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.
This tutorial demonstrates how to create insights from customer order data. The results show the list of products ordered that cost more than $15. Each document contains the number of units sold and the total sale value for each product.
The aggregation pipeline performs the following operations:
Unwinds an array field into separate documents
Matches a subset of documents by a field value
Groups documents by common field values
Adds computed fields to each result document
➤ 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 an orders collection, which contains documents describing product orders. Because each order contains multiple products, the first step of the aggregation unpacks the products array into individual product order documents.
To create the orders collection, use the insertMany() method:
db.orders.deleteMany({})db.orders.insertMany( [ { order_id: 6363763262239, products: [ { prod_id: "abc12345", name: "Asus Laptop", price: 431 }, { prod_id: "def45678", name: "Karcher Hose Set", price: 22 } ] }, { order_id: 1197372932325, products: [ { prod_id: "abc12345", name: "Asus Laptop", price: 429 } ] }, { order_id: 9812343774839, products: [ { prod_id: "pqr88223", name: "Morphy Richards Food Mixer", price: 431 }, { prod_id: "def45678", name: "Karcher Hose Set", price: 21 } ] }, { order_id: 4433997244387, products: [ { prod_id: "def45678", name: "Karcher Hose Set", price: 23 }, { prod_id: "jkl77336", name: "Picky Pencil Sharpener", price: 1 }, { prod_id: "xyz11228", name: "Russell Hobbs Chrome Kettle", price: 16 } ] }] )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.
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.
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.
#include <stdio.h>#include <bson/bson.h>#include <mongoc/mongoc.h>int main(void){ mongoc_init(); char *uri = "<connection string>"; mongoc_client_t* client = mongoc_client_new(uri); { const bson_t *doc; bson_t *pipeline = BCON_NEW("pipeline", "[", "]"); bson_destroy(pipeline); 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); } 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.
TipTo 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";This example uses an orders collection, which contains documents describing product orders. Because each order contains multiple products, the first step of the aggregation unpacks the products array into individual product order documents.
To create the orders collection 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");{ 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); } bson_destroy(filter);}{ size_t num_docs = 4; bson_t *docs[num_docs]; docs[0] = BCON_NEW( "order_id", BCON_INT64(6363763262239), "products", "[", "{", "prod_id", BCON_UTF8("abc12345"), "name", BCON_UTF8("Asus Laptop"), "price", BCON_INT32(431), "}", "{", "prod_id", BCON_UTF8("def45678"), "name", BCON_UTF8("Karcher Hose Set"), "price", BCON_INT32(22), "}", "]"); docs[1] = BCON_NEW( "order_id", BCON_INT64(1197372932325), "products", "[", "{", "prod_id", BCON_UTF8("abc12345"), "name", BCON_UTF8("Asus Laptop"), "price", BCON_INT32(429), "}", "]"); docs[2] = BCON_NEW( "order_id", BCON_INT64(9812343774839), "products", "[", "{", "prod_id", BCON_UTF8("pqr88223"), "name", BCON_UTF8("Morphy Richards Food Mixer"), "price", BCON_INT32(431), "}", "{", "prod_id", BCON_UTF8("def45678"), "name", BCON_UTF8("Karcher Hose Set"), "price", BCON_INT32(21), "}", "]"); docs[3] = BCON_NEW( "order_id", BCON_INT64(4433997244387), "products", "[", "{", "prod_id", BCON_UTF8("def45678"), "name", BCON_UTF8("Karcher Hose Set"), "price", BCON_INT32(23), "}", "{", "prod_id", BCON_UTF8("jkl77336"), "name", BCON_UTF8("Picky Pencil Sharpener"), "price", BCON_INT32(1), "}", "{", "prod_id", BCON_UTF8("xyz11228"), "name", BCON_UTF8("Russell Hobbs Chrome Kettle"), "price", BCON_INT32(16), "}", "]"); bson_error_t error; if (!mongoc_collection_insert_many(orders, (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]); }}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.
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.
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.
#include <iostream>#include <bsoncxx/builder/basic/document.hpp>#include <bsoncxx/builder/basic/kvp.hpp>#include <bsoncxx/json.hpp>#include <mongocxx/client.hpp>#include <mongocxx/instance.hpp>#include <mongocxx/pipeline.hpp>#include <mongocxx/uri.hpp>#include <chrono>using bsoncxx::builder::basic::kvp;using bsoncxx::builder::basic::make_document;using bsoncxx::builder::basic::make_array;int main() { mongocxx::instance instance; mongocxx::uri uri("<connection string>"); mongocxx::client client(uri); auto db = client["agg_tutorials_db"]; db.drop(); mongocxx::pipeline pipeline; 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.
TipTo 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"};This example uses an orders collection, which contains documents describing product orders. Because each order contains multiple products, the first step of the aggregation unpacks the products array into individual product order documents.
To create the orders collection and insert the sample data, add the following code to your application:
auto orders = db["orders"];std::vector<bsoncxx::document::value> order_docs = { bsoncxx::from_json(R"({ "order_id": 6363763262239, "products": [ { "prod_id": "abc12345", "name": "Asus Laptop", "price": 431 }, { "prod_id": "def45678", "name": "Karcher Hose Set", "price": 22 } ] })"), bsoncxx::from_json(R"({ "order_id": 1197372932325, "products": [ { "prod_id": "abc12345", "name": "Asus Laptop", "price": 429 } ] })"), bsoncxx::from_json(R"({ "order_id": 9812343774839, "products": [ { "prod_id": "pqr88223", "name": "Morphy Richards Food Mixer", "price": 431 }, { "prod_id": "def45678", "name": "Karcher Hose Set", "price": 21 } ] })"), bsoncxx::from_json(R"({ "order_id": 4433997244387, "products": [ { "prod_id": "def45678", "name": "Karcher Hose Set", "price": 23 }, { "prod_id": "jkl77336", "name": "Picky Pencil Sharpener", "price": 1 }, { "prod_id": "xyz11228", "name": "Russell Hobbs Chrome Kettle", "price": 16 } ] })")};orders.insert_many(order_docs); 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.
After you install the driver, paste the following code into your Program.cs file to create an app template for the aggregation tutorials.
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;var uri = "<connection string>";var client = new MongoClient(uri);var aggDB = client.GetDatabase("agg_tutorials_db");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.
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";This example uses an orders collection, which contains documents describing product orders. Because each order contains multiple products, the first step of the aggregation unpacks the products array into individual product order documents.
First, create C# classes to model the data in the orders collection:
public class Order{ [BsonId] public ObjectId Id { get; set; } public long OrderId { get; set; } [Required] public required List<Product> Products { get; set; }}public class OrderUnwound{ public long OrderId { get; set; } [Required] public required Product Products { get; set; }}public class Product{ [Required] public required string ProductId { get; set; } public string Name { get; set; } = ""; public int Price { get; set; }}To create the orders collection and insert the sample data, add the following code to your application:
var orders = aggDB.GetCollection<Order>("orders");orders.InsertMany(new List<Order>{ new Order { OrderId = 6363763262239L, Products = new List<Product> { new Product { ProductId = "abc12345", Name = "Asus Laptop", Price = 431 }, new Product { ProductId = "def45678", Name = "Karcher Hose Set", Price = 22 } } }, new Order { OrderId = 1197372932325L, Products = new List<Product> { new Product { ProductId = "abc12345", Name = "Asus Laptop", Price = 429 } } }, new Order { OrderId = 9812343774839L, Products = new List<Product> { new Product { ProductId = "pqr88223", Name = "Morphy Richards Food Mixer", Price = 431 }, new Product { ProductId = "def45678", Name = "Karcher Hose Set", Price = 21 } } }, new Order { OrderId = 4433997244387L, Products = new List<Product> { new Product { ProductId = "def45678", Name = "Karcher Hose Set", Price = 23 }, new Product { ProductId = "jkl77336", Name = "Picky Pencil Sharpener", Price = 1 }, new Product { ProductId = "xyz11228", Name = "Russell Hobbs Chrome Kettle", Price = 16 } } }});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.
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.
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 mainimport ( "context" "fmt" "log" "time" "go.mongodb.org/mongo-driver/v2/bson" "go.mongodb.org/mongo-driver/v2/mongo" "go.mongodb.org/mongo-driver/v2/mongo/options")func main() { const uri = "<connection string>" client, err := mongo.Connect(options.Client().ApplyURI(uri)) if err != nil { log.Fatal(err) } defer func() { if err = client.Disconnect(context.TODO()); err != nil { log.Fatal(err) } }() aggDB := client.Database("agg_tutorials_db") if err != nil { log.Fatal(err) } defer func() { if err := cursor.Close(context.TODO()); err != nil { log.Fatalf("failed to close cursor: %v", err) } }() var results []bson.D if err = cursor.All(context.TODO(), &results); err != nil { log.Fatalf("failed to decode results: %v", err) } for _, result := range results { res, _ := bson.MarshalExtJSON(result, false, false) fmt.Println(string(res)) }}For every tutorial, you must replace the connection string placeholder with your deployment's connection string.
TipTo learn how to locate your deployment's connection string, see the Create a MongoDB Cluster step of the Go 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";This example uses an orders collection, which contains documents describing product orders. Because each order contains multiple products, the first step of the aggregation unpacks the products array into individual product order documents.
First, create Go structs to model the data in the orders collection:
type Order struct { OrderID int `bson:"order_id"` Products []Product `bson:"products"`}type Product struct { ProductID string `bson:"prod_id"` Name string `bson:"name"` Price int `bson:"price"`}To create the orders collection and insert the sample data, add the following code to your application:
orders := aggDB.Collection("orders")orders.DeleteMany(context.TODO(), bson.D{})_, err = orders.InsertMany(context.TODO(), []interface{}{ Order{ OrderID: 6363763262239, Products: []Product{ {ProductID: "abc12345", Name: "Asus Laptop", Price: 431}, {ProductID: "def45678", Name: "Karcher Hose Set", Price: 22}, }, }, Order{ OrderID: 1197372932325, Products: []Product{ {ProductID: "abc12345", Name: "Asus Laptop", Price: 429}, }, }, Order{ OrderID: 9812343774839, Products: []Product{ {ProductID: "pqr88223", Name: "Morphy Richards Food Mixer", Price: 431}, {ProductID: "def45678", Name: "Karcher Hose Set", Price: 21}, }, }, Order{ OrderID: 4433997244387, Products: []Product{ {ProductID: "def45678", Name: "Karcher Hose Set", Price: 23}, {ProductID: "jkl77336", Name: "Picky Pencil Sharpene", Price: 1}, {ProductID: "xyz11228", Name: "Russell Hobbs Chrome Kettle", Price: 16}, }, },})if err != nil { log.Fatal(err)}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.
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.
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;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) { String uri = "<connection string>"; try (MongoClient mongoClient = MongoClients.create(uri)) { MongoDatabase aggDB = mongoClient.getDatabase("agg_tutorials_db"); List<Bson> pipeline = new ArrayList<>(); 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.
TipTo 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";This example uses an orders collection, which contains documents describing product orders. Because each order contains multiple products, the first step of the aggregation unpacks the products array into individual product order documents.
To create the orders collection 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");orders.insertMany( Arrays.asList( new Document("order_id", 6363763262239f) .append("products", Arrays.asList( new Document("prod_id", "abc12345") .append("name", "Asus Laptop") .append("price", 431), new Document("prod_id", "def45678") .append("name", "Karcher Hose Set") .append("price", 22) )), new Document("order_id", 1197372932325f) .append("products", Collections.singletonList( new Document("prod_id", "abc12345") .append("name", "Asus Laptop") .append("price", 429) )), new Document("order_id", 9812343774839f) .append("products", Arrays.asList( new Document("prod_id", "pqr88223") .append("name", "Morphy Richards Food Mixer") .append("price", 431), new Document("prod_id", "def45678") .append("name", "Karcher Hose Set") .append("price", 21) )), new Document("order_id", 4433997244387f) .append("products", Arrays.asList( new Document("prod_id", "def45678") .append("name", "Karcher Hose Set") .append("price", 23), new Document("prod_id", "jkl77336") .append("name", "Picky Pencil Sharpener") .append("price", 1), new Document("prod_id", "xyz11228") .append("name", "Russell Hobbs Chrome Kettle") .append("price", 16) )) ));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.
In addition to the driver, you must also add the following dependencies to your build.gradle.kts file and reload your project:
dependencies { implementation("org.jetbrains.kotlinx:kotlinx-serialization-core:1.5.1") 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.
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.exampleimport com.mongodb.client.model.*import com.mongodb.kotlin.client.coroutine.MongoClientimport kotlinx.coroutines.runBlockingimport kotlinx.datetime.LocalDateTimeimport kotlinx.datetime.toJavaLocalDateTimeimport kotlinx.serialization.Contextualimport kotlinx.serialization.Serializableimport org.bson.Documentimport org.bson.conversions.Bson@Serializabledata class MyClass( ...)suspend fun main() { val uri = "<connection string>" MongoClient.create(uri).use { mongoClient -> val aggDB = mongoClient.getDatabase("agg_tutorials_db") val pipeline = mutableListOf<Bson>() aggregationResult.collect { println(it) } }}For every tutorial, you must replace the connection string placeholder with your deployment's connection string.
TipTo 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"This example uses an orders collection, which contains documents describing product orders. Because each order contains multiple products, the first step of the aggregation unpacks the products array into individual product order documents.
First, create Kotlin data classes to model the data in the orders collection:
@Serializabledata class Order( val orderID: Float, val products: List<Product>)@Serializabledata class Product( val prodID: String, val name: String, val price: Int)To create the orders collection and insert the sample data, add the following code to your application:
val orders = aggDB.getCollection<Order>("orders")orders.deleteMany(Filters.empty())orders.insertMany( listOf( Order( 6363763262239f, listOf( Product("abc12345", "Asus Laptop", 431), Product("def45678", "Karcher Hose Set", 22) ) ), Order( 1197372932325f, listOf( Product("abc12345", "Asus Laptop", 429) ) ), Order( 9812343774839f, listOf( Product("pqr88223", "Morphy Richards Food Mixer", 431), Product("def45678", "Karcher Hose Set", 21) ) ), Order( 4433997244387f, listOf( Product("def45678", "Karcher Hose Set", 23), Product("jkl77336", "Picky Pencil Sharpener", 1), Product("xyz11228", "Russell Hobbs Chrome Kettle", 16) ) ) ))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.
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.
ImportantIn 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');const uri = '<connection-string>';const client = new MongoClient(uri);export async function run() { try { const aggDB = client.db('agg_tutorials_db'); const pipeline = []; 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.
TipTo 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";This example uses an orders collection, which contains documents describing product orders. Because each order contains multiple products, the first step of the aggregation unpacks the products array into individual product order documents.
To create the orders collection and insert the sample data, add the following code to your application:
const orders = aggDB.collection('orders');await orders.insertMany([ { order_id: 6363763262239, products: [ { prod_id: 'abc12345', name: 'Asus Laptop', price: 431, }, { prod_id: 'def45678', name: 'Karcher Hose Set', price: 22, }, ], }, { order_id: 1197372932325, products: [ { prod_id: 'abc12345', name: 'Asus Laptop', price: 429, }, ], }, { order_id: 9812343774839, products: [ { prod_id: 'pqr88223', name: 'Morphy Richards Food Mixer', price: 431, }, { prod_id: 'def45678', name: 'Karcher Hose Set', price: 21, }, ], }, { order_id: 4433997244387, products: [ { prod_id: 'def45678', name: 'Karcher Hose Set', price: 23, }, { prod_id: 'jkl77336', name: 'Picky Pencil Sharpener', price: 1, }, { prod_id: 'xyz11228', name: 'Russell Hobbs Chrome Kettle', price: 16, }, ], },]);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.
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.
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.
<?phprequire 'vendor/autoload.php';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;$uri = '<connection string>';$client = new Client($uri);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.
TipTo 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';This example uses an orders collection, which contains documents describing product orders. Because each order contains multiple products, the first step of the aggregation unpacks the products array into individual product order documents.
To create the orders collection and insert the sample data, add the following code to your application:
$orders = $client->agg_tutorials_db->orders;$orders->deleteMany([]);$orders->insertMany( [ [ 'order_id' => 6363763262239, 'products' => [ [ 'prod_id' => 'abc12345', 'name' => 'Asus Laptop', 'price' => 431, ], [ 'prod_id' => 'def45678', 'name' => 'Karcher Hose Set', 'price' => 22, ], ], ], [ 'order_id' => 1197372932325, 'products' => [ [ 'prod_id' => 'abc12345', 'name' => 'Asus Laptop', 'price' => 429, ], ], ], [ 'order_id' => 9812343774839, 'products' => [ [ 'prod_id' => 'pqr88223', 'name' => 'Morphy Richards Food Mixer', 'price' => 431, ], [ 'prod_id' => 'def45678', 'name' => 'Karcher Hose Set', 'price' => 21, ], ], ], [ 'order_id' => 4433997244387, 'products' => [ [ 'prod_id' => 'def45678', 'name' => 'Karcher Hose Set', 'price' => 23, ], [ 'prod_id' => 'jkl77336', 'name' => 'Picky Pencil Sharpener', 'price' => 1, ], [ 'prod_id' => 'xyz11228', 'name' => 'Russell Hobbs Chrome Kettle', 'price' => 16, ], ], ] ]);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.
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.
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.
from pymongo import MongoClienturi = "<connection-string>"client = MongoClient(uri)try: agg_db = client["agg_tutorials_db"] pipeline = [] 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.
TipTo 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"This example uses an orders collection, which contains documents describing product orders. Because each order contains multiple products, the first step of the aggregation unpacks the products array into individual product order documents.
To create the orders collection and insert the sample data, add the following code to your application:
orders_coll = agg_db["orders"]order_data = [ { "order_id": 6363763262239, "products": [ { "prod_id": "abc12345", "name": "Asus Laptop", "price": 431, }, { "prod_id": "def45678", "name": "Karcher Hose Set", "price": 22, }, ], }, { "order_id": 1197372932325, "products": [ { "prod_id": "abc12345", "name": "Asus Laptop", "price": 429, } ], }, { "order_id": 9812343774839, "products": [ { "prod_id": "pqr88223", "name": "Morphy Richards Food Mixer", "price": 431, }, { "prod_id": "def45678", "name": "Karcher Hose Set", "price": 21, }, ], }, { "order_id": 4433997244387, "products": [ { "prod_id": "def45678", "name": "Karcher Hose Set", "price": 23, }, { "prod_id": "jkl77336", "name": "Picky Pencil Sharpener", "price": 1, }, { "prod_id": "xyz11228", "name": "Russell Hobbs Chrome Kettle", "price": 16, }, ], },]orders_coll.insert_many(order_data)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.
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.
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 'mongo'require 'bson'uri = "<connection string>"Mongo::Client.new(uri) do |client| agg_db = client.use('agg_tutorials_db') aggregation_result.each do |doc| puts doc endendFor every tutorial, you must replace the connection string placeholder with your deployment's connection string.
TipTo 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"This example uses an orders collection, which contains documents describing product orders. Because each order contains multiple products, the first step of the aggregation unpacks the products array into individual product order documents.
To create the orders collection and insert the sample data, add the following code to your application:
orders = agg_db[:orders]orders.delete_many({})orders.insert_many( [ { order_id: 6363763262239, products: [ { prod_id: "abc12345", name: "Asus Laptop", price: 431, }, { prod_id: "def45678", name: "Karcher Hose Set", price: 22, }, ], }, { order_id: 1197372932325, products: [ { prod_id: "abc12345", name: "Asus Laptop", price: 429, }, ], }, { order_id: 9812343774839, products: [ { prod_id: "pqr88223", name: "Morphy Richards Food Mixer", price: 431, }, { prod_id: "def45678", name: "Karcher Hose Set", price: 21, }, ], }, { order_id: 4433997244387, products: [ { prod_id: "def45678", name: "Karcher Hose Set", price: 23, }, { prod_id: "jkl77336", name: "Picky Pencil Sharpener", price: 1, }, { prod_id: "xyz11228", name: "Russell Hobbs Chrome Kettle", price: 16, }, ], }, ])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.
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.
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;#[tokio::main]async fn main() mongodb::error::Result<()> { let uri = "<connection string>"; let client = Client::with_uri_str(uri).await?; let agg_db = client.database("agg_tutorials_db"); let mut pipeline = Vec::new(); 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.
TipTo 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";This example uses an orders collection, which contains documents describing product orders. Because each order contains multiple products, the first step of the aggregation unpacks the products array into individual product order documents.
First, create Rust structs to model the data in the orders collection:
#[derive(Debug, Serialize, Deserialize)]struct Product { prod_id: String, name: String, price: i32,}#[derive(Debug, Serialize, Deserialize)]struct Order { order_id: i64, products: Vec<Product>,}To create the orders collection and insert the sample data, add the following code to your application:
let orders_coll: Collection<Order> = agg_db.collection("orders");orders.delete_many(doc! {}).await?;let orders = vec![ Order { order_id: 6363763262239, products: vec![ Product { prod_id: "abc12345".to_string(), name: "Asus Laptop".to_string(), price: 431, }, Product { prod_id: "def45678".to_string(), name: "Karcher Hose Set".to_string(), price: 22, }, ], }, Order { order_id: 1197372932325, products: vec![Product { prod_id: "abc12345".to_string(), name: "Asus Laptop".to_string(), price: 429, }], }, Order { order_id: 9812343774839, products: vec![ Product { prod_id: "pqr88223".to_string(), name: "Morphy Richards Food Mixer".to_string(), price: 431, }, Product { prod_id: "def45678".to_string(), name: "Karcher Hose Set".to_string(), price: 21, }, ], }, Order { order_id: 4433997244387, products: vec![ Product { prod_id: "def45678".to_string(), name: "Karcher Hose Set".to_string(), price: 23, }, Product { prod_id: "jkl77336".to_string(), name: "Picky Pencil Sharpene".to_string(), price: 1, }, Product { prod_id: "xyz11228".to_string(), name: "Russell Hobbs Chrome Kettle".to_string(), price: 16, }, ], },];orders_coll.insert_many(orders).await?;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.
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.
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;import org.mongodb.scala.MongoClientimport org.mongodb.scala.bson.Documentimport org.mongodb.scala.model.{Accumulators, Aggregates, Field, Filters, Variable}import java.text.SimpleDateFormatobject FilteredSubset { def main(args: Array[String]): Unit = { val uri = "<connection string>" val mongoClient = MongoClient(uri) Thread.sleep(1000) val aggDB = mongoClient.getDatabase("agg_tutorials_db") val dateFormat = new SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss") Thread.sleep(1000) Thread.sleep(1000) mongoClient.close() }}For every tutorial, you must replace the connection string placeholder with your deployment's connection string.
TipTo 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"This example uses an orders collection, which contains documents describing product orders. Because each order contains multiple products, the first step of the aggregation unpacks the products array into individual product order documents.
To create the orders collection and insert the sample data, add the following code to your application:
val orders = aggDB.getCollection("orders")orders.deleteMany(Filters.empty()).subscribe( _ => {}, e => println("Error: " + e.getMessage),)orders.insertMany(Seq( Document( "order_id" -> 6363763262239L, "products" -> Seq( Document( "prod_id" -> "abc12345", "name" -> "Asus Laptop", "price" -> 431 ), Document( "prod_id" -> "def45678", "name" -> "Karcher Hose Set", "price" -> 22 ) ) ), Document( "order_id" -> 1197372932325L, "products" -> Seq( Document( "prod_id" -> "abc12345", "name" -> "Asus Laptop", "price" -> 429 ) ) ), Document( "order_id" -> 9812343774839L, "products" -> Seq( Document( "prod_id" -> "pqr88223", "name" -> "Morphy Richards Food Mixer", "price" -> 431 ), Document( "prod_id" -> "def45678", "name" -> "Karcher Hose Set", "price" -> 21 ) ) ), Document( "order_id" -> 4433997244387L, "products" -> Seq( Document( "prod_id" -> "def45678", "name" -> "Karcher Hose Set", "price" -> 23 ), Document( "prod_id" -> "jkl77336", "name" -> "Picky Pencil Sharpener", "price" -> 1 ), Document( "prod_id" -> "xyz11228", "name" -> "Russell Hobbs Chrome Kettle", "price" -> 16 ) ) ))).subscribe( _ => {}, e => println("Error: " + e.getMessage),)The following steps demonstrate how to create and run an aggregation pipeline to unpack array fields into separate documents and compute new values based on groups of common values.
db.orders.aggregate( [ { $unwind: { path: "$products" } }, { $match: { "products.price": { $gt: 15 } } }, { $group: { _id: "$products.prod_id", product: { $first: "$products.name" }, total_value: { $sum: "$products.price" }, quantity: { $sum: 1 } } }, { $set: { product_id: "$_id" } }, { $unset: [ "_id"] }] )The aggregation returns the following summary of customers' orders from 2020:
{ product: 'Asus Laptop', total_value: 860, quantity: 2, product_id: 'abc12345'}{ product: 'Morphy Richards Food Mixer', total_value: 431, quantity: 1, product_id: 'pqr88223'}{ product: 'Russell Hobbs Chrome Kettle', total_value: 16, quantity: 1, product_id: 'xyz11228'}{ product: 'Karcher Hose Set', total_value: 66, quantity: 3, product_id: 'def45678'}If you run this example, the order of documents in your results might differ from the order of documents on this page because the aggregation pipeline does not contain a sort stage.
First, add an $unwind stage to separate the entries in the products array into individual documents:
"{", "$unwind", "{", "path", BCON_UTF8("$products"), "}", "}",Next, add a $match stage that matches products with a products.price value greater than 15:
"{", "$match", "{", "products.price", "{", "$gt", BCON_INT32(15), "}", "}", "}",Add a $group stage to collect order documents by the value of the prod_id field. In this stage, add aggregation operations that create the following fields in the result documents:
product: the product name
total_value: the total value of all the sales of the product
quantity: the number of orders for the product
"{", "$group", "{","_id", BCON_UTF8("$products.prod_id"),"product", "{", "$first", BCON_UTF8("$products.name"), "}","total_value", "{", "$sum", BCON_UTF8("$products.price"), "}","quantity", "{", "$sum", BCON_INT32(1), "}","}", "}",Add a $set stage to recreate the product_id field from the values in the _id field that were set during the $group stage:
"{", "$set", "{", "product_id", BCON_UTF8("$_id"), "}", "}",Finally, add an $unset stage. The $unset stage removes the _id field from the result documents:
"{", "$unset", "[", BCON_UTF8("_id"), "]", "}",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);Finally, run the following commands in your shell to generate and run the executable:
gcc -o aggc agg-tutorial.c $(pkg-config --libs --cflags libmongoc-1.0)./aggcIf you encounter connection errors by running the preceding commands in one call, you can run them separately.
The aggregation returns the following summary of customers' orders from 2020:
{ "product" : "Asus Laptop", "total_value" : { "$numberInt" : "860" }, "quantity" : { "$numberInt" : "2" }, "product_id" : "abc12345" }{ "product" : "Karcher Hose Set", "total_value" : { "$numberInt" : "66" }, "quantity" : { "$numberInt" : "3" }, "product_id" : "def45678" }{ "product" : "Morphy Richards Food Mixer", "total_value" : { "$numberInt" : "431" }, "quantity" : { "$numberInt" : "1" }, "product_id" : "pqr88223" }{ "product" : "Russell Hobbs Chrome Kettle", "total_value" : { "$numberInt" : "16" }, "quantity" : { "$numberInt" : "1" }, "product_id" : "xyz11228" }The result documents contain details about the total value and quantity of orders for products that cost more than $15.
First, add an $unwind stage to separate the entries in the products array into individual documents:
pipeline.unwind("$products");Next, add a $match stage that matches products with a products.price value greater than 15:
pipeline.match(bsoncxx::from_json(R"({ "products.price": { "$gt": 15 }})"));Add a $group stage to collect order documents by the value of the prod_id field. In this stage, add aggregation operations that create the following fields in the result documents:
product: the product name
total_value: the total value of all the sales of the product
quantity: the number of orders for the product
pipeline.group(bsoncxx::from_json(R"({ "_id": "$products.prod_id", "product": { "$first": "$products.name" }, "total_value": { "$sum": "$products.price" }, "quantity": { "$sum": 1 }})"));Add an $addFields stage to recreate the product_id field from the values in the _id field that were set during the $group stage:
pipeline.add_fields(bsoncxx::from_json(R"({ "product_id": "$_id"})"));Finally, add an $unset stage. The $unset stage removes the _id field from the result documents:
pipeline.append_stage(bsoncxx::from_json(R"({ "$unset": ["_id"]})"));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.outThe aggregation returns the following summary of customers' orders from 2020:
{ "product" : "Karcher Hose Set", "total_value" : 66, "quantity" : 3, "product_id" : "def45678" }{ "product" : "Asus Laptop", "total_value" : 860, "quantity" : 2, "product_id" : "abc12345" }{ "product" : "Morphy Richards Food Mixer", "total_value" : 431, "quantity" : 1, "product_id" : "pqr88223" }{ "product" : "Russell Hobbs Chrome Kettle", "total_value" : 16, "quantity" : 1, "product_id" : "xyz11228" }The result documents contain details about the total value and quantity of orders for products that cost more than $15.
First, start the aggregation on the orders collection and chain an $unwind stage to separate the entries in the Products array into individual documents:
var results = orders.Aggregate() .Unwind<Order, OrderUnwound>(o => o.Products)Next, add a $match stage that matches products with a Products.Price value greater than 15:
.Match(o => o.Products.Price > 15)Add a $group stage to collect order documents by the value of the ProductId field. In this stage, add aggregation operations that create the following fields in the result documents:
ProductId: the product ID (the grouping key)
Product: the product name
TotalValue: the total value of all the sales of the product
Quantity: the number of orders for the product
.Group( id: o => o.Products.ProductId, group: g => new { ProductId = g.Key, Product = g.First().Products.Name, TotalValue = g.Sum(o => o.Products.Price), Quantity = g.Count(), });Finally, run the application in your IDE and inspect the results.
The aggregation returns the following summary of customers' orders from 2020:
{ ProductId = pqr88223, Product = Morphy Richards Food Mixer, TotalValue = 431, Quantity = 1 }{ ProductId = xyz11228, Product = Russell Hobbs Chrome Kettle, TotalValue = 16, Quantity = 1 }{ ProductId = abc12345, Product = Asus Laptop, TotalValue = 860, Quantity = 2 }{ ProductId = def45678, Product = Karcher Hose Set, TotalValue = 66, Quantity = 3 }The result documents contain details about the total value and quantity of orders for products that cost more than $15.
First, add an $unwind stage to separate the entries in the products array into individual documents:
unwindStage := bson.D{{Key: "$unwind", Value: bson.D{ {Key: "path", Value: "$products"},}}}Next, add a $match stage that matches products with a products.price value greater than 15:
matchStage := bson.D{{Key: "$match", Value: bson.D{ {Key: "products.price", Value: bson.D{{Key: "$gt", Value: 15}}},}}}Add a $group stage to collect order documents by the value of the prod_id field. In this stage, add aggregation operations that create the following fields in the result documents:
product: the product name
total_value: the total value of all the sales of the product
quantity: the number of orders for the product
groupStage := bson.D{{Key: "$group", Value: bson.D{ {Key: "_id", Value: "$products.prod_id"}, {Key: "product", Value: bson.D{{Key: "$first", Value: "$products.name"}}}, {Key: "total_value", Value: bson.D{{Key: "$sum", Value: "$products.price"}}}, {Key: "quantity", Value: bson.D{{Key: "$sum", Value: 1}}},}}}Add a $set stage to recreate the product_id field from the values in the _id field that were set during the $group stage:
setStage := bson.D{{Key: "$set", Value: bson.D{ {Key: "product_id", Value: "$_id"},}}}Finally, add an $unset stage. The $unset stage removes the _id field from the result documents:
unsetStage := bson.D{{Key: "$unset", Value: bson.A{"_id"}}}Add the following code to the end of your application to perform the aggregation on the orders collection:
pipeline := mongo.Pipeline{unwindStage, matchStage, groupStage, setStage, unsetStage}cursor, err := orders.Aggregate(context.TODO(), pipeline)Finally, run the following command in your shell to start your application:
The aggregation returns the following summary of customers' orders from 2020:
{"product":"Morphy Richards Food Mixer","total_value":431,"quantity":1,"product_id":"pqr88223"}{"product":"Russell Hobbs Chrome Kettle","total_value":16,"quantity":1,"product_id":"xyz11228"}{"product":"Karcher Hose Set","total_value":66,"quantity":3,"product_id":"def45678"}{"product":"Asus Laptop","total_value":860,"quantity":2,"product_id":"abc12345"}The result documents contain details about the total value and quantity of orders for products that cost more than $15.
First, add an $unwind stage to separate the entries in the products array into individual documents:
pipeline.add(Aggregates.unwind("$products"));Next, add a $match stage that matches products with a products.price value greater than 15:
pipeline.add(Aggregates.match( Filters.gt("products.price", 15)));Add a $group stage to collect order documents by the value of the prod_id field. In this stage, add aggregation operations that create the following fields in the result documents:
product: the product name
total_value: the total value of all the sales of the product
quantity: the number of orders for the product
pipeline.add(Aggregates.group( "$products.prod_id", Accumulators.first("product", "$products.name"), Accumulators.sum("total_value", "$products.price"), Accumulators.sum("quantity", 1)));Add a $set stage to recreate the product_id field from the values in the _id field that were set during the $group stage:
pipeline.add(Aggregates.set(new Field<>("product_id", "$_id")));Finally, add an $unset stage. The $unset stage removes the _id field from the result documents:
pipeline.add(Aggregates.unset("_id"));Add the following code to the end of your application to perform the aggregation on the orders collection:
AggregateIterable<Document> aggregationResult = orders.aggregate(pipeline);Finally, run the application in your IDE.
The aggregation returns the following summary of customers' orders from 2020:
{"product": "Asus Laptop", "total_value": 860, "quantity": 2, "product_id": "abc12345"}{"product": "Russell Hobbs Chrome Kettle", "total_value": 16, "quantity": 1, "product_id": "xyz11228"}{"product": "Karcher Hose Set", "total_value": 66, "quantity": 3, "product_id": "def45678"}{"product": "Morphy Richards Food Mixer", "total_value": 431, "quantity": 1, "product_id": "pqr88223"}The result documents contain details about the total value and quantity of orders for products that cost more than $15.
First, add an $unwind stage to separate the entries in the products array into individual documents:
pipeline.add( Aggregates.unwind("\$${Order::products.name}"))Next, add a $match stage that matches products with a products.price value greater than 15:
pipeline.add( Aggregates.match( Filters.gt("${Order::products.name}.${Product::price.name}", 15) ))Add a $group stage to collect order documents by the value of the prodID field. In this stage, add aggregation operations that create the following fields in the result documents:
product: the product name
total_value: the total value of all the sales of the product
quantity: the number of orders for the product
pipeline.add( Aggregates.group( "\$${Order::products.name}.${Product::prodID.name}", Accumulators.first("product", "\$${Order::products.name}.${Product::name.name}"), Accumulators.sum("total_value", "\$${Order::products.name}.${Product::price.name}"), Accumulators.sum("quantity", 1) ))Add a $set stage to recreate the product_id field from the values in the _id field that were set during the $group stage:
pipeline.add(Aggregates.set(Field("product_id", "\$_id")))Finally, add an $unset stage. The $unset stage removes the _id field from the result documents:
pipeline.add(Aggregates.unset("_id"))Add the following code to the end of your application to perform the aggregation on the orders collection:
val aggregationResult = orders.aggregate<Document>(pipeline)Finally, run the application in your IDE.
The aggregation returns the following summary of customers' orders from 2020:
Document{{product=Russell Hobbs Chrome Kettle, total_value=16, quantity=1, product_id=xyz11228}}Document{{product=Karcher Hose Set, total_value=66, quantity=3, product_id=def45678}}Document{{product=Morphy Richards Food Mixer, total_value=431, quantity=1, product_id=pqr88223}}Document{{product=Asus Laptop, total_value=860, quantity=2, product_id=abc12345}}The result documents contain details about the total value and quantity of orders for products that cost more than $15.
First, add an $unwind stage to separate the entries in the products array into individual documents:
pipeline.push({ $unwind: { path: '$products', },});Next, add a $match stage that matches products with a products.price value greater than 15:
pipeline.push({ $match: { 'products.price': { $gt: 15, }, },});Add a $group stage to collect order documents by the value of the prod_id field. In this stage, add aggregation operations that create the following fields in the result documents:
product: the product name
total_value: the total value of all the sales of the product
quantity: the number of orders for the product
pipeline.push({ $group: { _id: '$products.prod_id', product: { $first: '$products.name' }, total_value: { $sum: '$products.price' }, quantity: { $sum: 1 }, },});Add a $set stage to recreate the product_id field from the values in the _id field that were set during the $group stage:
pipeline.push({ $set: { product_id: '$_id', },});Finally, add an $unset stage. The $unset stage removes the _id field from the result documents:
pipeline.push({ $unset: ['_id'] });Add the following code to the end of your application to perform the aggregation on the orders collection:
const aggregationResult = await orders.aggregate(pipeline);Finally, execute the code in the file using your IDE or the command line.
The aggregation returns the following summary of customers' orders from 2020:
{ product: 'Asus Laptop', total_value: 860, quantity: 2, product_id: 'abc12345'}{ product: 'Morphy Richards Food Mixer', total_value: 431, quantity: 1, product_id: 'pqr88223'}{ product: 'Russell Hobbs Chrome Kettle', total_value: 16, quantity: 1, product_id: 'xyz11228'}{ product: 'Karcher Hose Set', total_value: 66, quantity: 3, product_id: 'def45678'}The result documents contain details about the total value and quantity of orders for products that cost more than $15.
First, add an $unwind stage to separate the entries in the products array into individual documents:
Stage::unwind( path: Expression::arrayFieldPath('products')),Next, add a $match stage that matches products with a products.price value greater than 15:
Stage::match( ['products.price' => Query::gt(15)]),Outside of your Pipeline instance, create a $group stage in a factory function to collect order documents by the value of the prod_id field. In this stage, add aggregation operations that create the following fields in the result documents:
product: the product name
total_value: the total value of all the sales of the product
quantity: the number of orders for the product
function groupByProductStage(){ return Stage::group( _id: Expression::stringFieldPath('products.prod_id'), product: Accumulator::first( Expression::stringFieldPath('products.name') ), total_value: Accumulator::sum( Expression::numberFieldPath('products.price'), ), quantity: Accumulator::sum(1) );}Then, in your Pipeline instance, call the groupByProductStage() function:
Add a $set stage to recreate the product_id field from the values in the _id field that were set during the $group stage:
Stage::set(product_id: Expression::stringFieldPath('_id')),Finally, add an $unset stage. The $unset stage removes the _id field from the result documents:
Add the following code to the end of your application to perform the aggregation on the orders collection:
$cursor = $orders->aggregate($pipeline);Finally, run the following command in your shell to start your application:
The aggregation returns the following summary of customers' orders from 2020:
{ "product": "Russell Hobbs Chrome Kettle", "total_value": 16, "quantity": 1, "product_id": "xyz11228"}{ "product": "Asus Laptop", "total_value": 860, "quantity": 2, "product_id": "abc12345"}{ "product": "Karcher Hose Set", "total_value": 66, "quantity": 3, "product_id": "def45678"}{ "product": "Morphy Richards Food Mixer", "total_value": 431, "quantity": 1, "product_id": "pqr88223"}The result documents contain details about the total value and quantity of orders for products that cost more than $15.
First, add an $unwind stage to separate the entries in the products array into individual documents:
pipeline.append({"$unwind": {"path": "$products"}})Next, add a $match stage that matches products with a products.price value greater than 15:
pipeline.append({"$match": {"products.price": {"$gt": 15}}})Add a $group stage to collect order documents by the value of the prod_id field. In this stage, add aggregation operations that create the following fields in the result documents:
product: the product name
total_value: the total value of all the sales of the product
quantity: the number of orders for the product
pipeline.append( { "$group": { "_id": "$products.prod_id", "product": {"$first": "$products.name"}, "total_value": {"$sum": "$products.price"}, "quantity": {"$sum": 1}, } })Add a $set stage to recreate the product_id field from the values in the _id field that were set during the $group stage:
pipeline.append({"$set": {"product_id": "$_id"}})Finally, add an $unset stage. The $unset stage removes the _id field from the result documents:
pipeline.append({"$unset": ["_id"]})Add the following code to the end of your application to perform the aggregation on the orders collection:
aggregation_result = orders_coll.aggregate(pipeline)Finally, run the following command in your shell to start your application:
The aggregation returns the following summary of customers' orders from 2020:
{'product': 'Asus Laptop', 'total_value': 860, 'quantity': 2, 'product_id': 'abc12345'}{'product': 'Karcher Hose Set', 'total_value': 66, 'quantity': 3, 'product_id': 'def45678'}{'product': 'Morphy Richards Food Mixer', 'total_value': 431, 'quantity': 1, 'product_id': 'pqr88223'}{'product': 'Russell Hobbs Chrome Kettle', 'total_value': 16, 'quantity': 1, 'product_id': 'xyz11228'}The result documents contain details about the total value and quantity of orders for products that cost more than $15.
First, add an $unwind stage to separate the entries in the products array into individual documents:
{ "$unwind": { path: "$products", },},Next, add a $match stage that matches products with a products.price value greater than 15:
{ "$match": { "products.price": { "$gt": 15, }, },},Add a $group stage to collect order documents by the value of the prod_id field. In this stage, add aggregation operations that create the following fields in the result documents:
product: the product name
total_value: the total value of all the sales of the product
quantity: the number of orders for the product
{ "$group": { _id: "$products.prod_id", product: { "$first": "$products.name" }, total_value: { "$sum": "$products.price" }, quantity: { "$sum": 1 }, },},Add a $set stage to recreate the product_id field from the values in the _id field that were set during the $group stage:
{ "$set": { product_id: "$_id", },},Finally, add an $unset stage. The $unset stage removes the _id field from the result documents:
Add the following code to the end of your application to perform the aggregation on the orders collection:
aggregation_result = orders.aggregate(pipeline)Finally, run the following command in your shell to start your application:
The aggregation returns the following summary of customers' orders from 2020:
{"product"=>"Asus Laptop", "total_value"=>860, "quantity"=>2, "product_id"=>"abc12345"}{"product"=>"Russell Hobbs Chrome Kettle", "total_value"=>16, "quantity"=>1, "product_id"=>"xyz11228"}{"product"=>"Karcher Hose Set", "total_value"=>66, "quantity"=>3, "product_id"=>"def45678"}{"product"=>"Morphy Richards Food Mixer", "total_value"=>431, "quantity"=>1, "product_id"=>"pqr88223"}The result documents contain details about the total value and quantity of orders for products that cost more than $15.
First, add an $unwind stage to separate the entries in the products array into individual documents:
pipeline.push(doc! { "$unwind": { "path": "$products" }});Next, add a $match stage that matches products with a products.price value greater than 15:
pipeline.push(doc! { "$match": { "products.price": { "$gt": 15 } }});Add a $group stage to collect order documents by the value of the prod_id field. In this stage, add aggregation operations that create the following fields in the result documents:
product: the product name
total_value: the total value of all the sales of the product
quantity: the number of orders for the product
pipeline.push(doc! { "$group": { "_id": "$products.prod_id", "product": { "$first": "$products.name" }, "total_value": { "$sum": "$products.price" }, "quantity": { "$sum": 1 } }});Add a $set stage to recreate the prod_id field from the values in the _id field that were set during the $group stage:
pipeline.push(doc! { "$set": { "prod_id": "$_id" }});Finally, add an $unset stage. The $unset stage removes the _id field from the result documents:
pipeline.push(doc! { "$unset": ["_id"]});Add the following code to the end of your application to perform the aggregation on the orders collection:
let mut cursor = orders_coll.aggregate(pipeline).await?;Finally, run the following command in your shell to start your application:
The aggregation returns the following summary of customers' orders from 2020:
Document({"product": String("Russell Hobbs Chrome Kettle"), "total_value": Int32(16), "quantity": Int32(1),"prod_id": String("xyz11228")})Document({"product": String("Morphy Richards Food Mixer"), "total_value": Int32(431), "quantity": Int32(1),"prod_id": String("pqr88223")})Document({"product": String("Karcher Hose Set"), "total_value": Int32(66), "quantity": Int32(3),"prod_id": String("def45678")})Document({"product": String("Asus Laptop"), "total_value": Int32(860), "quantity": Int32(2),"prod_id": String("abc12345")})The result documents contain details about the total value and quantity of orders for products that cost more than $15.
First, add an $unwind stage to separate the entries in the products array into individual documents:
Aggregates.unwind("$products"),Next, add a $match stage that matches products with a products.price value greater than 15:
Aggregates.filter(Filters.gt("products.price", 15)),Add a $group stage to collect order documents by the value of the prod_id field. In this stage, add aggregation operations that create the following fields in the result documents:
product: the product name
total_value: the total value of all the sales of the product
quantity: the number of orders for the product
Aggregates.group( "$products.prod_id", Accumulators.first("product", "$products.name"), Accumulators.sum("total_value", "$products.price"), Accumulators.sum("quantity", 1)),Add a $set stage to recreate the product_id field from the values in the _id field that were set during the $group stage:
Aggregates.set(Field("product_id", "$_id")),Finally, add an $unset stage. The $unset stage removes the _id field from the result documents:
Add the following code to the end of your application to perform the aggregation on the orders collection:
orders.aggregate(pipeline) .subscribe((doc: Document) => println(doc.toJson()), (e: Throwable) => println(s"Error: $e"))Finally, run the application in your IDE.
The aggregation returns the following summary of customers' orders from 2020:
{"product": "Morphy Richards Food Mixer", "total_value": 431, "quantity": 1, "product_id": "pqr88223"}{"product": "Karcher Hose Set", "total_value": 66, "quantity": 3, "product_id": "def45678"}{"product": "Russell Hobbs Chrome Kettle", "total_value": 16, "quantity": 1, "product_id": "xyz11228"}{"product": "Asus Laptop", "total_value": 860, "quantity": 2, "product_id": "abc12345"}The result documents contain details about the total value and quantity of orders for products that cost more than $15.
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