Speed up Elasticsearch more like this query - performance

Question

Whats wrong with this more like this query? It was written from scratch. It returns relevant results, but it is too slow (this example took 187.9 ms)
{
"query": {
"bool": {
"must": [{
"more_like_this": {
"fields": ["similarity.analyzed"],
"like": [{
"_id": 4
}, {
"_id": 550
}, {
"_id": 757
}],
"min_term_freq": 1,
"min_doc_freq": 1,
"analyzer": "searchkick_search2",
"minimum_should_match": "10%"
}
}, {
"range": {
"count_posts": {
"gt": 0
}
}
}],
"must_not": [{
"terms": {
"_id": [4, 550, 757]
}
}]
}
},
"size": 10
}
This query finds similar tags to given tags set.
similarity - text field, with all posts titles, joined with space.
count_posts - numeric field, which contains number of posts if each tag.
Running Elasticseach 7.8.0 on Ubuntu 18.04 as single node. Rails 5 app with Searchkick gem.

Answer 1

Whats wrong with this more like this query?
"like": [{
"_id": 4
}, {
"_id": 550
}, {
"_id": 757
}]
It acts like multi get API. It does the below things.
Get's all the documents mentioned by _id in like
Analyse the field using analyser option ptovided
Analyse the same fields from the matching docs of step1. List of tokenizer,s filters also adds some ms.
Calculate doc, term frequencies along with min match.
And you have two more conditions. Documentation says
A more complicated use case consists of mixing texts with documents already existing in the index.
Unfortunately, I don't think this can be optimised further. But you can add a text instead id in like to make it much better. Hope the query is not always taking more than 100ms due to caching.