使用NEST渗透

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

PercolatedQuery

T

.Search<T>()

现在，删除索引，以便我们从头开始

// delete the index if it already existsif (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

PercolatedQuery

percolator

{  "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 instancevar 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 1Console.WriteLine(searchResponse.documents.Count());

带有ID的渗透查询

"std_query"

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>()        )    ));