This tutorial illustrates how to construct an aggregation pipeline, perform the aggregation on a collection, and display the results using the language of your choice.
About This Task
This tutorial demonstrates how to combine data from a collection that describes product information with another collection that describes customer orders. The results show a list of all orders placed in 2020 and includes the product details associated with each order.
This aggregation performs a one-to-one join. A one-to-one join occurs when a document in one collection has a field value that matches a single document in another collection that has the same field value. The aggregation matches these documents on the field value and combines information from both sources into one result.
Note
A one-to-one join does not require the documents to have a one-to-one relationship. To learn more about this data relationship, see the Wikipedia entry about One-to-one (data model).
Before You Begin
➤ Use the Select your language drop-down menu in the upper-right to set the language of the following examples or select MongoDB Shell.
This example uses two collections:
orders: documents that describe individual orders for products in a shopproducts: documents that describe the products that a shop sells
An order can only contain one product. The aggregation uses a
one-to-one join to match an order document to the corresponding product
document. The aggregation joins the collections by the product_id field
that exists in documents in both collections.
To create the orders and products collections, use the
insertMany() method:
db.orders.insertMany( [ { customer_id: "[email protected]", orderdate: new Date("2020-05-30T08:35:52Z"), product_id: "a1b2c3d4", value: 431.43 }, { customer_id: "[email protected]", orderdate: new Date("2019-05-28T19:13:32Z"), product_id: "z9y8x7w6", value: 5.01 }, { customer_id: "[email protected]", orderdate: new Date("2020-01-01T08:25:37Z"), product_id: "ff11gg22hh33", value: 63.13 }, { customer_id: "[email protected]", orderdate: new Date("2020-12-26T08:55:46Z"), product_id: "a1b2c3d4", value: 429.65 } ] )
db.products.insertMany( [ { p_id: "a1b2c3d4", name: "Asus Laptop", category: "ELECTRONICS", description: "Good value laptop for students" }, { p_id: "z9y8x7w6", name: "The Day Of The Triffids", category: "BOOKS", description: "Classic post-apocalyptic novel" }, { p_id: "ff11gg22hh33", name: "Morphy Richardds Food Mixer", category: "KITCHENWARE", description: "Luxury mixer turning good cakes into great" }, { p_id: "pqr678st", name: "Karcher Hose Set", category: "GARDEN", description: "Hose + nozzles + winder for tidy storage" } ] )
Create the Template App
Before you begin following this aggregation tutorial, you must set up a new C app. You can use this app to connect to a MongoDB deployment, insert sample data into MongoDB, and run the aggregation pipeline.
Tip
To learn how to install the driver and connect to MongoDB, see the Get Started with the C Driver guide.
To learn more about performing aggregations in the C Driver, see the Aggregation guide.
After you install the driver, create a file called
agg-tutorial.c. Paste the following code in this file to create an
app template for the aggregation tutorials.
Important
In the following code, read the code comments to find the sections of the code that you must modify for the tutorial you are following.
If you attempt to run the code without making any changes, you will encounter a connection error.
int main(void) { mongoc_init(); / Replace the placeholder with your connection string. char *uri = "<connection string>"; mongoc_client_t* client = mongoc_client_new(uri); / Get a reference to relevant collections. / ... mongoc_collection_t *some_coll = mongoc_client_get_collection(client, "agg_tutorials_db", "some_coll"); / ... mongoc_collection_t *another_coll = mongoc_client_get_collection(client, "agg_tutorials_db", "another_coll"); / Delete any existing documents in collections if needed. / ... { / ... bson_t *filter = bson_new(); / ... bson_error_t error; / ... if (!mongoc_collection_delete_many(some_coll, filter, NULL, NULL, &error)) / ... { / ... fprintf(stderr, "Delete error: %s\n", error.message); / ... } / ... bson_destroy(filter); / ... } / Insert sample data into the collection or collections. / ... { / ... size_t num_docs = ...; / ... bson_t *docs[num_docs]; / ... / ... docs[0] = ...; / ... / ... bson_error_t error; / ... if (!mongoc_collection_insert_many(some_coll, (const bson_t **)docs, num_docs, NULL, NULL, &error)) / ... { / ... fprintf(stderr, "Insert error: %s\n", error.message); / ... } / ... / ... for (int i = 0; i < num_docs; i++) / ... { / ... bson_destroy(docs[i]); / ... } / ... } { const bson_t *doc; / Add code to create pipeline stages. bson_t *pipeline = BCON_NEW("pipeline", "[", / ... Add pipeline stages here. "]"); / Run the aggregation. / ... mongoc_cursor_t *results = mongoc_collection_aggregate(some_coll, MONGOC_QUERY_NONE, pipeline, NULL, NULL); bson_destroy(pipeline); / Print the aggregation results. while (mongoc_cursor_next(results, &doc)) { char *str = bson_as_canonical_extended_json(doc, NULL); printf("%s\n", str); bson_free(str); } bson_error_t error; if (mongoc_cursor_error(results, &error)) { fprintf(stderr, "Aggregation error: %s\n", error.message); } mongoc_cursor_destroy(results); } / Clean up resources. / ... mongoc_collection_destroy(some_coll); mongoc_client_destroy(client); mongoc_cleanup(); return EXIT_SUCCESS; }
For every tutorial, you must replace the connection string placeholder with your deployment's connection string.
Tip
To learn how to locate your deployment's connection string, see the Create a Connection String step of the C Get Started guide.
For example, if your connection string is
"mongodb+srv://mongodb-example:27017", your connection string assignment resembles
the following:
char *uri = "mongodb+srv://mongodb-example:27017";
Create the Collection
This example uses two collections:
orders: documents that describe individual orders for products in a shopproducts: documents that describe the products that a shop sells
An order can only contain one product. The aggregation uses a
one-to-one join to match an order document to the corresponding product
document. The aggregation joins the collections by the product_id field
that exists in documents in both collections.
To create the orders and products collections and insert the
sample data, add the following code to your application:
mongoc_collection_t *orders = mongoc_client_get_collection(client, "agg_tutorials_db", "orders"); mongoc_collection_t *products = mongoc_client_get_collection(client, "agg_tutorials_db", "products"); { bson_t *filter = bson_new(); bson_error_t error; if (!mongoc_collection_delete_many(orders, filter, NULL, NULL, &error)) { fprintf(stderr, "Delete error: %s\n", error.message); } if (!mongoc_collection_delete_many(products, filter, NULL, NULL, &error)) { fprintf(stderr, "Delete error: %s\n", error.message); } bson_destroy(filter); } { size_t num_docs = 4; bson_t *order_docs[num_docs]; order_docs[0] = BCON_NEW( "customer_id", BCON_UTF8("[email protected]"), "orderdate", BCON_DATE_TIME(1590822952000UL), / 2020-05-30T08:35:52Z "product_id", BCON_UTF8("a1b2c3d4"), "value", BCON_DOUBLE(431.43)); order_docs[1] = BCON_NEW( "customer_id", BCON_UTF8("[email protected]"), "orderdate", BCON_DATE_TIME(1559063612000UL), / 2019-05-28T19:13:32Z "product_id", BCON_UTF8("z9y8x7w6"), "value", BCON_DOUBLE(5.01)); order_docs[2] = BCON_NEW( "customer_id", BCON_UTF8("[email protected]"), "orderdate", BCON_DATE_TIME(1577869537000UL), / 2020-01-01T08:25:37Z "product_id", BCON_UTF8("ff11gg22hh33"), "value", BCON_DOUBLE(63.13)); order_docs[3] = BCON_NEW( "customer_id", BCON_UTF8("[email protected]"), "orderdate", BCON_DATE_TIME(1608976546000UL), / 2020-12-26T08:55:46Z "product_id", BCON_UTF8("a1b2c3d4"), "value", BCON_DOUBLE(429.65)); bson_error_t error; if (!mongoc_collection_insert_many(orders, (const bson_t **)order_docs, num_docs, NULL, NULL, &error)) { fprintf(stderr, "Insert error: %s\n", error.message); } for (int i = 0; i < num_docs; i++) { bson_destroy(order_docs[i]); } } { size_t num_docs = 4; bson_t *product_docs[num_docs]; product_docs[0] = BCON_NEW( "id", BCON_UTF8("a1b2c3d4"), "name", BCON_UTF8("Asus Laptop"), "category", BCON_UTF8("ELECTRONICS"), "description", BCON_UTF8("Good value laptop for students")); product_docs[1] = BCON_NEW( "id", BCON_UTF8("z9y8x7w6"), "name", BCON_UTF8("The Day Of The Triffids"), "category", BCON_UTF8("BOOKS"), "description", BCON_UTF8("Classic post-apocalyptic novel")); product_docs[2] = BCON_NEW( "id", BCON_UTF8("ff11gg22hh33"), "name", BCON_UTF8("Morphy Richardds Food Mixer"), "category", BCON_UTF8("KITCHENWARE"), "description", BCON_UTF8("Luxury mixer turning good cakes into great")); product_docs[3] = BCON_NEW( "id", BCON_UTF8("pqr678st"), "name", BCON_UTF8("Karcher Hose Set"), "category", BCON_UTF8("GARDEN"), "description", BCON_UTF8("Hose + nozzles + winder for tidy storage")); bson_error_t error; if (!mongoc_collection_insert_many(products, (const bson_t **)product_docs, num_docs, NULL, NULL, &error)) { fprintf(stderr, "Insert error: %s\n", error.message); } for (int i = 0; i < num_docs; i++) { bson_destroy(product_docs[i]); } }
Create the Template App
Before you begin following an aggregation tutorial, you must set up a new C++ app. You can use this app to connect to a MongoDB deployment, insert sample data into MongoDB, and run the aggregation pipeline.
Tip
To learn how to install the driver and connect to MongoDB, see the Get Started with C++ tutorial.
To learn more about using the C++ driver, see the API documentation.
To learn more about performing aggregations in the C++ Driver, see the Aggregation guide.
After you install the driver, create a file called
agg-tutorial.cpp. Paste the following code in this file to create an
app template for the aggregation tutorials.
Important
In the following code, read the code comments to find the sections of the code that you must modify for the tutorial you are following.
If you attempt to run the code without making any changes, you will encounter a connection error.
using bsoncxx::builder::basic::kvp; using bsoncxx::builder::basic::make_document; using bsoncxx::builder::basic::make_array; int main() { mongocxx::instance instance; / Replace the placeholder with your connection string. mongocxx::uri uri("<connection string>"); mongocxx::client client(uri); auto db = client["agg_tutorials_db"]; / Delete existing data in the database, if necessary. db.drop(); / Get a reference to relevant collections. / ... auto some_coll = db["..."]; / ... auto another_coll = db["..."]; / Insert sample data into the collection or collections. / ... some_coll.insert_many(docs); / Create an empty pipelne. mongocxx::pipeline pipeline; / Add code to create pipeline stages. / pipeline.match(make_document(...)); / Run the aggregation and print the results. auto cursor = orders.aggregate(pipeline); for (auto&& doc : cursor) { std::cout << bsoncxx::to_json(doc, bsoncxx::ExtendedJsonMode::k_relaxed) << std::endl; } }
For every tutorial, you must replace the connection string placeholder with your deployment's connection string.
Tip
To learn how to locate your deployment's connection string, see the Create a Connection String step of the C++ Get Started tutorial.
For example, if your connection string is
"mongodb+srv://mongodb-example:27017", your connection string assignment resembles
the following:
mongocxx::uri uri{"mongodb+srv://mongodb-example:27017"};
Create the Collection
This example uses two collections:
orders: documents that describe individual orders for products in a shopproducts: documents that describe the products that a shop sells
An order can only contain one product. The aggregation uses a
one-to-one join to match an order document to the corresponding product
document. The aggregation joins the collections by the product_id field
that exists in documents in both collections.
To create the orders and products collections and insert the
sample data, add the following code to your application:
auto orders = db["orders"]; auto products = db["products"]; std::vector<bsoncxx::document::value> order_docs = { bsoncxx::from_json(R"({ "customer_id": "[email protected]", "orderdate": {"$date": 1590821752000}, "product_id": "a1b2c3d4", "value": 431.43 })"), bsoncxx::from_json(R"({ "customer_id": "[email protected]", "orderdate": {"$date": 1559062412}, "product_id": "z9y8x7w6", "value": 5.01 })"), bsoncxx::from_json(R"({ "customer_id": "[email protected]", "orderdate": {"$date": 1577861137}, "product_id": "ff11gg22hh33", "value": 63.13 })"), bsoncxx::from_json(R"({ "customer_id": "[email protected]", "orderdate": {"$date": 1608972946000}, "product_id": "a1b2c3d4", "value": 429.65 })") }; orders.insert_many(order_docs); / Might throw an exception std::vector<bsoncxx::document::value> product_docs = { bsoncxx::from_json(R"({ "id": "a1b2c3d4", "name": "Asus Laptop", "category": "ELECTRONICS", "description": "Good value laptop for students" })"), bsoncxx::from_json(R"({ "id": "z9y8x7w6", "name": "The Day Of The Triffids", "category": "BOOKS", "description": "Classic post-apocalyptic novel" })"), bsoncxx::from_json(R"({ "id": "ff11gg22hh33", "name": "Morphy Richardds Food Mixer", "category": "KITCHENWARE", "description": "Luxury mixer turning good cakes into great" })"), bsoncxx::from_json(R"({ "id": "pqr678st", "name": "Karcher Hose Set", "category": "GARDEN", "description": "Hose + nozzles + winder for tidy storage" })") }; products.insert_many(product_docs); / Might throw an exception
Create the Template App
Before you begin following this aggregation tutorial, you must set up a new C#/.NET app. You can use this app to connect to a MongoDB deployment, insert sample data into MongoDB, and run the aggregation pipeline.
Tip
To learn how to install the driver and connect to MongoDB, see the C#/.NET Driver Quick Start guide.
To learn more about performing aggregations in the C#/.NET Driver, see the Aggregation guide.
After you install the driver, paste the following code into your
Program.cs file to create an app template for the aggregation
tutorials.
Important
In the following code, read the code comments to find the sections of the code that you must modify for the tutorial you are following.
If you attempt to run the code without making any changes, you will encounter a connection error.
using MongoDB.Bson; using MongoDB.Bson.Serialization.Attributes; using MongoDB.Driver; / Define data model classes. / ... public class MyClass { ... } / Replace the placeholder with your connection string. var uri = "<connection string>"; var client = new MongoClient(uri); var aggDB = client.GetDatabase("agg_tutorials_db"); / Get a reference to relevant collections. / ... var someColl = aggDB.GetCollection<MyClass>("someColl"); / ... var anotherColl = aggDB.GetCollection<MyClass>("anotherColl"); / Delete any existing documents in collections if needed. / ... someColl.DeleteMany(Builders<MyClass>.Filter.Empty); / Insert sample data into the collection or collections. / ... someColl.InsertMany(new List<MyClass> { ... }); / Add code to chain pipeline stages to the Aggregate() method. / ... var results = someColl.Aggregate().Match(...); / Print the aggregation results. foreach (var result in results.ToList()) { Console.WriteLine(result); }
For every tutorial, you must replace the connection string placeholder with your deployment's connection string.
Tip
To learn how to locate your deployment's connection string, see the Set Up a Free Tier Cluster in Atlas step of the C# Quick Start guide.
For example, if your connection string is
"mongodb+srv://mongodb-example:27017", your connection string assignment resembles
the following:
var uri = "mongodb+srv://mongodb-example:27017";
Create the Collection
This example uses two collections:
orders: documents that describe individual orders for products in a shopproducts: documents that describe the products that a shop sells
An order must contain one product. The aggregation uses a
one-to-one join to match an order document to the corresponding product
document. The aggregation joins the collections by the ProductId field
that exists in documents in both collections.
First, create C# classes to model the data in the orders and products
collections:
public class Order { [] public ObjectId Id { get; set; } public required string CustomerId { get; set; } public DateTime OrderDate { get; set; } public required string ProductId { get; set; } public double Value { get; set; } } public class Product { [] public required string Id { get; set; } public string Name { get; set; } = ""; public string Category { get; set; } = ""; public string Description { get; set; } = ""; }
To create the orders and products collections and insert the
sample data, add the following code to your application:
var orders = aggDB.GetCollection<Order>("orders"); var products = aggDB.GetCollection<Product>("products"); orders = aggDB.GetCollection<Order>("orders"); products = aggDB.GetCollection<Product>("products"); orders.InsertMany(new List<Order> { new Order() { CustomerId = "[email protected]", OrderDate = DateTime.Parse("2020-05-30T08:35:52Z"), ProductId = "a1b2c3d4", Value = 431.43 }, new Order() { CustomerId = "[email protected]", OrderDate = DateTime.Parse("2019-05-28T19:13:32Z"), ProductId = "z9y8x7w6", Value = 5.01 }, new Order() { CustomerId = "[email protected]", OrderDate = DateTime.Parse("2020-01-01T08:25:37Z"), ProductId = "ff11gg22hh33", Value = 63.13 }, new Order() { CustomerId = "[email protected]", OrderDate = DateTime.Parse("2020-12-26T08:55:46Z"), ProductId = "a1b2c3d4", Value = 429.65 } }); products.InsertMany(new List<Product> { new Product() { Id = "a1b2c3d4", Name = "Asus Laptop", Category = "ELECTRONICS", Description = "Good value laptop for students" }, new Product() { Id = "z9y8x7w6", Name = "The Day Of The Triffids", Category = "BOOKS", Description = "Classic post-apocalyptic novel" }, new Product() { Id = "ff11gg22hh33", Name = "Morphy Richardds Food Mixer", Category = "KITCHENWARE", Description = "Luxury mixer turning good cakes into great" }, new Product() { Id = "pqr678st", Name = "Karcher Hose Set", Category = "GARDEN", Description = "Hose + nozzles + winder for tidy storage" } });
Create the Template App
Before you begin following this aggregation tutorial, you must set up a new Go app. You can use this app to connect to a MongoDB deployment, insert sample data into MongoDB, and run the aggregation pipeline.
Tip
To learn how to install the driver and connect to MongoDB, see the Go Driver Quick Start guide.
To learn more about performing aggregations in the Go Driver, see the Aggregation guide.
After you install the driver, create a file called
agg_tutorial.go. Paste the following code in this file to create an
app template for the aggregation tutorials.
Important
In the following code, read the code comments to find the sections of the code that you must modify for the tutorial you are following.
If you attempt to run the code without making any changes, you will encounter a connection error.
const uri = "mongodb+srv://mongodb-example:27017";
Create the Collection
This example uses two collections:
orders: documents that describe individual orders for products in a shopproducts: documents that describe the products that a shop sells
An order can only contain one product. The aggregation uses a
one-to-one join to match an order document to the corresponding product
document. The aggregation joins the collections by the product_id field
that exists in documents in both collections.
First, create Go structs to model the data in the orders and products
collections:
type Order struct { CustomerID string `bson:"customer_id"` OrderDate bson.DateTime `bson:"orderdate"` ProductID string `bson:"product_id"` Value float32 `bson:"value"` } type Product struct { ID string `bson:"id"` Name string `bson:"name"` Category string `bson:"category"` Description string `bson:"description"` }
To create the orders and products collections and insert the
sample data, add the following code to your application:
orders := aggDB.Collection("orders") products := aggDB.Collection("products") _, err = orders.InsertMany(ctx, []interface{}{ Order{ CustomerID: "[email protected]", OrderDate: bson.NewDateTimeFromTime(time.Date(2020, 5, 30, 8, 35, 52, 0, time.UTC)), ProductID: "a1b2c3d4", Value: 431.43, }, Order{ CustomerID: "[email protected]", OrderDate: bson.NewDateTimeFromTime(time.Date(2019, 5, 28, 19, 13, 32, 0, time.UTC)), ProductID: "z9y8x7w6", Value: 5.01, }, Order{ CustomerID: "[email protected]", OrderDate: bson.NewDateTimeFromTime(time.Date(2020, 01, 8, 25, 37, 0, time.UTC)), ProductID: "ff11gg22hh33", Value: 63.13, }, Order{ CustomerID: "[email protected]", OrderDate: bson.NewDateTimeFromTime(time.Date(2020, 12, 26, 8, 55, 46, 0, time.UTC)), ProductID: "a1b2c3d4", Value: 429.65, }, }) if err != nil { log.Fatal(err) } _, err = products.InsertMany(ctx, []interface{}{ Product{ ID: "a1b2c3d4", Name: "Asus Laptop", Category: "ELECTRONICS", Description: "Good value laptop for students", }, Product{ ID: "z9y8x7w6", Name: "The Day Of The Triffids", Category: "BOOKS", Description: "Classic post-apocalyptic novel", }, Product{ ID: "ff11gg22hh33", Name: "Morphy Richardds Food Mixer", Category: "KITCHENWARE", Description: "Luxury mixer turning good cakes into great", }, Product{ ID: "pqr678st", Name: "Karcher Hose Set", Category: "GARDEN", Description: "Hose + nozzles + winder for tidy storage", }, }) if err != nil { log.Fatal(err) }
Create the Template App
Before you begin following an aggregation tutorial, you must set up a new Java app. You can use this app to connect to a MongoDB deployment, insert sample data into MongoDB, and run the aggregation pipeline.
Tip
To learn how to install the driver and connect to MongoDB, see the Get Started with the Java Driver guide.
To learn more about performing aggregations in the Java Sync Driver, see the Aggregation guide.
After you install the driver, create a file called
AggTutorial.java. Paste the following code in this file to create an
app template for the aggregation tutorials.
Important
In the following code, read the code comments to find the sections of the code that you must modify for the tutorial you are following.
If you attempt to run the code without making any changes, you will encounter a connection error.
package org.example; / Modify imports for each tutorial as needed. import com.mongodb.client.*; import com.mongodb.client.model.Accumulators; import com.mongodb.client.model.Aggregates; import com.mongodb.client.model.Field; import com.mongodb.client.model.Filters; import com.mongodb.client.model.Sorts; import com.mongodb.client.model.Variable; import org.bson.Document; import org.bson.conversions.Bson; import java.time.LocalDateTime; import java.util.ArrayList; import java.util.Arrays; import java.util.Collections; import java.util.List; public class AggTutorial { public static void main(String[] args) { / Replace the placeholder with your connection string. String uri = "<connection string>"; try (MongoClient mongoClient = MongoClients.create(uri)) { MongoDatabase aggDB = mongoClient.getDatabase("agg_tutorials_db"); / Get a reference to relevant collections. / ... MongoCollection<Document> someColl = ... / ... MongoCollection<Document> anotherColl = ... / Insert sample data into the collection or collections. / ... someColl.insertMany(...); / Create an empty pipeline array. List<Bson> pipeline = new ArrayList<>(); / Add code to create pipeline stages. / ... pipeline.add(...); / Run the aggregation. / ... AggregateIterable<Document> aggregationResult = / someColl.aggregate(pipeline); / Print the aggregation results. for (Document document : aggregationResult) { System.out.println(document.toJson()); } } } }
For every tutorial, you must replace the connection string placeholder with your deployment's connection string.
Tip
To learn how to locate your deployment's connection string, see the Create a Connection String step of the Java Sync Quick Start guide.
For example, if your connection string is
"mongodb+srv://mongodb-example:27017", your connection string assignment resembles
the following:
String uri = "mongodb+srv://mongodb-example:27017";
Create the Collection
This example uses two collections:
orders: documents that describe individual orders for products in a shopproducts: documents that describe the products that a shop sells
An order can only contain one product. The aggregation uses a
one-to-one join to match an order document to the corresponding product
document. The aggregation joins the collections by the product_id field
that exists in documents in both collections.
To create the orders and products collections and insert the
sample data, add the following code to your application:
MongoDatabase aggDB = mongoClient.getDatabase("agg_tutorials_db"); MongoCollection<Document> orders = aggDB.getCollection("orders"); MongoCollection<Document> products = aggDB.getCollection("products"); orders.insertMany( Arrays.asList( new Document("customer_id", "[email protected]") .append("orderdate", LocalDateTime.parse("2020-05-30T08:35:52")) .append("product_id", "a1b2c3d4") .append("value", 431.43), new Document("customer_id", "[email protected]") .append("orderdate", LocalDateTime.parse("2019-05-28T19:13:32")) .append("product_id", "z9y8x7w6") .append("value", 5.01), new Document("customer_id", "[email protected]") .append("orderdate", LocalDateTime.parse("2020-01-01T08:25:37")) .append("product_id", "ff11gg22hh33") .append("value", 63.13), new Document("customer_id", "[email protected]") .append("orderdate", LocalDateTime.parse("2020-12-26T08:55:46")) .append("product_id", "a1b2c3d4") .append("value", 429.65) ) ); products.insertMany( Arrays.asList( new Document("id", "a1b2c3d4") .append("name", "Asus Laptop") .append("category", "ELECTRONICS") .append("description", "Good value laptop for students"), new Document("id", "z9y8x7w6") .append("name", "The Day Of The Triffids") .append("category", "BOOKS") .append("description", "Classic post-apocalyptic novel"), new Document("id", "ff11gg22hh33") .append("name", "Morphy Richardds Food Mixer") .append("category", "KITCHENWARE") .append("description", "Luxury mixer turning good cakes into great"), new Document("id", "pqr678st") .append("name", "Karcher Hose Set") .append("category", "GARDEN") .append("description", "Hose + nozzles + winder for tidy storage") ) );
Create the Template App
Before you begin following an aggregation tutorial, you must set up a new Kotlin app. You can use this app to connect to a MongoDB deployment, insert sample data into MongoDB, and run the aggregation pipeline.
Tip
To learn how to install the driver and connect to MongoDB, see the Kotlin Driver Quick Start guide.
To learn more about performing aggregations in the Kotlin Driver, see the Aggregation guide.
In addition to the driver, you must also add the following dependencies
to your build.gradle.kts file and reload your project:
dependencies { / Implements Kotlin serialization implementation("org.jetbrains.kotlinx:kotlinx-serialization-core:1.5.1") / Implements Kotlin date and time handling implementation("org.jetbrains.kotlinx:kotlinx-datetime:0.6.1") }
After you install the driver, create a file called
AggTutorial.kt. Paste the following code in this file to create an
app template for the aggregation tutorials.
Important
In the following code, read the code comments to find the sections of the code that you must modify for the tutorial you are following.
If you attempt to run the code without making any changes, you will encounter a connection error.
package org.example / Modify imports for each tutorial as needed. import com.mongodb.client.model.* import com.mongodb.kotlin.client.coroutine.MongoClient import kotlinx.coroutines.runBlocking import kotlinx.datetime.LocalDateTime import kotlinx.datetime.toJavaLocalDateTime import kotlinx.serialization.Contextual import kotlinx.serialization.Serializable import org.bson.Document import org.bson.conversions.Bson / Define data classes. data class MyClass( ... ) suspend fun main() { / Replace the placeholder with your connection string. val uri = "<connection string>" MongoClient.create(uri).use { mongoClient -> val aggDB = mongoClient.getDatabase("agg_tutorials_db") / Get a reference to relevant collections. / ... val someColl = ... / Delete any existing documents in collections if needed. / ... someColl.deleteMany(empty()) / Insert sample data into the collection or collections. / ... someColl.insertMany( ... ) / Create an empty pipeline. val pipeline = mutableListOf<Bson>() / Add code to create pipeline stages. / ... pipeline.add(...) / Run the aggregation. / ... val aggregationResult = someColl.aggregate<Document>(pipeline) / Print the aggregation results. aggregationResult.collect { println(it) } } }
For every tutorial, you must replace the connection string placeholder with your deployment's connection string.
Tip
To learn how to locate your deployment's connection string, see the Connect to your Cluster step of the Kotlin Driver Quick Start guide.
For example, if your connection string is
"mongodb+srv://mongodb-example:27017", your connection string assignment resembles
the following:
val uri = "mongodb+srv://mongodb-example:27017"
Create the Collection
This example uses two collections:
orders: documents that describe individual orders for products in a shopproducts: documents that describe the products that a shop sells
An order can only contain one product. The aggregation uses a
one-to-one join to match an order document to the corresponding product
document. The aggregation joins the collections by the product_id field
that exists in documents in both collections.
First, create Kotlin data classes to model the data in the orders and products
collections:
data class Order( val customerID: String, val orderDate: LocalDateTime, val productID: String, val value: Double ) data class Product( val ID: String, val name: String, val category: String, val description: String )
To create the orders and products collections and insert the
sample data, add the following code to your application:
val orders = aggDB.getCollection<Order>("orders") val products = aggDB.getCollection<Product>("products") orders.deleteMany(Filters.empty()); products.deleteMany(Filters.empty()); orders.insertMany( listOf( Order("[email protected]", LocalDateTime.parse("2020-05-30T08:35:52"), "a1b2c3d4", 431.43), Order("[email protected]", LocalDateTime.parse("2019-05-28T19:13:32"), "z9y8x7w6", 5.01), Order("[email protected]", LocalDateTime.parse("2020-01-01T08:25:37"), "ff11gg22hh33", 63.13), Order("[email protected]", LocalDateTime.parse("2020-12-26T08:55:46"), "a1b2c3d4", 429.65) ) ) products.insertMany( listOf( Product("a1b2c3d4", "Asus Laptop", "ELECTRONICS", "Good value laptop for students"), Product("z9y8x7w6", "The Day Of The Triffids", "BOOKS", "Classic post-apocalyptic novel"), Product( "ff11gg22hh33", "Morphy Richardds Food Mixer", "KITCHENWARE", "Luxury mixer turning good cakes into great" ), Product("pqr678st", "Karcher Hose Set", "GARDEN", "Hose + nozzles + winder for tidy storage") ) )
Create the Template App
Before you begin following this aggregation tutorial, you must set up a new Node.js app. You can use this app to connect to a MongoDB deployment, insert sample data into MongoDB, and run the aggregation pipeline.
Tip
To learn how to install the driver and connect to MongoDB, see the Node.js Driver Quick Start guide.
To learn more about performing aggregations in the Node.js Driver, see the Aggregation guide.
After you install the driver, create a file to run the tutorial template. Paste the following code in this file to create an app template for the aggregation tutorials.
Important
In the following code, read the code comments to find the sections of the code that you must modify for the tutorial you are following.
If you attempt to run the code without making any changes, you will encounter a connection error.
const { MongoClient } = require('mongodb'); / Replace the placeholder with your connection string. const uri = '<connection-string>'; const client = new MongoClient(uri); export async function run() { try { const aggDB = client.db('agg_tutorials_db'); / Get a reference to relevant collections. / ... const someColl = / ... const anotherColl = / Delete any existing documents in collections. / ... await someColl.deleteMany({}); / Insert sample data into the collection or collections. / ... const someData = [ ... ]; / ... await someColl.insertMany(someData); / Create an empty pipeline array. const pipeline = []; / Add code to create pipeline stages. / ... pipeline.push({ ... }) / Run the aggregation. / ... const aggregationResult = ... / Print the aggregation results. for await (const document of aggregationResult) { console.log(document); } } finally { await client.close(); } } run().catch(console.dir);
For every tutorial, you must replace the connection string placeholder with your deployment's connection string.
Tip
To learn how to locate your deployment's connection string, see the Create a Connection String step of the Node.js Quick Start guide.
For example, if your connection string is
"mongodb+srv://mongodb-example:27017", your connection string assignment resembles
the following:
const uri = "mongodb+srv://mongodb-example:27017";
Create the Collection
This example uses two collections:
orders: documents that describe individual orders for products in a shopproducts: documents that describe the products that a shop sells
An order can only contain one product. The aggregation uses a
one-to-one join to match an order document to the corresponding product
document. The aggregation joins the collections by the product_id field
that exists in documents in both collections.
To create the orders and products collections and insert the
sample data, add the following code to your application:
const orders = aggDB.collection('orders'); const products = aggDB.collection('products'); await orders.insertMany([ { customer_id: '[email protected]', orderdate: new Date('2020-05-30T08:35:52Z'), product_id: 'a1b2c3d4', value: 431.43, }, { customer_id: '[email protected]', orderdate: new Date('2019-05-28T19:13:32Z'), product_id: 'z9y8x7w6', value: 5.01, }, { customer_id: '[email protected]', orderdate: new Date('2020-01-01T08:25:37Z'), product_id: 'ff11gg22hh33', value: 63.13, }, { customer_id: '[email protected]', orderdate: new Date('2020-12-26T08:55:46Z'), product_id: 'a1b2c3d4', value: 429.65, }, ]); await products.insertMany([ { id: 'a1b2c3d4', name: 'Asus Laptop', category: 'ELECTRONICS', description: 'Good value laptop for students', }, { id: 'z9y8x7w6', name: 'The Day Of The Triffids', category: 'BOOKS', description: 'Classic post-apocalyptic novel', }, { id: 'ff11gg22hh33', name: 'Morphy Richardds Food Mixer', category: 'KITCHENWARE', description: 'Luxury mixer turning good cakes into great', }, { id: 'pqr678st', name: 'Karcher Hose Set', category: 'GARDEN', description: 'Hose + nozzles + winder for tidy storage', }, ]);
Create the Template App
Before you begin following this aggregation tutorial, you must set up a new PHP app. You can use this app to connect to a MongoDB deployment, insert sample data into MongoDB, and run the aggregation pipeline.
Tip
To learn how to install the PHP library and connect to MongoDB, see the Get Started with the PHP Library tutorial.
To learn more about performing aggregations in the PHP library, see the Aggregation guide.
After you install the library, create a file called
agg_tutorial.php. Paste the following code in this file to create an
app template for the aggregation tutorials.
Important
In the following code, read the code comments to find the sections of the code that you must modify for the tutorial you are following.
If you attempt to run the code without making any changes, you will encounter a connection error.
require 'vendor/autoload.php'; / Modify imports for each tutorial as needed. use MongoDB\Client; use MongoDB\BSON\UTCDateTime; use MongoDB\Builder\Pipeline; use MongoDB\Builder\Stage; use MongoDB\Builder\Type\Sort; use MongoDB\Builder\Query; use MongoDB\Builder\Expression; use MongoDB\Builder\Accumulator; use function MongoDB\object; / Replace the placeholder with your connection string. $uri = '<connection string>'; $client = new Client($uri); / Get a reference to relevant collections. / ... $someColl = $client->agg_tutorials_db->someColl; / ... $anotherColl = $client->agg_tutorials_db->anotherColl; / Delete any existing documents in collections if needed. / ... $someColl->deleteMany([]); / Insert sample data into the collection or collections. / ... $someColl->insertMany(...); / Add code to create pipeline stages within the Pipeline instance. / ... $pipeline = new Pipeline(...); / Run the aggregation. / ... $cursor = $someColl->aggregate($pipeline); / Print the aggregation results. foreach ($cursor as $doc) { echo json_encode($doc, JSON_PRETTY_PRINT), PHP_EOL; }
For every tutorial, you must replace the connection string placeholder with your deployment's connection string.
Tip
To learn how to locate your deployment's connection string, see the Create a Connection String step of the Get Started with the PHP Library tutorial.
For example, if your connection string is
"mongodb+srv://mongodb-example:27017", your connection string assignment resembles
the following:
$uri = 'mongodb+srv://mongodb-example:27017';
Create the Collection
This example uses two collections:
orders: documents that describe individual orders for products in a shopproducts: documents that describe the products that a shop sells
An order can only contain one product. The aggregation uses a
one-to-one join to match an order document to the corresponding product
document. The aggregation joins the collections by the product_id field
that exists in documents in both collections.
To create the orders and products collections and insert the
sample data, add the following code to your application:
$orders = $client->agg_tutorials_db->orders; $products = $client->agg_tutorials_db->products; $orders->deleteMany([]); $products->deleteMany([]); $orders->insertMany( [ [ 'customer_id' => '[email protected]', 'orderdate' => new UTCDateTime(new DateTimeImmutable('2020-05-30T08:35:52')), 'product_id' => 'a1b2c3d4', 'value' => 431.43 ], [ 'customer_id' => '[email protected]', 'orderdate' => new UTCDateTime(new DateTimeImmutable('2019-05-28T19:13:32')), 'product_id' => 'z9y8x7w6', 'value' => 5.01 ], [ 'customer_id' => '[email protected]', 'orderdate' => new UTCDateTime(new DateTimeImmutable('2020-01-01T08:25:37')), 'product_id' => 'ff11gg22hh33', 'value' => 63.13, ], [ 'customer_id' => '[email protected]', 'orderdate' => new UTCDateTime(new DateTimeImmutable('2020-12-26T08:55:46')), 'product_id' => 'a1b2c3d4', 'value' => 429.65 ], ] ); $products->insertMany( [ [ 'id' => 'a1b2c3d4', 'name' => 'Asus Laptop', 'category' => 'ELECTRONICS', 'description' => 'Good value laptop for students', ], [ 'id' => 'z9y8x7w6', 'name' => 'The Day Of The Triffids', 'category' => 'BOOKS', 'description' => 'Classic post-apocalyptic novel', ], [ 'id' => 'ff11gg22hh33', 'name' => 'Morphy Richardds Food Mixer', 'category' => 'KITCHENWARE', 'description' => 'Luxury mixer turning good cakes into great', ], [ 'id' => 'pqr678st', 'name' => 'Karcher Hose Set', 'category' => 'GARDEN', 'description' => 'Hose + nozzles + winder for tidy storage', ], ] );
Create the Template App
Before you begin following this aggregation tutorial, you must set up a new Python app. You can use this app to connect to a MongoDB deployment, insert sample data into MongoDB, and run the aggregation pipeline.
Tip
To learn how to install PyMongo and connect to MongoDB, see the Get Started with PyMongo tutorial.
To learn more about performing aggregations in PyMongo, see the Aggregation guide.
After you install the library, create a file called
agg_tutorial.py. Paste the following code in this file to create an
app template for the aggregation tutorials.
Important
In the following code, read the code comments to find the sections of the code that you must modify for the tutorial you are following.
If you attempt to run the code without making any changes, you will encounter a connection error.
# Modify imports for each tutorial as needed. from pymongo import MongoClient # Replace the placeholder with your connection string. uri = "<connection-string>" client = MongoClient(uri) try: agg_db = client["agg_tutorials_db"] # Get a reference to relevant collections. # ... some_coll = agg_db["some_coll"] # ... another_coll = agg_db["another_coll"] # Delete any existing documents in collections if needed. # ... some_coll.delete_many({}) # Insert sample data into the collection or collections. # ... some_coll.insert_many(...) # Create an empty pipeline array. pipeline = [] # Add code to create pipeline stages. # ... pipeline.append({...}) # Run the aggregation. # ... aggregation_result = ... # Print the aggregation results. for document in aggregation_result: print(document) finally: client.close()
For every tutorial, you must replace the connection string placeholder with your deployment's connection string.
Tip
To learn how to locate your deployment's connection string, see the Create a Connection String step of the Get Started with the PHP Library tutorial.
For example, if your connection string is
"mongodb+srv://mongodb-example:27017", your connection string assignment resembles
the following:
uri = "mongodb+srv://mongodb-example:27017"
Create the Collection
This example uses two collections:
orders: documents that describe individual orders for products in a shopproducts: documents that describe the products that a shop sells
An order can only contain one product. The aggregation uses a
one-to-one join to match an order document to the corresponding product
document. The aggregation joins the collections by the product_id field
that exists in documents in both collections.
To create the orders and products collections and insert the
sample data, add the following code to your application:
orders_coll = agg_db["orders"] products_coll = agg_db["products"] order_data = [ { "customer_id": "[email protected]", "orderdate": datetime(2020, 5, 30, 8, 35, 52), "product_id": "a1b2c3d4", "value": 431.43, }, { "customer_id": "[email protected]", "orderdate": datetime(2019, 5, 28, 19, 13, 32), "product_id": "z9y8x7w6", "value": 5.01, }, { "customer_id": "[email protected]", "orderdate": datetime(2020, 1, 8, 25, 37), "product_id": "ff11gg22hh33", "value": 63.13, }, { "customer_id": "[email protected]", "orderdate": datetime(2020, 12, 26, 8, 55, 46), "product_id": "a1b2c3d4", "value": 429.65, }, ] orders_coll.insert_many(order_data) product_data = [ { "id": "a1b2c3d4", "name": "Asus Laptop", "category": "ELECTRONICS", "description": "Good value laptop for students", }, { "id": "z9y8x7w6", "name": "The Day Of The Triffids", "category": "BOOKS", "description": "Classic post-apocalyptic novel", }, { "id": "ff11gg22hh33", "name": "Morphy Richardds Food Mixer", "category": "KITCHENWARE", "description": "Luxury mixer turning good cakes into great", }, { "id": "pqr678st", "name": "Karcher Hose Set", "category": "GARDEN", "description": "Hose + nozzles + winder for tidy storage", }, ] products_coll.insert_many(product_data)
Create the Template App
Before you begin following this aggregation tutorial, you must set up a new Ruby app. You can use this app to connect to a MongoDB deployment, insert sample data into MongoDB, and run the aggregation pipeline.
Tip
To learn how to install the Ruby Driver and connect to MongoDB, see the Get Started with the Ruby Driver guide.
To learn more about performing aggregations in the Ruby Driver, see the Aggregation guide.
After you install the driver, create a file called
agg_tutorial.rb. Paste the following code in this file to create an
app template for the aggregation tutorials.
Important
In the following code, read the code comments to find the sections of the code that you must modify for the tutorial you are following.
If you attempt to run the code without making any changes, you will encounter a connection error.
# typed: strict require 'mongo' require 'bson' # Replace the placeholder with your connection string. uri = "<connection string>" Mongo::Client.new(uri) do |client| agg_db = client.use('agg_tutorials_db') # Get a reference to relevant collections. # ... some_coll = agg_db[:some_coll] # Delete any existing documents in collections if needed. # ... some_coll.delete_many({}) # Insert sample data into the collection or collections. # ... some_coll.insert_many( ... ) # Add code to create pipeline stages within the array. # ... pipeline = [ ... ] # Run the aggregation. # ... aggregation_result = some_coll.aggregate(pipeline) # Print the aggregation results. aggregation_result.each do |doc| puts doc end end
For every tutorial, you must replace the connection string placeholder with your deployment's connection string.
Tip
To learn how to locate your deployment's connection string, see the Create a Connection String step of the Ruby Get Started guide.
For example, if your connection string is
"mongodb+srv://mongodb-example:27017", your connection string assignment resembles
the following:
uri = "mongodb+srv://mongodb-example:27017"
Create the Collection
This example uses two collections:
orders: documents that describe individual orders for products in a shopproducts: documents that describe the products that a shop sells
An order can only contain one product. The aggregation uses a
one-to-one join to match an order document to the corresponding product
document. The aggregation joins the collections by the product_id field
that exists in documents in both collections.
To create the orders and products collections and insert the
sample data, add the following code to your application:
orders = agg_db[:orders] products = agg_db[:products] orders.delete_many({}) products.delete_many({}) orders.insert_many( [ { customer_id: "[email protected]", orderdate: DateTime.parse("2020-05-30T08:35:52Z"), product_id: "a1b2c3d4", value: 431.43, }, { customer_id: "[email protected]", orderdate: DateTime.parse("2019-05-28T19:13:32Z"), product_id: "z9y8x7w6", value: 5.01, }, { customer_id: "[email protected]", orderdate: DateTime.parse("2020-01-01T08:25:37Z"), product_id: "ff11gg22hh33", value: 63.13, }, { customer_id: "[email protected]", orderdate: DateTime.parse("2020-12-26T08:55:46Z"), product_id: "a1b2c3d4", value: 429.65, }, ] ) products.insert_many( [ { id: "a1b2c3d4", name: "Asus Laptop", category: "ELECTRONICS", description: "Good value laptop for students", }, { id: "z9y8x7w6", name: "The Day Of The Triffids", category: "BOOKS", description: "Classic post-apocalyptic novel", }, { id: "ff11gg22hh33", name: "Morphy Richardds Food Mixer", category: "KITCHENWARE", description: "Luxury mixer turning good cakes into great", }, { id: "pqr678st", name: "Karcher Hose Set", category: "GARDEN", description: "Hose + nozzles + winder for tidy storage", }, ] )
Create the Template App
Before you begin following this aggregation tutorial, you must set up a new Rust app. You can use this app to connect to a MongoDB deployment, insert sample data into MongoDB, and run the aggregation pipeline.
Tip
To learn how to install the driver and connect to MongoDB, see the Rust Driver Quick Start guide.
To learn more about performing aggregations in the Rust Driver, see the Aggregation guide.
After you install the driver, create a file called
agg-tutorial.rs. Paste the following code in this file to create an
app template for the aggregation tutorials.
Important
In the following code, read the code comments to find the sections of the code that you must modify for the tutorial you are following.
If you attempt to run the code without making any changes, you will encounter a connection error.
use mongodb::{ bson::{doc, Document}, options::ClientOptions, Client, }; use futures::stream::TryStreamExt; use std::error::Error; / Define structs. / #[derive(Debug, Serialize, Deserialize)] / struct MyStruct { ... } async fn main() mongodb::error::Result<()> { / Replace the placeholder with your connection string. let uri = "<connection string>"; let client = Client::with_uri_str(uri).await?; let agg_db = client.database("agg_tutorials_db"); / Get a reference to relevant collections. / ... let some_coll: Collection<T> = agg_db.collection("..."); / ... let another_coll: Collection<T> = agg_db.collection("..."); / Delete any existing documents in collections if needed. / ... some_coll.delete_many(doc! {}).await?; / Insert sample data into the collection or collections. / ... some_coll.insert_many(vec![...]).await?; / Create an empty pipeline. let mut pipeline = Vec::new(); / Add code to create pipeline stages. / pipeline.push(doc! { ... }); / Run the aggregation and print the results. let mut results = some_coll.aggregate(pipeline).await?; while let Some(result) = results.try_next().await? { println!("{:?}\n", result); } Ok(()) }
For every tutorial, you must replace the connection string placeholder with your deployment's connection string.
Tip
To learn how to locate your deployment's connection string, see the Create a Connection String step of the Rust Quick Start guide.
For example, if your connection string is
"mongodb+srv://mongodb-example:27017", your connection string assignment resembles
the following:
let uri = "mongodb+srv://mongodb-example:27017";
Create the Collection
This example uses two collections:
orders: documents that describe individual orders for products in a shopproducts: documents that describe the products that a shop sells
An order can only contain one product. The aggregation uses a
one-to-one join to match an order document to the corresponding product
document. The aggregation joins the collections by the product_id field
that exists in documents in both collections.
First, create Rust structs to model the data in the orders and products
collections:
struct Order { customer_id: String, order_date: DateTime, product_id: String, value: f32, } struct Product { id: String, name: String, category: String, description: String, }
To create the orders and products collections and insert the
sample data, add the following code to your application:
let orders: Collection<Order> = agg_db.collection("orders"); let products: Collection<Product> = agg_db.collection("products"); orders.delete_many(doc! {}).await?; products.delete_many(doc! {}).await?; let order_docs = vec![ Order { customer_id: "[email protected]".to_string(), order_date: DateTime::builder().year(2020).month(5).day(30).hour(8).minute(35).second(52).build().unwrap(), product_id: "a1b2c3d4".to_string(), value: 431.43, }, Order { customer_id: "[email protected]".to_string(), order_date: DateTime::builder().year(2019).month(5).day(28).hour(19).minute(13).second(32).build().unwrap(), product_id: "z9y8x7w6".to_string(), value: 5.01, }, Order { customer_id: "[email protected]".to_string(), order_date: DateTime::builder().year(2020).month(1).day(1).hour(8).minute(25).second(37).build().unwrap(), product_id: "ff11gg22hh33".to_string(), value: 63.13, }, Order { customer_id: "[email protected]".to_string(), order_date: DateTime::builder().year(2020).month(12).day(26).hour(8).minute(55).second(46).build().unwrap(), product_id: "a1b2c3d4".to_string(), value: 429.65, }, ]; orders.insert_many(order_docs).await?; let product_docs = vec![ Product { id: "a1b2c3d4".to_string(), name: "Asus Laptop".to_string(), category: "ELECTRONICS".to_string(), description: "Good value laptop for students".to_string(), }, Product { id: "z9y8x7w6".to_string(), name: "The Day Of The Triffids".to_string(), category: "BOOKS".to_string(), description: "Classic post-apocalyptic novel".to_string(), }, Product { id: "ff11gg22hh33".to_string(), name: "Morphy Richardds Food Mixer".to_string(), category: "KITCHENWARE".to_string(), description: "Luxury mixer turning good cakes into great".to_string(), }, Product { id: "pqr678st".to_string(), name: "Karcher Hose Set".to_string(), category: "GARDEN".to_string(), description: "Hose + nozzles + winder for tidy storage".to_string(), }, ]; products.insert_many(product_docs).await?;
Create the Template App
Before you begin following an aggregation tutorial, you must set up a new Scala app. You can use this app to connect to a MongoDB deployment, insert sample data into MongoDB, and run the aggregation pipeline.
Tip
To learn how to install the driver and connect to MongoDB, see the Get Started with the Scala Driver guide.
To learn more about performing aggregations in the Scala Driver, see the Aggregation guide.
After you install the driver, create a file called
AggTutorial.scala. Paste the following code in this file to create an
app template for the aggregation tutorials.
Important
In the following code, read the code comments to find the sections of the code that you must modify for the tutorial you are following.
If you attempt to run the code without making any changes, you will encounter a connection error.
package org.example; / Modify imports for each tutorial as needed. import org.mongodb.scala.MongoClient import org.mongodb.scala.bson.Document import org.mongodb.scala.model.{Accumulators, Aggregates, Field, Filters, Variable} import java.text.SimpleDateFormat object FilteredSubset { def main(args: Array[String]): Unit = { / Replace the placeholder with your connection string. val uri = "<connection string>" val mongoClient = MongoClient(uri) Thread.sleep(1000) val aggDB = mongoClient.getDatabase("agg_tutorials_db") / Get a reference to relevant collections. / ... val someColl = aggDB.getCollection("someColl") / ... val anotherColl = aggDB.getCollection("anotherColl") / Delete any existing documents in collections if needed. / ... someColl.deleteMany(Filters.empty()).subscribe(...) / If needed, create the date format template. val dateFormat = new SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss") / Insert sample data into the collection or collections. / ... someColl.insertMany(...).subscribe(...) Thread.sleep(1000) / Add code to create pipeline stages within the Seq. / ... val pipeline = Seq(...) / Run the aggregation and print the results. / ... someColl.aggregate(pipeline).subscribe(...) Thread.sleep(1000) mongoClient.close() } }
For every tutorial, you must replace the connection string placeholder with your deployment's connection string.
Tip
To learn how to locate your deployment's connection string, see the Create a Connection String step of the Scala Driver Get Started guide.
For example, if your connection string is
"mongodb+srv://mongodb-example:27017", your connection string
assignment resembles the following:
val uri = "mongodb+srv://mongodb-example:27017"
Create the Collection
This example uses two collections:
orders: documents that describe individual orders for products in a shopproducts: documents that describe the products that a shop sells
An order can only contain one product. The aggregation uses a
one-to-one join to match an order document to the corresponding product
document. The aggregation joins the collections by the product_id field
that exists in documents in both collections.
To create the orders and products collections and insert the
sample data, add the following code to your application:
val orders = aggDB.getCollection("orders") val products = aggDB.getCollection("products") orders.deleteMany(Filters.empty()).subscribe( _ => {}, e => println("Error: " + e.getMessage), ) products.deleteMany(Filters.empty()).subscribe( _ => {}, e => println("Error: " + e.getMessage), ) val dateFormat = new SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss") orders.insertMany( Seq( Document( "customer_id" -> "[email protected]", "orderdate" -> dateFormat.parse("2020-05-30T08:35:52"), "product_id" -> "a1b2c3d4", "value" -> 431.43 ), Document( "customer_id" -> "[email protected]", "orderdate" -> dateFormat.parse("2019-05-28T19:13:32"), "product_id" -> "z9y8x7w6", "value" -> 5.01 ), Document( "customer_id" -> "[email protected]", "orderdate" -> dateFormat.parse("2020-01-01T08:25:37"), "product_id" -> "ff11gg22hh33", "value" -> 63.13 ), Document( "customer_id" -> "[email protected]", "orderdate" -> dateFormat.parse("2020-12-26T08:55:46"), "product_id" -> "a1b2c3d4", "value" -> 429.65 ) ) ).subscribe( _ => {}, e => println("Error: " + e.getMessage), ) products.insertMany( Seq( Document( "id" -> "a1b2c3d4", "name" -> "Asus Laptop", "category" -> "ELECTRONICS", "description" -> "Good value laptop for students" ), Document( "id" -> "z9y8x7w6", "name" -> "The Day Of The Triffids", "category" -> "BOOKS", "description" -> "Classic post-apocalyptic novel" ), Document( "id" -> "ff11gg22hh33", "name" -> "Morphy Richardds Food Mixer", "category" -> "KITCHENWARE", "description" -> "Luxury mixer turning good cakes into great" ), Document( "id" -> "pqr678st", "name" -> "Karcher Hose Set", "category" -> "GARDEN", "description" -> "Hose + nozzles + winder for tidy storage" ) ) ).subscribe( _ => {}, e => println("Error: " + e.getMessage), )
Steps
The following steps demonstrate how to create and run an aggregation pipeline to join collections on a single common field.
Run the aggregation pipeline.
db.orders.aggregate( [ / Stage 1: Match orders that were placed in 2020 { $match: { orderdate: { $gte: new Date("2020-01-01T00:00:00Z"), $lt: new Date("2021-01-01T00:00:00Z") } } }, / Stage 2: Link the collections { $lookup: { from: "products", localField: "product_id", foreignField: "p_id", as: "product_mapping" } }, / Stage 3: Create new document fields { $set: { product_mapping: { $first: "$product_mapping" } } }, { $set: { product_name: "$product_mapping.name", product_category: "$product_mapping.category" } }, / Stage 4: Remove unneeded fields { $unset: ["_id", "product_id", "product_mapping"] } ] )
In this example, the $lookup stage always outputs a
product_mapping array that contains one document. The $set
stage after the $lookup stage uses $first to extract the
document from the product_mapping array. If you use this
pipeline in a setting where the $lookup stage outputs an array
of more than one document, consider using an explicit { $limit:
1 } stage in the $lookup stage.
Note
If a supporting index on the foreignField does not exist, a
$lookup operation that performs an equality match with a single
join will likely have poor performance. For more information,
see and Lookup Performance Considerations and
Create an Index.
Interpret the aggregation results.
The aggregated results contain three documents. The documents
represent customer orders that occurred in 2020, with the
product_name and product_category of the ordered product:
{ customer_id: '[email protected]', orderdate: ISODate('2020-05-30T08:35:52.000Z'), value: 431.43, product_name: 'Asus Laptop', product_category: 'ELECTRONICS' } { customer_id: '[email protected]', orderdate: ISODate('2020-01-01T08:25:37.000Z'), value: 63.13, product_name: 'Morphy Richardds Food Mixer', product_category: 'KITCHENWARE' } { customer_id: '[email protected]', orderdate: ISODate('2020-12-26T08:55:46.000Z'), value: 429.65, product_name: 'Asus Laptop', product_category: 'ELECTRONICS' }
The result consists of documents that contain fields from
documents in the orders collection and the products collection
joined by matching the product_id field present in each original
document.
Add a match stage for orders in 2020.
Add a $match stage that matches
orders placed in 2020:
"{", "$match", "{", "orderdate", "{", "$gte", BCON_DATE_TIME(1577836800000UL), "$lt", BCON_DATE_TIME(1609459200000UL), "}", "}", "}",
Add a lookup stage to link the collections.
Next, add a $lookup stage. The
$lookup stage joins the product_id field in the orders
collection to the id field in the products collection:
"{", "$lookup", "{", "from", BCON_UTF8("products"), "localField", BCON_UTF8("product_id"), "foreignField", BCON_UTF8("id"), "as", BCON_UTF8("product_mapping"), "}", "}",
Add set stages to create new document fields.
Next, add two $set stages to the pipeline.
The first $set stage sets the product_mapping field
to the first element in the product_mapping object
created in the previous $lookup stage.
The second $set stage creates two new fields, product_name
and product_category, from the values in the
product_mapping object field:
"{", "$set", "{", "product_mapping", "{", "$first", BCON_UTF8("$product_mapping"), "}", "}", "{", "$set", "{", "product_name", BCON_UTF8("$product_mapping.name"), "product_category", BCON_UTF8("$product_mapping.category"), "}", "}",
Tip
Because this is a one-to-one join, the $lookup stage
adds only one array element to the input document. The pipeline
uses the $first operator to retrieve the data from
this element.
Add an unset stage to remove unneeded fields.
Finally, add an $unset stage. The
$unset stage removes unnecessary fields from the document:
"{", "$unset", "[", BCON_UTF8("_id"), BCON_UTF8("product_id"), BCON_UTF8("product_mapping"), "]", "}",
Run the aggregation pipeline.
Add the following code to the end of your application to perform
the aggregation on the orders collection:
mongoc_cursor_t *results = mongoc_collection_aggregate(orders, MONGOC_QUERY_NONE, pipeline, NULL, NULL); bson_destroy(pipeline);
Ensure that you clean up the collection resources by adding the following line to your cleanup statements:
mongoc_collection_destroy(orders); mongoc_collection_destroy(products);
Finally, run the following commands in your shell to generate and run the executable:
gcc -o aggc agg-tutorial.c $(pkg-config --libs --cflags libmongoc-1.0) ./aggc
Tip
If you encounter connection errors by running the preceding commands in one call, you can run them separately.
Interpret the aggregation results.
The aggregated result contains three documents. The documents
represent customer orders that occurred in 2020, with the
product_name and product_category of the ordered product:
{ "customer_id" : "[email protected]", "orderdate" : { "$date" : { "$numberLong" : "1590822952000" } }, "value" : { "$numberDouble" : "431.43000000000000682" }, "product_name" : "Asus Laptop", "product_category" : "ELECTRONICS" } { "customer_id" : "[email protected]", "orderdate" : { "$date" : { "$numberLong" : "1577869537000" } }, "value" : { "$numberDouble" : "63.130000000000002558" }, "product_name" : "Morphy Richardds Food Mixer", "product_category" : "KITCHENWARE" } { "customer_id" : "[email protected]", "orderdate" : { "$date" : { "$numberLong" : "1608976546000" } }, "value" : { "$numberDouble" : "429.64999999999997726" }, "product_name" : "Asus Laptop", "product_category" : "ELECTRONICS" }
The result consists of documents that contain fields from
documents in the orders collection and the products
collection, joined by matching the product_id field present in
each original document.
Add a match stage for orders in 2020.
Add a $match stage that matches
orders placed in 2020:
pipeline.match(bsoncxx::from_json(R"({ "orderdate": { "$gte": {"$date": 1577836800}, "$lt": {"$date": 1609459200000} } })"));
Add a lookup stage to link the collections.
Next, add a $lookup stage. The
$lookup stage joins the product_id field in the orders
collection to the id field in the products collection:
pipeline.lookup(bsoncxx::from_json(R"({ "from": "products", "localField": "product_id", "foreignField": "id", "as": "product_mapping" })"));
Add addFields stages to create new document fields.
Next, add two $addFields
stages to the pipeline.
The first $addFields stage sets the product_mapping field
to the first element in the product_mapping object
created in the previous $lookup stage.
The second $addFields stage creates two new fields, product_name
and product_category, from the values in the
product_mapping object field:
pipeline.add_fields(bsoncxx::from_json(R"({ "product_mapping": {"$first": "$product_mapping"} })")); pipeline.add_fields(bsoncxx::from_json(R"({ "product_name": "$product_mapping.name", "product_category": "$product_mapping.category" })"));
Tip
Because this is a one-to-one join, the $lookup stage
adds only one array element to the input document. The pipeline
uses the $first operator to retrieve the data from this element.
Add an unset stage to remove unneeded fields.
Finally, add an $unset stage. The
$unset stage removes unnecessary fields from the document:
pipeline.append_stage(bsoncxx::from_json(R"({ "$unset": ["_id", "product_id", "product_mapping"] })"));
Run the aggregation pipeline.
Add the following code to the end of your application to perform
the aggregation on the orders collection:
auto cursor = orders.aggregate(pipeline);
Finally, run the following command in your shell to start your application:
c++ --std=c++17 agg-tutorial.cpp $(pkg-config --cflags --libs libmongocxx) -o ./app.out ./app.out
Interpret the aggregation results.
The aggregated result contains three documents. The documents
represent customer orders that occurred in 2020, with the
product_name and product_category of the ordered product:
{ "customer_id" : "[email protected]", "orderdate" : { "$date" : "2020-05-30T06:55:52Z" }, "value" : 431.43000000000000682, "product_name" : "Asus Laptop", "product_category" : "ELECTRONICS" } { "customer_id" : "[email protected]", "orderdate" : { "$date" : "1970-01-19T06:17:41.137Z" }, "value" : 63.130000000000002558, "product_name" : "Morphy Richardds Food Mixer", "product_category" : "KITCHENWARE" } { "customer_id" : "[email protected]", "orderdate" : { "$date" : "2020-12-26T08:55:46Z" }, "value" : 429.64999999999997726, "product_name" : "Asus Laptop", "product_category" : "ELECTRONICS" }
The result consists of documents that contain fields from
documents in the orders collection and the products
collection, joined by matching the product_id field present in
each original document.
Add a match stage for orders in 2020.
First, start the aggregation on the orders collection and
chain a $match stage that matches orders placed in 2020: