Cross field search with different analyzers

Elastic Stack Elasticsearch
1

I have the following (simplified) mapping:

{
"settings": {
    "index": {
        "number_of_shards": "2",
        "number_of_replicas": "1"
    },
    "analysis": {
        "analyzer": {
            "rebuilt_whitespace": {
                "tokenizer": "whitespace",
                "filter": ["lowercase"]
            }
        }
    }
},
"mappings": {
    "properties": {
        "tags": {
            "fielddata": true,
            "type": "text",
            "analyzer": "rebuilt_whitespace",
            "fields": {
                "length": {
                    "type": "token_count",
                    "analyzer": "rebuilt_whitespace"
                }
            }
        },
        "category": {
            "type": "text",
            "fields": {
                "raw": {
                    "type": "keyword"
                }
            }
        }
    }
}
}

Say I have a query like "c++ programming" I'd like to be able to have results that match "programming" in category and "c++" in tags (but not "c"). Using cross_fields (with multi_match or query_string) to do this doesn't work as they are using different analyzers. Anything else I have been able to find seem to go on compromise on something else. Also note I have more fields than those listed here, so doing "exhaustive" matching won't work.
Basically I want to be able to perform:

+(tags:programming | category:programming) +(tags:c++ | category:c)

Note the different analyzers used for "c++". Is this possible?

2

I haven't found a solution to this. Some threads a mentioning using query_string with default_operatorset to AND, but this is still not giving me the right results.
Alternatively, I can set search_analyzer at query time to force them into the same groups, but this is not optimal either.
Any advice is greatly appreciated!

3

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