Advanced Mongoose Queries Part 01

Mongoose, a popular ODM (Object Data Modeling) library for MongoDB in Node.js, provides powerful querying capabilities to work with complex document structures. In this article, we'll explore some advanced querying features in Mongoose, including $elemMatch, aggregation pipelines, and bulkWrite operations. These techniques are essential for building efficient and scalable applications.

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1. $elemMatch – Filtering Arrays of Subdocuments

Use Case

When documents contain arrays of objects (like orders), you might want to query based on specific key-value pairs within that array.

Example Schema

const CustomerSchema = new mongoose.Schema({
  name: String,
  is_active: Boolean,
  orders: [
    {
      product: String,
      quantity: Number,
      status: String,
    },
  ],
});

Query Example

const pendingOrders = await Customer.find({
  orders: {
    $elemMatch: {
      product: "Laptop",
      status: "pending",
    },
  },
});

Explanation

• $elemMatch ensures that at least one element in the orders array satisfies both conditions: product = 'Laptop' and status = 'pending'.
• Without $elemMatch, MongoDB might incorrectly match documents where one order has product: 'Laptop' and another has status: 'pending'.

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2. Aggregation Pipeline – Extracting Unique Values

Use Case

Extract all unique cities where customers have placed shipped orders.

Aggregation Query

const customerCitiesCursor = Customer.aggregate([
  { $match: { is_active: true } },
  { $unwind: "$orders" },
  { $match: { "orders.status": { $regex: /^shipped$/i } } },
  { $group: { _id: null, cities: { $addToSet: "$orders.city" } } },
]).cursor();

Step-by-Step Explanation

1. $match: Filters documents to include only active customers.
2. $unwind: Deconstructs the orders array so that each element becomes its own document.
3. $match: Filters orders where the status is 'shipped' (case-insensitive).
4. $group: Aggregates all matching cities into an array, using $addToSet to avoid duplicates.
5. .cursor(): Returns a cursor for memory-efficient streaming, which is useful when dealing with large datasets.

Result

A list of distinct cities where shipped orders exist.

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3. bulkWrite – Batch Write Operations

Use Case

Perform multiple write operations (insert, update, delete) in a single database call to improve performance.

Sample Usage

const bulkOps = [
  {
    updateOne: {
      filter: { name: "John Doe" },
      update: { $set: { is_active: false } },
    },
  },
  {
    deleteOne: {
      filter: { name: "Jane Smith" },
    },
  },
  {
    insertOne: {
      document: { name: "New Customer", is_active: true, orders: [] },
    },
  },
];

const result = await Customer.bulkWrite(bulkOps);

Explanation

• updateOne: Updates a single document matching the filter.
• deleteOne: Deletes one document.
• insertOne: Adds a new document.

Result Object

The result from bulkWrite contains details like:

• nInserted, nMatched, nModified, nDeleted
• upsertedIds, nUpserted

This allows you to monitor the impact of your batch operations.

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Summary

Advanced Mongoose queries like $elemMatch, aggregation pipelines, and bulkWrite enable you to:

• Perform precise filtering within arrays
• Extract and manipulate data efficiently
• Optimize performance with batched write operations

These techniques are vital when working with complex MongoDB data structures in production-grade Node.js applications.

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Bonus Tip

If you regularly use these patterns, consider wrapping them in reusable repository or service classes for cleaner and more maintainable code — especially in a NestJS architecture.