Hi all,

I have a set of extracted terms, with associated relevancy scores and other 
metadata, from each document. I'd like to scale _score by the relevancy of 
the matched terms. It seems to me there are at least two approaches for 
solving this problem:

1) Nested Document:

In this case, my mapping would look like:

{
    "contentDocument": {
        "properties": {
            "content": {
                "type": "string"
            },
            "terms": {
                "type": "nested",
                "fields": {
                    "text": {
                        "type": "string",
                    },
                    "relevance": {
                        "type": "float"
                    }
                }
            }
        }
    }
}

Then I could query using:

{
    "query": {
        "nested": {
            "score_mode": "max",
            "path": "terms",
            "query": {
                "function_score": {
                    "boost_mode": "replace",
                    "score_mode": "multiply",
                    "query": {
                        "match": {
                            "terms.text": "<my text>"
                        }
                    },
                    "functions": [
                        {
                            "field_value_factor": {
                                "field": "terms.relevance"
                            }
                        }
                    ]
                }
            }
        }
    }
}

This seems to work as expected on the small prototype I've built.

2. Parent/Child Documents:

In this case, my mapping would look like:

{
    "contentDocument": {
        "properties": {
            "content": {
                "type": "string"
            }
        }
    }
}
{
    "termDocument": {
        "_parent": {
            "type": "contentDocument"
        },
        "properties": {
            "text": {
                "type": "string"
            },
            "relevance": {
                "type": "float"
            }
        }
    }
}

Then I could query using:

{
    "query": {
        "has_child": {
            "type": "termDocument",
            "score_mode": "max",
            "query": {
                "function_score": {
                    "boost_mode": "replace",
                    "score_mode": "multiply",
                    "query": {
                        "match": {
                            "text": "<my text>"
                        }
                    },
                    "functions": [
                        {
                            "field_value_factor": {
                                "field": "termDocument.relevance"
                            }
                        }
                    ]
                }
            }
        }
    }
}

This also seems to work in the prototype. 

So, both options seem to work, which is great! However, I'm not sure if 
there are any performance (or other) concerns with approaches? We will have 
millions of documents (and associated terms), so we need our solution to 
scale well. It seems to me that the nested approach is conceptually more 
straightforward, so I'm leaning in that directly, but wanted to get input 
for larger ES community. 

Please let me know if there is any other options that might work better! 
I've also considered using payloads:

https://groups.google.com/forum/#!searchin/elasticsearch/Scott$20Decker%7Csort:date/elasticsearch/gEcBVhSynnY/4N1XD5NyseMJ

However, I'm not sure that will work for us as there is metadata, other 
than relevancy, I'd like to store about each term.

Thank you!

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