Elasticsearch filter multiple terms with only matching results and not any of them - elasticsearch

Question

How I can get only filtered matching results with all the multi term search. I have this sample table where titleid is a mapping int field and personid is a keyword:
titleid:1,personid:a
titleid:3,personid:a
titleid:1,personid:b
titleid:2,personid:b
titleid:1,personid:c
titleid:5,personid:c
The expeted result is:
titleid:1
With a sample query like this one:
{query:
{bool:
{filter:
{must:[
{terms : {fields: {personid:[a,b,c]}}
]
}}}}
I have the following results:
titleid: 1,2,3,5
Maybe this will help, I did the query in sql and got the expected result. What I did was ask the query to give me the sum of titleid that matches the quantity of searched parameters. This is only to be more self explained, the idea is to use elasticsearch.
select titleid
from (
select count(titleid) as title_count, titleid
from table1
where personid in ('a','b','c')
group by titleid
) as vw
where title_count = 3

Answer 1

if you only want records with titleid == 1 AND personid == 'a' you can filter on both fields. only the boolean query uses must, should, and most_not. with a filter since it's filtering (eg, removing) by definition it's a must
"query": {
"bool": {
"filter": [
{
"term": {
"titleId": { "value": 1 }
}
},
{
"term": {
"personid": { "value": "a" }
}
}
]
}
}
UPDATE::
Now your question looks like you want to filter and aggregate your results and then aggregate on those. There's a few metrics and bucket aggregations
Using bucket selector aggregation (this isn't tested but should be very close if not correct)
{
"aggs" : {
"title_id" : {
"filter" : { "terms": { "personid": ["a","b","c"] } },
"aggs" : {
"id_count" : { "count" : { "field" : "titleid" } }
}
},
aggs": {
"count_filter": {
"bucket_selector": {
"buckets_path": {
"the_doc_count": "_count"
},
"script": "the_doc_count == 3"
}
}
}
}
}
However, be aware that Pipeline aggregations work on the outputs produced from other aggregations, so the overall amount of work that needs to be done to calculate the initial doc_counts will be the same. Since the script parts needs to be executed for each input bucket, the opetation might potentially be slow for high cardinality fields as in thousands of thousands of terms.