elasticsearch-multifacet-interaction-example.sense

# Clear our example index
DELETE /vehicle

# Post a few car records with make, model and year
POST /vehicle/car/_bulk
{"index":{}}
{"make":"Saturn","model":"LW","year":2005}
{"index":{}}
{"make":"Honda","model":"Civic","year":2005}
{"index":{}}
{"make":"Honda","model":"Civic","year":2007}
{"index":{}}
{"make":"Honda","model":"Accord","year":2012}
{"index":{}}
{"make":"Saturn","model":"Ion","year":2001}
{"index":{}}
{"make":"Toyota","model":"Prius","year":2012}
{"index":{}}
{"make":"Toyota","model":"Corolla","year":2007}
{"index":{}}
{"make":"Ford","model":"Focus","year":2005}

# Display simple term aggregations for each field
GET /vehicle/car/_search
{
  "size": 10,
  "aggs": {
    "make": {
      "terms": {
        "field": "make"
      }
    },
    "model": {
      "terms": {
        "field": "model"
      }
    },
    "year": {
      "terms": {
        "field": "year"
      }
    }
  }
}

# If "honda" and "saturn" are selected for the "make" field filter...
#
#   * In the result set, return the union of of cars with either "honda" OR "saturn"
#     as the make
#   * For the make aggregation, apply a pipeline that a) sets the global aggregation to
#     escape the context of primary query filter b) aggregates the resulting documents by
#     the "make" term. We label the nested aggregations with an underscore for convenience
#     when parsing the result.
#   * For the model and year aggregations, just do a simple terms agg as we can apply the
#     primary query filters
GET /vehicle/car/_search
{
  "size": 10,
  "query": {
    "constant_score": {
      "filter": {
        "terms": {
          "make": ["honda", "saturn"]
        }
      }
    }
  },
  "aggs": {
    "make": {
      "global": {},
      "aggs": {
        "_":  {
          "terms": {
            "field": "make"
          }
        }
      }
    },
    "model": {
      "terms": {
        "field": "model"
      }
    },
    "year": {
      "terms": {
        "field": "year"
      }
    }
  }
}

# If "honda" and "saturn" are selected for "make" and "2005" and "2001" are selected
# for "year"...
#
#   * In the result set, return all cars that are either "honda" or "saturn" and
#     made either in "2001" or "2005"
#   * For the make aggregation, apply a pipeline that a) sets the global aggregation to
#     escape the normal query context b) filters the result to match only vehicles 
#     made in "2001" or "2005", c) aggregates the resulting documents by the "make" term.
#   * For the model aggregation, just do a simple terms agg to apply the primary query
#     filters
#   * For the year aggregation, apply a pipeline that a) sets the global aggregation to
#     escape the normal query context b) filters the result to match only vehicles 
#     with a make of "honda" or "saturn", c) aggregates the resulting documents by the
#     "year" term
GET /vehicle/car/_search
{
  "size": 10,
  "query": {
    "constant_score": {
      "filter": {
        "and": {
          "filters": [
            {
              "terms": {
                "make": ["honda", "saturn"]
              }
            },
            {
              "terms": {
                "year": [2001, 2005]
              }
            }
          ]
        }
        
      }
    }
  },
  "aggs": {
    "make": {
      "global": {},
      "aggs": {
        "_": {
          "filter": {
            "terms": {
              "year": [2001, 2005]
            }
          },
          "aggs": {
            "_": {
              "terms": {
                "field": "make"
              }
            }
          }
        }
      }
    },
    "model": {
      "terms": {
        "field": "model"
      }
    },
    "year": {
      "global": {},
      "aggs": {
        "_": {
          "filter": {
            "terms": {
              "make": ["honda", "saturn"]
            }
          },
          "aggs": {
            "_": {
              "terms": {
                "field": "year"
              }
            }
          }
        }
      }
    }
  }
}