GA发布后，有计划改进 5.x
的文档。我知道在许多地方文档可能会更清晰，对此领域的任何帮助将不胜感激:)

Percolate查询的文档是从集成测试中生成的。在此使用来自其他问题的详细信息，拉出所有示例的示例。首先，让我们定义POCO模型

public class LogEntryModel
{
    public string Message { get; set; }

    public DateTimeOffset Timestamp { get; set; }
}

public class PercolatedQuery
{
    public string Id { get; set; }

    public QueryContainer Query { get; set; }
}

我们将流畅地映射所有属性，而不是使用映射属性。流畅的映射功能最强大，可以表达所有在Elasticsearch中进行映射的方式。

现在，创建连接设置和客户端以使用Elasticsearch。

var pool = new SingleNodeConnectionPool(new Uri($"http://localhost:9200"));
var logIndex = "log_entries";
var connectionSettings = new ConnectionSettings(pool)
    // infer mapping for logs
    .InferMappingFor<LogEntryModel>(m => m
        .IndexName(logIndex)
        .TypeName("log_entry")
    )
    // infer mapping for percolated queries
    .InferMappingFor<PercolatedQuery>(m => m
        .IndexName(logIndex)
        .TypeName("percolated_query")
    );

var client = new ElasticClient(connectionSettings);

我们可以指定索引名称和类型名称来推断我们的POCO。也就是说，当NEST使用LogEntryModel或PercolatedQuery作为请求中的泛型类型参数（例如Tin.Search<T>()）发出请求时，如果未在请求中指定它们，则将使用推断的索引名和类型名。

现在，删除索引，以便我们从头开始

// delete the index if it already exists
if (client.IndexExists(logIndex).Exists)
    client.DeleteIndex(logIndex);

并创建索引

client.CreateIndex(logIndex, c => c
    .Settings(s => s
        .NumberOfShards(1)
        .NumberOfReplicas(0)
    )
    .Mappings(m => m
        .Map<LogEntryModel>(mm => mm
            .AutoMap()
        )
        .Map<PercolatedQuery>(mm => mm
            .AutoMap()
            .Properties(p => p
                // map the query field as a percolator type
                .Percolator(pp => pp
                    .Name(n => n.Query)
                )
            )
        )
    )
);

上的Query属性PercolatedQuery被映射为percolator类型。这是Elasticsearch5.0中的新功能。映射请求看起来像

{
  "settings": {
    "index.number_of_replicas": 0,
    "index.number_of_shards": 1
  },
  "mappings": {
    "log_entry": {
      "properties": {
        "message": {
          "fields": {
            "keyword": {
              "type": "keyword"
            }
          },
          "type": "text"
        },
        "timestamp": {
          "type": "date"
        }
      }
    },
    "percolated_query": {
      "properties": {
        "id": {
          "fields": {
            "keyword": {
              "type": "keyword"
            }
          },
          "type": "text"
        },
        "query": {
          "type": "percolator"
        }
      }
    }
  }
}

现在，我们准备为查询建立索引

client.Index(new PercolatedQuery
{
    Id = "std_query",
    Query = new MatchQuery
    {
        Field = Infer.Field<LogEntryModel>(entry => entry.Message),
        Query = "just a text"
    }
}, d => d.Index(logIndex).Refresh(Refresh.WaitFor));

索引查询后，让我们对文档进行渗透

var logEntry = new LogEntryModel
{
    Timestamp = DateTimeOffset.UtcNow,
    Message = "some log message text"
};

// run percolator on the logEntry instance
var searchResponse = client.Search<PercolatedQuery>(s => s
    .Query(q => q
        .Percolate(p => p
            // field that contains the query
            .Field(f => f.Query)
            // details about the document to run the stored query against.
            // NOTE: This does not index the document, only runs percolation
            .DocumentType<LogEntryModel>()
            .Document(logEntry)
        )
    )
);

// outputs 1
Console.WriteLine(searchResponse.Documents.Count());

带有ID的渗透查询"std_query"返回到searchResponse.Documents

{
  "took" : 117,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "failed" : 0
  },
  "hits" : {
    "total" : 1,
    "max_score" : 0.2876821,
    "hits" : [
      {
        "_index" : "log_entries",
        "_type" : "percolated_query",
        "_id" : "std_query",
        "_score" : 0.2876821,
        "_source" : {
          "id" : "std_query",
          "query" : {
            "match" : {
              "message" : {
                "query" : "just a text"
              }
            }
          }
        }
      }
    ]
  }
}

这是渗透文档实例的示例。渗滤还可以对已经建立索引的文档运行

var searchResponse = client.Search<PercolatedQuery>(s => s
    .Query(q => q
        .Percolate(p => p
            // field that contains the query
            .Field(f => f.Query)
            // percolate an already indexed log entry
            .DocumentType<LogEntryModel>()
            .Id("log entry id")
            .Index<LogEntryModel>()
            .Type<LogEntryModel>()
        )
    )
);