如何使用 Atlas Search $search
查询运行 $unionWith
从 v6.0 开始,MongoDB $unionWith
聚合阶段支持 $unionWith
pipeline
选项内的 $search
。使用 $unionWith
,您可以将来自同一个数据库中多个集合的 $search
结果合并到结果集中。
本教程演示针对 sample_training
数据库中的 companies
和 inspections
集合,如何运行带有 $search
的 $unionWith
查询。本教程将引导您完成以下步骤:
为
sample_training
数据库中的companies
和inspections
collection设置具有动态映射的 Atlas Search 索引。使用
$search
运行$unionWith
查询,对companies
集合中名为mobile
的公司与inspections
集合中具有相同或相似企业名称的公司进行联合。
开始之前,请确保 Atlas 集群满足先决条件中所述的要求。
注意
要运行带有 $search
的 $unionWith
查询,您的集群必须运行 MongoDB v6.0 或更高版本。
要创建 Atlas Search 索引,您必须拥有 Project Data Access Admin
或更高的项目访问权限。
创建 Atlas Search 搜索索引
在本部分中,您将在sample_training
数据库中companies
collection的所有字段上创建一个名为default
的 Atlas Search 搜索索引。您将在sample_training
数据库的inspections
collection中的所有字段上创建另一个名为default
的 Atlas Search 搜索索引。您必须为每个collection执行以下步骤。
AtlasGoClusters在Atlas中,Go项目的 页面。
如果尚未显示,请从导航栏上的 Organizations 菜单中选择包含所需项目的组织。
如果尚未显示,请从导航栏的Projects菜单中选择所需的项目。
如果尚未出现,请单击侧边栏中的 Clusters(集群)。
会显示集群页面。
转到集群的 Atlas Search 页面。
您可以从侧边栏、 Data Explorer 或集群详细信息页面转到 Atlas Search 页面。
在侧边栏中,单击 Services 标题下的 Atlas Search。
从 Select data source 下拉菜单中选择您的集群并单击 Go to Atlas Search。
将显示 Atlas Search 页面。
单击集群的对应 Browse Collections 按钮。
展开数据库并选择集合。
单击该集合的 Search Indexes 标签页。
将显示 Atlas Search 页面。
单击集群的名称。
单击 Atlas Search 标签页。
将显示 Atlas Search 页面。
输入 Index Name(索引名称),然后设置 Database and Collection(数据库和集合)。
在 Index Name 字段中输入
default
。如果将索引命名为
default
,则在使用 $search 管道阶段时无需指定index
参数。如果您为索引指定了自定义名称,则必须在index
参数中指定此名称。在Database and Collection部分中,找到
sample_training
数据库,然后选择集合。要为
companies
collection创建索引,请选择companies
。要为
inspections
collection创建索引,请选择inspections
。
指定索引定义。
以下索引定义对集合中的支持类型字段动态创建索引。您可以使用 Atlas Search Visual Editor 或 Atlas Search JSON Editor 在 Atlas 用户界面中创建索引。
单击 Next(连接)。
查看collection的
"default"
索引定义。
单击 Next(连接)。
查看索引定义。
您的索引定义应类似于以下示例:
{ "mappings": { "dynamic": true } }
运行带有 $search
的 $unionWith
以搜索集合
➤ 使用选择语言下拉菜单设置本节中示例的语言。
在本部分中,您将连接到 Atlas 集群,并针对sample_training
数据库中的索引集合运行样本查询。
通过 mongosh
连接到您的集群。
在终端窗口中打开mongosh
并连接到集群。 有关连接的详细说明,请参阅通过mongosh
连接。
切换到 sample_training
数据库。
在 mongosh
提示符下运行以下命令:
use sample_training
switched to db sample_training
使用 Atlas Search $search
查询运行以下 $unionWith
。
以下查询分别在companies
和inspections
collection中搜索name
和business_name
字段中的术语mobile
。
此查询使用以下阶段:
$search
可搜索名称中包含mobile
的公司。$unionWith
以执行以下操作:使用子管道中的
$search
阶段搜索名称中包含mobile
的公司的检查。对
companies
中的文档和inspections
collection中的文档执行并集。
$set
阶段添加一个名为source
的新字段,用于标识输出文档的集合。
db.companies.aggregate([ { "$search": { "text": { "query": "Mobile", "path": "name" } } }, { "$project": { "score": { "$meta": "searchScore" }, "_id": 0, "number_of_employees": 1, "founded_year": 1, "name": 1 } }, { "$set": { "source": "companies" } }, { "$limit": 3 }, { "$unionWith": { "coll": "inspections", "pipeline": [ { "$search": { "text": { "query": "Mobile", "path": "business_name" } } }, { "$set": { "source": "inspections" } }, { "$project": { "score": { "$meta": "searchScore" }, "source": 1, "_id": 0, "business_name": 1, "address": 1 } }, { "$limit": 3 }, { "$sort": { "score": -1 } } ] } } ])
[ { name: 'XLR8 Mobile', number_of_employees: 21, founded_year: 2006, score: 2.0815043449401855, source: 'companies' }, { name: 'Pulse Mobile', number_of_employees: null, founded_year: null, score: 2.0815043449401855, source: 'companies' }, { name: 'T-Mobile', number_of_employees: null, founded_year: null, score: 2.0815043449401855, source: 'companies' }, { business_name: 'T. MOBILE', address: { city: 'BROOKLYN', zip: 11209, street: '86TH ST', number: 440 }, score: 2.900916337966919, source: 'inspections' }, { business_name: 'BOOST MOBILE', address: { city: 'BRONX', zip: 10458, street: 'E FORDHAM RD', number: 261 }, score: 2.900916337966919, source: 'inspections' }, { business_name: 'SPRING MOBILE', address: { city: 'SOUTH RICHMOND HILL', zip: 11419, street: 'LIBERTY AVE', number: 12207 }, score: 2.900916337966919, source: 'inspections' } ]
此查询使用以下阶段:
$search
可搜索名称中包含mobile
的公司。$project
阶段到:在结果中仅包含指定字段。
添加名为
score
的字段。
$addFields
阶段添加以下新字段:一个名为
source
的新字段,用于标识输出文档的集合。字段名称
source_count
,显示输出文档的计数。
$unionWith
以执行以下操作:使用子管道中的
$search
阶段搜索名称中包含mobile
的公司的检查。对
companies
中的文档和inspections
collection中的文档执行并集。
$project
阶段到:在结果中仅包含指定字段。
添加名为
score
的字段。
$limit
阶段将输出限制为每个集合的3
个结果。$set
阶段添加以下新字段:一个名为
source
的新字段,用于标识输出文档的集合。一个名为
source_count
的新字段,显示输出文档的计数。
db.companies.aggregate([ { "$search": { "text": { "query": "mobile", "path": "name", "score": { "boost": { "value": 1.6 } } } } }, { "$project": { "score": { "$meta": "searchScore" }, "_id": 0, "number_of_employees": 1, "founded_year": 1, "name": 1 } }, { "$addFields": { "source": "companies", "source_count": "$$SEARCH_META.count.lowerBound" } }, { "$limit": 3 }, { "$unionWith": { "coll": "inspections", "pipeline": [ { "$search": { "text": { "query": "mobile", "path": "business_name" } } }, { "$project": { "score": { "$meta": "searchScore" }, "business_name": 1, "address": 1, "_id": 0 } }, { "$limit": 3 }, { "$set": { "source": "inspections", "source_count": "$$SEARCH_META.count.lowerBound" } }, { "$sort": { "score": -1 } } ] } }, { "$facet": { "allDocs": [], "totalCount": [ { "$group": { "_id": "$source", "firstCount": { "$first": "$source_count" } } }, { "$project": { "totalCount": { "$sum": "$firstCount" } } } ] } } ])
[ { allDocs: [ { name: 'XLR8 Mobile', number_of_employees: 21, founded_year: 2006, score: 3.33040714263916, source: 'companies', source_count: Long("52") }, { name: 'Pulse Mobile', number_of_employees: null, founded_year: null, score: 3.33040714263916, source: 'companies', source_count: Long("52") }, { name: 'T-Mobile', number_of_employees: null, founded_year: null, score: 3.33040714263916, source: 'companies', source_count: Long("52") }, { business_name: 'T. MOBILE', address: { city: 'BROOKLYN', zip: 11209, street: '86TH ST', number: 440 }, score: 2.900916337966919, source: 'inspections', source_count: Long("456") }, { business_name: 'BOOST MOBILE', address: { city: 'BRONX', zip: 10458, street: 'E FORDHAM RD', number: 261 }, score: 2.900916337966919, source: 'inspections', source_count: Long("456") }, { business_name: 'SPRING MOBILE', address: { city: 'SOUTH RICHMOND HILL', zip: 11419, street: 'LIBERTY AVE', number: 12207 }, score: 2.900916337966919, source: 'inspections', source_count: Long("456") } ], totalCount: [ { _id: 'companies', totalCount: Long("52") }, { _id: 'inspections', totalCount: Long("456") } ] } ]
在 MongoDB Compass 中连接到您的集群。
打开 MongoDB Compass 并连接到您的集群。有关连接的详细说明,请参阅通过 Compass 连接。
针对该集合运行 Atlas Search 查询。
以下查询分别在companies
和inspections
collection中搜索name
和business_name
字段中的术语mobile
。
若要在 MongoDB Compass 中运行此查询:
单击 Aggregations 标签页。
单击 Select...,然后从下拉菜单中选择阶段并为该阶段添加查询,以配置以下每个管道阶段。单击 Add Stage 以添加其他阶段。
此查询使用以下阶段:
$search
可搜索名称中包含mobile
的公司。$unionWith
以执行以下操作:使用子管道中的
$search
阶段搜索名称中包含mobile
的公司的检查。对
companies
中的文档和inspections
collection中的文档执行并集。
$set
阶段添加一个名为source
的新字段,用于标识输出文档的集合。
管道阶段查询$search
{ "text": { "query": "Mobile", "path": "name" } } $project
{ "score": { "$meta": "searchScore", }, "_id": 0, "number_of_employees": 1, "founded_year": 1, "name": 1 } $set
{ "source": "companies" } $limit
3
$unionWith
{ "coll": "inspections", "pipeline": [ { "$search": { "text": { "query": "Mobile", "path": "business_name", } } }, { "$set": { "source": "inspections", } }, { "$project": { "score": { "$meta": "searchScore" }, "source": 1, "_id": 0, "business_name": 1, "address": 1 } }, { "$limit": 3 }, { "$sort": { "score": -1 } } ] } 如果已启用 Auto Preview,则 MongoDB Compass 将在
$project
管道阶段旁边显示以下文档:name: "XLR8 Mobile" number_of_employees: 21 founded_year: 2006 score: 2.0815043449401855 source: "companies" name: "Pulse Mobile" number_of_employees: null founded_year: null score: 2.0815043449401855 source: "companies" name: "T-Mobile" number_of_employees: null founded_year: null score: 2.0815043449401855 source: "companies" business_name: "T. MOBILE" address: Object source: "inspections" score: 2.900916337966919 business_name: "BOOST MOBILE" address: Object source: "inspections" score: 2.900916337966919 business_name: "SPRING MOBILE" address: Object source: "inspections" score: 2.900916337966919 此查询使用以下阶段:
$search
可搜索名称中包含mobile
的公司。$project
阶段到:在结果中仅包含指定字段。
添加名为
score
的字段。
$addFields
阶段添加以下新字段:一个名为
source
的新字段,用于标识输出文档的集合。字段名称
source_count
,显示输出文档的计数。
$unionWith
以执行以下操作:使用子管道中的
$search
阶段搜索名称中包含mobile
的公司的检查。对
companies
中的文档和inspections
collection中的文档执行并集。
$project
阶段到:在结果中仅包含指定字段。
添加名为
score
的字段。
$limit
阶段将输出限制为每个集合的3
个结果。$set
阶段添加以下新字段:一个名为
source
的新字段,用于标识输出文档的集合。一个名为
source_count
的新字段,显示输出文档的计数。
管道阶段查询$search
{ text: { query: "mobile", path: "name", score: { boost: { value: 1.6 } } } } $project
{ "score": { "$meta": "searchScore", }, "_id": 0, "number_of_employees": 1, "founded_year": 1, "name": 1 } $addFields
{ source: "companies", source_count: "$$SEARCH_META.count.lowerBound" } $limit
3
$unionWith
{ coll: "inspections", pipeline: [ { $search: { text: { query: "mobile", path: "business_name" } } }, { $project: { score: { $meta: "searchScore" }, business_name: 1, address: 1, _id: 0 } }, { $limit: 3, }, { $set: { source: "inspections", source_count: "$$SEARCH_META.count.lowerBound" } }, { $sort: { score: -1 } } ] } $facet
{ allDocs: [], totalCount: [ { $group: { _id: "$source", firstCount: { $first: "$source_count" } } }, { $project: { totalCount: { $sum: "$firstCount" } } } ] } 如果已启用 Auto Preview,则 MongoDB Compass 将在
$project
管道阶段旁边显示以下文档:allDocs: Array (6) 0: Object name: "XLR8 Mobile" number_of_employees: 21 founded_year: 2006 score: 3.33040714263916 source: "companies" source_count: 52 1: Object name: "Pulse Mobile" number_of_employees: null founded_year: null score: 3.33040714263916 source: "companies" source_count: 52 2: Object name: "T-Mobile" number_of_employees: null founded_year: null score: 3.33040714263916 source: "companies" source_count: 52 3: Object business_name: "T. MOBILE" address: Object score: 2.900916337966919 source: "inspections" source_count: 456 4: Object business_name: "BOOST MOBILE" address: Object score: 2.900916337966919 source: "inspections" source_count: 456 5: Object business_name: "SPRING MOBILE" address: Object score: 2.900916337966919 source: "inspections" source_count: 456 totalCount: Array (2) 0: Object _id: "companies" totalCount: 52 1: Object _id: "inspections" totalCount: 456
将查询复制并粘贴到 Program.cs
文件中。
以下查询分别在companies
和inspections
collection中搜索name
和business_name
字段中的术语mobile
。
此查询使用以下阶段:
$search
可搜索名称中包含mobile
的公司。$unionWith
以执行以下操作:使用子管道中的
$search
阶段搜索名称中包含mobile
的公司的检查。对
companies
中的文档和inspections
collection中的文档执行并集。
$set
阶段添加一个名为source
的新字段,用于标识输出文档的集合。
1 using MongoDB.Bson; 2 using MongoDB.Driver; 3 using MongoDB.Driver.Search; 4 5 public class Program 6 { 7 public static void Main(string[] args) 8 { 9 // connect to your Atlas cluster 10 string connectionString = "<connection-string>"; 11 var client = new MongoClient(connectionString); 12 13 // define namespace 14 var database = client.GetDatabase("sample_training"); 15 var collection = database.GetCollection<BsonDocument>("companies"); 16 17 // define pipeline stage 18 var searchStage1 = new BsonDocument("$search", new BsonDocument{{ "text", new BsonDocument 19 {{ "query", "Mobile" },{ "path", "name" }} 20 }}); 21 var projectStage1 = new BsonDocument("$project", new BsonDocument{ 22 { "score", new BsonDocument("$meta", "searchScore") }, 23 { "_id", 0 },{ "number_of_employees", 1 },{ "founded_year", 1 },{ "name", 1 } 24 }); 25 var setStage1 = new BsonDocument("$set", new BsonDocument{{ "source", "companies" }}); 26 var limitStage1 = new BsonDocument("$limit", 3); 27 28 // define subpipeline 29 var searchStage2 = new BsonDocument("$search", new BsonDocument{{ "text", new BsonDocument 30 {{ "query", "Mobile" },{ "path", "business_name" }} 31 }}); 32 var setStage2 = new BsonDocument("$set", new BsonDocument{ { "source", "inspections" } }); 33 var projectStage2 = new BsonDocument("$project", new BsonDocument{ 34 { "score", new BsonDocument("$meta", "searchScore") }, 35 { "source", 1 }, { "_id", 0 }, { "business_name", 1 }, { "address", 1 } 36 }); 37 var limitStage2 = new BsonDocument("$limit", 3); 38 var sortStage2 = new BsonDocument("$sort", new BsonDocument{{ "score", -1 }}); 39 var unionWithPipeline = new List<BsonDocument>{searchStage2, setStage2, projectStage2, limitStage2, sortStage2}; 40 var unionWithStage = new BsonDocument("$unionWith", new BsonDocument 41 { 42 { "coll", "inspections" }, 43 { "pipeline", new BsonArray(unionWithPipeline) } 44 }); 45 var aggregationPipeline = new List<BsonDocument> {searchStage1, projectStage1, setStage1, limitStage1,unionWithStage}; 46 47 // run pipeline 48 var result = collection.Aggregate<BsonDocument>(aggregationPipeline).ToList(); 49 50 //print results 51 foreach (var document in result) 52 { 53 Console.WriteLine(document); 54 } 55 } 56 }
此查询使用以下阶段:
$search
可搜索名称中包含mobile
的公司。$project
阶段到:在结果中仅包含指定字段。
添加名为
score
的字段。
$addFields
阶段添加以下新字段:一个名为
source
的新字段,用于标识输出文档的集合。字段名称
source_count
,显示输出文档的计数。
$unionWith
以执行以下操作:使用子管道中的
$search
阶段搜索名称中包含mobile
的公司的检查。对
companies
中的文档和inspections
collection中的文档执行并集。
$project
阶段到:在结果中仅包含指定字段。
添加名为
score
的字段。
$limit
阶段将输出限制为每个集合的3
个结果。$set
阶段添加以下新字段:一个名为
source
的新字段,用于标识输出文档的集合。一个名为
source_count
的新字段,显示输出文档的计数。
1 using MongoDB.Bson; 2 using MongoDB.Driver; 3 4 public class Program 5 { 6 public static void Main(string[] args) 7 { 8 // connect to your Atlas cluster 9 var client = new MongoClient("<connection-string>"); 10 11 // define namespace 12 var database = client.GetDatabase("sample_training"); 13 var collection = database.GetCollection<BsonDocument>("companies"); 14 15 // define pipeline 16 var pipeline = new BsonDocument[] 17 { 18 new BsonDocument("$search", new BsonDocument{ 19 { "text", new BsonDocument{ 20 { "query", "mobile" }, { "path", "name" }, 21 { "score", new BsonDocument{ 22 { "boost", new BsonDocument{ { "value", 1.6 } }} 23 }} 24 }} 25 }), 26 new BsonDocument("$project", new BsonDocument{ 27 { "score", new BsonDocument("$meta", "searchScore") }, 28 { "_id", 0 }, 29 { "number_of_employees", 1 }, { "founded_year", 1 }, { "name", 1 } 30 }), 31 new BsonDocument("$addFields", new BsonDocument{ 32 { "source", "companies" }, 33 { "source_count", "$$SEARCH_META.count.lowerBound" } 34 }), 35 new BsonDocument("$limit", 3), 36 new BsonDocument("$unionWith", new BsonDocument{ 37 { "coll", "inspections" }, 38 { "pipeline", new BsonArray{ 39 new BsonDocument("$search", new BsonDocument{ 40 { "text", new BsonDocument{ 41 { "query", "mobile" }, 42 { "path", "business_name" } 43 }} 44 }), 45 new BsonDocument("$project", new BsonDocument{ 46 { "score", new BsonDocument("$meta", "searchScore") }, 47 { "business_name", 1 }, { "address", 1 }, { "_id", 0 } 48 }), 49 new BsonDocument("$limit", 3), 50 new BsonDocument("$set", new BsonDocument{ 51 { "source", "inspections" }, 52 { "source_count", "$$SEARCH_META.count.lowerBound" } 53 }), 54 new BsonDocument("$sort", new BsonDocument{ 55 { "score", -1 } 56 }) 57 }} 58 }), 59 new BsonDocument("$facet", new BsonDocument{ 60 { "allDocs", new BsonArray() }, 61 { "totalCount", new BsonArray{ 62 new BsonDocument("$group", new BsonDocument{ 63 { "_id", "$source" }, 64 { "firstCount", new BsonDocument("$first", "$source_count") } 65 }), 66 new BsonDocument("$project", new BsonDocument{ 67 { "totalCount", new BsonDocument("$sum", "$firstCount") } 68 }) 69 }} 70 }) 71 }; 72 73 // run pipeline 74 var result = collection.Aggregate<BsonDocument>(pipeline).ToList(); 75 76 //print results 77 foreach (var document in result) 78 { 79 Console.WriteLine(document); 80 } 81 } 82 }
替换查询中的 <connection-string>
,然后保存该文件。
确保您的连接字符串包含数据库用户的档案。要了解详情,请参阅通过驱动程序连接。
编译并运行 Program.cs
文件。
dotnet run search-with-unionwith.csproj
{ "name" : "XLR8 Mobile", "number_of_employees" : 21, "founded_year" : 2006, "score" : 2.0815043449401855, "source" : "companies" } { "name" : "Pulse Mobile", "number_of_employees" : null, "founded_year" : null, "score" : 2.0815043449401855, "source" : "companies" } { "name" : "T-Mobile", "number_of_employees" : null, "founded_year" : null, "score" : 2.0815043449401855, "source" : "companies" } { "business_name" : "T. MOBILE", "address" : { "city" : "BROOKLYN", "zip" : 11209, "street" : "86TH ST", "number" : 440 }, "source" : "inspections", "score" : 2.9009163379669189 } { "business_name" : "BOOST MOBILE", "address" : { "city" : "BRONX", "zip" : 10458, "street" : "E FORDHAM RD", "number" : 261 }, "source" : "inspections", "score" : 2.9009163379669189 } { "business_name" : "SPRING MOBILE", "address" : { "city" : "SOUTH RICHMOND HILL", "zip" : 11419, "street" : "LIBERTY AVE", "number" : 12207 }, "source" : "inspections", "score" : 2.9009163379669189 }
dotnet run search-with-unionwith.csproj
{ "allDocs" : [ { "name" : "XLR8 Mobile", "number_of_employees" : 21, "founded_year" : 2006, "score" : 3.3304071426391602, "source" : "companies", "source_count" : NumberLong(52) }, { "name" : "Pulse Mobile", "number_of_employees" : null, "founded_year" : null, "score" : 3.3304071426391602, "source" : "companies", "source_count" : NumberLong(52) }, { "name" : "T-Mobile", "number_of_employees" : null, "founded_year" : null, "score" : 3.3304071426391602, "source" : "companies", "source_count" : NumberLong(52) }, { "business_name" : "T. MOBILE", "address" : { "city" : "BROOKLYN", "zip" : 11209, "street" : "86TH ST", "number" : 440 }, "score" : 2.9009163379669189, "source" : "inspections", "source_count" : NumberLong(456) }, { "business_name" : "BOOST MOBILE", "address" : { "city" : "BRONX", "zip" : 10458, "street" : "E FORDHAM RD", "number" : 261 }, "score" : 2.9009163379669189, "source" : "inspections", "source_count" : NumberLong(456) }, { "business_name" : "SPRING MOBILE", "address" : { "city" : "SOUTH RICHMOND HILL", "zip" : 11419, "street" : "LIBERTY AVE", "number" : 12207 }, "score" : 2.9009163379669189, "source" : "inspections", "source_count" : NumberLong(456) } ], "totalCount" : [ { "_id" : "companies", "totalCount" : NumberLong(52) }, { "_id" : "inspections", "totalCount" : NumberLong(456) } ] }
将查询复制并粘贴到 search-with-unionwith-query.go
文件中。
以下查询分别在companies
和inspections
collection中搜索name
和business_name
字段中的术语mobile
。
此查询使用以下阶段:
$search
可搜索名称中包含mobile
的公司。$unionWith
以执行以下操作:使用子管道中的
$search
阶段搜索名称中包含mobile
的公司的检查。对
companies
中的文档和inspections
collection中的文档执行并集。
$set
阶段添加一个名为source
的新字段,用于标识输出文档的集合。
1 package main 2 import ( 3 "context" 4 "fmt" 5 "time" 6 7 "go.mongodb.org/mongo-driver/bson" 8 "go.mongodb.org/mongo-driver/mongo" 9 "go.mongodb.org/mongo-driver/mongo/options" 10 ) 11 12 func main() { 13 var err error 14 // connect to the Atlas cluster 15 ctx := context.Background() 16 client, err := mongo.Connect(ctx, options.Client().ApplyURI("<connection-string>")) 17 if err != nil { 18 panic(err) 19 } 20 defer client.Disconnect(ctx) 21 22 // set namespace 23 collection := client.Database("sample_training").Collection("companies") 24 // define pipeline 25 searchStage := bson.D{{"$search", bson.D{ 26 {"text", bson.D{ 27 {"query", "Mobile"}, {"path", "name"}, 28 }}, 29 }}} 30 projectStage := bson.D{{"$project", bson.D{ 31 {"score", bson.D{{"$meta", "searchScore"}}}, 32 {"_id", 0}, 33 {"number_of_employees", 1}, 34 {"founded_year", 1}, 35 {"name", 1}, 36 }}} 37 setStage := bson.D{{"$set", bson.D{{"source", "companies"}}}} 38 limitStage := bson.D{{"$limit", 5}} 39 uinionWithStage := bson.D{{"$unionWith", bson.D{ 40 {"coll", "inspections"}, 41 {"pipeline", bson.A{ 42 bson.D{{"$search", bson.D{ 43 {"text", bson.D{ 44 {"query", "Mobile"}, {"path", "business_name"}, 45 }}, 46 }}}, 47 bson.D{{"$set", bson.D{{"source", "inspections"}}}}, 48 bson.D{{"$project", bson.D{ 49 {"score", bson.D{{"$meta", "searchScore"}}}, 50 {"source", 1}, 51 {"_id", 0}, 52 {"business_name", 1}, 53 {"address", 1}, 54 }}}, 55 bson.D{{"$limit", 3}}, 56 bson.D{{"$sort", bson.D{{"score", -1}}}}, 57 }}, 58 }}} 59 // specify the amount of time the operation can run on the server 60 opts := options.Aggregate().SetMaxTime(5 * time.Second) 61 // run pipeline 62 cursor, err := collection.Aggregate(ctx, mongo.Pipeline{searchStage, projectStage, setStage, limitStage, uinionWithStage}, opts) 63 if err != nil { 64 panic(err) 65 } 66 // print results 67 var results []bson.D 68 if err = cursor.All(context.TODO(), &results); err != nil { 69 panic(err) 70 } 71 for _, result := range results { 72 fmt.Println(result) 73 } 74 }
此查询使用以下阶段:
$search
可搜索名称中包含mobile
的公司。$project
阶段到:在结果中仅包含指定字段。
添加名为
score
的字段。
$addFields
阶段添加以下新字段:一个名为
source
的新字段,用于标识输出文档的集合。字段名称
source_count
,显示输出文档的计数。
$unionWith
以执行以下操作:使用子管道中的
$search
阶段搜索名称中包含mobile
的公司的检查。对
companies
中的文档和inspections
collection中的文档执行并集。
$project
阶段到:在结果中仅包含指定字段。
添加名为
score
的字段。
$limit
阶段将输出限制为每个集合的3
个结果。$set
阶段添加以下新字段:一个名为
source
的新字段,用于标识输出文档的集合。一个名为
source_count
的新字段,显示输出文档的计数。
1 package main 2 import ( 3 "context" 4 "fmt" 5 "time" 6 7 "go.mongodb.org/mongo-driver/bson" 8 "go.mongodb.org/mongo-driver/mongo" 9 "go.mongodb.org/mongo-driver/mongo/options" 10 ) 11 12 func main() { 13 var err error 14 // connect to the Atlas cluster 15 ctx := context.Background() 16 client, err := mongo.Connect(ctx, options.Client().ApplyURI("<connection-string>")) 17 if err != nil { 18 panic(err) 19 } 20 defer client.Disconnect(ctx) 21 // set namespace 22 collection := client.Database("sample_training").Collection("companies") 23 // define pipeline 24 searchStage := bson.D{{"$search", bson.D{ 25 {"text", bson.D{ 26 {"query", "Mobile"}, {"path", "name"}, {"score", bson.D{{"boost", bson.D{{"value", 1.6}}}}}, 27 }}, 28 }}} 29 projectStage := bson.D{{"$project", bson.D{ 30 {"score", bson.D{{"$meta", "searchScore"}}}, 31 {"_id", 0}, 32 {"number_of_employees", 1}, 33 {"founded_year", 1}, 34 {"name", 1}, 35 }}} 36 addFieldsStage := bson.D{{"$set", bson.D{ 37 {"source", "companies"}, 38 {"source_count", "$$SEARCH_META.count.lowerBound"}, 39 }}} 40 limitStage := bson.D{{"$limit", 3}} 41 uinionWithStage := bson.D{{"$unionWith", bson.D{ 42 {"coll", "inspections"}, 43 {"pipeline", bson.A{ 44 bson.D{{"$search", bson.D{ 45 {"text", bson.D{ 46 {"query", "mobile"}, {"path", "business_name"}, 47 }}, 48 }}}, 49 bson.D{{"$project", bson.D{ 50 {"score", bson.D{{"$meta", "searchScore"}}}, 51 {"business_name", 1}, 52 {"address", 1}, 53 {"_id", 0}, 54 }}}, 55 bson.D{{"$limit", 3}}, 56 bson.D{{"$set", bson.D{ 57 {"source", "inspections"}, 58 {"source_count", "$$SEARCH_META.count.lowerBound"}, 59 }}}, 60 bson.D{{"$sort", bson.D{{"score", -1}}}}, 61 }}, 62 }}} 63 facetStage := bson.D{{"$facet", bson.D{ 64 {"allDocs", bson.A{}}, 65 {"totalCount", bson.A{ 66 bson.D{ 67 {"$group", bson.D{ 68 {"_id", "$source"}, 69 {"firstCount", bson.D{{"$first", "$source_count"}}}, 70 }}, 71 }, 72 bson.D{{"$project", bson.D{{"totalCount", bson.D{{"$sum", "$firstCount"}}}}}}, 73 }}, 74 }}} 75 // specify the amount of time the operation can run on the server 76 opts := options.Aggregate().SetMaxTime(5 * time.Second) 77 // run pipeline 78 cursor, err := collection.Aggregate(ctx, mongo.Pipeline{searchStage, projectStage, addFieldsStage, limitStage, uinionWithStage, facetStage}, opts) 79 if err != nil { 80 panic(err) 81 } 82 // print results 83 var results []bson.D 84 if err = cursor.All(context.TODO(), &results); err != nil { 85 panic(err) 86 } 87 for _, result := range results { 88 fmt.Println(result) 89 } 90 }
替换查询中的 <connection-string>
,然后保存该文件。
确保您的连接字符串包含数据库用户的档案。要了解详情,请参阅通过驱动程序连接。
运行命令以查询您的集合。
go run search-with-unionwith-query.go
[{name XLR8 Mobile} {number_of_employees 21} {founded_year 2006} {score 3.33040714263916} {source companies} {source_count 52}] [{name Pulse Mobile} {number_of_employees <nil>} {founded_year <nil>} {score 3.33040714263916} {source companies} {source_count 52}] [{name T-Mobile} {number_of_employees <nil>} {founded_year <nil>} {score 3.33040714263916} {source companies} {source_count 52}] [{business_name T. MOBILE} {address [{city BROOKLYN} {zip 11209} {street 86TH ST} {number 440}]} {score 2.900916337966919} {source inspections} {source_count 456}] [{business_name BOOST MOBILE} {address [{city BRONX} {zip 10458} {street E FORDHAM RD} {number 261}]} {score 2.900916337966919} {source inspections} {source_count 456}] [{business_name SPRING MOBILE} {address [{city SOUTH RICHMOND HILL} {zip 11419} {street LIBERTY AVE} {number 12207}]} {score 2.900916337966919} {source inspections} {source_count 456}]
go run search-with-unionwith-query.go
[ {allDocs [ [{name XLR8 Mobile} {number_of_employees 21} {founded_year 2006} {score 3.33040714263916} {source companies} {source_count 52}] [{name Pulse Mobile} {number_of_employees <nil>} {founded_year <nil>} {score 3.33040714263916} {source companies} {source_count 52}] [{name T-Mobile} {number_of_employees <nil>} {founded_year <nil>} {score 3.33040714263916} {source companies} {source_count 52}] [{business_name T. MOBILE} {address [{city BROOKLYN} {zip 11209} {street 86TH ST} {number 440}]} {score 2.900916337966919} {source inspections} {source_count 456}] [{business_name BOOST MOBILE} {address [{city BRONX} {zip 10458} {street E FORDHAM RD} {number 261}]} {score 2.900916337966919} {source inspections} {source_count 456}] [{business_name SPRING MOBILE} {address [{city SOUTH RICHMOND HILL} {zip 11419} {street LIBERTY AVE} {number 12207}]} {score 2.900916337966919} {source inspections} {source_count 456}] ]} {totalCount [ [{_id inspections} {totalCount 456}] [{_id companies} {totalCount 52}] ]} ]
将查询复制并粘贴到 SearchWithUnionwithQuery.java
文件中。
以下查询分别在companies
和inspections
collection中搜索name
和business_name
字段中的术语mobile
。
此查询使用以下阶段:
$search
可搜索名称中包含mobile
的公司。$unionWith
以执行以下操作:使用子管道中的
$search
阶段搜索名称中包含mobile
的公司的检查。对
companies
中的文档和inspections
collection中的文档执行并集。
$set
阶段添加一个名为source
的新字段,用于标识输出文档的集合。
1 import com.mongodb.client.MongoClients; 2 import com.mongodb.client.MongoClient; 3 import com.mongodb.client.MongoDatabase; 4 import org.bson.Document; 5 import java.util.ArrayList; 6 import java.util.Arrays; 7 import java.util.List; 8 9 public class SearchWithUnionwith { 10 public static void main(String[] args) { 11 // connect to Atlas cluster 12 try (MongoClient mongoClient = MongoClients.create("<connection-string>")) { 13 // get database name 14 MongoDatabase database = mongoClient.getDatabase("sample_training"); 15 // define pipeline 16 List<Document> pipeline1 = Arrays.asList( 17 new Document("$search", new Document("text", 18 new Document("query", "Mobile") 19 .append("path", "name"))), 20 new Document("$project", new Document("score", 21 new Document("$meta", "searchScore")) 22 .append("_id", 0) 23 .append("number_of_employees", 1) 24 .append("founded_year", 1) 25 .append("name", 1)), 26 new Document("$set", new Document("source", "companies")), 27 new Document("$limit", 3) 28 ); 29 30 List<Document> pipeline2 = Arrays.asList( 31 new Document("$search", new Document("text", 32 new Document("query", "Mobile") 33 .append("path", "business_name"))), 34 new Document("$set", new Document("source", "inspections")), 35 new Document("$project", new Document("score", 36 new Document("$meta", "searchScore")) 37 .append("source", 1) 38 .append("_id", 0) 39 .append("business_name", 1) 40 .append("address", 1)), 41 new Document("$limit", 3), 42 new Document("$sort", new Document("score", -1)) 43 ); 44 45 List<Document> unionWithStage = new ArrayList<>(); 46 Document unionWith = new Document("$unionWith", new Document("coll", "inspections") 47 .append("pipeline", pipeline2)); 48 unionWithStage.add(unionWith); 49 50 List<Document> finalPipeline = new ArrayList<>(pipeline1); 51 finalPipeline.addAll(unionWithStage); 52 // run pipeline and print results 53 database.getCollection("companies").aggregate(finalPipeline) 54 .forEach(doc -> System.out.println(doc.toJson())); 55 } 56 } 57 }
此查询使用以下阶段:
$search
可搜索名称中包含mobile
的公司。$project
阶段到:在结果中仅包含指定字段。
添加名为
score
的字段。
$addFields
阶段添加以下新字段:一个名为
source
的新字段,用于标识输出文档的集合。字段名称
source_count
,显示输出文档的计数。
$unionWith
以执行以下操作:使用子管道中的
$search
阶段搜索名称中包含mobile
的公司的检查。对
companies
中的文档和inspections
collection中的文档执行并集。
$project
阶段到:在结果中仅包含指定字段。
添加名为
score
的字段。
$limit
阶段将输出限制为每个集合的3
个结果。$set
阶段添加以下新字段:一个名为
source
的新字段,用于标识输出文档的集合。一个名为
source_count
的新字段,显示输出文档的计数。
1 import com.mongodb.client.MongoClients; 2 import com.mongodb.client.MongoCollection; 3 import com.mongodb.client.MongoClient; 4 import org.bson.Document; 5 6 public class SearchWithUnionwith { 7 public static void main(String[] args) { 8 // connect to Atlas cluster 9 try (MongoClient mongoClient = MongoClients.create("<connection-string>")) { 10 // define namespace 11 MongoCollection<Document> collection = mongoClient.getDatabase("sample_training").getCollection("companies"); 12 // define pipeline 13 Document searchStage = new Document("$search", new Document("text", 14 new Document("query", "mobile") 15 .append("path", "name") 16 .append("score", new Document("boost", new Document("value", 1.6))) 17 ) 18 ); 19 20 Document projectStage = new Document("$project", new Document("score", new Document("$meta", "searchScore")) 21 .append("_id", 0) 22 .append("number_of_employees", 1) 23 .append("founded_year", 1) 24 .append("name", 1) 25 ); 26 27 Document addFieldsStage = new Document("$addFields", new Document("source", "companies") 28 .append("source_count", "$$SEARCH_META.count.lowerBound") 29 ); 30 31 Document limitStage = new Document("$limit", 3); 32 33 Document unionWithStage = new Document("$unionWith", new Document("coll", "inspections") 34 .append("pipeline", java.util.Arrays.asList( 35 new Document("$search", new Document("text", 36 new Document("query", "mobile") 37 .append("path", "business_name") 38 )), 39 new Document("$project", new Document("score", new Document("$meta", "searchScore")) 40 .append("business_name", 1) 41 .append("address", 1) 42 .append("_id", 0) 43 ), 44 new Document("$limit", 3), 45 new Document("$set", new Document("source", "inspections") 46 .append("source_count", "$$SEARCH_META.count.lowerBound") 47 ), 48 new Document("$sort", new Document("score", -1)) 49 )) 50 ); 51 52 Document facetStage = new Document("$facet", new Document("allDocs", java.util.Arrays.asList()) 53 .append("totalCount", java.util.Arrays.asList( 54 new Document("$group", new Document("_id", "$source") 55 .append("firstCount", new Document("$first", "$source_count")) 56 ), 57 new Document("$project", new Document("totalCount", 58 new Document("$sum", "$firstCount") 59 )) 60 )) 61 ); 62 // run pipeline and print results 63 collection.aggregate(java.util.Arrays.asList( 64 searchStage, projectStage, addFieldsStage, limitStage, unionWithStage, facetStage 65 )).forEach(doc -> System.out.println(doc.toJson())); 66 } 67 } 68 }
注意
要在 Maven 环境中运行示例代码,请将以下代码添加到文件中的 import 语句上方。
package com.mongodb.drivers;
替换查询中的 <connection-string>
,然后保存该文件。
确保您的连接字符串包含数据库用户的档案。要了解详情,请参阅通过驱动程序连接。
编译并运行 UinionwithWithSearchQuery.java
文件。
javac SearchWithUnionwithQuery.java java SearchWithUnionwithQuery
{"name": "XLR8 Mobile", "number_of_employees": 21, "founded_year": 2006, "score": 2.0815043449401855, "source": "companies"} {"name": "Pulse Mobile", "number_of_employees": null, "founded_year": null, "score": 2.0815043449401855, "source": "companies"} {"name": "T-Mobile", "number_of_employees": null, "founded_year": null, "score": 2.0815043449401855, "source": "companies"} {"business_name": "T. MOBILE", "address": {"city": "BROOKLYN", "zip": 11209, "street": "86TH ST", "number": 440}, "source": "inspections", "score": 2.900916337966919} {"business_name": "BOOST MOBILE", "address": {"city": "BRONX", "zip": 10458, "street": "E FORDHAM RD", "number": 261}, "source": "inspections", "score": 2.900916337966919} {"business_name": "SPRING MOBILE", "address": {"city": "SOUTH RICHMOND HILL", "zip": 11419, "street": "LIBERTY AVE", "number": 12207}, "source": "inspections", "score": 2.900916337966919}
javac SearchWithUnionwithQuery.java java SearchWithUnionwithQuery
{ "allDocs": [ {"name": "XLR8 Mobile", "number_of_employees": 21, "founded_year": 2006, "score": 3.33040714263916, "source": "companies", "source_count": 52}, {"name": "Pulse Mobile", "number_of_employees": null, "founded_year": null, "score": 3.33040714263916, "source": "companies", "source_count": 52}, {"name": "T-Mobile", "number_of_employees": null, "founded_year": null, "score": 3.33040714263916, "source": "companies", "source_count": 52}, {"business_name": "T. MOBILE", "address": {"city": "BROOKLYN", "zip": 11209, "street": "86TH ST", "number": 440}, "score": 2.900916337966919, "source": "inspections", "source_count": 456}, {"business_name": "BOOST MOBILE", "address": {"city": "BRONX", "zip": 10458, "street": "E FORDHAM RD", "number": 261}, "score": 2.900916337966919, "source": "inspections", "source_count": 456}, {"business_name": "SPRING MOBILE", "address": {"city": "SOUTH RICHMOND HILL", "zip": 11419, "street": "LIBERTY AVE", "number": 12207}, "score": 2.900916337966919, "source": "inspections", "source_count": 456} ], "totalCount": [ {"_id": "companies", "totalCount": 52}, {"_id": "inspections", "totalCount": 456} ] }
将查询复制并粘贴到 SearchWithUnionwithQuery.kt
文件中。
以下查询分别在companies
和inspections
collection中搜索name
和business_name
字段中的术语mobile
。
此查询使用以下阶段:
$search
可搜索名称中包含mobile
的公司。$unionWith
以执行以下操作:使用子管道中的
$search
阶段搜索名称中包含mobile
的公司的检查。对
companies
中的文档和inspections
collection中的文档执行并集。
$set
阶段添加一个名为source
的新字段,用于标识输出文档的集合。
1 import com.mongodb.kotlin.client.coroutine.MongoClient 2 import kotlinx.coroutines.runBlocking 3 import org.bson.Document 4 5 fun main() { 6 // connect to Atlas cluster 7 val uri = "<connection-string>" 8 val mongoClient = MongoClient.create(uri) 9 10 // set namespace 11 val database = mongoClient.getDatabase("sample_training") 12 val collection = database.getCollection<Document>("companies") 13 14 runBlocking { 15 // define pipeline 16 val pipeline1 = listOf( 17 Document("\$search", Document("text", 18 Document("query", "Mobile") 19 .append("path", "name"))), Document("\$project", Document("score", 20 Document("\$meta", "searchScore")) 21 .append("_id", 0) 22 .append("number_of_employees", 1) 23 .append("founded_year", 1) 24 .append("name", 1)), Document("\$set", Document("source", "companies")), 25 Document("\$limit", 3) 26 ) 27 28 val pipeline2 = listOf( 29 Document( 30 "\$search", Document( 31 "text", 32 Document("query", "Mobile") 33 .append("path", "business_name") 34 ) 35 ), 36 Document("\$set", Document("source", "inspections")), 37 Document( 38 "\$project", Document( 39 "score", 40 Document("\$meta", "searchScore") 41 ) 42 .append("source", 1) 43 .append("_id", 0) 44 .append("business_name", 1) 45 .append("address", 1) 46 ), 47 Document("\$limit", 3), 48 Document("\$sort", Document("score", -1)) 49 ) 50 51 val unionWithStage: MutableList<Document> = ArrayList() 52 val unionWith = Document( 53 "\$unionWith", Document("coll", "inspections") 54 .append("pipeline", pipeline2) 55 ) 56 unionWithStage.add(unionWith) 57 val finalPipeline: MutableList<Document> = ArrayList(pipeline1) 58 finalPipeline.addAll(unionWithStage) 59 60 // run pipeline and print results 61 val resultsFlow = collection.aggregate<Document>(finalPipeline) 62 resultsFlow.collect { println(it) } 63 64 } 65 mongoClient.close() 66 }
此查询使用以下阶段:
$search
可搜索名称中包含mobile
的公司。$project
阶段到:在结果中仅包含指定字段。
添加名为
score
的字段。
$addFields
阶段添加以下新字段:一个名为
source
的新字段,用于标识输出文档的集合。字段名称
source_count
,显示输出文档的计数。
$unionWith
以执行以下操作:使用子管道中的
$search
阶段搜索名称中包含mobile
的公司的检查。对
companies
中的文档和inspections
collection中的文档执行并集。
$project
阶段到:在结果中仅包含指定字段。
添加名为
score
的字段。
$limit
阶段将输出限制为每个集合的3
个结果。$set
阶段添加以下新字段:一个名为
source
的新字段,用于标识输出文档的集合。一个名为
source_count
的新字段,显示输出文档的计数。
1 import com.mongodb.kotlin.client.coroutine.MongoClient 2 import kotlinx.coroutines.runBlocking 3 import org.bson.Document 4 import java.util.* 5 6 fun main() { 7 // connect to Atlas cluster 8 val uri = "<connection-string>" 9 val mongoClient = MongoClient.create(uri) 10 11 // set namespace 12 val database = mongoClient.getDatabase("sample_training") 13 val collection = database.getCollection<Document>("companies") 14 15 runBlocking { 16 // define pipeline stages 17 val searchStage = Document( 18 "\$search", Document( 19 "text", 20 Document("query", "mobile") 21 .append("path", "name") 22 .append("score", Document("boost", Document("value", 1.6))) 23 ) 24 ) 25 26 val projectStage = Document( 27 "\$project", Document("score", Document("\$meta", "searchScore")) 28 .append("_id", 0) 29 .append("number_of_employees", 1) 30 .append("founded_year", 1) 31 .append("name", 1) 32 ) 33 34 val addFieldsStage = Document( 35 "\$addFields", Document("source", "companies") 36 .append("source_count", "$\$SEARCH_META.count.lowerBound") 37 ) 38 39 val limitStage = Document("\$limit", 3) 40 41 val unionWithStage = Document( 42 "\$unionWith", Document("coll", "inspections") 43 .append( 44 "pipeline", Arrays.asList( 45 Document( 46 "\$search", Document( 47 "text", 48 Document("query", "mobile") 49 .append("path", "business_name") 50 ) 51 ), 52 Document( 53 "\$project", Document("score", Document("\$meta", "searchScore")) 54 .append("business_name", 1) 55 .append("address", 1) 56 .append("_id", 0) 57 ), 58 Document("\$limit", 3), 59 Document( 60 "\$set", Document("source", "inspections") 61 .append("source_count", "$\$SEARCH_META.count.lowerBound") 62 ), 63 Document("\$sort", Document("score", -1)) 64 ) 65 ) 66 ) 67 68 val facetStage = Document( 69 "\$facet", Document("allDocs", Arrays.asList<Any>()) 70 .append( 71 "totalCount", Arrays.asList( 72 Document( 73 "\$group", Document("_id", "\$source") 74 .append("firstCount", Document("\$first", "\$source_count")) 75 ), 76 Document( 77 "\$project", Document( 78 "totalCount", 79 Document("\$sum", "\$firstCount") 80 ) 81 ) 82 ) 83 ) 84 ) 85 86 // run pipeline and print results 87 val resultsFlow = collection.aggregate<Document>( 88 listOf( 89 searchStage, 90 projectStage, 91 addFieldsStage, 92 limitStage, 93 unionWithStage, 94 facetStage 95 ) 96 ) 97 resultsFlow.collect { println(it) } 98 99 } 100 mongoClient.close() 101 }
替换查询中的 <connection-string>
,然后保存该文件。
确保您的连接字符串包含数据库用户的档案。要了解详情,请参阅通过驱动程序连接。
运行 SearchWithUnionwithQuery.kt
文件。
当你在 IDE 中运行 SearchWithUnionwithQuery.kt
程序时,它会打印以下文档:
Document{{name=XLR8 Mobile, number_of_employees=21, founded_year=2006, score=2.0815043449401855, source=companies}} Document{{name=Pulse Mobile, number_of_employees=null, founded_year=null, score=2.0815043449401855, source=companies}} Document{{name=Mobile Trend, number_of_employees=null, founded_year=2003, score=2.0815043449401855, source=companies}} Document{{business_name=T-MOBILE, address=Document{{city=BROOKLYN, zip=11229, street=AVENUE U, number=1616}}, source=inspections, score=2.900916337966919}} Document{{business_name=BOOST MOBILE, address=Document{{city=BRONX, zip=10458, street=E FORDHAM RD, number=261}}, source=inspections, score=2.900916337966919}} Document{{business_name=SPRING MOBILE, address=Document{{city=SOUTH RICHMOND HILL, zip=11419, street=LIBERTY AVE, number=12207}}, source=inspections, score=2.900916337966919}}
当您在 IDE 中运行 SearchWithUnionwithQuery.kt
程序时,它会打印以下结果:
Document{{allDocs=[Document{{name=XLR8 Mobile, number_of_employees=21, founded_year=2006, score=3.33040714263916, source=companies, source_count=52}}, Document{{name=Pulse Mobile, number_of_employees=null, founded_year=null, score=3.33040714263916, source=companies, source_count=52}}, Document{{name=Mobile Trend, number_of_employees=null, founded_year=2003, score=3.33040714263916, source=companies, source_count=52}}, Document{{business_name=T-MOBILE, address=Document{{city=BROOKLYN, zip=11229, street=AVENUE U, number=1616}}, score=2.900916337966919, source=inspections, source_count=456}}, Document{{business_name=BOOST MOBILE, address=Document{{city=BRONX, zip=10458, street=E FORDHAM RD, number=261}}, score=2.900916337966919, source=inspections, source_count=456}}, Document{{business_name=SPRING MOBILE, address=Document{{city=SOUTH RICHMOND HILL, zip=11419, street=LIBERTY AVE, number=12207}}, score=2.900916337966919, source=inspections, source_count=456}}], totalCount=[Document{{_id=inspections, totalCount=456}}, Document{{_id=companies, totalCount=52}}]}}
将示例查询复制并粘贴到 search-with-unionwith-query.js
文件中。
以下查询分别在companies
和inspections
collection中搜索name
和business_name
字段中的术语mobile
。
此查询使用以下阶段:
$search
可搜索名称中包含mobile
的公司。$unionWith
以执行以下操作:使用子管道中的
$search
阶段搜索名称中包含mobile
的公司的检查。对
companies
中的文档和inspections
collection中的文档执行并集。
$set
阶段添加一个名为source
的新字段,用于标识输出文档的集合。
1 const MongoClient = require("mongodb").MongoClient; 2 const assert = require("assert"); 3 4 const agg = [ 5 { 6 '$search': { 7 'text': { 'query': 'Mobile', 'path': 'name' } 8 } 9 }, { 10 '$project': { 11 'score': { '$meta': 'searchScore' }, 12 '_id': 0, 'number_of_employees': 1, 'founded_year': 1, 'name': 1 13 } 14 }, { 15 '$set': { 'source': 'companies' } 16 }, { 17 '$limit': 3 18 }, { 19 '$unionWith': { 20 'coll': 'inspections', 21 'pipeline': [ 22 { 23 '$search': { 24 'text': { 'query': 'Mobile', 'path': 'business_name' } 25 } 26 }, { 27 '$set': { 'source': 'inspections' } 28 }, { 29 '$project': { 30 'score': { '$meta': 'searchScore' }, 31 'source': 1, '_id': 0, 'business_name': 1, 'address': 1 32 } 33 }, { 34 '$limit': 3 35 }, { 36 '$sort': { 'score': -1 } 37 } 38 ] 39 } 40 } 41 ]; 42 43 MongoClient.connect( 44 "<connection-string>", 45 { useNewUrlParser: true, useUnifiedTopology: true }, 46 async function (connectErr, client) { 47 assert.equal(null, connectErr); 48 const coll = client.db("sample_training").collection("companies"); 49 let cursor = await coll.aggregate(agg); 50 await cursor.forEach((doc) => console.log(doc)); 51 client.close(); 52 } 53 );
此查询使用以下阶段:
$search
可搜索名称中包含mobile
的公司。$project
阶段到:在结果中仅包含指定字段。
添加名为
score
的字段。
$addFields
阶段添加以下新字段:一个名为
source
的新字段,用于标识输出文档的集合。字段名称
source_count
,显示输出文档的计数。
$unionWith
以执行以下操作:使用子管道中的
$search
阶段搜索名称中包含mobile
的公司的检查。对
companies
中的文档和inspections
collection中的文档执行并集。
$project
阶段到:在结果中仅包含指定字段。
添加名为
score
的字段。
$limit
阶段将输出限制为每个集合的3
个结果。$set
阶段添加以下新字段:一个名为
source
的新字段,用于标识输出文档的集合。一个名为
source_count
的新字段,显示输出文档的计数。
1 const MongoClient = require("mongodb").MongoClient; 2 const assert = require("assert"); 3 4 const agg = [ 5 {'$search': { 'text': { 6 'query': 'mobile', 7 'path': 'name', 8 'score': { 9 'boost': { 'value': 1.6 } 10 } 11 }}}, 12 {'$project': { 13 'score': { '$meta': 'searchScore' }, 14 '_id': 0, 15 'number_of_employees': 1, 16 'founded_year': 1, 17 'name': 1 18 }}, 19 {'$addFields': { 20 'source': 'companies', 21 'source_count': '$$SEARCH_META.count.lowerBound' 22 }}, 23 {'$limit': 3}, 24 {'$unionWith': { 25 'coll': 'inspections', 26 'pipeline': [ 27 {'$search': { 28 'text': { 'query': 'mobile', 'path': 'business_name' } 29 }}, 30 {'$project': { 31 'score': { '$meta': 'searchScore' }, 32 'business_name': 1, 33 'address': 1, 34 '_id': 0 35 }}, 36 {'$limit': 3}, 37 {'$set': { 38 'source': 'inspections', 39 'source_count': '$$SEARCH_META.count.lowerBound' 40 }}, 41 {'$sort': { 'score': -1 } } 42 ] 43 }}, 44 {'$facet': { 45 'allDocs': [], 46 'totalCount': [ 47 {'$group': { 48 '_id': '$source', 49 'firstCount': { '$first': '$source_count' } 50 }}, 51 {'$project': { 52 'totalCount': { '$sum': '$firstCount' } 53 }} 54 ] 55 }} 56 ]; 57 58 MongoClient.connect( 59 "<connection-string>", 60 { useNewUrlParser: true, useUnifiedTopology: true }, 61 async function (connectErr, client) { 62 assert.equal(null, connectErr); 63 const coll = client.db("sample_training").collection("companies"); 64 let cursor = await coll.aggregate(agg); 65 await cursor.forEach((doc) => console.log(doc)); 66 client.close(); 67 } 68 );
替换查询中的 <connection-string>
,然后保存该文件。
确保您的连接字符串包含数据库用户的档案。要了解详情,请参阅通过驱动程序连接。
查询您的集合。
运行以下命令来查询您的集合:
node unionwith-with-search-query.js
{ name: 'SoftBank Mobile', number_of_employees: null, founded_year: null, score: 2.0815043449401855, source: 'companies' } { name: 'Mobile Factory', number_of_employees: 53, founded_year: 2001, score: 2.0815043449401855, source: 'companies' } { name: 'ZOOZ Mobile', number_of_employees: 5, founded_year: 2008, score: 2.0815043449401855, source: 'companies' } { business_name: 'T. MOBILE', address: { city: 'BROOKLYN', zip: 11209, street: '86TH ST', number: 440 }, source: 'inspections', score: 2.900916337966919 } { business_name: 'BOOST MOBILE', address: { city: 'BRONX', zip: 10458, street: 'E FORDHAM RD', number: 261 }, source: 'inspections', score: 2.900916337966919 } { business_name: 'T-MOBILE', address: { city: 'BROOKLYN', zip: 11229, street: 'AVENUE U', number: 1616 }, source: 'inspections', score: 2.900916337966919 }
node unionwith-with-search-query.js
{ allDocs: [ { name: 'XLR8 Mobile', number_of_employees: 21, founded_year: 2006, score: 3.33040714263916, source: 'companies', source_count: 52 }, { name: 'Pulse Mobile', number_of_employees: null, founded_year: null, score: 3.33040714263916, source: 'companies', source_count: 52 }, { name: 'T-Mobile', number_of_employees: null, founded_year: null, score: 3.33040714263916, source: 'companies', source_count: 52 }, { business_name: 'T. MOBILE', address: [Object], score: 2.900916337966919, source: 'inspections', source_count: 456 }, { business_name: 'BOOST MOBILE', address: [Object], score: 2.900916337966919, source: 'inspections', source_count: 456 }, { business_name: 'SPRING MOBILE', address: [Object], score: 2.900916337966919, source: 'inspections', source_count: 456 } ], totalCount: [ { _id: 'companies', totalCount: 52 }, { _id: 'inspections', totalCount: 456 } ] }
将查询复制并粘贴到 search-with-unionwith-query.py
文件中。
以下查询分别在companies
和inspections
collection中搜索name
和business_name
字段中的术语mobile
。
此查询使用以下阶段:
$search
可搜索名称中包含mobile
的公司。$unionWith
以执行以下操作:使用子管道中的
$search
阶段搜索名称中包含mobile
的公司的检查。对
companies
中的文档和inspections
collection中的文档执行并集。
$set
阶段添加一个名为source
的新字段,用于标识输出文档的集合。
1 import pymongo 2 import dns 3 4 client = pymongo.MongoClient('<connection-string>') 5 result = client['sample_training']['companies'].aggregate([ 6 { 7 '$search': { 8 'text': { 'query': 'Mobile', 'path': 'name' } 9 } 10 }, { 11 '$project': { 12 'score': { '$meta': 'searchScore' }, '_id': 0, 'number_of_employees': 1, 'founded_year': 1, 'name': 1 13 } 14 }, { 15 '$set': { 'source': 'companies' } 16 }, { 17 '$limit': 3 18 }, { 19 '$unionWith': { 20 'coll': 'inspections', 21 'pipeline': [ 22 { 23 '$search': { 24 'text': { 'query': 'Mobile', 'path': 'business_name' } 25 } 26 }, { 27 '$set': { 'source': 'inspections' } 28 }, { 29 '$project': { 30 'score': { '$meta': 'searchScore' }, 'source': 1, '_id': 0, 'business_name': 1, 'address': 1 31 } 32 }, { 33 '$limit': 3 34 }, { 35 '$sort': { 'score': -1 } 36 } 37 ] 38 } 39 } 40 ]) 41 42 for i in result: 43 print(i)
此查询使用以下阶段:
$search
可搜索名称中包含mobile
的公司。$project
阶段到:在结果中仅包含指定字段。
添加名为
score
的字段。
$addFields
阶段添加以下新字段:一个名为
source
的新字段,用于标识输出文档的集合。字段名称
source_count
,显示输出文档的计数。
$unionWith
以执行以下操作:使用子管道中的
$search
阶段搜索名称中包含mobile
的公司的检查。对
companies
中的文档和inspections
collection中的文档执行并集。
$project
阶段到:在结果中仅包含指定字段。
添加名为
score
的字段。
$limit
阶段将输出限制为每个集合的3
个结果。$set
阶段添加以下新字段:一个名为
source
的新字段,用于标识输出文档的集合。一个名为
source_count
的新字段,显示输出文档的计数。
1 import pymongo 2 import dns 3 4 client = pymongo.MongoClient('<connection-string>') 5 result = client['sample_training']['companies'].aggregate([ 6 {'$search': { 'text': { 7 'query': 'mobile', 8 'path': 'name', 9 'score': { 'boost': { 'value': 1.6 } } 10 }}}, 11 {'$project': { 12 'score': { '$meta': 'searchScore' }, 13 '_id': 0, 14 'number_of_employees': 1, 15 'founded_year': 1, 16 'name': 1 17 }}, 18 {'$addFields': { 19 'source': 'companies', 20 'source_count': '$$SEARCH_META.count.lowerBound' 21 }}, 22 {'$limit': 3}, 23 {'$unionWith': { 24 'coll': 'inspections', 25 'pipeline': [ 26 {'$search': { 'text': { 27 'query': 'mobile', 28 'path': 'business_name' 29 }} }, 30 {'$project': { 31 'score': { '$meta': 'searchScore' }, 32 'business_name': 1, 33 'address': 1, 34 '_id': 0 35 }}, 36 {'$limit': 3}, 37 {'$set': { 38 'source': 'inspections', 39 'source_count': '$$SEARCH_META.count.lowerBound' 40 }}, 41 {'$sort': { 'score': -1 }} 42 ] 43 }}, 44 {'$facet': { 45 'allDocs': [], 46 'totalCount': [ 47 {'$group': { 48 '_id': '$source', 49 'firstCount': { '$first': '$source_count' } 50 }}, 51 {'$project': { 52 'totalCount': { '$sum': '$firstCount' } 53 }} 54 ] 55 }} 56 ]) 57 58 for i in result: 59 print(i)
替换查询中的 <connection-string>
,然后保存该文件。
确保您的连接字符串包含数据库用户的档案。要了解详情,请参阅通过驱动程序连接。
运行命令以查询您的集合。
python search-with-unionwith-query.py
{'name': 'XLR8 Mobile', 'number_of_employees': 21, 'founded_year': 2006, 'score': 2.0815043449401855, 'source': 'companies'} {'name': 'Pulse Mobile', 'number_of_employees': None, 'founded_year': None, 'score': 2.0815043449401855, 'source': 'companies'} {'name': 'T-Mobile', 'number_of_employees': None, 'founded_year': None, 'score': 2.0815043449401855, 'source': 'companies'} {'business_name': 'T. MOBILE', 'address': {'city': 'BROOKLYN', 'zip': 11209, 'street': '86TH ST', 'number': 440}, 'source': 'inspections', 'score': 2.900916337966919} {'business_name': 'BOOST MOBILE', 'address': {'city': 'BRONX', 'zip': 10458, 'street': 'E FORDHAM RD', 'number': 261}, 'source': 'inspections', 'score': 2.900916337966919} {'business_name': 'SPRING MOBILE', 'address': {'city': 'SOUTH RICHMOND HILL', 'zip': 11419, 'street': 'LIBERTY AVE', 'number': 12207}, 'source': 'inspections', 'score': 2.900916337966919}
python search-with-unionwith-query.py
{ 'allDocs': [ {'name': 'XLR8 Mobile', 'number_of_employees': 21, 'founded_year': 2006, 'score': 3.33040714263916, 'source': 'companies', 'source_count': 52}, {'name': 'Pulse Mobile', 'number_of_employees': None, 'founded_year': None, 'score': 3.33040714263916, 'source': 'companies', 'source_count': 52}, {'name': 'T-Mobile', 'number_of_employees': None, 'founded_year': None, 'score': 3.33040714263916, 'source': 'companies', 'source_count': 52}, {'business_name': 'T. MOBILE', 'address': {'city': 'BROOKLYN', 'zip': 11209, 'street': '86TH ST', 'number': 440}, 'score': 2.900916337966919, 'source': 'inspections', 'source_count': 456}, {'business_name': 'BOOST MOBILE', 'address': {'city': 'BRONX', 'zip': 10458, 'street': 'E FORDHAM RD', 'number': 261}, 'score': 2.900916337966919, 'source': 'inspections', 'source_count': 456}, {'business_name': 'SPRING MOBILE', 'address': {'city': 'SOUTH RICHMOND HILL', 'zip': 11419, 'street': 'LIBERTY AVE', 'number': 12207}, 'score': 2.900916337966919, 'source': 'inspections', 'source_count': 456} ], 'totalCount': [ {'_id': 'companies', 'totalCount': 52}, {'_id': 'inspections', 'totalCount': 456} ] }