39深入聚合数据分析_实战date hitogram之统计每月电视销量

date_histogram，按照我们指定的某个date类型的日期field，以及日期interval，按照一定的日期间隔，去划分bucket

date interval = 1m， 一个月

2017-01-01~2017-01-31，就是一个bucket 2017-02-01~2017-02-28，就是一个bucket

然后会去扫描每个数据的date field，判断date落在哪个bucket中，就将其放入那个bucket

2017-01-05，就将其放入2017-01-01~2017-01-31，就是一个bucket

min_doc_count：即使某个日期interval，2017-01-01~2017-01-31中，一条数据都没有，那么这个区间也是要返回的，不然默认是会过滤掉这个区间的 extended_bounds，min，max：划分bucket的时候，会限定在这个起始日期，和截止日期内

GET /tvs/sales/_search
{
   "size" : 0,
   "aggs": {
      "sales": {
         "date_histogram": {
            "field": "sold_date",
            "interval": "month", 
            "format": "yyyy-MM-dd",
            "min_doc_count" : 0, 
            "extended_bounds" : { 
                "min" : "2016-01-01",
                "max" : "2017-12-31"
            }
         }
      }
   }
}

响应结果

{
  "took": 5,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "skipped": 0,
    "failed": 0
  },
  "hits": {
    "total": 8,
    "max_score": 0,
    "hits": []
  },
  "aggregations": {
    "sales": {
      "buckets": [
        {
          "key_as_string": "2016-01-01",
          "key": 1451606400000,
          "doc_count": 0
        },
        {
          "key_as_string": "2016-02-01",
          "key": 1454284800000,
          "doc_count": 0
        },
        {
          "key_as_string": "2016-03-01",
          "key": 1456790400000,
          "doc_count": 0
        },
        {
          "key_as_string": "2016-04-01",
          "key": 1459468800000,
          "doc_count": 0
        },
        {
          "key_as_string": "2016-05-01",
          "key": 1462060800000,
          "doc_count": 1
        },
        {
          "key_as_string": "2016-06-01",
          "key": 1464739200000,
          "doc_count": 0
        },
        {
          "key_as_string": "2016-07-01",
          "key": 1467331200000,
          "doc_count": 1
        },
        {
          "key_as_string": "2016-08-01",
          "key": 1470009600000,
          "doc_count": 1
        },
        {
          "key_as_string": "2016-09-01",
          "key": 1472688000000,
          "doc_count": 0
        },
        {
          "key_as_string": "2016-10-01",
          "key": 1475280000000,
          "doc_count": 1
        },
        {
          "key_as_string": "2016-11-01",
          "key": 1477958400000,
          "doc_count": 2
        },
        {
          "key_as_string": "2016-12-01",
          "key": 1480550400000,
          "doc_count": 0
        },
        {
          "key_as_string": "2017-01-01",
          "key": 1483228800000,
          "doc_count": 1
        },
        {
          "key_as_string": "2017-02-01",
          "key": 1485907200000,
          "doc_count": 1
        },
        {
          "key_as_string": "2017-03-01",
          "key": 1488326400000,
          "doc_count": 0
        },
        {
          "key_as_string": "2017-04-01",
          "key": 1491004800000,
          "doc_count": 0
        },
        {
          "key_as_string": "2017-05-01",
          "key": 1493596800000,
          "doc_count": 0
        },
        {
          "key_as_string": "2017-06-01",
          "key": 1496275200000,
          "doc_count": 0
        },
        {
          "key_as_string": "2017-07-01",
          "key": 1498867200000,
          "doc_count": 0
        },
        {
          "key_as_string": "2017-08-01",
          "key": 1501545600000,
          "doc_count": 0
        },
        {
          "key_as_string": "2017-09-01",
          "key": 1504224000000,
          "doc_count": 0
        },
        {
          "key_as_string": "2017-10-01",
          "key": 1506816000000,
          "doc_count": 0
        },
        {
          "key_as_string": "2017-11-01",
          "key": 1509494400000,
          "doc_count": 0
        },
        {
          "key_as_string": "2017-12-01",
          "key": 1512086400000,
          "doc_count": 0
        }
      ]
    }
  }
}