elasticsearch - 了解elasticsearch查询说明

Question

我试图理解 flex 文档中的Explain API评分:
https://www.elastic.co/guide/en/elasticsearch/reference/current/search-explain.html

当我仅凭几个文档就无法在自己的简单索引中弄清楚时，我尝试在上述文档页面上重现计算结果。

在示例中，它显示了一个1.3862944的“值”，其描述为:“idf，计算为log(1 +(docCount-docFreq + 0.5)/(docFreq + 0.5))”。在“详细信息”下，它为字段提供以下值:docFreq:1.0，docCount:5.0

使用提供的docFreq和docCount值，我将其计算为:log(1 +(5.0-1.0 + 0.5)/(1.0 + 0.5))= 0.602，与示例中的1.3862944不同。

我无法获得任何匹配的值。

我读错了吗？

以下是整个帖子

GET /twitter/_doc/0/_explain   
{ 
  "query" : {
    "match" : { "message" : "elasticsearch" }
  }
}

这将产生以下结果:

{
   "_index": "twitter",
   "_type": "_doc",
   "_id": "0",
   "matched": true,
   "explanation": {
       "value": 1.6943599,
       "description": "weight(message:elasticsearch in 0) [PerFieldSimilarity], result of:",
       "details": [
       {
        "value": 1.6943599,
        "description": "score(doc=0,freq=1.0 = termFreq=1.0\n), product of:",
        "details": [
           {
              "value": 1.3862944,  <== This is the one I am trying
              "description": "idf, computed as log(1 + (docCount - docFreq + 0.5) / (docFreq + 0.5)) from:",
              "details": [
                 {
                    "value": 1.0,
                    "description": "docFreq",
                    "details": []
                 },
                 {
                    "value": 5.0,
                    "description": "docCount",
                    "details": []
                  }
               ]
           },
            {
              "value": 1.2222223,
              "description": "tfNorm, computed as (freq * (k1 + 1)) / (freq + k1 * (1 - b + b * fieldLength / avgFieldLength)) from:",
              "details": [
                 {
                    "value": 1.0,
                    "description": "termFreq=1.0",
                    "details": []
                 },
                 {
                    "value": 1.2,
                    "description": "parameter k1",
                    "details": []
                 },
                 {
                    "value": 0.75,
                    "description": "parameter b",
                    "details": []
                 },
                 {
                    "value": 5.4,
                    "description": "avgFieldLength",
                    "details": []
                 },
                 {
                    "value": 3.0,
                    "description": "fieldLength",
                    "details": []
                 }
              ]
           }
        ]
     }
  ]
}
}

Answer 1

一如既往的解释非常准确，让我帮助您了解这些计算:

这是初始公式:

log(1 + (5.0 - 1.0 + 0.5) / (1.0 + 0.5))

下一步将是:

log(1 + 4.5 / 1.5)

多一个:

log(4) = ?

这是棘手的部分。您将此log视为以10为底的对数。但是，如果您看一下Lucene scorer的代码，您会发现它是一个ln，它恰好是1.386294
部分代码:

public float idf(long docFreq, long numDocs) {
    return (float)(Math.log(numDocs/(double)(docFreq+1)) + 1.0);
  }

其中Math.log定义如下:

public static double log(double a)

Returns the natural logarithm (base e) of a double value.