Top 10 Concepts to know when using MongoDB as a Beginner

If you’re starting to explore the world of NoSQL databases, you’ve landed in the right place. MongoDB is one of the most popular and powerful NoSQL databases available today. It’s designed for modern applications that require handling flexible data structures, scaling seamlessly, and integrating with technologies such as AI and real-time analytics.

What Makes MongoDB Stand Out?

It is a document-based model. Unlike traditional relational databases, it stores data in a flexible, JSON-like document, known as BSON or Binary JSON, which helps you move fast, iterate quickly, and build features without worrying about rigid schemas. BSON gives more edge over a JSON structure, and it allows you to store advanced datatypes like Decimal128, ObjectId, Date, and Binary.

In this article, we’ll walk you through the top 10 concepts every MongoDB beginner should know. From the basics of CRUD operations to powerful tools like aggregations, indexes, change streams, vector search, data federation, online archiving, and the features offered by MongoDB Atlas, we’ve got you covered.

By the end, you’ll have a strong foundation to build modern, scalable, and efficient applications with MongoDB.

1. Documents and Collections

Before we get into the details of learning deeper concepts, let's take the first step toward understanding how data is being stored inside MongoDB. As mentioned above, MongoDB stores JSON-like documents known as BSON. This provides the flexibility of JSON with the performance and type safety needed for a high-performance document database.

In MongoDB, the data stored is known as documents, and a group of documents is stored together inside the collections. With respect to relational databases, the below table will help you understand better:

Now that we know the naming conventions used in MongoDB for data being stored, in the next section, let us understand how to perform operations on the data.

2. CRUD Operations

One of the first concepts that a MongoDB beginner should understand is performing create, read, update, and delete (CRUD) operations with documents stored inside collections.

2.1 Create or Insert Documents

Use insertOne() or insertMany() to add new documents to a collection.

InsertOne():

db.collection.insertOne({
  name: "Alice",
  email: "[email protected]",
  age: 28
});

InsertMany():

db.collection.insertMany([{
  name: "Alice",
  email: "[email protected]",
  age: 28
},
{
  name: "Bob",
  email: "[email protected]",
  age: 29
}, 
{
  name: "Claire",
  email: "[email protected]",
  age: 35
}
]);

Also, it is important to remember that the _id, which acts as the primary key, is automatically inserted while inserting new documents. Hence, let MongoDB auto-generate the _id unless you have a compelling reason to use custom IDs.

2.2 Read Documents

Use find() or findOne() to read the documents from the collection.

db.collection.find({ age: { $gt: 25 } }); 
// This finds all documents where age is > 25

To have a better query performance, it is always recommended to use indexed fields in queries.

2.3 Update Documents

Use updateOne() or updateMany() to modify existing documents.

db.collection.updateOne(
  { email: "[email protected]" },
  { $set: { age: 29 } }
);

2.4 Delete Documents

Use deleteOne() or deleteMany() to remove documents from a collection.

db.collection.deleteMany({ age: { $lt: 18 } }); // Deletes underage users

Now that you know how basic queries work, it's time we dive a little deeper and explore operators, projections, and filtering.

3. Query Language and Filters

Querying with MongoDB documents is expressive, flexible, and designed to work naturally with JSON-like documents. MongoDB's query language is built to handle both simple and complex data models with ease using the operators.

Let us discuss a few operators which are commonly used in MongoDB queries.

3.1 Comparison Operators

MongoDB provides rich comparison operators that let you filter results based on conditions.

3.2 Logical Operators

Common logical operators include:

$and, $or, $nor, $not

Example: To find all documents where age is greater than equal 25 AND status is active:

db.collection.find({
  $and: [
    { age: { $gte: 25 } },
    { status: "active" }
  ]
});

3.3 Projections

In order to process only documents that are required as part of the query response, making use of projections is preferred. To do so, you can use:

db.collections.find(
  { age: { $gt: 25 } },
  { name: 1, email: 1, _id: 0 }
);

4. Working with Arrays and Subdocuments

In addition to MongoDB storing regular JSON-type documents, it also stores sub-documents and arrays to store information. Let us understand how you would query inside a subdocument and in an array.

Let’s assume we need to find users whose native city is New York. To do so, we can use dot(.) notation.

```db.users.find({ "address.city": "New York" });```

Similarly, for querying inside arrays, we can make use of $elemMatch to find the document. Example:

```db.users.find({
  hobbies: { $elemMatch: { name: "gaming", frequency: "daily" } }
});```

Read more about query operators in MongoDB.

While querying documents in MongoDB is powerful and flexible, it's equally important to consider query performance, especially as your data grows. Writing efficient queries is only part of the equation; optimizing them requires a solid understanding of indexes.

In the next section, we’ll explore the different types of indexes MongoDB supports and how they can significantly improve the speed and efficiency of your queries. Whether you're filtering, sorting, or joining data, indexes play a critical role in building high-performance applications.

5. Indexes

When an application moves to production, data volume naturally increases, and performance becomes a key priority. While well-written queries play an important role, most performance gains at this stage come from using indexes effectively. That said, we also assume that the data model has been thoughtfully designed to support the application's access patterns.

• Indexes in MongoDB are special data structures that store a subset of your collection’s data in a way that makes it faster to search.
• Under the hood, MongoDB uses a B+ tree data structure to implement these indexes, allowing it to quickly traverse and locate matching values.
• Without indexes, MongoDB has no choice but to perform a collection scan, checking each document in the collection one by one.
• This can be very costly in terms of performance, especially as the dataset grows.
• However, when indexes are applied to the right fields, MongoDB can narrow down its search to a much smaller subset of documents. This significantly reduces query time and improves the overall responsiveness of your application.
• It’s important to note, though, that indexing every field is not a good practice. Each index consumes additional memory and slows down write operations. The key is to index strategically, focusing on fields that are frequently used in query filters, sorting, or join operations.
• MongoDB allows different types of indexes to be created and each index has its own advantage.

The below table summarises different types of indexes and their advantages in MongoDB. Learn more about indexes in the official MongoDB documentation.

6. Aggregation Framework

Now that you know how to query data and improve the performance of the application, it’s time we understand one of the greatest features of MongoDB: Aggregations.

• Aggregations in MongoDB allow you to process and transform data directly within the database.
• You can generate totals and reports, reshape documents, filter complex datasets, and much more without the need to push the data to the application layer.
• Aggregations are pipelines in MongoDB, which means a sequence of stages where each stage transforms the data before passing it to the next.

The below diagram represents aggregation stages:

Let us understand a few stages of an aggregation:

6.1 $match: This stage filters documents, allowing only those that satisfy the specified condition(s) to proceed to the subsequent pipeline stage.

• Common use cases: Utilized for pre-aggregation data filtering and for the reduction of the document set requiring processing.

6.2 $project: This stage reshapes each document within the data stream, enabling the inclusion or exclusion of fields, as well as the addition of new fields.

• Common use cases: Employed for the selection of specific fields, the renaming of existing fields, and the creation of computed fields.

6.3 $group: This stage groups documents based on a designated identifier expression and subsequently applies accumulator expressions to each defined group.

• Common use cases: Applied for the computation of aggregate values such as sums, averages, counts, minimums, and maximums across grouped data.

6.4 $sort: This stage sorts all incoming documents and returns them in the specified order.

• Common use cases: Used for ordering results for presentation or for subsequent processing operations.

6.5 $limit: This stage passes only the initial 'n' documents to the subsequent stage.

• Common use cases: Implemented for constraining the number of results returned, particularly in "top N" query scenarios.

6.6 $skip: This stage bypasses the initial 'n' documents and forwards the remaining documents to the next stage.

• Common use cases: Used for ordering results for presentation or for subsequent processing operations.

6.7 $unwind: This stage deconstructs an array field from the input documents, generating a separate document for each element of the array.

• Common use cases: Utilized for flattening array fields to facilitate processing and for performing operations on individual elements within an array.

6.8 $lookup: This stage executes a left outer join operation with an unsharded collection residing within the same database, thereby incorporating relevant documents from the "joined" collection for processing.

• Common use cases: Applied for integrating data from distinct collections, thereby facilitating a unified view of interconnected data.

6.9 $out: This stage writes the results of the aggregation pipeline to a specified collection.

• Common use cases: Utilized for the persistent storage of aggregation outcomes for reporting or further analytical purposes, and for the creation of materialized views.

6.10 $merge: This stage writes the aggregated results to a designated collection. The '$merge' operator additionally offers the capability to integrate the results into an existing collection, allowing for more flexible management of output data.

• Common use cases: Similar to the '$out' stage, but providing enhanced flexibility through various merge strategies, including insertion, replacement, retention of existing documents, and merging operations into the target collection.

7. Schema Design and Data Validation

By now, if you have not yet fallen in love with MongoDB, you’re about to. In this section, we will be talking about the flexible schema and the advantages that it has in modern day development.

• This flexibility is powerful, but it also puts the responsibility on you, as the developer, to design your data models thoughtfully.
• A well-designed schema can drastically improve performance, reduce complexity, and make your application easier to maintain.
• While modelling data, MongoDB follows a rule of thumb: Data that is accessed together is stored together.

There are two ways to store related data: embedding and referencing.

7.1 Embedding

Embedding is the practice of storing related data within a single document. For example, a `user` document might embed their `address` and `contact` information directly within it.

db.collection.insertOne({
  name: "Jane Doe",
  email: "[email protected]",
  address: {
    street: "123 Main St",
    city: "Anytown",
    zip: "12345"
  },
  contact: {
    phone: "555-1234",
    emergency: {
      name: "John Doe",
      phone: "555-5678"
    }
  }
});

7.2 Referencing

Referencing, also known as normalization, is the practice of storing related data in separate documents and linking them using references (typically the `_id` of the referenced document). This is similar to foreign keys in relational databases. Let’s do the same example using referencing:

collection:

db.collection.insertOne({
  _id: 1,
  name: "Jane Doe",
  email: "[email protected]",
  addressId: 101 // Reference to the address document
});

addresses:

// Address Document
db.addresses.insertOne({
  _id: 101,
  street: "123 Main St",
  city: "Anytown",
  zip: "12345"
});```

To embed or to reference is always a question when an application developer plans for schema designs. The solution is it always depends on how you want to access data and what data is most frequently accessed. To learn more about embedding versus referencing, you can refer to the documentation page, which also provides examples.

8. Change Streams

In the previous section, we saw very briefly how the flexible storage capability of MongoDB makes it an ideal match for modern data applications. Now, these modern data applications also expect the application to react with data changing over time.

MongoDB’s Change Streams make this possible by allowing your application to listen for changes in the database as they happen. With Change Streams, you can subscribe to real-time events like document inserts, updates, deletes, and more, without polling or additional overhead.

• The applications leverage change streams to subscribe to real-time changes.
• These could be on a collection, an entire database, or even across the entire deployment.
• Since the change streams are built on top of MongoDB’s aggregation framework, one can go beyond simply listening and can also filter for specific operations or transform the event data before it reaches your application.

To store data which changes with time, the perfect data structure will be time series collections.

9. Time series data

MongoDB’s time series collections are designed to store data specifically which changes over time, for example, sensor data, logs, or performance metrics. It’s comprised of three major components:

• Time: When the data is recorded.
• Metadata: This is stored as a metaField. It is a label or tag that identifies a data series and rarely changes.
• Measurements: These are the data points tracked at increments in time. Generally, these are key-value pairs that change over time.

The time series data is stored inside a time series collection where the writes are organized so that data from the same source is stored alongside other data points from a similar point in time.

Advantages:

• Optimized for insert-heavy workloads
• Built-in support for data expiration
• Ideal for monitoring, IoT, financial, and analytics use cases

To create a time series collection:

db.createCollection(
   "weather",
   {
      timeseries: {
         timeField: "timestamp",
         metaField: "metadata",
         granularity: "seconds"
      },
      expireAfterSeconds: 86400
   }
)

10. MongoDB and AI

In the previous sections, we explored how MongoDB supports modern application development with flexibility, scalability, and performance. Now, let’s take it a step further and look at how AI-powered applications can benefit from MongoDB’s capabilities.

From enabling fast and intelligent text search to powering advanced vector search for semantic understanding, MongoDB offers a solid foundation for building AI-driven features, whether you're working on chatbots, recommendation systems, or natural language processing tasks.

Atlas Search:

• MongoDB has the support for full-text searches, allowing users to index and search string content within the documents. Without the need to have an explicit service for search capability for the applications, MongoDB’s Atlas Search feature allows you to build applications like keyword search, blog post search, product lookup, or FAQ search all within the same databases.
• This feature allows you to perform keyword search, language search supported for around 15 different languages, autocomplete, and fuzzy search.
• If you wish to learn more about how to use them, you can play around on Atlas Search Playground. Also, visit our blog for a better idea of how to build applications with MongoDB’s Atlas Search.

Vector Search:

• MongoDB’s search capability isn’t limited to fuzzy search. Rather, it expands and gives the capability to perform semantic search. This is possible with MongoDB’s Vector Search.
• Atlas Vector Search enables you to query data based on its semantic meaning, combine vector search with full-text search, and filter your queries on other fields in your collection, so you can retrieve the most relevant results for your use case.
• To perform vector search in MongoDB, create a vector search index on the Atlas cluster. These are different from regular indexes and allow semantic search on the data.

To create a vector search index:

{
  "fields":[
    {
      "type": "vector",
      "path": "<field-to-index>",
      "numDimensions": <number-of-dimensions>,
      "similarity": "euclidean | cosine | dotProduct"
    }]
}

The search will be performed using the $search aggregation operation:

{
  "$vectorSearch": {
    "exact": true | false,
    "filter": {<filter-specification>},
    "index": "<index-name>",
    "limit": <number-of-results>,
    "numCandidates": <number-of-candidates>,
    "path": "<field-to-search>",
    "queryVector": [<array-of-numbers>]
  }
}

Therefore, MongoDB handles the approximate nearest neighbor search behind the scenes, making it scalable and fast for production AI use cases.

MongoDB Atlas and Cloud Tooling

With all of these capabilities and scaling applications, managing the database becomes increasingly complex. That is where the MongoDB Atlas comes in.

MongoDB Atlas is a fully managed cloud database service that takes care of deployment, scaling, performance tuning, backups, and security so you can focus on building applications, not managing infrastructure. With a great UI and CLI support, it provides support for all major cloud providers, like Amazon AWS, Google Cloud, and Microsoft Azure.

With MongoDB Atlas, and with just a few clicks (or lines of code), you can:

• Deploy a MongoDB cluster in the cloud.
• Scale vertically or horizontally.
• Monitor performance with built-in dashboards.
• Set up backups and restore points.
• Enforce security with IP whitelisting, encryption, and access controls.

Other major capabilities that MongoDB Atlas provides are:

• Search indexes: On MongoDB Atlas, add powerful, full-text search capabilities to your application, backed by Lucene, so there is no need to run a separate search engine.
• Vector Search: Store and query vector embeddings natively in MongoDB.
• Atlas Charts: On MongoDB Atlas, you can also visualise your data using Charts.
• Online archival: Store data on different storage spaces, like that of Amazon S3, when it is no longer accessed or is archived.
• Data federation: MongoDB Atlas also gives the capability to access data across different MongoDB clusters and storage sources (like S3) using a single connection and query.

With all these capabilities, MongoDB Atlas makes everything seamless.

Conclusion

MongoDB gives you the flexibility to model data your way, the tools to scale without friction, and the power to innovate without limits. By now, we have explored the top concepts every beginner should know when working with MongoDB.

But this is just the beginning!

MongoDB is constantly evolving to meet the needs of modern developers building fast, flexible, and intelligent applications. As you continue learning, you’ll uncover even more ways MongoDB can support your use cases—from real-time analytics and IoT to semantic search and gen AI.

MongoDB has a lot to offer, and each of the capabilities can be learned in depth and through different skill badges, official documentation, and MongoDB University certifications and full trained programs.

Skills like these will help you reinforce what you've learned, showcase your progress, and flaunt the badges on your socials.