Awesome book. I read the pre-release and it was a great deep dive into various topics that will help improve your search application. I highly recommend for anyone who is working with Elasticsearch (I think they also have a Solr appendix, but I was only interested in ES)!
(author here) -- yeah we focus on relevance exclusively. Other topics like analytics, scale, devops, performance, data integration would be better handled by one of the pure Elasticsearch or Solr books (Solr in Action, ES in Action, ES the Definitive Guide).
I've only read this and the Definitive Guide to ES. "Relevant Search" is more focused on working through how to tune search results and understand why things are happening and how you can make them work in your favor. They work through a lot of great examples and go much deeper than anything else I've come across.
This is not a Elasticsearch focused book. Rather, it is using ES as a tool in order to provide an concrete and practical walkthrough about how to achieve semantic relevancy in real world application. It is not concerning too much about the engineering side of ES, e.g. Master/Datanode or replication that kind of stuff, but more on the quality of text search.
As the author has put, you could use Solr but will still find this book relevant(sorry) to you.
As someone working on search products I know at first hand how hard relevance is. The best thing would be mind-reading, but we have little progress so far. Therefore we have to assume what is most relevant for our customers (luckily they help with that part)
Another nice aspect of Relevant Search is that its code is mostly ipython/jupyter notebooks here https://github.com/o19s/relevant-search-book, along with a Vagrant box and installation instructions, so you can spend more time focusing on learning and experimenting with the ideas than on installation and setup.
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I wish my customers weren't so unwilling to let me install neural taps. I'm just trying to improve the experience!