Cacher is the code snippet organizer for pro developers

1. two sample documents

POST /aggstest/test/1
{
    "categories": [
        {
            "type": "book",
            "words": [
                {"word":"storm","count":277},
                {"word":"pooh","count":229}
            ]
        },
        {
            "type": "magazine",
            "words": [
                {"word":"vibe","count":100},
                {"word":"sunny","count":50}
            ]
        }
    ]
}


POST /aggstest/test/2
{
    "categories": [
        {
            "type": "book",
            "words": [
                {"word":"rain","count":160},
                {"word":"jurassic park","count":150}
            ]
        },
        {
            "type": "megazine",
            "words": [
                {"word":"tech","count":200},
                {"word":"homes","count":30}
            ]
        }
    ]
}


2. aggs query
GET /aggstest/test/_search
{
  "size": 0,
  "query": {
    "filtered": {
      "filter": {
        "bool": {
          "must": [
            {
              "term": {
                "categories.type": "book"
              }
            },
            {
              "term": {
                "categories.words.word": "storm"
              }
            }
          ]
        }
      }
    }
  },
  "aggs": {
    "filtered": {
      "filter": {
        "bool": {
          "must": [
            {
              "term": {
                "categories.type": "book"
              }
            }
          ]
        }
      },
      "aggs": {
        "book_category": {
          "terms": {
            "field": "categories.words.word",
            "size": 10
          }
        }
      }
    }
  },
  "post_filter": {
    "term": {
      "categories.type": "book"
    }
  }
}


3. result
{
   "took": 5,
   "timed_out": false,
   "_shards": {
      "total": 5,
      "successful": 5,
      "failed": 0
   },
   "hits": {
      "total": 1,
      "max_score": 0,
      "hits": []
   },
   "aggregations": {
      "filtered": {
         "doc_count": 1,
         "book_category": {
            "doc_count_error_upper_bound": 0,
            "sum_other_doc_count": 0,
            "buckets": [
               {
                  "key": "pooh",
                  "doc_count": 1
               },
               {
                  "key": "storm",
                  "doc_count": 1
               },
               {
                  "key": "sunny",
                  "doc_count": 1
               },
               {
                  "key": "vibe",
                  "doc_count": 1
               }
            ]
         }
      }
   }
}

========================

Expected aggs result set should not include "sunny" and "vibe" because it's "magazine" type.

I used filter query and post_filter, but I couldn't get only "book" type aggs result.