截断命中结果处理器

truncate_hits 响应处理器在达到指定的命中数量后，会丢弃后续返回的搜索结果命中项。truncate_hits 处理器设计用于与 oversample 请求处理器协同工作，但也可以单独使用。

target_size 参数（指定从何处开始截断）是可选的。如果未指定，UDB-SX 将使用由 oversample 处理器设置的 original_size 变量（如果可用）。

以下是一个常见的使用模式：

在请求管道中添加 oversample 处理器，以获取更大的结果集。
在响应管道中，应用一个重排序处理器（可能会提升原本请求的前 N 项之后的结果）或 collapse 处理器（可能在去重后丢弃一些结果）。
应用 truncate 处理器，以返回（最多）原始请求数量的命中结果。

请求体字段

下表列出了所有请求字段。

字段	数据类型	描述
`target_size`	整数	要返回的最大搜索命中数 (>=0)。如果未指定，处理器将尝试读取 `original_size` 变量，如果该变量不可用，则会失败。可选。
`context_prefix`	字符串	可用于从特定作用域读取 `original_size` 变量，以避免冲突。可选。
`tag`	字符串	处理器的标识符。可选。
`description`	字符串	处理器的描述信息。可选。
`ignore_failure`	布尔值	如果设置为 `true`，UDB-SX 将忽略此处理器的任何失败，并继续运行搜索管道中的其余处理器。可选。默认值为 `false`。

示例

以下示例演示了如何使用包含 truncate 处理器的搜索管道。

准备工作

创建一个包含多个文档的索引 my_index：

POST /_bulk
{ "create":{"_index":"my_index","_id":1}}
{ "doc": { "title" : "document 1" }}
{ "create":{"_index":"my_index","_id":2}}
{ "doc": { "title" : "document 2" }}
{ "create":{"_index":"my_index","_id":3}}
{ "doc": { "title" : "document 3" }}
{ "create":{"_index":"my_index","_id":4}}
{ "doc": { "title" : "document 4" }}
{ "create":{"_index":"my_index","_id":5}}
{ "doc": { "title" : "document 5" }}
{ "create":{"_index":"my_index","_id":6}}
{ "doc": { "title" : "document 6" }}
{ "create":{"_index":"my_index","_id":7}}
{ "doc": { "title" : "document 7" }}
{ "create":{"_index":"my_index","_id":8}}
{ "doc": { "title" : "document 8" }}
{ "create":{"_index":"my_index","_id":9}}
{ "doc": { "title" : "document 9" }}
{ "create":{"_index":"my_index","_id":10}}
{ "doc": { "title" : "document 10" }}

创建搜索管道

以下请求创建了一个名为 my_pipeline 的搜索管道，其中包含一个 truncate_hits 响应处理器，该处理器会丢弃前五个之后的命中结果：

PUT /_search/pipeline/my_pipeline 
{
  "response_processors": [
    {
      "truncate_hits" : {
        "tag" : "truncate_1",
        "description" : "This processor will discard results after the first 5.",
        "target_size" : 5
      }
    }
  ]
}

使用搜索管道

在不使用搜索管道的情况下，搜索 my_index 中的文档：

POST /my_index/_search
{
  "size": 8
}

响应包含八个命中项：

响应

{
  "took" : 13,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 10,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "my_index",
        "_id" : "1",
        "_score" : 1.0,
        "_source" : {
          "doc" : {
            "title" : "document 1"
          }
        }
      },
      {
        "_index" : "my_index",
        "_id" : "2",
        "_score" : 1.0,
        "_source" : {
          "doc" : {
            "title" : "document 2"
          }
        }
      },
      {
        "_index" : "my_index",
        "_id" : "3",
        "_score" : 1.0,
        "_source" : {
          "doc" : {
            "title" : "document 3"
          }
        }
      },
      {
        "_index" : "my_index",
        "_id" : "4",
        "_score" : 1.0,
        "_source" : {
          "doc" : {
            "title" : "document 4"
          }
        }
      },
      {
        "_index" : "my_index",
        "_id" : "5",
        "_score" : 1.0,
        "_source" : {
          "doc" : {
            "title" : "document 5"
          }
        }
      },
      {
        "_index" : "my_index",
        "_id" : "6",
        "_score" : 1.0,
        "_source" : {
          "doc" : {
            "title" : "document 6"
          }
        }
      },
      {
        "_index" : "my_index",
        "_id" : "7",
        "_score" : 1.0,
        "_source" : {
          "doc" : {
            "title" : "document 7"
          }
        }
      },
      {
        "_index" : "my_index",
        "_id" : "8",
        "_score" : 1.0,
        "_source" : {
          "doc" : {
            "title" : "document 8"
          }
        }
      }
    ]
  }
}

若要使用管道进行搜索，请在 search_pipeline 查询参数中指定管道名称：

POST /my_index/_search?search_pipeline=my_pipeline
{
  "size": 8
}

尽管请求了8个命中项且总共有10个可用，但响应只包含5个命中项：

响应

{
  "took" : 3,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 10,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "my_index",
        "_id" : "1",
        "_score" : 1.0,
        "_source" : {
          "doc" : {
            "title" : "document 1"
          }
        }
      },
      {
        "_index" : "my_index",
        "_id" : "2",
        "_score" : 1.0,
        "_source" : {
          "doc" : {
            "title" : "document 2"
          }
        }
      },
      {
        "_index" : "my_index",
        "_id" : "3",
        "_score" : 1.0,
        "_source" : {
          "doc" : {
            "title" : "document 3"
          }
        }
      },
      {
        "_index" : "my_index,
        "_id" : "4",
        "_score" : 1.0,
        "_source" : {
          "doc" : {
            "title" : "document 4"
          }
        }
      },
      {
        "_index" : "my_index",
        "_id" : "5",
        "_score" : 1.0,
        "_source" : {
          "doc" : {
            "title" : "document 5"
          }
        }
      }
    ]
  }
}

超采样、折叠和截断命中项

以下是一个更贴近实际的示例，您将使用 oversample 请求许多候选文档，使用 collapse 去除重复特定字段的文档（以获得更多样化的结果），然后使用 truncate 返回原始请求的文档数量（以避免从集群返回巨大的结果负载）。

准备工作

创建许多包含将用于折叠的字段的文档：

POST /_bulk
{ "create":{"_index":"my_index","_id":1}}
{ "title" : "document 1", "color":"blue" }
{ "create":{"_index":"my_index","_id":2}}
{ "title" : "document 2", "color":"blue" }
{ "create":{"_index":"my_index","_id":3}}
{ "title" : "document 3", "color":"red" }
{ "create":{"_index":"my_index","_id":4}}
{ "title" : "document 4", "color":"red" }
{ "create":{"_index":"my_index","_id":5}}
{ "title" : "document 5", "color":"yellow" }
{ "create":{"_index":"my_index","_id":6}}
{ "title" : "document 6", "color":"yellow" }
{ "create":{"_index":"my_index","_id":7}}
{ "title" : "document 7", "color":"orange" }
{ "create":{"_index":"my_index","_id":8}}
{ "title" : "document 8", "color":"orange" }
{ "create":{"_index":"my_index","_id":9}}
{ "title" : "document 9", "color":"green" }
{ "create":{"_index":"my_index","_id":10}}
{ "title" : "document 10", "color":"green" }

创建一个仅根据 color 字段进行折叠的管道：

PUT /_search/pipeline/collapse_pipeline
{
  "response_processors": [
    {
      "collapse" : {
        "field": "color"
      }
    }
  ]
}

创建另一个执行超采样、折叠，然后截断结果的管道：

PUT /_search/pipeline/oversampling_collapse_pipeline
{
  "request_processors": [
    {
      "oversample": {
        "sample_factor": 3
      }
    }
  ],
  "response_processors": [
    {
      "collapse" : {
        "field": "color"
      }
    },
    {
      "truncate_hits": {
        "description": "Truncates back to the original size before oversample increased it."
      }
    }
  ]
}

不使用超采样的折叠

在此示例中，您请求在根据 color 字段折叠之前的前三个文档。由于前两个文档具有相同的 color，第二个被丢弃，请求返回第一个和第三个文档：

POST /my_index/_search?search_pipeline=collapse_pipeline
{
  "size": 3
}

响应

{
  "took" : 2,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 10,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "my_index",
        "_id" : "1",
        "_score" : 1.0,
        "_source" : {
          "title" : "document 1",
          "color" : "blue"
        }
      },
      {
        "_index" : "my_index",
        "_id" : "3",
        "_score" : 1.0,
        "_source" : {
          "title" : "document 3",
          "color" : "red"
        }
      }
    ]
  },
  "profile" : {
    "shards" : [ ]
  }
}

超采样、折叠和截断

现在，您将使用 oversampling_collapse_pipeline，它请求前9个文档（将大小乘以3），按 color 去重，然后返回前3个命中项：

POST /my_index/_search?search_pipeline=oversampling_collapse_pipeline
{
  "size": 3
}

响应

{
  "took" : 2,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 10,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "my_index,
        "_id" : "1",
        "_score" : 1.0,
        "_source" : {
          "title" : "document 1",
          "color" : "blue"
        }
      },
      {
        "_index" : "my_index",
        "_id" : "3",
        "_score" : 1.0,
        "_source" : {
          "title" : "document 3",
          "color" : "red"
        }
      },
      {
        "_index" : "my_index",
        "_id" : "5",
        "_score" : 1.0,
        "_source" : {
          "title" : "document 5",
          "color" : "yellow"
        }
      }
    ]
  },
  "profile" : {
    "shards" : [ ]
  }
}