elasticsearch - 在 Elasticsearch 中查找不同的内部对象

Question

我们试图在 Elasticsearch 中找到不同的内部对象。这将是我们案例的最小示例。我们遇到了类似以下映射的问题(更改类型或索引或添加新字段不会有问题，但结构应保持原样):

{
  "building": {
    "properties": {
      "street": {
        "type": "string",
        "store": "yes",
        "index": "not_analyzed"
      },
      "house number": {
        "type": "string",
        "store": "yes",
        "index": "not_analyzed"
      },
      "city": {
        "type": "string",
        "store": "yes",
        "index": "not_analyzed"
      },
      "people": {
        "type": "object",
        "store": "yes",
        "index": "not_analyzed",
        "properties": {
          "firstName": {
            "type": "string",
            "store": "yes",
            "index": "not_analyzed"
          },
          "lastName": {
            "type": "string",
            "store": "yes",
            "index": "not_analyzed"
          }
        }
      }
    }
  }
}

假设我们有这个示例数据:

{
  "buildings": [
    {
      "street": "Baker Street",
      "house number": "221 B",
      "city": "London",
      "people": [
        {
          "firstName": "John",
          "lastName": "Doe"
        },
        {
          "firstName": "Jane",
          "lastName": "Doe"
        }
      ]
    },
    {
      "street": "Baker Street",
      "house number": "5",
      "city": "London",
      "people": [
        {
          "firstName": "John",
          "lastName": "Doe"
        }
      ]
    },
    {
      "street": "Garden Street",
      "house number": "1",
      "city": "London",
      "people": [
        {
          "firstName": "Jane",
          "lastName": "Smith"
        }
      ]
    }
  ]
}

当我们查询街道“Baker Street”(以及所需的任何其他选项)时，我们希望获得以下列表:

[
    {
      "firstName": "John",
      "lastName": "Doe"
    },
    {
      "firstName": "Jane",
      "lastName": "Doe"
    }
]

格式没有太大关系，但我们应该能够解析名字和姓氏。只是，由于我们的实际数据集要大得多，我们需要条目是不同的。

我们正在使用 Elasticsearch 1.7。

Answer 1

我们终于解决了问题。

我们的解决方案(如我们所料)是一个预先计算的 people_all 字段。但是，我们没有使用 copy_to 或 transform，而是像在导入数据时编写其他字段一样编写它。该字段如下所示:

"people": {
  "type": "nested",
  ..
  "properties": {
    "firstName": {
      "type": "string",
      "store": "yes",
      "index": "not_analyzed"
    },
    "lastName": {
      "type": "string",
      "store": "yes",
      "index": "not_analyzed"
    },
    "people_all": {
      "type": "string",
      "index": "not_analyzed"
    }
  }
}

请注意people_all字段的"index": "not_analyzed"。这对于拥有完整的桶很重要。如果您不使用它，我们的示例将返回 3 个桶“john”、“jane”和“doe”。

写入这个新字段后，我们可以按如下方式运行聚合:

{
  "size": 0,
  "query": {
    "term": {
      "street": "Baker Street"
    }
  },
  "aggs": {
    "people_distinct": {
      "nested": {
        "path": "people"
      },
      "aggs": {
        "people_all_distinct": {
          "terms": {
            "field": "people.people_all",
            "size": 0
          }
        }
      }
    }
  }
}

我们返回以下响应:

{
  "took": 2,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "failed": 0
  },
  "hits": {
    "total": 2,
    "max_score": 0.0,
    "hits": []
  },
  "aggregations": {
    "people_distinct": {
      "doc_count": 3,
      "people_name_distinct": {
        "doc_count_error_upper_bound": 0,
        "sum_other_doc_count": 0,
        "buckets": [
          {
            "key": "John Doe",
            "doc_count": 2
          },
          {
            "key": "Jane Doe",
            "doc_count": 1
          }
        ]
      }
    }
  }
}

在响应中的桶中，我们现在能够创建不同的人物对象。

如果有更好的方法来实现我们的目标，请告诉我们。解析存储桶不是最佳解决方案，在每个存储桶中包含字段 firstName 和 lastName 会更奇特。