Dataframe 具有某些字段和某些条件的弹性搜索中的查询？

我有关于产品的数据，其中有一些字段_id、Shop、ProductVerion。。。。它在弹性搜索中建立了索引。我想用商店搜索产品的最高产品版本

例：

版本可能是相同的

现在，我想得到以下产品：

Shop Amazon and ProducVersion 333
or Shop Ebay and ProductVersion 444
or Shop Alibaba and ProductVersion 444.

但我不知道。

---

请帮帮我

如果您提供可复制的示例会更好，但看起来您需要使用：和：

---

我用一些示例文档进行了尝试。我将版本字段保留为数字字段

这些是我尝试使用的示例文档

[
  {
    "_index": "test",
    "_type": "doc",
    "_id": "12334",
    "_score": 1,
    "_source": {
      "shopName": "amazon",
      "version": 341
    }
  },
  {
    "_index": "test",
    "_type": "doc",
    "_id": "123",
    "_score": 1,
    "_source": {
      "shopName": "amazon",
      "version": 3412
    }
  },
  {
    "_index": "test",
    "_type": "doc",
    "_id": "1233",
    "_score": 1,
    "_source": {
      "shopName": "amazon",
      "version": 341
    }
  },
  {
    "_index": "test",
    "_type": "doc",
    "_id": "1238",
    "_score": 1,
    "_source": {
      "shopName": "alibaba",
      "version": 34120
    }
  },
  {
    "_index": "test",
    "_type": "doc",
    "_id": "1239",
    "_score": 1,
    "_source": {
      "shopName": "alibaba",
      "version": 3414
    }
  },
  {
    "_index": "test",
    "_type": "doc",
    "_id": "123910",
    "_score": 1,
    "_source": {
      "shopName": "alibaba",
      "version": 124
    }
  }
]

正如@demas所指定的，我继续使用术语聚合和热门搜索聚合

indexName/_search

{
  "size": 0,
  "aggs": {
    "shop": {
      "terms": {
        "field": "shopName.keyword"
      },
      "aggs": {
        "product": {
          "top_hits": {
            "size": 1,
            "sort": [
              {
                "version": {
                  "order": "desc"
                }
              }
            ]
          }
        }
      }
    }
  }
}

这将为您提供包含每个商店的最高产品版本号的文档，如下所示

{
  "took": 8,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "skipped": 0,
    "failed": 0
  },
  "hits": {
    "total": 6,
    "max_score": 0,
    "hits": []
  },
  "aggregations": {
    "shop": {
      "doc_count_error_upper_bound": 0,
      "sum_other_doc_count": 0,
      "buckets": [
        {
          "key": "alibaba",
          "doc_count": 3,
          "product": {
            "hits": {
              "total": 3,
              "max_score": null,
              "hits": [
                {
                  "_index": "test",
                  "_type": "doc",
                  "_id": "1238",
                  "_score": null,
                  "_source": {
                    "shopName": "alibaba",
                    "version": 34120
                  },
                  "sort": [
                    34120
                  ]
                }
              ]
            }
          }
        },
        {
          "key": "amazon",
          "doc_count": 3,
          "product": {
            "hits": {
              "total": 3,
              "max_score": null,
              "hits": [
                {
                  "_index": "test",
                  "_type": "doc",
                  "_id": "123",
                  "_score": null,
                  "_source": {
                    "shopName": "amazon",
                    "version": 3412
                  },
                  "sort": [
                    3412
                  ]
                }
              ]
            }
          }
        }
      ]
    }
  }
} 

---

如果您向我们展示您的映射会更容易。请向我们提供一些样本文档作为json元素以及相关字段的映射。请提供您的elasticsearch版本和您使用的映射的详细信息/example Document我在这里没有看到提及的版本！谢谢你的回答！我不知道答案是否正确，我看不到显示的结果数量。嗨，我只使用聚合来获取详细信息。它拥有每个shopName版本号最高的产品的文档。您还可以通过检查聚合结果中的total字段来查看每个shopName的可用文档数。我已经测试了此查询，但它似乎仍然采用较低的版本。如何检查聚合结果中的total字段？？您可以通过检查每个shopname聚合结果的total字段来查看它。你能提供你的索引映射吗。产品版本号有可能以文本格式保存。这可能是您获得较低产品版本结果的原因。

indexName/_search

{
  "size": 0,
  "aggs": {
    "shop": {
      "terms": {
        "field": "shopName.keyword"
      },
      "aggs": {
        "product": {
          "top_hits": {
            "size": 1,
            "sort": [
              {
                "version": {
                  "order": "desc"
                }
              }
            ]
          }
        }
      }
    }
  }
}

{
  "took": 8,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "skipped": 0,
    "failed": 0
  },
  "hits": {
    "total": 6,
    "max_score": 0,
    "hits": []
  },
  "aggregations": {
    "shop": {
      "doc_count_error_upper_bound": 0,
      "sum_other_doc_count": 0,
      "buckets": [
        {
          "key": "alibaba",
          "doc_count": 3,
          "product": {
            "hits": {
              "total": 3,
              "max_score": null,
              "hits": [
                {
                  "_index": "test",
                  "_type": "doc",
                  "_id": "1238",
                  "_score": null,
                  "_source": {
                    "shopName": "alibaba",
                    "version": 34120
                  },
                  "sort": [
                    34120
                  ]
                }
              ]
            }
          }
        },
        {
          "key": "amazon",
          "doc_count": 3,
          "product": {
            "hits": {
              "total": 3,
              "max_score": null,
              "hits": [
                {
                  "_index": "test",
                  "_type": "doc",
                  "_id": "123",
                  "_score": null,
                  "_source": {
                    "shopName": "amazon",
                    "version": 3412
                  },
                  "sort": [
                    3412
                  ]
                }
              ]
            }
          }
        }
      ]
    }
  }
}