Elasticsearch 之（27）cardinality算法之优化内存开销以及HLL算法

1、cardinality语法

es，去重，cartinality metric，对每个bucket中的指定的field进行去重，取去重后的count，类似于count(distcint)
cardinality，count(distinct)，5%的错误率，性能在100ms左右

{
  "size" : 0,
  "aggs" : {
      "months" : {
        "date_histogram": {
          "field": "sold_date",
          "interval": "month"
        },
        "aggs": {
          "distinct_colors" : {
              "cardinality" : {
                "field" : "brand"
              }
          }
        }
      }
  }
}

{
  "took": 70,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "failed": 0
  },
  "hits": {
    "total": 8,
    "max_score": 0,
    "hits": []
  },
  "aggregations": {
    "group_by_sold_date": {
      "buckets": [
        {
          "key_as_string": "2016-05-01T00:00:00.000Z",
          "key": 1462060800000,
          "doc_count": 1,
          "distinct_brand_cnt": {
            "value": 1
          }
        },
        {
          "key_as_string": "2016-06-01T00:00:00.000Z",
          "key": 1464739200000,
          "doc_count": 0,
          "distinct_brand_cnt": {
            "value": 0
          }
        },
        {
          "key_as_string": "2016-07-01T00:00:00.000Z",
          "key": 1467331200000,
          "doc_count": 1,
          "distinct_brand_cnt": {
            "value": 1
          }
        },
        {
          "key_as_string": "2016-08-01T00:00:00.000Z",
          "key": 1470009600000,
          "doc_count": 1,
          "distinct_brand_cnt": {
            "value": 1
          }
        },
        {
          "key_as_string": "2016-09-01T00:00:00.000Z",
          "key": 1472688000000,
          "doc_count": 0,
          "distinct_brand_cnt": {
            "value": 0
          }
        },
        {
          "key_as_string": "2016-10-01T00:00:00.000Z",
          "key": 1475280000000,
          "doc_count": 1,
          "distinct_brand_cnt": {
            "value": 1
          }
        },
        {
          "key_as_string": "2016-11-01T00:00:00.000Z",
          "key": 1477958400000,
          "doc_count": 2,
          "distinct_brand_cnt": {
            "value": 1
          }
        },
        {
          "key_as_string": "2016-12-01T00:00:00.000Z",
          "key": 1480550400000,
          "doc_count": 0,
          "distinct_brand_cnt": {
            "value": 0
          }
        },
        {
          "key_as_string": "2017-01-01T00:00:00.000Z",
          "key": 1483228800000,
          "doc_count": 1,
          "distinct_brand_cnt": {
            "value": 1
          }
        },
        {
          "key_as_string": "2017-02-01T00:00:00.000Z",
          "key": 1485907200000,
          "doc_count": 1,
          "distinct_brand_cnt": {
            "value": 1
          }
        }
      ]
    }
  }
}

2、precision_threshold优化准确率和内存开销

GET /tvs/sales/_search
{
    "size" : 0,
    "aggs" : {
        "distinct_brand" : {
            "cardinality" : {
              "field" : "brand",
              "precision_threshold" : 100 
            }
        }
    }
}

brand去重，如果brand（品牌）的unique value，在100个以内，小米，长虹，三星，TCL，HTL。。。

在多少个unique value以内，cardinality，几乎保证100%准确
cardinality算法，会占用precision_threshold * 8 byte 内存消耗，100 * 8 = 800个字节
占用内存很小而且unique value如果的确在值以内，那么可以确保100%准确
100，数百万的unique value，错误率在5%以内

precision_threshold，值设置的越大，占用内存越大，可以确保更多unique value的场景下，100%的准确

field，去重，count，这时候，unique value，10000，
precision_threshold=10000，
10000 * 8 = 80000 个byte，
80000 / 1024 ≈ 80KB

3、HyperLogLog++ (HLL)算法性能优化

cardinality底层算法：HLL算法，HLL算法的性能
会对所有的uqniue value取hash值，通过hash值近似去求distcint count，误差

默认情况下，发送一个cardinality请求的时候，会动态地对所有的field value，取hash值; 将取hash值的操作，前移到建立索引的时候

创建索引时， brand field type 增加创建其hash值索引

PUT /tvs/
{
  "mappings": {
    "sales": {
      "properties": {
        "brand": {
          "type": "text",
          "fields": {
            "hash": {
              "type": "murmur3" 
            }
          }
        }
      }
    }
  }
}

根据hash值作引进行cartinality metric

GET /tvs/sales/_search
{
    "size" : 0,
    "aggs" : {
        "distinct_brand" : {
            "cardinality" : {
              "field" : "brand.hash",
              "precision_threshold" : 100 
            }
        }
    }
}