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Elastic 文档 ›Elasticsearch 指南 [8.17] ›聚合 ›管道聚合

桶相关性聚合

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桶相关性聚合

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一个兄弟管道聚合，它在配置的兄弟多桶聚合上执行相关性函数。

参数

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buckets_path
（必需，字符串）包含一组要关联的值的桶的路径。有关语法，请参阅 buckets_path 语法。
function

（必需，对象）要执行的相关性函数。

function 的属性
count_correlation

（必需*，对象）用于计算计数相关性的配置。此函数旨在确定术语值与给定指标的相关性。因此，它需要满足以下要求。

  • buckets_path 必须指向 _count 指标。
  • 所有 bucket_path 计数值的总计数必须小于或等于 indicator.doc_count。
  • 当使用此函数时，需要进行初始计算以收集所需的 indicator 值。
count_correlation 的属性
indicator

（必需，对象）用于关联配置的 bucket_path 值的指标。

indicator 的属性
doc_count
（必需，整数）最初创建 expectations 的文档总数。它必须大于或等于 buckets_path 中所有值的总和，因为这是术语值关联的原始超集数据。
expectations
（必需，数组）用于关联配置的 bucket_path 值的数字数组。此值的长度必须始终等于 bucket_path 返回的桶数。
fractions
（可选，数组）用于平均和计算方差的分数数组。如果预先计算的数据和 buckets_path 存在已知差距，则应使用此数组。如果提供了 fractions，则其长度必须等于 expectations。

语法

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一个 bucket_correlation 聚合单独看起来像这样

{
  "bucket_correlation": {
    "buckets_path": "range_values>_count", 
    "function": {
      "count_correlation": { 
        "indicator": {
          "expectations": [...],
          "doc_count": 10000
        }
      }
    }
  }
}

包含要关联的值的桶。

相关性函数定义。

示例

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以下代码片段将字段 version 中的各个术语与 latency 指标相关联。未显示的是 latency 指标值的预计算，这是使用 百分位数聚合完成的。

此示例仅使用 10 秒的百分位数。

resp = client.search(
    index="correlate_latency",
    size="0",
    filter_path="aggregations",
    aggs={
        "buckets": {
            "terms": {
                "field": "version",
                "size": 2
            },
            "aggs": {
                "latency_ranges": {
                    "range": {
                        "field": "latency",
                        "ranges": [
                            {
                                "to": 0
                            },
                            {
                                "from": 0,
                                "to": 105
                            },
                            {
                                "from": 105,
                                "to": 225
                            },
                            {
                                "from": 225,
                                "to": 445
                            },
                            {
                                "from": 445,
                                "to": 665
                            },
                            {
                                "from": 665,
                                "to": 885
                            },
                            {
                                "from": 885,
                                "to": 1115
                            },
                            {
                                "from": 1115,
                                "to": 1335
                            },
                            {
                                "from": 1335,
                                "to": 1555
                            },
                            {
                                "from": 1555,
                                "to": 1775
                            },
                            {
                                "from": 1775
                            }
                        ]
                    }
                },
                "bucket_correlation": {
                    "bucket_correlation": {
                        "buckets_path": "latency_ranges>_count",
                        "function": {
                            "count_correlation": {
                                "indicator": {
                                    "expectations": [
                                        0,
                                        52.5,
                                        165,
                                        335,
                                        555,
                                        775,
                                        1000,
                                        1225,
                                        1445,
                                        1665,
                                        1775
                                    ],
                                    "doc_count": 200
                                }
                            }
                        }
                    }
                }
            }
        }
    },
)
print(resp)
const response = await client.search({
  index: "correlate_latency",
  size: 0,
  filter_path: "aggregations",
  aggs: {
    buckets: {
      terms: {
        field: "version",
        size: 2,
      },
      aggs: {
        latency_ranges: {
          range: {
            field: "latency",
            ranges: [
              {
                to: 0,
              },
              {
                from: 0,
                to: 105,
              },
              {
                from: 105,
                to: 225,
              },
              {
                from: 225,
                to: 445,
              },
              {
                from: 445,
                to: 665,
              },
              {
                from: 665,
                to: 885,
              },
              {
                from: 885,
                to: 1115,
              },
              {
                from: 1115,
                to: 1335,
              },
              {
                from: 1335,
                to: 1555,
              },
              {
                from: 1555,
                to: 1775,
              },
              {
                from: 1775,
              },
            ],
          },
        },
        bucket_correlation: {
          bucket_correlation: {
            buckets_path: "latency_ranges>_count",
            function: {
              count_correlation: {
                indicator: {
                  expectations: [
                    0, 52.5, 165, 335, 555, 775, 1000, 1225, 1445, 1665, 1775,
                  ],
                  doc_count: 200,
                },
              },
            },
          },
        },
      },
    },
  },
});
console.log(response);
POST correlate_latency/_search?size=0&filter_path=aggregations
{
  "aggs": {
    "buckets": {
      "terms": { 
        "field": "version",
        "size": 2
      },
      "aggs": {
        "latency_ranges": {
          "range": { 
            "field": "latency",
            "ranges": [
              { "to": 0.0 },
              { "from": 0, "to": 105 },
              { "from": 105, "to": 225 },
              { "from": 225, "to": 445 },
              { "from": 445, "to": 665 },
              { "from": 665, "to": 885 },
              { "from": 885, "to": 1115 },
              { "from": 1115, "to": 1335 },
              { "from": 1335, "to": 1555 },
              { "from": 1555, "to": 1775 },
              { "from": 1775 }
            ]
          }
        },
        "bucket_correlation": { 
          "bucket_correlation": {
            "buckets_path": "latency_ranges>_count",
            "function": {
              "count_correlation": {
                "indicator": {
                   "expectations": [0, 52.5, 165, 335, 555, 775, 1000, 1225, 1445, 1665, 1775],
                   "doc_count": 200
                }
              }
            }
          }
        }
      }
    }
  }
}
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包含范围聚合和桶相关性聚合的术语桶。两者都用于计算术语值与延迟的相关性。

延迟字段上的范围聚合。这些范围是参考延迟字段的百分位数创建的。

桶相关性聚合，它计算每个范围内的术语值数量与先前计算的指标值的相关性。

以下可能是响应

{
  "aggregations" : {
    "buckets" : {
      "doc_count_error_upper_bound" : 0,
      "sum_other_doc_count" : 0,
      "buckets" : [
        {
          "key" : "1.0",
          "doc_count" : 100,
          "latency_ranges" : {
            "buckets" : [
              {
                "key" : "*-0.0",
                "to" : 0.0,
                "doc_count" : 0
              },
              {
                "key" : "0.0-105.0",
                "from" : 0.0,
                "to" : 105.0,
                "doc_count" : 1
              },
              {
                "key" : "105.0-225.0",
                "from" : 105.0,
                "to" : 225.0,
                "doc_count" : 9
              },
              {
                "key" : "225.0-445.0",
                "from" : 225.0,
                "to" : 445.0,
                "doc_count" : 0
              },
              {
                "key" : "445.0-665.0",
                "from" : 445.0,
                "to" : 665.0,
                "doc_count" : 0
              },
              {
                "key" : "665.0-885.0",
                "from" : 665.0,
                "to" : 885.0,
                "doc_count" : 0
              },
              {
                "key" : "885.0-1115.0",
                "from" : 885.0,
                "to" : 1115.0,
                "doc_count" : 10
              },
              {
                "key" : "1115.0-1335.0",
                "from" : 1115.0,
                "to" : 1335.0,
                "doc_count" : 20
              },
              {
                "key" : "1335.0-1555.0",
                "from" : 1335.0,
                "to" : 1555.0,
                "doc_count" : 20
              },
              {
                "key" : "1555.0-1775.0",
                "from" : 1555.0,
                "to" : 1775.0,
                "doc_count" : 20
              },
              {
                "key" : "1775.0-*",
                "from" : 1775.0,
                "doc_count" : 20
              }
            ]
          },
          "bucket_correlation" : {
            "value" : 0.8402398981360937
          }
        },
        {
          "key" : "2.0",
          "doc_count" : 100,
          "latency_ranges" : {
            "buckets" : [
              {
                "key" : "*-0.0",
                "to" : 0.0,
                "doc_count" : 0
              },
              {
                "key" : "0.0-105.0",
                "from" : 0.0,
                "to" : 105.0,
                "doc_count" : 19
              },
              {
                "key" : "105.0-225.0",
                "from" : 105.0,
                "to" : 225.0,
                "doc_count" : 11
              },
              {
                "key" : "225.0-445.0",
                "from" : 225.0,
                "to" : 445.0,
                "doc_count" : 20
              },
              {
                "key" : "445.0-665.0",
                "from" : 445.0,
                "to" : 665.0,
                "doc_count" : 20
              },
              {
                "key" : "665.0-885.0",
                "from" : 665.0,
                "to" : 885.0,
                "doc_count" : 20
              },
              {
                "key" : "885.0-1115.0",
                "from" : 885.0,
                "to" : 1115.0,
                "doc_count" : 10
              },
              {
                "key" : "1115.0-1335.0",
                "from" : 1115.0,
                "to" : 1335.0,
                "doc_count" : 0
              },
              {
                "key" : "1335.0-1555.0",
                "from" : 1335.0,
                "to" : 1555.0,
                "doc_count" : 0
              },
              {
                "key" : "1555.0-1775.0",
                "from" : 1555.0,
                "to" : 1775.0,
                "doc_count" : 0
              },
              {
                "key" : "1775.0-*",
                "from" : 1775.0,
                "doc_count" : 0
              }
            ]
          },
          "bucket_correlation" : {
            "value" : -0.5759855613334943
          }
        }
      ]
    }
  }
}
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