This is not a case of 'ignoring the theory', it's a case of you being pedantic.

I didn't say "replace 'like' queries with identical functionality", I said 'replace', as in 'supersede' and 'improve upon'. Except for insane cases such as the search 'ant' matching the word 'pedANTic', which is to say whenever whole tokens are intended to be matched, postgresql's full text search will replace 'like' queries for you with better functionality (such as matching non-continuous tokens in a search text) and better performance (assuming an index is created).

However, having full control of the tokenization pipeline means you can certainly satisfy any special cases your old 'like' queries would satisfy. For example, in Solr it is trivial to tokenize additionally on word/camel case/number-letter boundaries, so that the tokens generated from something like EM22C can be EM 22 C, EM22 C, EM 22C, EM22C, etc.

That is a specific example, so please don't respond that I am ignorant of the theory, I fully realize that you can index any string, not just the exact ones I mentioned there.

Speaking of being pedantic, Postgresql's full text search is not an 'inverted index'. It is not an index at all, although it can use indexing to improve performance. In addition to GIN (Generalized Inverted Index) it can also use GiST (Generalized Search Tree)

You seem to be implying that the primary purpose of like queries is to do full text search, which is of course not true. E.g. perhaps you need prefix matching, or perhaps you are implementing an autocomplete box where you want partial matching, or you are searching over filenames and want partial matching, the latter two both being "insane" cases in your world.

And yes, there are GiST indexes for text in addition to inverted indexes. I didn't bring these up because you are comparing primarily to products using inverted indexes. My point is to illustrate that these products are actually mostly the same, with differences primarily around tooling.

No one is arguing that Solr and Lucene don't have advantages over PostgreSQL's text search. But I strongly disagree with your claim that "full text search lacks the feature set to be a reasonable solution to most search use cases."

You are quick to claim that postgresql cannot use customize tokenizers or cannot scale or doesn't have features. Then in the same paragraph say that well, yes, it can do both these things but it's harder to work with. You also wipe all the other advantages that postgresql brings under the rug with a dismissive "oh well these are minor situations." I guess they are minor in your products, but they are certainly not minor in others.

If you only need text search, or are using a database other than postgres, then Solr and Lucene are great choices. On the other hand, if you are using PostgreSQL already, its' full text search is entirely reasonable and feature rich for many if not most search cases.

> You may need to use a trigram index, which you cannot do with ElasticSearch and Lucene

You definitely can create an analyzer to generate trigrams in Elasticsearch. Unless you mean something different with "trigram index" than indexing trigrams?