Unpack Arrays & Group Differently

Minimum MongoDB Version: 4.2

Scenario

You want to generate a retail report to list the total value and quantity of expensive products sold (valued over 15 dollars). The source data is a list of shop orders, where each order contains the set of products purchased as part of the order.

Sample Data Population

Drop any old version of the database (if it exists) and then populate a new orders collection where each document contains an array of products purchased:

use book-unpack-array-group-differently;
db.dropDatabase();

// Insert 4 records into the orders collection each with 1+ product items
db.orders.insertMany([
  {
    "order_id": 6363763262239,
    "products": [
      {
        "prod_id": "abc12345",
        "name": "Asus Laptop",
        "price": NumberDecimal("431.43"),
      },
      {
        "prod_id": "def45678",
        "name": "Karcher Hose Set",
        "price": NumberDecimal("22.13"),
      },
    ],
  },
  {
    "order_id": 1197372932325,
    "products": [
      {
        "prod_id": "abc12345",
        "name": "Asus Laptop",
        "price": NumberDecimal("429.99"),
      },
    ],
  },
  {
    "order_id": 9812343774839,
    "products": [
      {
        "prod_id": "pqr88223",
        "name": "Morphy Richardds Food Mixer",
        "price": NumberDecimal("431.43"),
      },
      {
        "prod_id": "def45678",
        "name": "Karcher Hose Set",
        "price": NumberDecimal("21.78"),
      },
    ],
  },
  {
    "order_id": 4433997244387,
    "products": [
      {
        "prod_id": "def45678",
        "name": "Karcher Hose Set",
        "price": NumberDecimal("23.43"),
      },
      {
        "prod_id": "jkl77336",
        "name": "Picky Pencil Sharpener",
        "price": NumberDecimal("0.67"),
      },
      {
        "prod_id": "xyz11228",
        "name": "Russell Hobbs Chrome Kettle",
        "price": NumberDecimal("15.76"),
      },
    ],
  },
]);

Aggregation Pipeline

Define a single pipeline ready to perform the aggregation:

var pipeline = [
  // Unpack each product from the each order's product as a new separate record
  {"$unwind": {
    "path": "$products",
  }},

  // Match only products valued greater than 15.00
  {"$match": {
    "products.price": {
      "$gt": NumberDecimal("15.00"),
    },
  }},
  
  // Group by product type, capturing each product's total value + quantity
  {"$group": {
    "_id": "$products.prod_id",
    "product": {"$first": "$products.name"},
    "total_value": {"$sum": "$products.price"},
    "quantity": {"$sum": 1},
  }},

  // Set product id to be the value of the field that was grouped on
  {"$set": {
    "product_id": "$_id",
  }},
  
  // Omit unwanted fields
  {"$unset": [
    "_id",
  ]},   
];

Execution

Execute the aggregation using the defined pipeline and also view its explain plan:

db.orders.aggregate(pipeline);
db.orders.explain("executionStats").aggregate(pipeline);

Expected Results

Four documents should be returned, representing only the four expensive products that were referenced multiple times in the customer orders, each showing the product's total order value and amount sold as shown below:

[
  {
    product_id: 'pqr88223',
    product: 'Morphy Richardds Food Mixer',
    total_value: NumberDecimal('431.43'),
    quantity: 1
  },
  {
    product_id: 'abc12345',
    product: 'Asus Laptop',
    total_value: NumberDecimal('861.42'),
    quantity: 2
  },
  {
    product_id: 'def45678',
    product: 'Karcher Hose Set',
    total_value: NumberDecimal('67.34'),
    quantity: 3
  },
  {
    product_id: 'xyz11228',
    product: 'Russell Hobbs Chrome Kettle',
    total_value: NumberDecimal('15.76'),
    quantity: 1
  }
]

Note, the order of fields shown for each document may vary.

Observations

  • Unwinding Arrays. The $unwind stage is a powerful concept, although often unfamiliar to many developers initially. Distilled down, it does one simple thing: it generates a new record for each element in an array field of every input document. If a source collection has 3 documents and each document contains an array of 4 elements, then performing an $unwind on each record's array field produces 12 records (3 x 4).

  • Introducing A Partial Match. The current example pipeline scans all documents in the collection and then filters out unpacked products where price > 15.00. If the pipeline executed this filter as the first stage, it would incorrectly produce some result product records with a value of 15 dollars or less. This would be the case for an order composed of both inexpensive and expensive products. However, you can still improve the pipeline by including an additional 'partial match' filter at the start of the pipeline for products valued at over 15 dollars. The aggregation could leverage an index (on products.price), resulting in a partial rather than full collection scan. This extra filter stage is beneficial if the input data set is large and many customer orders are for inexpensive items only. This approach is described in the chapter Pipeline Performance Considerations.