聚合数据分析date histogram之统计每月电视销量

bucket,分组操作,hitogram,按照某个值指定的interval,划分一个一个的bucket
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,区划分bucket
date histogram = 1m
2017-01-01~2017-01-31,就是一个bucket
2017-02-01~2017-02-28,就是一个bucket
然后回去扫描每个数据的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": {
    "group_by_sold_date": {
      "date_histogram": {
        "field": "sold_date",
        "interval": "month",
        "format": "yyyy-MM-dd",
        "min_doc_count": 0,
        "extended_bounds": {
          "min": "2016-01-01",
          "max": "2017-01-31"
        }
      }
    }
  }
}

{
  "took": 8,
  "timed_out": false,
  "_shards": {
    "total": 3,
    "successful": 3,
    "skipped": 0,
    "failed": 0
  },
  "hits": {
    "total": 8,
    "max_score": 0,
    "hits": []
  },
  "aggregations": {
    "group_by_sold_date": {
      "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
        }
      ]
    }
  }
}