标签:ica scan 响应 equals images 排序 pat ring ges
Elasticsearch.Net与NEST是Elasticsearch为C#提供的一套客户端驱动,方便C#调用Elasticsearch服务接口。Elasticsearch.Net是较基层的对Elasticsearch服务接口请求响应的实现,NEST是在前者基础之上进行的封装。本文是针对NEST的使用的总结。
Install-Package NEST
包含以下dll
NEST.dll
Elasticsearch.Net.dll
Newtonsoft.Json.dll
在Elasticsearch中,文档(Document
)归属于一种类型(type),而这些类型存在于索引(index)中.
类比传统关系型数据库:
Relational DB -> Databases -> Tables -> Rows -> Columns Elasticsearch -> Indices -> Types -> Documents -> Fields
mm
(毫米)cm
(厘米)m
(米)km
(千米)in
(英寸)ft
(英尺)yd
(码)mi
(英里)nmi
or NM
(海里)s => s .Query(q => q .Term(p => p.Name, "elasticsearch") )
var searchRequest = new SearchRequest<VendorPriceInfo> { Query = new TermQuery { Field = "name", Value = "elasticsearch" } };
//单node
Var node = new Uri(“……”);
var settings = new ConnectionSettings(node);
//多uri
Var uris = new Uri [] {
new Uri(“……”),
new Uri(“……”)
};
//多node
Var nodes = new Node [] {
new Node (new Uri(“……”)),
new Node (new Uri(“……”))
};
var pool = new StaticConnectionPool(nodes);
var pool = new StaticConnectionPool(uris);
var settings = new ConnectionSettings(pool);
var client = new ElasticClient(settings);
//对单节点请求 IConnectionPool pool = new SingleNodeConnectionPool(urls.FirstOrDefault()); //请求时随机请求各个正常节点,不请求异常节点,异常节点恢复后会重新被请求 IConnectionPool pool = new StaticConnectionPool(urls); IConnectionPool pool = new SniffingConnectionPool(urls); //false.创建客户端时,随机选择一个节点作为客户端的请求对象,该节点异常后不会切换其它节点 //true,请求时随机请求各个正常节点,不请求异常节点,但异常节点恢复后不会重新被请求 pool.SniffedOnStartup = true; //创建客户端时,选择第一个节点作为请求主节点,该节点异常后会切换其它节点,待主节点恢复后会自动切换回来 IConnectionPool pool = new StickyConnectionPool(urls);
索引选择
var settings = new ConnectionSettings().DefaultIndex("defaultindex");
方式2:
var settings = new ConnectionSettings().MapDefaultTypeIndices(m => m.Add(typeof(Project), "projects") );
方式3:
client.Search<VendorPriceInfo>(s => s.Index("test-index")); client.Index(data,o=>o.Index("test-index"));
优先级:方式3 > 方式2 > 方式1
1) 默认以“Id”字段值作为索引唯一Id值,无“Id”属性,Es自动生成唯一Id值,添加数据时统一类型数据唯一ID已存在相等值,将只做更新处理。
注:自动生成的ID有22个字符长,URL-safe, Base64-encoded string universally unique identifiers, 或者叫UUIDs。
2) 标记唯一Id值
[ElasticsearchType(IdProperty = "priceID")] public class VendorPriceInfo { public Int64 priceID { get; set; } public int oldID { get; set; } public int source { get; set; } }
3) 索引时指定
client.Index(data, o => o.Id(data.vendorName));
优先级: 3) > 2) > 1)
1) 默认类型为索引数据的类名(自动转换为全小写)
2) 标记类型
[ElasticsearchType(Name = "datatype")] public class VendorPriceInfo { public Int64 priceID { get; set; } public int oldID { get; set; } public int source { get; set; } }
3) 索引时指定
client.Index(data, o => o.Type(new TypeName() { Name = "datatype", Type = typeof(VendorPriceInfo) })); 或 client.Index(data, o => o.Type<MyClass>());//使用 2)标记的类型
优先级:3)> 2) > 1)
client.CreateIndex("test2"); //基本配置 IIndexState indexState=new IndexState() { Settings = new IndexSettings() { NumberOfReplicas = 1,//副本数 NumberOfShards = 5//分片数 } }; client.CreateIndex("test2", p => p.InitializeUsing(indexState)); //创建并Mapping client.CreateIndex("test-index3", p => p.InitializeUsing(indexState).Mappings(m => m.Map<VendorPriceInfo>(mp => mp.AutoMap())));
注:索引名称必须小写
client.IndexExists("test2");
删除:
client.DeleteIndex("test2");
Open/Close:
client.OpenIndex("index"); client.CloseIndex("index");
每个类型拥有自己的映射(mapping)或者模式定义(schema definition)。一个映射定义了字段类型,每个字段的数据类型,以及字段被Elasticsearch处理的方式。映射还用于设置关联到类型上的元数据。
var resule = client.GetMapping<VendorPriceInfo>();
/// <summary> /// VendorPrice 实体 /// </summary> [ElasticsearchType(IdProperty = "priceID", Name = "VendorPriceInfo")] public class VendorPriceInfo { [Number(NumberType.Long)] public Int64 priceID { get; set; } [Date(Format = "mmddyyyy")] public DateTime modifyTime { get; set; } /// <summary> /// 如果string 类型的字段不需要被分析器拆分,要作为一个正体进行查询,需标记此声明,否则索引的值将被分析器拆分 /// </summary> [String(Index = FieldIndexOption.NotAnalyzed)] public string pvc_Name { get; set; } /// <summary> /// 设置索引时字段的名称 /// </summary> [String(Name = "PvcDesc")] public string pvc_Desc { get; set; } /// <summary> /// 如需使用坐标点类型需添加坐标点特性,在maping时会自动映射类型 /// </summary> [GeoPoint(Name = "ZuoBiao",LatLon = true)] public GeoLocation Location { get; set; } }
//根据对象类型自动映射 var result= client.Map<VendorPriceInfo>(m => m.AutoMap()); //手动指定 var result1 = client.Map<VendorPriceInfo>(m => m.Properties(p => p .GeoPoint(gp => gp.Name(n => n.Location)// 坐标点类型 .Fielddata(fd => fd .Format(GeoPointFielddataFormat.Compressed)//格式 array doc_values compressed disabled .Precision(new Distance(2, DistanceUnit.Meters)) //精确度 )) .String(s => s.Name(n => n.b_id))//string 类型 )); //在原有字段下新增字段(用于存储不同格式的数据,查询方法查看SearchBaseDemo) //eg:在 vendorName 下添加无需分析器分析的值 temp var result2 = client.Map<VendorPriceInfo>( m => m .Properties(p => p.String(s => s.Name(n => n.vendorName).Fields(fd => fd.String(ss => ss.Name("temp").Index(FieldIndexOption.NotAnalyzed))))));
注:映射时已存在的字段将无法重新映射,只有新加的字段能映射成功。
所以我们也可以使用Index来更新已存在文档,只需对应文档的唯一id。
var data = new VendorPriceInfo() { vendorName = "测试"}; client.Index(data);
var datas = new List<VendorPriceInfo> { new VendorPriceInfo(){priceID = 1,vendorName = "test1"}, new VendorPriceInfo(){priceID = 2,vendorName = "test2"}}; client.IndexMany(datas);
DocumentPath<VendorPriceInfo> deletePath=new DocumentPath<VendorPriceInfo>(7); client.Delete(deletePath); 或 IDeleteRequest request = new DeleteRequest("test3", "vendorpriceinfo", 0); client.Delete(request);
Indices indices = "test-1"; Types types = "vendorpriceinfo"; //批量删除 需要es安装 delete-by-query插件 var result = client.DeleteByQuery<VendorPriceInfo>(indices, types, dq => dq.Query( q => q.TermRange(tr => tr.Field(fd => fd.priceID).GreaterThanOrEquals("5").LessThanOrEquals("10"))) );
DocumentPath<VendorPriceInfo> deletePath=new DocumentPath<VendorPriceInfo>(2); Var response=client.Update(deletePath,(p)=>p.Doc(new VendorPriceInfo(){vendorName = "test2update..."})); //或 IUpdateRequest<VendorPriceInfo, VendorPriceInfo> request = new UpdateRequest<VendorPriceInfo, VendorPriceInfo>(deletePath) { Doc = new VendorPriceInfo() { priceID = 888, vendorName = "test4update........" } }; var response = client.Update<VendorPriceInfo, VendorPriceInfo>(request);
IUpdateRequest<VendorPriceInfo, VendorPriceInfoP> request = new UpdateRequest<VendorPriceInfo, VendorPriceInfoP>(deletePath) { Doc = new VendorPriceInfoP() { priceID = 888, vendorName = "test4update........" } }; var response = client.Update(request);
IUpdateRequest<VendorPriceInfo, object> request = new UpdateRequest<VendorPriceInfo, object>(deletePath) { Doc = new { priceID = 888, vendorName = " test4update........" } }; var response = client.Update(request); //或 client.Update<VendorPriceInfo, object>(deletePath, upt => upt.Doc(new { vendorName = "ptptptptp" }));
注:更新时根据唯一id更新
var response = client.Get(new DocumentPath<VendorPriceInfo>(0)); //或 var response = client.Get(new DocumentPath<VendorPriceInfo>(0),pd=>pd.Index("test4").Type("v2")); //多个 var response = client.MultiGet(m => m.GetMany<VendorPriceInfo>(new List<long> { 1, 2, 3, 4 }));
注:获取时根据唯一id获取
var result = client.Search<VendorPriceInfo>( s => s .Explain() //参数可以提供查询的更多详情。 .FielddataFields(fs => fs //对指定字段进行分析 .Field(p => p.vendorFullName) .Field(p => p.cbName) ) .From(0) //跳过的数据个数 .Size(50) //返回数据个数 .Query(q => q.Term(p => p.vendorID, 100) // 主要用于精确匹配哪些值,比如数字,日期,布尔值或 not_analyzed的字符串(未经分析的文本数据类型): && q.Term(p => p.vendorName.Suffix("temp"), "姓名") //用于自定义属性的查询 (定义方法查看MappingDemo) && q.Bool( //bool 查询 b => b .Must(mt => mt //所有分句必须全部匹配,与 AND 相同 .TermRange(p => p.Field(f => f.priceID).GreaterThan("0").LessThan("1"))) //指定范围查找 .Should(sd => sd //至少有一个分句匹配,与 OR 相同 .Term(p => p.priceID, 32915), sd => sd.Terms(t => t.Field(fd => fd.priceID).Terms(new[] {10, 20, 30})),//多值 //|| //sd.Term(p => p.priceID, 1001) //|| //sd.Term(p => p.priceID, 1005) sd => sd.TermRange(tr => tr.GreaterThan("10").LessThan("12").Field(f => f.vendorPrice)) ) .MustNot(mn => mn//所有分句都必须不匹配,与 NOT 相同 .Term(p => p.priceID, 1001) , mn => mn.Bool(//bool 过滤 ,bool 查询与 bool 过滤相似,用于合并多个查询子句。不同的是,bool 过滤可以直接给出是否匹配成功, 而bool 查询要计算每一个查询子句的 _score (相关性分值)。 bb=>bb.Must(mt=>mt .Match(mc=>mc.Field(fd=>fd.carName).Query("至尊")) )) ) ) )//查询条件 .Sort(st => st.Ascending(asc => asc.vendorPrice))//排序 .Source(sc => sc.Include(ic => ic .Fields( fd => fd.vendorName, fd => fd.vendorID, fd => fd.priceID, fd => fd.vendorPrice))) //返回特定的字段 ); //TResult var result1 = client.Search<VendorPriceInfo, VendorPriceInfoP>(s => s.Query( q => q.MatchAll() ) .Size(15) );
或
var result = client.Search<VendorPriceInfo>(new SearchRequest() { Sort =new List<ISort> { new SortField { Field = "vendorPrice", Order = SortOrder.Ascending } }, Size = 10, From = 0, Query = new TermQuery() { Field = "priceID", Value = 6 } || new TermQuery( { Field = "priceID", Value = 8 } });
//分页最大限制(from+size<=10000)
int pageSize = 10; int pageIndex = 1; var result = client.Search<VendorPriceInfo>(s => s.Query(q => q .MatchAll()) .Size(pageSize) .From((pageIndex - 1) * pageSize) .Sort(st => st.Descending(d => d.priceID)));
string scrollid = ""; var result = client.Search<VendorPriceInfo>(s => s.Query(q => q.MatchAll()) .Size(100) .SearchType(SearchType.Scan) .Scroll("1m"));//scrollid过期时间 //得到滚动扫描的id scrollid = result.ScrollId; //执行滚动扫描得到数据 返回数据量是 result.Shards.Successful*size(查询成功的分片数*size) result = client.Scroll<VendorPriceInfo>("1m", scrollid); //得到新的id scrollid = result.ScrollId;
// 在原分值基础上 设置不同匹配的加成值 具体算法为lucene内部算法
var result = client.Search<VendorPriceInfo>(s => s .Query(q => q.Term(t => t .Field(f => f.cityID).Value(2108).Boost(4)) || q.Term(t => t .Field(f => f.pvcId).Value(2103).Boost(1)) ) .Size(3000) .Sort(st => st.Descending(SortSpecialField.Score)) );
//使用functionscore计算得分
var result1 = client.Search<VendorPriceInfo>(s => s .Query(q=>q.FunctionScore(f=>f
//查询区 .Query(qq => qq.Term(t => t .Field(fd => fd.cityID).Value(2108)) || qq.Term(t => t .Field(fd => fd.pvcId).Value(2103)) ) .Boost(1.0) //functionscore 对分值影响 .BoostMode(FunctionBoostMode.Replace)//计算boost 模式 ;Replace为替换 .ScoreMode(FunctionScoreMode.Sum) //计算score 模式;Sum为累加
//逻辑区 .Functions(fun=>fun .Weight(w => w.Weight(2).Filter(ft => ft .Term(t => t .Field(fd => fd.cityID).Value(2108))))//匹配cityid +2 .Weight(w => w.Weight(1).Filter(ft => ft .Term(t => t .Field(fd => fd.pvcId).Value(2103))))//匹配pvcid +1 ) ) ) .Size(3000) .Sort(st => st.Descending(SortSpecialField.Score).Descending(dsc=>dsc.priceID)) );
//结果中 cityid=2108,得分=2; pvcid=2103 ,得分=1 ,两者都满足的,得分=3
var result = client.Search<VendorPriceInfo>(s => s .Query(q => q.QueryString(m => m.Fields(fd=>fd.Field(fdd=>fdd.carName).Field(fdd=>fdd.carGearBox)) .Query("手自一体") ) ) .From(0) .Size(15) );
var result=client.Search<VendorPriceInfo>(s=>s .Query(q=>q .Match(m=>m.Field(f=>f.carName) .Query("尊贵型") ) ) .From(0) .Size(15) ); //多字段匹配 var result1 = client.Search<VendorPriceInfo>(s => s .Query(q => q .MultiMatch(m => m.Fields(fd=>fd.Fields(f=>f.carName,f=>f.carGearBox)) .Query("尊贵型") ) ) .From(0) .Size(15) );
var result = client.Search<VendorPriceInfo>(s => s .Query(q => q.MatchPhrase(m => m.Field(f => f.carName) .Query("尊贵型") ) ) .From(0) .Size(15) );
const double lat = 39.8694890000; const double lon = 116.4206470000; const double distance = 2000.0; //1 var result = client.Search<VendorPriceInfo>(s => s .Query(q => q .Bool(b => b.Must(m => m .GeoDistance(gd => gd .Location(lat, lon) .Distance(distance, DistanceUnit.Meters) .Field(fd => fd.Location) )) ) ) .From(0) .Size(15) ); //2 var location = new GeoLocation(lat, lon); var distancei = new Distance(distance, DistanceUnit.Meters); var result1 = client.Search<VendorPriceInfo>(s => s .Query(q => q .Bool(b => b.Must(m => m .Exists(e => e.Field(fd => fd.Location)) ) ) && q.GeoDistance(gd => gd .Location(location) .Distance(distancei) .Field(fd => fd.Location) ) ) .From(0) .Size(15) ); //3 var result2 = client.Search<VendorPriceInfo>(s => s .Query(q => q .Bool(b=>b .Must(m=>m.MatchAll()) .Filter(f=>f .GeoDistance(g => g .Name("named_query") .Field(p => p.Location) .DistanceType(GeoDistanceType.Arc) .Location(lat,lon) .Distance("2000.0m") ) ) ) ) .From(0) .Size(15) );
var result = client.Search<VendorPriceInfo>(s => s .From(0) .Size(15) .Aggregations(ag=>ag .ValueCount("Count", vc => vc.Field(fd => fd.vendorPrice))//总数 .Sum("vendorPrice_Sum", su => su.Field(fd => fd.vendorPrice))//求和 .Max("vendorPrice_Max", m => m.Field(fd => fd.vendorPrice))//最大值 .Min("vendorPrice_Min", m => m.Field(fd => fd.vendorPrice))//最小值 .Average("vendorPrice_Avg", avg => avg.Field(fd => fd.vendorPrice))//平均值 .Terms("vendorID_group", t => t.Field(fd => fd.vendorID).Size(100))//分组 ) );
//每个经销商 的平均报价 var result = client.Search<VendorPriceInfo>(s => s .Size(0) .Aggregations(ag => ag .Terms("vendorID_group", t => t .Field(fd => fd.vendorID) .Size(100) .Aggregations(agg => agg.Average("vendorID_Price_Avg", av => av.Field(fd => fd.vendorPrice))) )//分组 .Cardinality("vendorID_group_count", dy => dy.Field(fd => fd.vendorID))//分组数量 .ValueCount("Count", c => c.Field(fd => fd.vendorID))//总记录数 ) );
//每个经销商下 每个品牌 的平均报价 var result = client.Search<VendorPriceInfo>(s => s .Size(0) .Aggregations(ag => ag .Terms("vendorID_group", //vendorID 分组 t => t.Field(fd => fd.vendorID) .Size(100) .Aggregations(agg => agg .Terms("vendorID_cbID_group", //cbID分组 tt => tt.Field(fd => fd.cbID) .Size(50) .Aggregations(aggg => aggg .Average("vendorID_cbID_Price_Avg", av => av.Field(fd => fd.vendorPrice))//Price avg .Max("vendorID_cbID_Price_Max", m => m.Field(fd => fd.vendorPrice))//Price max .Min("vendorID_cbID_Price_Min", m => m.Field(fd => fd.vendorPrice))//Price min .ValueCount("vendorID_cbID_Count", m => m.Field(fd => fd.cbID))//该经销商对该品牌 报价数 count ) ) .Cardinality("vendorID_cbID_group_count", dy => dy.Field(fd => fd.cbID))//分组数量 .ValueCount("vendorID_Count", c => c.Field(fd => fd.vendorID))//该经销商的报价数 ) ) .Cardinality("vendorID_group_count",dy=>dy.Field(fd=>fd.vendorID))//分组数量 .ValueCount("Count",c=>c.Field(fd=>fd.priceID))//总记录数 ) //分组 );
(转)Elasticsearch .net client NEST使用说明 2.x
标签:ica scan 响应 equals images 排序 pat ring ges
原文地址:http://www.cnblogs.com/ywcz060/p/6028789.html