First, fix the terminology: in Elasticsearch, "parent / child" refers to completely separate documents, where the child document points to the parent document. Parent and children are kept in the same shard, but they can be updated independently.
In the example above, what you are trying to achieve can be accomplished with nested docs.
Your locations field currently has a type:"object" value. This means that the values in each place are aligned to look something like this:
{ "locations.category": [5322606, 5883712, 5322605], "locations.subCategory": [6032961], "locations.order": [1, 3, 2] }
In other words, the “under” fields are aligned into multi-valued fields, which is useless for you because there is no correlation between category: 5322606 and order: 1 .
However, if you change locations to type:"nested" , then it will index each location as a separate document, which means that each location can be requested independently using the dedicated nested query and filter .
By default, a nested query returns _score depending on how well each location matches, but in your case you want to return the highest order field value of all the matching child elements. To do this, you need to use the custom_score request.
So, let's start by creating an index with the appropriate mapping:
curl -XPUT 'http://127.0.0.1:9200/test/?pretty=1' -d ' { "mappings" : { "products" : { "properties" : { "locations" : { "type" : "nested", "properties" : { "order" : { "type" : "long" }, "subCategory" : { "type" : "long" }, "category" : { "type" : "long" } } }, "id" : { "type" : "long" } } } } } '
We index your doc example:
curl -XPOST 'http://127.0.0.1:9200/test/products?pretty=1' -d ' { "locations" : [ { "order" : 1, "category" : 5322606 }, { "order" : 3, "subCategory" : null, "category" : 5883712 }, { "order" : 2, "subCategory" : 6032961, "category" : 5322605 } ], "id" : 5331880 } '
And now we can search for it using the queries we discussed above:
curl -XGET 'http://127.0.0.1:9200/test/products/_search?pretty=1' -d ' { "query" : { "nested" : { "query" : { "custom_score" : { "script" : "doc[\u0027locations.order\u0027].value", "query" : { "constant_score" : { "filter" : { "and" : [ { "term" : { "category" : 5322605 } }, { "term" : { "subCategory" : 6032961 } } ] } } } } }, "score_mode" : "max", "path" : "locations" } } } '
Note: single quotes in the script were escaped as \u0027 to bypass shell quoting. The script looks like this: "doc['locations.order'].value"
If you look at _score from the results, you will see that it used the order value from the associated location :
{ "hits" : { "hits" : [ { "_source" : { "locations" : [ { "order" : 1, "category" : 5322606 }, { "order" : 3, "subCategory" : null, "category" : 5883712 }, { "order" : 2, "subCategory" : 6032961, "category" : 5322605 } ], "id" : 5331880 }, "_score" : 2, "_index" : "test", "_id" : "cXTFUHlGTKi0hKAgUJFcBw", "_type" : "products" } ], "max_score" : 2, "total" : 1 }, "timed_out" : false, "_shards" : { "failed" : 0, "successful" : 5, "total" : 5 }, "took" : 9 }