要学更多的metric
    countavg
    count:bucketterms,自动就会有一个doc_count,就相当于是count
    avg:avg aggs,求平均值
    max:求一个bucket内,指定field值最大的那个数据
    min:求一个bucket内,指定field值最小的那个数据
    sum:求一个bucket内,指定field值的总和
    一般来说,90%的常见的数据分析的操作,metric,无非就是count,avg,max,min,sum

    1. GET /tvs/_search
    2. {
    3. "size" : 0,
    4. "aggs": {
    5. "colors": {
    6. "terms": {
    7. "field": "color"
    8. },
    9. "aggs": {
    10. "avg_price": { "avg": { "field": "price" } },
    11. "min_price" : { "min": { "field": "price"} },
    12. "max_price" : { "max": { "field": "price"} },
    13. "sum_price" : { "sum": { "field": "price" } }
    14. }
    15. }
    16. }
    17. }

    求总和,就可以拿到一个颜色下的所有电视的销售总额

    {
      "took": 16,
      "timed_out": false,
      "_shards": {
        "total": 5,
        "successful": 5,
        "failed": 0
      },
      "hits": {
        "total": 8,
        "max_score": 0,
        "hits": []
      },
      "aggregations": {
        "group_by_color": {
          "doc_count_error_upper_bound": 0,
          "sum_other_doc_count": 0,
          "buckets": [
            {
              "key": "红色",
              "doc_count": 4,
              "max_price": {
                "value": 8000
              },
              "min_price": {
                "value": 1000
              },
              "avg_price": {
                "value": 3250
              },
              "sum_price": {
                "value": 13000
              }
            },
            {
              "key": "绿色",
              "doc_count": 2,
              "max_price": {
                "value": 3000
              },
              "min_price": {
                "value":
              }, 1200
              "avg_price": {
                "value": 2100
              },
              "sum_price": {
                "value": 4200
              }
            },
            {
              "key": "蓝色",
              "doc_count": 2,
              "max_price": {
                "value": 2500
              },
              "min_price": {
                "value": 1500
              },
              "avg_price": {
                "value": 2000
              },
              "sum_price": {
                "value": 4000
              }
            }
          ]
        }
      }
    }