elastic cache if you are on AWS, are much more efficient at both inserts and reads.

"Elasticsearch and Solr are fast" is relative, caching infrastructure is often measured in single-digit millisecond range, same for inserts. These search engines are at least measured in 10's of milliseconds for reads, and much higher for writes.

I've heard of setups where ES was used for what is it really good for: full context search and used in parallel with a secondary storage. In these setups data was not stored (but it can be) - "store": "no" - and after searching with ES in its indices, the actual records were retrieved from the second storage level - usually a RDBMS - given that ES was holding a reference to the actual record in the RDBMS (an ID of some sort). If you're not happy with whatever secondary storage gives in you in terms of speed and "search" in general I don't see why you couldn't setup an ES cluster to give you the missing piece.

The disadvantage here is the time spent architecting the ES data structure because ES is not as good as a RDBMS at representing relationships. And it really doesn't need to, its main job and purpose is different. And is, actually, happier with a denormalized set of data to search over.

Another disadvantage is the complexity of keeping in sync the two storage systems which will require some thinking ahead. But, once the initial setup and architecture is in place, it should be easy afterwards.

the only recommended way of using a search engine is to create indices that match your most frequently accessed denormalised data access patterns. You can call it a cache if you want. For searching it's perfect, as it's fast enough.Recommended thing to add cache for there - statistics for "aggregated" queries - "Top 100 hotels in Europe", as a good example of it.