How does Couchbase perform with aggregations?

Couchbase Server
4

Here is an elasticsearch query I’m using to aggregate data. The data is denormalized and is one “table” or document.

A document just has a dept_id, dept_name, sub_dept_id, sub_dept_name, item_id, item_name, location, week_date

Each dept will have about 104 million records max. Currently we have 3 depts. We insert about 3 million documents weekly.

GET weekly_sales/_search
{
  "size": 1, 
  "query": {
    "bool": {
      "filter": [
        {
          "range": {
            "week_date": {
            "format": "yyyy-MM-dd", 
            "gte": "2016-01-01",
            "lte": "2017-02-21"
          }
          }
        },
        {
          "term": {
            "department_id": "4"
          }
        },
        {
          "term": {
            "sub_dept_id": "20"
          }
        }
      ]
    }
  },
  "aggs": {
    "group_by_location": {
      "terms": {
        "size": 10000,
        "field": "location"
      },
      "aggs": {
        "group_by_item_id": {
            "terms": {
              "size": 10000,
              "field": "item_id"
          },
      "aggs": {
        "total_sales" :{
          "sum": {
            "field": "total_sales"
          }
        },
        "agg_source": {
          "top_hits": {
            "size": 1, 
            "_source": {
              "includes": ["department_id", "dept_name", "sub_dept_id", "sub_dept_name","item_id", "item_name", "location"]
            }
          }
        }
      }
        }
      }
    }
  }
}

My end result is to have something similar to(this is how elasticsearch returns data, couch might return something better or cleaner i hope):

    {
      "grouped_by_location": [
        {    
          {
            "location": 1,
            "grouped_by_item_id: [
              "item_id": 333
              {
                "dept_id":"1",
                "dept_name":"dept1",
                "sub_dept_id":"2",
                "sub_dept_name":"subDeptName",            
                "item_name":"item333",
                "total_sales_sum": 48584943
              }    
            ]
          }
        }
      ]
    }