Elasticsearch(五)范围限定的聚合查询rest请求与java-api

在之前的聚合查询中,我们都是针对全局进行数据进行操作的,接下来我们要通过Elasticsearch的查询,本篇主要讲解的是,根据我们的条件先对数据进行过滤,之后再进行相关的聚合操作.

ps:数据还是请用我们第一篇中的数据ElasticSearch超强聚合查询(一)

案例一:先查询出制作厂商是福特公司的汽车,然后在车子的颜色进行聚合查询

rest-api:

GET /cars/transactions/_search
{
    "query" : {
        "match" : {
            "make" : "ford"
        }
    },
    "aggs" : {
        "colors" : {
            "terms" : {
              "field" : "color"
            }
        }
    }
}

• java-api

    @Test
    public void testWithFilter(){
        SearchResponse response = transportClient.prepareSearch("cars")
                .setTypes("transactions")
                .setQuery(
                        //查找条件
                        QueryBuilders.matchQuery("make","ford")
                )
                //根据条件查找到的结果再进行聚合
                .addAggregation(
                        AggregationBuilders
                                .terms("colors")
                                .field("color")
                )
                .get();
        Aggregation colors = response.getAggregations().get("colors");
        System.out.println(colors);
    }

• 返回结果

{
...
   "hits": {
      "total": 2,
      "max_score": 1.6931472,
      "hits": [
            //由于我们并没有设置返回的文档数量,所以查询的结果也一并返回
         {
            "_source": {
               "price": 25000,
               "color": "blue",
               "make": "ford",
               "sold": "2014-02-12"
            }
         },
         {
            "_source": {
               "price": 30000,
               "color": "green",
               "make": "ford",
               "sold": "2014-05-18"
            }
         }
      ]
   },
   "aggregations": {
      "colors": {
         "buckets": [
            {
               "key": "blue",
               "doc_count": 1
            },
            {
               "key": "green",
               "doc_count": 1
            }
         ]
      }
   }
}

案例二:使用过滤后的数据聚合,再与全局数据聚合,多个聚合之间的对比,在官方文档中定义这种查询为全局桶查询.

比方说我们想知道福特汽车与 所有 汽车平均售价的比较。我们可以用普通的聚合（查询范围内的）得到第一个信息，然后用 全局 桶获得第二个信息。

rest-api

GET /cars/transactions/_search
{
    "size" : 0,
    "query" : {
        "match" : {
            "make" : "ford"
        }
    },
    "aggs" : {
        "single_avg_price": {
            "avg" : { "field" : "price" } 
        },
        "all": {
            "global" : {}, 
            "aggs" : {
                "avg_price": {
                    "avg" : { "field" : "price" } 
                }

            }
        }
    }
}

• java-api

@Test
    public void compareQuery(){
        SearchResponse response = transportClient.prepareSearch("cars")
                .setTypes("transactions")
                .setQuery(
                        //查找条件
                        QueryBuilders.matchQuery("make","ford")
                )
                .addAggregation(
                        AggregationBuilders.avg("single_avg_price")
                            .field("price")

                        )
                .addAggregation(
                        //在添加一个全局数据的查询
                        AggregationBuilders.global("avg_price_name")
                            .subAggregation(
                                    AggregationBuilders.avg("avg_price")
                                        .field("price")
                            )
                )
                .get();

        Aggregation avg_price_name = response.getAggregations().get("avg_price_name");
        System.out.println(avg_price_name);
    }

• result

{
  "took": 6,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "skipped": 0,
    "failed": 0
  },
  "hits": {
    "total": 1,
    "max_score": 0.6931472,
    "hits": [
      {
        "_index": "cars",
        "_type": "transactions",
        "_id": "AWIFx7jO5idSEZHc8G0Q",
        "_score": 0.6931472,
        "_source": {
          "price": 30000,
          "color": "green",
          "make": "ford",
          "sold": "2014-05-18"
        }
      }
    ]
  },
  "aggregations": {
  //这个是全局的平均结果
    "avg_price_name": {
      "doc_count": 7,
      "avg_price": {
        "value": 26714.285714285714
      }
    },
    //只针对福特的聚合结果
    "single_avg_price": {
      "value": 30000.0
    }
  }
}