Add mongodb content

bin/roadmap-content.cjs

@@ -111,8 +111,13 @@ async function run() {
     const currTopicUrl = topicId.replace(/^\d+-/g, '/').replace(/:/g, '/');
     const contentFilePath = topicUrlToPathMapping[currTopicUrl];
 
+    if (!contentFilePath) {
+      console.log(`Missing file for: ${currTopicUrl}`);
+      return;
+    }
+
     const currentFileContent = fs.readFileSync(contentFilePath, 'utf8');
-    const isFileEmpty = currentFileContent.replace(/^#.+/, ``).trim() == '';
+    const isFileEmpty = currentFileContent.replace(/^#.+/, ``).trim() === '';
 
     if (!isFileEmpty) {
       console.log(`Ignoring ${topicId}. Not empty.`);

src/data/roadmaps/mongodb/content/100-mongodb-basics/100-sql-vs-nosql.md

@@ -1 +1,41 @@
-# Sql vs nosql
+# SQL vs NoSQL
+
+When discussing databases, it's essential to understand the difference between SQL and NoSQL databases, as each has its own set of advantages and limitations. In this section, we'll briefly compare and contrast the two, so you can determine which one suits your needs better.
+
+## SQL Databases
+
+SQL (Structured Query Language) databases are also known as relational databases. They have a predefined schema, and data is stored in tables consisting of rows and columns. SQL databases follow the ACID (Atomicity, Consistency, Isolation, Durability) properties to ensure reliable transactions. Some popular SQL databases include MySQL, PostgreSQL, and Microsoft SQL Server.
+
+**Advantages of SQL databases:**
+
+- **Predefined schema**: Ideal for applications with a fixed structure.
+- **ACID transactions**: Ensures data consistency and reliability.
+- **Support for complex queries**: Rich SQL queries can handle complex data relationships and aggregation operations.
+- **Scalability**: Vertical scaling by adding more resources to the server (e.g., RAM, CPU).
+
+**Limitations of SQL databases:**
+
+- **Rigid schema**: Data structure updates are time-consuming and can lead to downtime.
+- **Scaling**: Difficulties in horizontal scaling and sharding of data across multiple servers.
+- **Not well-suited for hierarchical data**: Requires multiple tables and JOINs to model tree-like structures.
+
+## NoSQL Databases
+
+NoSQL (Not only SQL) databases refer to non-relational databases, which don't follow a fixed schema for data storage. Instead, they use a flexible and semi-structured format like JSON documents, key-value pairs, or graphs. MongoDB, Cassandra, Redis, and Couchbase are some popular NoSQL databases.
+
+**Advantages of NoSQL databases:**
+
+- **Flexible schema**: Easily adapts to changes without disrupting the application.
+- **Scalability**: Horizontal scaling by partitioning data across multiple servers (sharding).
+- **Fast**: Designed for faster read and writes, often with a simpler query language.
+- **Handling large volumes of data**: Better suited to managing big data and real-time applications.
+- **Support for various data structures**: Different NoSQL databases cater to various needs, like document, graph, or key-value stores.
+
+**Limitations of NoSQL databases:**
+
+- **Limited query capabilities**: Some NoSQL databases lack complex query and aggregation support or use specific query languages.
+- **Weaker consistency**: Many NoSQL databases follow the BASE (Basically Available, Soft state, Eventual consistency) properties that provide weaker consistency guarantees than ACID-compliant databases.
+
+## MongoDB: A NoSQL Database
+
+This guide focuses on MongoDB, a popular NoSQL database that uses a document-based data model. MongoDB has been designed with flexibility, performance, and scalability in mind. With its JSON-like data format (BSON) and powerful querying capabilities, MongoDB is an excellent choice for modern applications dealing with diverse and large-scale data.

src/data/roadmaps/mongodb/content/100-mongodb-basics/101-what-is-mongodb.md

@@ -1 +1,33 @@
-# What is mongodb
+# What is MongoDB
+
+MongoDB is an open-source, document-based, and cross-platform NoSQL database that offers high performance, high availability, and easy scalability. It differs from traditional relational databases by utilizing a flexible, schema-less data model built on top of BSON (Binary JSON), allowing for non-structured data to be easily stored and queried.
+
+## Key Features of MongoDB
+
+- **Document-oriented**: MongoDB stores data in JSON-like documents (BSON format), meaning that the data model is very flexible and can adapt to real-world object representations easily.
+
+- **Scalability**: MongoDB offers automatic scaling, as it can be scaled horizontally by sharding (partitioning data across multiple servers) and vertically by adding storage capacity.
+
+- **Indexing**: To enhance query performance, MongoDB supports indexing on any attribute within a document.
+
+- **Replication**: MongoDB provides high availability through replica sets, which are primary and secondary nodes that maintain copies of the data.
+
+- **Aggregation**: MongoDB features a powerful aggregation framework to perform complex data operations, such as transformations, filtering, and sorting.
+
+- **Support for ad hoc queries**: MongoDB supports searching by field, range, and regular expression queries.
+
+## When to use MongoDB
+
+MongoDB is a suitable choice for various applications, including:
+
+- **Big Data**: MongoDB's flexible data model and horizontal scalability make it a great fit for managing large volumes of unstructured or semi-structured data.
+
+- **Real-time analytics**: MongoDB's aggregation framework and indexing capabilities help analyze and process data in real-time.
+
+- **Content management**: With its dynamic schema, MongoDB can handle diverse content types, making it a suitable choice for content management systems.
+
+- **Internet of Things (IoT) applications**: MongoDB can capture and store data from a large number of devices and sensors, proving beneficial in IoT scenarios.
+
+- **Mobile applications**: MongoDB provides a flexible data model, which is an essential requirement for the dynamic nature and varying data types of mobile applications.
+
+In conclusion, MongoDB is a powerful and versatile NoSQL database that can efficiently handle unstructured and semi-structured data, making it an excellent choice for various applications and industries.

src/data/roadmaps/mongodb/content/100-mongodb-basics/102-when-to-use-mongodb.md

@@ -1 +1,31 @@
-# When to use mongodb
+# When to use MongoDB?
+
+MongoDB is an ideal database solution in various scenarios. Let's discuss some of the key situations when you should consider using MongoDB.
+
+##  Handling Large Volumes of Data
+
+When dealing with large amounts of data that may require extensive read and write operations, MongoDB is an excellent choice due to its high performance and horizontal scaling. By leveraging replication and sharding, you can distribute data across multiple servers, reducing the workload on a single machine.
+
+##  Flexible Schema
+
+If your application requires a flexible data model that allows for changes in the data structure over time, MongoDB is a suitable choice. This flexibility comes from its document-based structure, which allows developers to store any JSON-like data without the need to define the schema beforehand.
+
+##  High Availability
+
+MongoDB's built-in replication feature allows you to create multiple copies of your data, ensuring high availability and fault tolerance. This means your application will remain accessible in the event of hardware failure or data center outages.
+
+##  Real-Time Analytics & Reporting
+
+MongoDB offers excellent support for real-time analytics and reporting. With its aggregation pipeline and map-reduce functionality, you can extract valuable insights from your data and perform complex data manipulations easily.
+
+##  Geo-spatial Queries
+
+If your application deals with location-based data, MongoDB provides built-in support for geospatial indexing and querying. This makes it easier to work with location-based services and applications, such as GPS tracking or location-based search features.
+
+##  Rapid Application Development
+
+Due to its flexibility and ease of use, MongoDB is a good choice for startups and agile development teams that require quick iterations and frequent schema changes. It allows developers to focus on implementing features without the burden of managing rigid database structures.
+
+## Summary
+
+In conclusion, you should consider using MongoDB when dealing with large volumes of data, requiring a flexible schema, needing high availability, handling location-based data, or aiming for rapid application development. However, always evaluate its suitability based on your specific project requirements and performance goals.

src/data/roadmaps/mongodb/content/100-mongodb-basics/103-what-is-mongodb-atlas.md

@@ -1 +1,19 @@
-# What is mongodb atlas
+# What is MongoDB Atlas?
+
+MongoDB Atlas is a fully managed cloud-based database service built and maintained by MongoDB. The Atlas platform is available on major cloud providers like AWS, Azure, and Google Cloud Platform, allowing developers to deploy, manage, and scale their MongoDB clusters in a seamless and efficient manner. 
+
+Some of the standout features and benefits of MongoDB Atlas include:
+
+- **Database as a Service (DBaaS)**: MongoDB Atlas takes care of database-related operations like backups, monitoring, scaling, and security, allowing developers to focus on their application logic.
+
+- **Global Cluster Support**: Atlas enables the creation of globally distributed clusters. Data can be stored and replicated across multiple geographies for improved performance, high availability, and reduced latency.
+
+- **Security**: Atlas offers built-in security features, such as end-to-end encryption, role-based access control, and IP whitelisting. This ensures your data remains secure and compliant with industry standards.
+
+- **Performance**: MongoDB Atlas provides tools for monitoring and optimizing the performance of your database. Advanced features like performance advisor and index suggestions help keep your database running at optimal speed.
+
+- **Easy Scaling**: With Atlas, you can easily scale your cluster either vertically or horizontally, depending on your requirements. Atlas supports auto-scaling of both storage and compute resources.
+
+- **Data Automation and Integration**: Atlas allows seamless integration with other services, like BI tools and serverless functions. The platform also supports easy data migration from on-premises or cloud-based deployments.
+
+To summarize, MongoDB Atlas is a powerful and versatile database service that simplifies and enhances the process of deploying, managing, and scaling MongoDB instances in the cloud. With its robust set of features and security capabilities, Atlas is an ideal choice for developers who want to build and maintain scalable and efficient applications using MongoDB.

src/data/roadmaps/mongodb/content/100-mongodb-basics/104-mongodb-terminology.md

@@ -1 +1,25 @@
-# Mongodb terminology
+# MongoDB Terminology
+
+This section of the guide will introduce you to the basic terminology used while working with MongoDB. Understanding these terms will help you to grasp the fundamentals of MongoDB and make it easier for you to follow along with the rest of the guide.
+
+## MongoDB Terminology
+
+- **Database:** A MongoDB database is used to store and manage a set of collections. It consists of various collections, indexes, and other essential data structures required to store the data efficiently.
+
+- **Collection:** A collection in MongoDB is a group of documents. The name of a collection must be unique within its database. Collections can be viewed as the table equivalencies in a relational database.
+
+- **Document:** A document is a record in a MongoDB collection. It is comprised of a set of fields, similar to a row in a relational database. However, unlike tables in a relational database, no schema or specific structure is enforced on the documents within a collection.
+
+- **Field:** A field in MongoDB is a key-value pair inside a document. It can store various types of data, including strings, numbers, arrays, and other documents. Fields in MongoDB can be seen as columns in a relational database.
+
+- **Index:** Indexes in MongoDB are data structures that improve the speed of common search operations. They store a small portion of the dataset in a well-organized structure. This structure allows MongoDB to search and sort documents faster by reducing the number of documents it has to scan.
+
+- **Query:** A query in MongoDB is used to retrieve data from the database. It retrieves specific documents or subsets of documents from a collection based on a given condition.
+
+- **Cursor:** A cursor is a pointer to the result set of a query. It allows developers to process individual documents from the result set in an efficient manner.
+
+- **Aggregation:** Aggregation in MongoDB is the process of summarizing and transforming the data stored in collections. It is used to run complex analytical operations on the dataset or create summary reports.
+
+- **Replica Set:** A replica set in MongoDB is a group of mongodb instances that maintain the same data set. It provides redundancy, high availability, and automatic failover in case the primary node becomes unreachable.
+
+- **Sharding:** Sharding is a method of distributing data across multiple machines. It is used in MongoDB to horizontally scale the database by partitioning the dataset into smaller, more manageable chunks called shards.

src/data/roadmaps/mongodb/content/100-mongodb-basics/index.md

@@ -1 +1,34 @@
-# Mongodb basics
+# MongoDB Basics
+
+MongoDB is a popular NoSQL database system that stores data in Flexible JSON-like documents, making it suitable for working with large scale and unstructured data.
+
+## Key MongoDB Concepts
+
+- **Database**: Stores all your collections within a MongoDB instance.
+- **Collection**: A group of related documents, similar to a table in a relational database.
+- **Document**: A single record within a collection, which is stored as BSON (Binary JSON) format.
+- **Field**: A key-value pair within a document.
+- **_id**: A unique identifier automatically generated for each document within a collection.
+
+## Basic Operations
+
+* **Insert**: To insert a single document, use `db.collection.insertOne()`. For inserting multiple documents, use `db.collection.insertMany()`.
+* **Find**: Fetch documents from a collection using `db.collection.find()`, and filter the results with query criteria like `{field: value}`. To fetch only one document, use `db.collection.findOne()`.
+* **Update**: Update fields or entire documents by using update operators like `$set` and `$unset` with `db.collection.updateOne()` or `db.collection.updateMany()`.
+* **Delete**: Remove documents from a collection using `db.collection.deleteOne()` or `db.collection.deleteMany()` with query criteria.
+* **Drop**: Permanently delete a collection or a database using `db.collection.drop()` and `db.dropDatabase()`.
+
+## Indexes and Aggregations
+
+- **Indexes**: Improve the performance of searches by creating indexes on fields within a collection using `db.collection.createIndex()` or build compound indexes for querying multiple fields.
+- **Aggregations**: Perform complex data processing tasks like filtering, grouping, transforming, and sorting using aggregation operations like `$match`, `$group`, `$project`, and `$sort`.
+
+## Data Modeling
+
+MongoDB's flexible schema allows for various data modeling techniques, including:
+
+- **Embedded Documents**: Store related data together in a single document, which is suitable for one-to-one or one-to-few relationships.
+- **Normalization**: Store related data in separate documents with references between them, suitable for one-to-many or many-to-many relationships.
+- **Hybrid Approach**: Combine embedded documents and normalization to balance performance and storage needs.
+
+In conclusion, MongoDB's flexible and feature-rich design makes it a powerful choice for modern applications dealing with large scale and unstructured data. Understanding the basics of MongoDB can help you effectively use it as your data storage solution.

src/data/roadmaps/mongodb/content/101-datatypes/100-bson-vs-json.md

@@ -1 +1,28 @@
-# Bson vs json
+# BSON vs JSON
+
+In MongoDB, data is stored in a binary format called BSON (Binary JSON), which is a superset of JSON (JavaScript Object Notation). While both BSON and JSON are used to represent data in MongoDB, they have some key differences.
+
+## BSON
+
+BSON is a binary-encoded serialization of JSON-like documents. It is designed to be efficient in storage, traversability, and encoding/decoding. Some of its key features include:
+
+- **Binary Encoding**: BSON encodes data in a binary format, which offers better performance and allows the storage of data types not supported by JSON.
+- **Support for Additional Data Types**: BSON supports more data types compared to JSON, such as `Date`, `Binary`, `ObjectId`, and `Decimal128`. This makes it possible to represent diverse data more accurately in MongoDB documents.
+- **Efficient Traversability**: In BSON, the size of each element is encoded, which makes it easy to skip over elements, thus making the traversal faster.
+
+## JSON
+
+JSON is a lightweight and human-readable data representation format that can be easily parsed and generated by many programming languages. It is used widely as a medium for transmitting data over the web. Some features of JSON include:
+
+- **Human-readable**: JSON is textual with a simple structure, making it easy for humans to read and write.
+- **Interoperable**: JSON can be easily parsed and generated by many different programming languages, making it a popular choice for data interchange between applications.
+- **Limited Data Types**: JSON supports fewer data types compared to BSON, such as strings, numbers, booleans, and `null`. This means that some data, like dates or binary data, must be represented as strings or custom objects in JSON.
+
+## Summary
+
+While BSON and JSON are related, they serve different purposes in the context of MongoDB:
+
+- BSON is the binary format used by MongoDB to store and retrieve data efficiently with support for additional native data types.
+- JSON, being a more human-readable and widely used format, is typically used for data interchange between MongoDB and applications.
+
+By using BSON internally, MongoDB can take advantage of its benefits in storage, traversability, and a richer data type representation while still providing the interoperability and readability of JSON through query interfaces and drivers.

src/data/roadmaps/mongodb/content/101-datatypes/101-embedded-documents-arrays.md

@@ -1 +1,61 @@
-# Embedded documents arrays
+# Embedded Documents and Arrays
+
+In MongoDB, one of the powerful features is the ability to store complex data structures like Embedded Documents Arrays. These are essentially arrays of sub-documents (also known as nested documents) that can be stored within a single document. This allows us to model complex data relationships in a highly efficient way while maintaining good performance.
+
+## What are Embedded Documents Arrays?
+
+Embedded Documents Arrays are used when you need to represent a 'one-to-many' or hierarchical relationship between data. Instead of using separate collections and references, you can embed the related documents directly into the main document using an array.
+
+Here's an example of a document containing an embedded array of sub-documents:
+
+```javascript
+{
+    _id: 1,
+    name: 'John Doe',
+    addresses: [
+        {
+            street: '123 Main St',
+            city: 'New York',
+            zipcode: '10001'
+        },
+        {
+            street: '456 Broadway',
+            city: 'Los Angeles',
+            zipcode: '90001'
+        }
+    ]
+}
+```
+
+In this example, the `addresses` field represents an array of embedded sub-documents that contain the address details for the user.
+
+## Advantages
+
+Embedded Documents Arrays offer a few key advantages:
+
+- **Read/Write Performance**: Since related data is stored together within the same document, read and write operations can be faster, as they don't require multiple queries or updates.
+- **Data Consistency**: By storing related data together, you can easily maintain consistency and ensure that related data is always in-sync without having to rely on joins or cross-references.
+- **Scalability**: Embedded arrays can be nested, allowing you to represent complex data structures while maintaining the benefits of a flexible schema and high performance.
+
+## When to Use Embedded Documents Arrays
+
+Consider using Embedded Documents Arrays when:
+
+- You have a one-to-many relationship
+- The embedded data does not grow unbounded
+- The embedded data is strongly related to the parent document
+- You can benefit from improved read/write performance
+
+Keep in mind that MongoDB has a document size limitation of 16MB, so if you expect the embedded data to grow over time, you should consider alternative approaches, such as using separate collections and referencing them instead.
+
+## Querying Embedded Documents Arrays
+
+Querying documents with embedded arrays is easy thanks to MongoDB's built-in array query operators, such as `$elemMatch`, `$all`, and `$size`. You can also use dot notation to search and update embedded sub-documents.
+
+For example, to find all users with a specific street address, you would use the following query:
+
+```javascript
+db.users.find({'addresses.street': '123 Main St'})
+```
+
+Overall, Embedded Documents Arrays are a powerful feature in MongoDB, allowing you to store complex data relationships in a performant and efficient manner. Use them wisely to take full advantage of MongoDB's flexibility and scalability.

src/data/roadmaps/mongodb/content/101-datatypes/102-double.md

@@ -1 +1,37 @@
-# Double
+# Double
+
+As a NoSQL database, MongoDB supports a wide range of data types that make it highly versatile for various data storage needs. In this section, we will focus on Double data type.
+
+## Double
+
+A Double in MongoDB is a 64-bit floating-point number used to store numerical values that require high precision. This data type is suitable for situations where fractional values or very large numbers are needed (e.g., decimal numbers, scientific calculations, etc.).
+
+Here's a quick example:
+
+```javascript
+{
+    "_id" : ObjectId("5d5c361494341a5f5c529cdc"),
+    "name" : "Pi",
+    "value" : 3.141592653589793
+}
+```
+
+In actual usage, if you try to store a number with a decimal part, MongoDB will save it as a Double. In the example above, the value of Pi is stored as a Double.
+
+Keep in mind that very large numbers, with or without a decimal part, could also be stored as Double.
+
+## BSON.Double
+
+In MongoDB, Double data type is represented as BSON.Double - BSON being the binary serialization format that MongoDB uses to store documents in a binary format (which is also more space-efficient).
+
+When querying the stored data, you can explicitly cast the value as a Double:
+
+```javascript
+db.my_collection.find({"value": {$type: "double"}})
+```
+
+It's important to always remember that although MongoDB provides flexibility in terms of storage, it is crucial to understand the impact of using various data types on performance and storage efficiency.
+
+That's all you need to know about the Double data type in MongoDB. Now, you can store numerical values with high precision.
+
+In the next section, we will cover another data type in MongoDB.

src/data/roadmaps/mongodb/content/101-datatypes/103-string.md

@@ -1 +1,41 @@
-# String
+# String
+
+A string in MongoDB represents the sequence of characters or text. It's a powerful and flexible data type that can hold anything, from names and descriptions to lengthy texts. Strings in MongoDB are UTF-8 encoded, which makes them compatible with a wide range of characters from many languages.
+
+Here's a quick overview of strings in MongoDB:
+
+**Characteristics:**
+
+- UTF-8 encoded: Supports various characters from multiple languages.
+- Flexible: Can hold any text, making it suitable for storing different kinds of information.
+
+**How to use strings in MongoDB:**
+
+When creating a document in a MongoDB collection, you can simply store the data as a string using key-value pairs. Here's an example:
+
+```javascript
+{
+    "name": "John Doe",
+    "city": "New York",
+    "description": "A software developer working at XYZ company.",
+}
+```
+
+In this example, `name`, `city`, and `description` are keys with string values: `"John Doe"`, `"New York"`, and `"A software developer working at XYZ company."`. 
+
+**Queries with strings:**
+
+You can also perform various queries using strings in MongoDB. Some common query operators used for string manipulation are:
+
+- `$regex`: Use regular expressions to search for patterns within the string values.
+- `$text`: Perform a text search on the specified fields in a collection.
+
+An example of a query with `$regex`:
+
+```javascript
+db.collection.find({ "name": { "$regex": "J.*" } })
+```
+
+This query searches for all documents in the collection with a `name` field starting with the letter `"J"`.
+
+In summary, strings are an essential data type in MongoDB that can store a wide range of texts and support multiple languages with UTF-8 encoding. They can be used to create flexible documents and perform various queries.

src/data/roadmaps/mongodb/content/101-datatypes/104-array.md

@@ -1 +1,83 @@
-# Array
+# Array
+
+In this section, we will discuss the `Array` datatype in MongoDB. Arrays are used to store multiple values in a single field of a MongoDB document. Arrays can contain values of different data types, including strings, numbers, dates, objects, and other embedded arrays.
+
+## Why use Arrays?
+
+Arrays are useful when you want to store multiple related items as part of a single document. For example, you might have a list of tags for a blog post or the ingredients for a recipe. Using arrays simplifies querying the data, as you can easily search for documents that contain a specific item in an array or match several items at once.
+
+## Creating Arrays
+
+To create an array in MongoDB, simply include it as a field in a document using the square bracket notation (`[]`). You can add values to the array while creating the document or update it later with new items.
+
+Example of creating an array in a document:
+
+```javascript
+{
+  "_id": ObjectId("123xyz"),
+  "name": "John Doe",
+  "hobbies": ["reading", "swimming", "coding"]
+}
+```
+
+## Querying Arrays
+
+MongoDB provides various operators such as `$in`, `$all`, and `$size`, for querying documents with arrays. The following are some examples:
+
+- Finding documents with a specific item in an array:
+
+```javascript
+db.collection.find({ hobbies: "swimming" });
+```
+
+- Finding documents with any of the specified items in an array:
+
+```javascript
+db.collection.find({ hobbies: { $in: ["swimming", "coding"] } });
+```
+
+- Finding documents with all specified items in an array:
+
+```javascript
+db.collection.find({ hobbies: { $all: ["reading", "coding"] } });
+```
+
+- Finding documents with a specific array size:
+
+```javascript
+db.collection.find({ hobbies: { $size: 3 } });
+```
+
+## Updating Arrays
+
+You can update documents containing arrays by using operators like `$push`, `$addToSet`, `$pull`, and `$pop`.
+
+- Adding a new item to an array:
+
+```javascript
+db.collection.updateOne({ _id: ObjectId("123xyz") }, { $push: { hobbies: "painting" } });
+```
+
+- Adding unique items to an array:
+
+```javascript
+db.collection.updateOne({ _id: ObjectId("123xyz") }, { $addToSet: { hobbies: "painting" } });
+```
+
+- Removing an item from an array:
+
+```javascript
+db.collection.updateOne({ _id: ObjectId("123xyz") }, { $pull: { hobbies: "reading" } });
+```
+
+- Removing the first or last item from an array:
+
+```javascript
+// Remove the first item (use $pop with -1)
+db.collection.updateOne({ _id: ObjectId("123xyz") }, { $pop: { hobbies: -1 } });
+
+// Remove the last item (use $pop with 1)
+db.collection.updateOne({ _id: ObjectId("123xyz") }, { $pop: { hobbies: 1 } });
+```
+
+In this section, we've covered the essentials of using the `Array` datatype in MongoDB. With this knowledge, you can efficiently model and query data that requires multiple related items within a single document.

src/data/roadmaps/mongodb/content/101-datatypes/105-object.md

@@ -1 +1,55 @@
-# Object
+# Object
+
+In MongoDB, the Object data type (or BSON data type) is used to represent embedded documents, which are essentially documents inside another document. An object is a key-value pair, where the key is a string and the value can be of any data type supported by MongoDB, including other objects or arrays. This data type is fundamental to MongoDB's flexibility and the schema-less design of the database.
+
+## Object Structure
+
+Objects in MongoDB are represented in BSON (Binary JSON) format, which is a binary-encoded version of JSON. BSON helps speed up data processing and supports the use of additional data types not available in standard JSON. BSON documents are hierarchical and can contain other BSON documents, arrays, and other complex data types.
+
+Here's an example of an object in MongoDB:
+
+```javascript
+{
+  "_id": ObjectId("507f191e810c19729de860ea"),
+  "name": "Alice",
+  "age": 28,
+  "address": {
+    "street": "Main Street",
+    "city": "New York",
+    "state": "NY"
+  }
+}
+```
+
+In this example, the `_id` field contains an ObjectId data type, the `name` and `age` fields contain string and integer data types, respectively, and the `address` field contains a nested object.
+
+## Querying Objects
+
+To query objects in MongoDB, you can use dot notation to access nested fields. For example, to find all documents with an address in New York City, you would use the following query:
+
+```javascript
+db.collection.find({
+  "address.city": "New York"
+});
+```
+
+## Updating Objects
+
+When updating documents with objects, it's important to use appropriate update operators to ensure the correct update behavior. For example, using `$set` to modify specific fields of the object:
+
+```javascript
+db.collection.updateOne(
+  { "name": "Alice" },
+  { "$set": { "address.city": "Los Angeles" } }
+);
+```
+
+This operation would only update the `city` field in the `address` object without affecting other fields within the object.
+
+## Aggregation Operations
+
+The MongoDB aggregation framework also supports handling objects for various data manipulations. For instance, you can use `$project`, `$group`, or `$unwind` functions to extract data from objects or manipulate object fields as needed.
+
+Keep in mind that MongoDB encourages denormalized data storage for the sake of query performance, so you should first consider your application requirements and choose a suitable level of normalization or denormalization for your schema design.
+
+To sum up, the object data type is a versatile aspect of MongoDB's data model, allowing for nesting and structured data storage. Understanding how to work with objects and leverage their functionality is crucial for mastering MongoDB.

src/data/roadmaps/mongodb/content/101-datatypes/106-binary-data.md

@@ -1 +1,50 @@
-# Binary data
+# Binary data
+
+Binary Data is a datatype in MongoDB that is used to store binary content like images, audio files, or any other data that can be represented in binary format. This datatype is particularly useful when you need to store large files, manipulate raw binary data, or work with data that cannot be encoded as UTF-8 strings. 
+
+In MongoDB, binary data is represented using the BSON Binary type, which uses a binary format for encoding and decoding data. The BSON Binary type has several subtypes to better categorize the kind of binary data being stored, such as `B_GENERAL`, `B_FUNCTION`, and `B_BINARY`.
+
+## Advantages of using Binary Data
+
+- **Storage:** Storing files directly in the MongoDB database removes the necessity for an additional file storage system and eases the retrieval and management of the files.
+- **Efficiency:** Binary data can be more efficiently stored and processed than textual representations of the same data.
+- **Interoperability:** Storing data in binary format allows for seamless communication between systems using different character encodings and serialization formats.
+
+## Working with Binary Data in MongoDB
+
+To work with binary data in MongoDB, you will need to utilize the `Binary` class provided by your MongoDB driver. This class offers methods to create, encode, and decode binary data objects.
+
+Here's an example of creating a binary data object using the `Binary` class in Python:
+
+```python
+from bson.binary import Binary
+from bson import ObjectId
+
+# Create a binary data object
+image_data = open("image.jpg", "rb").read()
+binary_image_data = Binary(image_data)
+
+# Storing binary data in a MongoDB collection
+data_collection = db.collection_name
+document = {
+    "name": "Sample Image",
+    "image_data": binary_image_data,
+}
+stored_data = data_collection.insert_one(document)
+```
+
+When it comes to retrieving binary data from the database, you can use your MongoDB driver's `find` method to query the required document and access the binary field.
+
+For example, in Python:
+
+```python
+# Retrieve binary data from the database
+document = data_collection.find_one({"name": "Sample Image"})
+retrieved_image_data = document["image_data"]
+
+# Save the retrieved binary data to a new file
+with open("retrieved_image.jpg", "wb") as f:
+    f.write(retrieved_image_data)
+```
+
+Keep in mind that storing large binary files in a MongoDB database might result in performance issues. In such cases, consider using a separate file storage system or MongoDB's [GridFS](https://docs.mongodb.com/manual/core/gridfs/) to store and manage binary data.

src/data/roadmaps/mongodb/content/101-datatypes/107-undefined.md

@@ -1 +1,26 @@
-# Undefined
+# Undefined
+
+In this section, we will discuss the "undefined" datatype in MongoDB. This datatype was originally used in the early versions of MongoDB, but now it is deprecated and should not be used in new applications.
+
+## What is 'undefined'?
+
+An 'undefined' datatype in MongoDB is a data type that signifies that the value of a field has not been set or has been removed. It represents the absence of a value.
+
+## Why should it not be used?
+
+In the newer versions of MongoDB, it is recommended to use the `null` value for representing missing or undefined values in the database. Although the `undefined` datatype is still supported for backward compatibility, it is advised to avoid the use of it, as the `null` value is more widely accepted and understood.
+
+Here is an example to show the difference between `null` and `undefined`:
+
+```javascript
+{
+  "field1": null,
+  "field2": undefined
+}
+```
+
+In this example, `field1` has a `null` value, while `field2` has an `undefined` value. However, it is recommended to use `null` instead of `undefined` to maintain better readability and compatibility.
+
+## Conclusion
+
+In summary, while the 'undefined' datatype exists in MongoDB, it is now considered deprecated and should be avoided. Instead, it is suggested to use the `null` value to represent fields with missing or undefined values in your database. This will ensure better compatibility and readability of your code when using MongoDB.

src/data/roadmaps/mongodb/content/101-datatypes/108-object-id.md

@@ -1 +1,59 @@
-# Object id
+# ObjectId
+
+Object ID is a unique identifier in MongoDB and one of its primary datatypes. It is the default identifier created by MongoDB when you insert a document into a collection without specifying an `_id`.
+
+## Structure of an Object ID
+
+An Object ID consists of 12 bytes, where:
+
+- The first 4 bytes represent the timestamp of the document's creation in seconds since the Unix epoch.
+- The next 3 bytes contain a unique identifier of the machine where the document was created, usually calculated using its hostname.
+- Following that, 2 bytes represent the process ID of the system where the document was created.
+- The last 3 bytes are a counter that starts from a random value, incremented for each new document created.
+
+## Benefits of Object ID
+
+- The generation of the Object ID is unique, ensuring that no two documents have the same `_id` value in a collection.
+- The structure of the Object ID provides important information about the document's creation, such as when and where it was created.
+- The Object ID enables efficient indexing and high performance in large-scale MongoDB deployments.
+
+## Working with Object ID
+
+Here are a few examples of how to work with Object IDs in MongoDB:
+
+**1. Inserting a document without specifying an `_id`:**
+
+```javascript
+db.collection.insertOne({ "title": "Example" });
+```
+
+**Output:**
+```javascript
+{
+  "_id": ObjectId("60c4237a89293ddc1ef23245"),
+  "title": "Example"
+}
+```
+
+**2. Creating Object ID manually:**
+
+```javascript
+const { ObjectId } = require("mongodb");
+const objectId = new ObjectId();
+```
+
+**3. Converting Object ID to a string:**
+
+```javascript
+const objectIdStr = objectId.toString();
+```
+
+**4. Converting a string back to an Object ID:**
+
+```javascript
+const objectIdFromStr = ObjectId(objectIdStr);
+```
+
+## Conclusion
+
+The Object ID datatype in MongoDB is a very powerful and efficient way to uniquely identify documents in a collection. Its structure provides valuable information about the document's creation, and its design ensures high performance and scalability for large-scale MongoDB deployments. Understanding and effectively utilizing Object IDs is essential for successful MongoDB usage.

src/data/roadmaps/mongodb/content/101-datatypes/109-boolean.md

@@ -1 +1,45 @@
-# Boolean
+# Boolean
+
+The `Boolean` data type in MongoDB is used to store true or false values. Booleans are used when you want to represent a binary state, where a field can have one of two possible values. MongoDB supports the standard `true` and `false` literals for this data type. 
+
+Examples of usage can include representing active/inactive statuses, toggling settings (e.g., sending email notifications), and denoting the presence/absence of a specific feature.
+
+## Storing Boolean Data
+
+To store a boolean data value in a MongoDB document, you may use the `true` or `false` literals. Here's an example of a document containing a boolean field named `isActive`:
+
+```javascript
+{
+    "name": "John Doe",
+    "isActive": true,
+    "email": "john.doe@example.com"
+}
+```
+
+## Querying Data by Boolean Value
+
+When you need to query documents based on a boolean value, you can use a query filter that specifies the desired boolean value. For example, if you want to find all active users in the `users` collection:
+
+```javascript
+db.users.find({ "isActive": true })
+```
+
+Similarly, you can retrieve all inactive users with the following query:
+
+```javascript
+db.users.find({ "isActive": false })
+```
+
+## Updating Boolean Data
+
+Updating or modifying boolean values is as simple as using the `$set` operator with the desired new value. Let's say we want to deactivate a user:
+
+```javascript
+db.users.updateOne({ "name": "John Doe" }, { $set: { "isActive": false } })
+```
+
+This would change the user's `isActive` field value to `false` in the document.
+
+## Conclusion
+
+Boolean data types in MongoDB provide a simple and efficient way to represent binary states. Utilize booleans to store true/false values and streamline queries, updates, and other operations to manage data with binary characteristics.

src/data/roadmaps/mongodb/content/101-datatypes/110-date.md

@@ -1 +1,63 @@
-# Date
+# Date
+
+In MongoDB, the *Date* datatype is used to store the date and time values in a specific format. This is essential when working with date-based data, such as recording timestamps, scheduling events, or organizing data based on time. 
+
+## Date Format
+
+MongoDB internally stores dates as the number of milliseconds since the Unix epoch (January 1, 1970). This BSON data format makes it efficient for storing and querying date values. However, when working with dates in your application, it is common to use a human-readable format such as ISO 8601.
+
+## Working with Date
+
+To create a new Date instance, you can use the JavaScript `Date` object. Here's an example:
+
+```javascript
+const currentDate = new Date();
+```
+
+When inserting a document with a Date field, you can store the date value as follows:
+
+```javascript
+db.events.insertOne({ title: "Sample Event", eventDate: new Date() });
+```
+
+You can also specifically store the current date and time using MongoDB's `$currentDate` operator:
+
+```javascript
+db.events.insertOne({ title: "Sample Event", eventDate: { $currentDate: { $type: "date" } } });
+```
+
+## Querying Dates
+
+To query documents based on date values, you can perform comparisons using various query operators such as `$lt`, `$lte`, `$gt`, `$gte`, and `$eq`. Here are some examples:
+
+```javascript
+// Find events that are happening before a certain date
+const filterDate = new Date("2021-12-31");
+db.events.find({ eventDate: { $lt: filterDate } });
+
+// Find events that are happening after a certain date
+const filterDate = new Date("2022-01-01");
+db.events.find({ eventDate: { $gt: filterDate } });
+```
+
+## Date Aggregations
+
+MongoDB also provides aggregation functions for working with date values. Some common operations include `$year`, `$month`, `$dayOfMonth`, `$hour`, and `$minute`.
+
+Example using the `$dayOfYear` and `$year` operators:
+
+```javascript
+db.events.aggregate([
+    {
+        $group: {
+            _id: {
+                year: { $year: "$eventDate" },
+                day: { $dayOfYear: "$eventDate" },
+            },
+            count: { $sum: 1 },
+        },
+    },
+]);
+```
+
+This query groups events by the day and year, providing a count of events for each day.

src/data/roadmaps/mongodb/content/101-datatypes/111-null.md

@@ -1 +1,34 @@
-# Null
+# Null
+
+In MongoDB, the `null` data type represents a missing value or a field that's purposely set to have no value. This is an important data type when you need to represent the absence of a value in a specific field, for example, when a field is optional in your documents.
+
+## Null in BSON
+
+MongoDB uses BSON (Binary JSON) as its data model for storage. In BSON, the `null` data type is represented by the type number `0x0A`.
+
+## Using Null Values in MongoDB
+
+Here's an example to illustrate how to use the `null` data type in MongoDB:
+
+```javascript
+db.users.insertOne({
+  "name": "Alice",
+  "email": "alice@example.com",
+  "phone": null
+});
+```
+
+In this example, we're inserting a new document into the `users` collection with the name, email, and phone fields. For the phone field, instead of leaving it out, we explicitly set it to `null`, making it clear that Alice might have a phone number, but it's currently unknown.
+
+## Comparison with Null
+
+When comparing values to `null`, MongoDB will use the following rules:
+
+- Equality: `null` is equal to `null`.
+- Inequalities: `null` is considered lower than any other value when it comes to inequalities.
+
+Keep in mind that there are cases when a field is missing from a document, it might be considered as having a `null` value (depending on the query).
+
+## Conclusion
+
+In MongoDB, the `null` data type helps you to represent missing values or fields that shouldn't have a defined value. By setting a field to `null`, you can preserve the structure of your documents and improve the readability of your database design.

src/data/roadmaps/mongodb/content/101-datatypes/112-regex.md

@@ -1 +1,49 @@
-# Regex
+# Regular Expression
+
+In MongoDB, regular expressions (regex) are a powerful data type that allows you to search for patterns within text strings. They can be used in query operations to find documents that match a specific pattern and are particularly useful when working with text-based data or when you don't have an exact match for your query.
+
+## Creating a Regular Expression
+
+In MongoDB, you can create a regular expression using the `/pattern/flags` syntax or by using the BSON type `RegExp`. Here's an example:
+
+```javascript
+// Creating a regex to find documents containing the word 'example'
+var regex = /example/i; // Using JavaScript regex syntax with 'i' flag (case-insensitive)
+var bsonRegex = new RegExp('example', 'i'); // Using BSON RegExp type
+```
+
+Both methods will result in the same regex pattern, with the `i` flag indicating case-insensitivity.
+
+## Querying with Regular Expressions
+
+You can use regular expressions in MongoDB queries using the `$regex` operator or by directly passing the regex pattern:
+
+```javascript
+db.collection.find({ field: /example/i }); // Using plain regex pattern
+db.collection.find({ field: { $regex: /example/i } }); // Using $regex operator
+```
+
+## Regular Expression Flags
+
+MongoDB supports the following regex flags to provide flexibility in pattern matching:
+
+* `i`: Case-insensitive match
+* `m`: Multi-line match
+* `x`: Ignore whitespace and comments in the pattern
+* `s`: Allow `.` to match all characters, including newlines
+
+Example:
+
+```javascript
+db.collection.find({ field: { $regex: /example/im } }); // Case-insensitive and multi-line match
+```
+
+## Escaping Special Characters
+
+In regex patterns, certain characters have special meanings, such as `.` (matches any character), `*` (matches zero or more repetitions). To search for a literal character that has a special meaning in regex, you must escape it with a backslash (`\`):
+
+```javascript
+db.collection.find({ field: /example\.com/i }); // Search for 'example.com'
+```
+
+Regular expressions in MongoDB allow you to search for complex patterns within text strings effectively. By understanding the basic syntax and flags, you can enhance your querying capabilities to find the exact data you need.

src/data/roadmaps/mongodb/content/101-datatypes/113-javascript.md

@@ -1 +1,64 @@
-# Javascript
+# JavaScript
+
+In MongoDB, JavaScript is a valuable data type that allows you to store and manipulate code within the database effectively. This data type can be beneficial when working with complex data structures and scenarios that require more flexibility than what the standard BSON types offer. In this section, we will discuss the JavaScript data type, its usage, and some limitations.
+
+## Usage
+
+You can store JavaScript directly within MongoDB as a string value, and you can also access JavaScript functions in the context of the `mongo` shell or MongoDB server. To store JavaScript code, you can use the `Code` BSON data type or the `$function` operator, introduced in version 4.4.
+
+Here's an example of storing JavaScript code in a MongoDB document:
+
+```javascript
+db.scripts.insert({
+  name: "helloWorld",
+  code: new Code("function() { return 'Hello World!'; }")
+});
+```
+
+And here is an example using the `$function` operator:
+
+```javascript
+db.collection.aggregate([
+  {
+    $addFields: {
+      volume: {
+        $function: {
+          body: "function(l, w, h) { return l * w * h; }",
+          args: ["$length", "$width", "$height"],
+          lang: "js"
+        }
+      }
+    }
+  }
+]);
+```
+
+## Working with JavaScript Functions and Map-Reduce
+
+You can utilize JavaScript functions with MongoDB's Map-Reduce framework. Map-Reduce is a technique that processes large datasets by applying a map function to each document and then reducing the results according to a reduce function. JavaScript functions can significantly increase the flexibility and expressiveness of these operations.
+
+An example of Map-Reduce using JavaScript functions:
+
+```javascript
+var map = function() {
+  emit(this.category, this.price);
+};
+
+var reduce = function(key, values) {
+  return Array.sum(values);
+};
+
+db.products.mapReduce(map, reduce, { out: "total_by_category" });
+```
+
+## Limitations
+
+While incredibly flexible, there are some limitations when using JavaScript in MongoDB:
+
+- **Performance**: JavaScript execution in MongoDB is slower compared to native BSON queries, so it should not be the first choice for high-performance applications.
+
+- **Concurrency**: JavaScript in MongoDB is single-threaded, which can lead to reduced concurrency and potential blocking if several operations rely on JavaScript code execution.
+
+- **Security**: Storing and executing JavaScript code may present security risks like code injection attacks. Ensure proper precautions, such as validation and role management, are in place to minimize such risks.
+
+In conclusion, MongoDB's support for JavaScript as a data type brings flexibility and expressiveness to the database. However, be aware of the performance, concurrency, and security implications when working with JavaScript in your MongoDB applications.

src/data/roadmaps/mongodb/content/101-datatypes/114-symbol.md

@@ -1 +1,21 @@
-# Symbol
+# Symbol
+
+The `Symbol` datatype is a legacy data type in MongoDB. It was primarily used to store textual data with some additional metadata but is now **deprecated** and advised not to be used for new projects.
+
+The `Symbol` datatype is functionally equivalent to the `String` datatype. The BSON encoding of both Symbol and String is identical, but the Symbol datatype was used to differentiate these two and provide a more powerful and flexible way to extend the MongoDB system for application-specific needs.
+
+It's also worth mentioning that most MongoDB drivers, including the official driver, do not support the Symbol data type as a separate type. They simply map it to their string representations.
+
+Although you might encounter Symbols in older databases, it's recommended to use the `String` datatype for new projects or migrate existing symbols to strings, as they don't provide any advantage over the `String` datatype.
+
+Below is a simple example of how a `Symbol` was stored in MongoDB (note that this is not recommended for new projects):
+
+```javascript
+{
+  "_id" : ObjectId("6190e2d973f6e571b47537a0"),
+  "title" : Symbol("Hello World"),
+  "description" : "A simple example of the Symbol datatype"
+}
+```
+
+In conclusion, the `Symbol` datatype is a deprecated legacy datatype in MongoDB that served to store textual data with additional metadata. For new projects, it's highly recommended to use the `String` datatype instead.

src/data/roadmaps/mongodb/content/101-datatypes/115-int.md

@@ -1 +1,37 @@
-# Int
+# Int32 / Int
+
+In MongoDB, the `int` (short for integer) data type is used for storing whole numbers without a fractional component. Integers can be either positive or negative and are commonly used in scenarios requiring counting or ranking, such as user's ages, product quantity, or the number of upvotes.
+
+## Overview
+
+In MongoDB, integers can be represented in different sizes depending on the range of values required for a specific application. These sizes are as follows:
+
+- `Int32`: Represents 32-bit integer values between -2^31 and 2^31-1.
+- `Int64`: Represents 64-bit integer values between -2^63 and 2^63-1.
+
+By default, MongoDB uses 64-bit integers (`Int64`) when storing integer values for greater flexibility in accommodating various value ranges. However, you can also choose to use 32-bit integers (`Int32`) for smaller value ranges if necessary.
+
+## Usage
+
+To store an integer value in a MongoDB document, you can simply include the integer as the value for a field within the document. For example:
+
+```javascript
+{
+  "name": "John Doe",
+  "age": 30,
+  "upvotes": 150
+}
+```
+
+Here, `age` and `upvotes` are both integer values representing the age and the number of upvotes of a user.
+
+If you specifically need to store an integer as a 32-bit or 64-bit value, you can use a driver-specific method or construct BSON objects using the appropriate BSON data type for integers. For example, in the Node.js MongoDB driver, you can use the `Int32` and `Long` constructors from the `mongodb` package:
+
+```javascript
+const { Int32, Long } = require("mongodb");
+
+const myInt32 = new Int32(42); // Creates a 32-bit integer
+const myInt64 = new Long(9007199254740991); // Creates a 64-bit integer
+```
+
+Remember that choosing the appropriate integer size can help optimize storage and performance within your MongoDB application. Use `Int32` for smaller value ranges and `Int64` for larger value ranges as needed.

src/data/roadmaps/mongodb/content/101-datatypes/116-long.md

@@ -1 +1,41 @@
-# Long
+# Int64 / Long
+
+The `Long` data type in MongoDB is a 64-bit integer, which is useful when you need to store large integral values beyond the range of the standard `int` (32-bit integer) data type. The range for the `Long` data type is from `-2^63` to `2^63 - 1`. This data type is suitable for applications that require high-precision numerical data, such as analytics and scientific calculations.
+
+## Syntax
+
+To define a field with the `Long` data type in MongoDB, you can use the `$numberLong` keyword. Here's an example of a document with a field named `largeValue` defined as a `Long` data type:
+
+```json
+{
+  "largeValue": { "$numberLong": "1234567890123456789" }
+}
+```
+
+## Usage
+
+You can use the `Long` data type to store and query large integral values in your MongoDB collections. To insert a document with a `Long` field, you can use the following syntax:
+
+```javascript
+db.collection.insert({
+  "largeValue": NumberLong("1234567890123456789")
+});
+```
+
+To query documents that have a `Long` field with a specific value, you can use the following syntax:
+
+```javascript
+db.collection.find({
+  "largeValue": NumberLong("1234567890123456789")
+});
+```
+
+## Considerations
+
+When using the `Long` data type in MongoDB, keep the following considerations in mind:
+
+- JavaScript uses the [IEEE 754 floating-point](https://en.wikipedia.org/wiki/Double-precision_floating-point_format) representation for numbers, which may cause a loss of precision when storing and manipulating large integral values. To avoid this, always manipulate `Long` values using MongoDB's built-in `NumberLong()` function, as shown in the examples above.
+
+- When using the `Long` data type, be aware of the performance trade-offs. Operations on 64-bit integers typically require more processing power and storage space compared to 32-bit integers. If you don't need the extra range provided by the `Long` data type, consider using the `int` data type instead.
+
+- If you need to store extremely large numbers that exceed the range of the `Long` data type, you may want to consider using the [`Decimal128`](https://docs.mongodb.com/manual/reference/bson-types/#decimal128) data type, which provides 128-bit decimal-based floating-point numbers with 34 decimal digits of precision.

src/data/roadmaps/mongodb/content/101-datatypes/117-timestamp.md

@@ -1 +1,34 @@
-# Timestamp
+# Timestamp
+
+A "Timestamp" in MongoDB is a specific datatype used for tracking the time of an event or a document modification. It's a 64-bit value containing a 4-byte incrementing ordinal for operations within a given second and a 4-byte timestamp representing the seconds since the Unix epoch (Jan 1, 1970).
+
+## When to use Timestamp
+
+Timestamps are mainly used for internal MongoDB operations, such as replication and sharding. They can be useful in tracking the order of operations in a distributed system and ensuring data consistency across multiple nodes.
+
+## Creating and Querying Timestamps
+
+To create a Timestamp, you can use the BSON Timestamp type. The syntax is as follows:
+
+```javascript
+new Timestamp(t, i)
+```
+
+Where `t` is the seconds since the Unix epoch, and `i` is an incrementing ordinal for operations within a given second.
+
+For example, to create a Timestamp for the current time:
+
+```javascript
+var currentTimestamp = new Timestamp(Math.floor(new Date().getTime() / 1000), 1);
+```
+
+To query documents based on their Timestamp, you can use the `$gt`, `$gte`, `$lt`, or `$lte` query operators:
+
+```javascript
+// Find all documents with a Timestamp greater than a specified date
+db.collection.find({ "timestampFieldName": { "$gt": new Timestamp(Math.floor(new Date('2021-01-01').getTime() / 1000), 1) }});
+```
+
+Keep in mind that using Timestamps for application purposes is generally not recommended, as their main purpose is to serve internal MongoDB operations. Instead, consider using the `Date` datatype for general-purpose time tracking in your application.
+
+Overall, Timestamps are a powerful tool in MongoDB for managing operations in distributed systems and maintaining data consistency.

src/data/roadmaps/mongodb/content/101-datatypes/118-decimal128.md

@@ -1 +1,35 @@
-# Decimal128
+# Decimal128
+
+`Decimal128` is a high-precision 128-bit decimal-based floating-point data type in MongoDB. It provides greater precision and a larger range for storing decimal numbers compared to other common floating-point data types like `Double`.
+
+## Key Features
+
+- **Precision**: Decimal128 allows for precise storage of decimal numbers with up to 34 decimal points, making it suitable for financial and scientific applications where exact calculations are crucial.
+- **Range**: Decimal128 supports a wide range of values, ranging from -10^6145 to 10^6145, as well as the smallest non-zero positive and negative numbers around ±10^-6143.
+- **IEEE 754-2008 compliant**: Decimal128 follows the decimal floating-point arithmetic encoding set by the IEEE 754-2008 international standard, ensuring consistent and accurate results across diverse platforms and systems.
+
+## Usage
+
+To specify a `Decimal128` value in MongoDB, use the `$numberDecimal` keyword followed by the decimal value enclosed in quotes. Here's an example demonstrating the insertion of a decimal128 data type:
+
+```javascript
+db.example.insertOne({
+  "amount": {
+    "$numberDecimal": "1234.567890123456789012345678901234"
+  }
+});
+```
+
+Alternatively, with the help of the JavaScript BSON library, you can use the `Decimal128.fromString()` function to create a Decimal128 value from a string:
+
+```javascript
+const { Decimal128 } = require('bson');
+
+const decimalValue = Decimal128.fromString('1234.567890123456789012345678901234');
+db.example.insertOne({ amount: decimalValue });
+```
+
+## Considerations
+
+- When querying decimal values, note that MongoDB compares decimal numbers using their mathematical values, rather than their string representation.
+- Due to the high precision of the `Decimal128` data type, you may encounter rounding differences between MongoDB and other systems or libraries when performing calculations involving mixed data types. To mitigate this, ensure that all operands are converted to the same data type (preferably, `Decimal128`) before performing calculations.

src/data/roadmaps/mongodb/content/101-datatypes/119-min-key.md

@@ -1 +1,47 @@
-# Min key
+# Min Key
+
+In this section, we will discuss the "Min Key" data type in MongoDB. It represents the lowest possible BSON value in the sorting order, making it useful when you need to compare values across documents.
+
+## What is Min Key?
+
+Min Key is a unique data type in MongoDB that is used to represent the smallest value possible when performing sorting operations. It is often used in queries or schema design when you need to ensure that a specific field has the lowest possible value compared to other BSON types.
+
+## How to use Min Key
+
+To use Min Key in MongoDB, you can utilize the `MinKey()` function. Here's an example demonstrating how to insert a document with Min Key data type:
+
+```javascript
+// Import the MinKey class from the BSON module
+const { MinKey } = require("bson");
+
+// Create an instance of the MinKey class
+const minValue = new MinKey();
+
+// Insert a document with a field `priority` having the MinKey value
+db.myCollection.insertOne({ name: "example", priority: minValue });
+```
+
+This will insert a document with a `priority` field set to the Min Key value.
+
+## Use cases
+
+- As a default value on a field when you want to ensure that it will always have the lowest possible value for sorting purposes.
+
+```javascript
+// Example schema with a field default set to MinKey
+const mySchema = new Schema({
+  name: String,
+  priority: { type: Schema.Types.MinKey, default: new MinKey() },
+});
+```
+
+- When you need to find a document with the minimum value for a specific field.
+
+```javascript
+// Find the document with the lowest priority
+db.myCollection.find().sort({ priority: 1 }).limit(1);
+```
+
+## Conclusion
+
+In this section, we've learned about the "Min Key" data type in MongoDB. We discussed how it is used to represent the smallest value in the BSON data types and its various use cases in sorting and querying the data.

src/data/roadmaps/mongodb/content/101-datatypes/120-max-key.md

@@ -1 +1,37 @@
-# Max key
+# Max Key
+
+Max Key is a special data type in MongoDB that is used mainly for sorting and comparing values. It has the unique characteristic of being greater than all other BSON types during the sorting process. This makes Max Key quite useful when you need to create a document that should always appear after other documents in a sorted query or when you are setting a limit for a range of data, and you want to ensure that nothing exceeds that limit.
+
+Here is a brief summary of Max Key:
+
+## Properties
+
+- Max Key is a constant that holds the value greater than any other BSON data type value.
+- It is used for comparing and sorting values in MongoDB collections.
+- Max Key is a part of the BSON data type, which is the primary data format used in MongoDB for storing, querying, and returning documents.
+- Max Key is not to be confused with a regular value in a document and is primarily used for internal purposes.
+
+## Usage
+To use Max Key in your MongoDB implementation, you can insert it into your document using MongoDB syntax as follows:
+
+```javascript
+{
+  _id: ObjectId("some_id_value"),
+  field1: "value1",
+  myMaxKeyField: MaxKey()
+}
+```
+
+In this example, `myMaxKeyField` is assigned the Max Key value.
+
+When you want to sort or compare documents in a collection, Max Key will help ensure that a document will always come last in the results when compared with other BSON types.
+
+Here is an example of how Max Key can be used in a range query:
+
+```javascript
+db.my_collection.find({age: {$lte: MaxKey()}});
+```
+
+This query will return all the documents in `my_collection` where the `age` field is less than or equal to Max Key, essentially retrieving everything, as no value can be greater than Max Key.
+
+In summary, Max Key plays an essential role in MongoDB by providing a constant value that is always greater than other BSON types, thus ensuring proper sorting and comparing behavior in your implementation.

src/data/roadmaps/mongodb/content/101-datatypes/index.md

@@ -1 +1,119 @@
-# Datatypes
+# Data Model and Data Types
+
+In MongoDB, data is stored in BSON format, which supports various data types. Understanding these data types is essential as they play a crucial role in schema design and query performance. The following is a brief summary of the different data types supported in MongoDB.
+
+##  ObjectId
+
+`ObjectId` is a 12-byte identifier used as a unique identifier for documents in a collection. It is the default value generated for the `_id` field, ensuring uniqueness within the collection.
+
+##  String
+
+`String` is used to store text data. It must be a valid UTF-8 encoded string.
+
+```javascript
+{
+   "name": "John Doe",
+}
+```
+
+##  Boolean
+
+`Boolean` is used to store true or false values.
+
+```javascript
+{
+   "isActive": true,
+}
+```
+
+##  Integer
+
+`Integer` is used to store an integer value. MongoDB supports two integer types: 32-bit (`int`) and 64-bit (`long`).
+
+```javascript
+{
+   "age": 28,
+}
+```
+
+##  Double
+
+`Double` is used to store floating-point numbers.
+
+```javascript
+{
+   "price": 12.99,
+}
+```
+
+##  Date
+
+`Date` is used to store the date and time in Unix time format (milliseconds timestamp since January 1, 1970, 00:00:00 UTC).
+
+```javascript
+{
+   "createdAt": ISODate("2019-02-18T19:29:22.381Z"),
+}
+```
+
+##  Array
+
+`Array` is used to store a list of values in a single field. The values can be of different data types.
+
+```javascript
+{
+   "tags": ["mongodb", "database", "noSQL"],
+}
+```
+
+##  Object
+
+`Object` is used to store embedded documents, meaning a document can contain another document.
+
+```javascript
+{
+   "address": { "street": "123 Main St", "city": "San Francisco", "state": "CA" },
+}
+```
+
+##  Null
+
+`Null` is used to store a null value, representing the absence of a value or the field.
+
+```javascript
+{
+   "middleName": null,
+}
+```
+
+##  Binary Data
+
+`Binary Data` is used to store binary data or byte arrays.
+
+```javascript
+{
+   "data": BinData(0, "c3VyZS4="),
+}
+```
+
+##  Code
+
+`Code` is used to store JavaScript code.
+
+```javascript
+{
+   "script": Code("function() { return 'Hello, World!'; }"),
+}
+```
+
+##  Regular Expression
+
+`Regular Expression` is used to store regular expressions.
+
+```javascript
+{
+   "pattern": /^mongodb/i,
+}
+```
+
+Understanding and using the appropriate data types while designing your MongoDB schema can significantly improve the performance, storage, and retrieval of your data. Don't forget to consider the specific use cases of your application when choosing data types.

src/data/roadmaps/mongodb/content/102-collections/100-counting-documents.md

@@ -1 +1,53 @@
-# Counting documents
+# Counting Documents
+
+When working with MongoDB, you might often need to know the number of documents present in a collection. MongoDB provides a few methods to efficiently count documents in a collection. In this section, we will discuss the following methods:
+
+- `countDocuments()`
+- `estimatedDocumentCount()`
+
+##  countDocuments()
+
+The `countDocuments()` method is used to count the number of documents in a collection based on a specified filter. It provides an accurate count that may involve reading all documents in the collection.
+
+**Syntax:**
+
+```javascript
+collection.countDocuments(filter, options)
+```
+
+* `filter`: (Optional) A query that will filter the documents before the count is applied.
+* `options`: (Optional) Additional options for the count operation such as `skip`, `limit`, and `collation`.
+
+**Example:**
+
+```javascript
+db.collection('orders').countDocuments({ status: 'completed' }, (err, count) => {
+  console.log('Number of completed orders: ', count);
+});
+```
+
+In the example above, we count the number of documents in the `orders` collection that have a`status` field equal to `'completed'`.
+
+##  estimatedDocumentCount()
+
+The `estimatedDocumentCount()` method provides an approximate count of documents in the collection, without applying any filters. This method uses the collection's metadata to determine the count and is generally faster than `countDocuments()`.
+
+**Syntax:**
+
+```javascript
+collection.estimatedDocumentCount(options)
+```
+
+* `options`: (Optional) Additional options for the count operation such as `maxTimeMS`.
+
+**Example:**
+
+```javascript
+db.collection('orders').estimatedDocumentCount((err, count) => {
+  console.log('Estimated number of orders: ', count);
+});
+```
+
+In the example above, we get the estimated number of documents in the `orders` collection.
+
+Keep in mind that you should use the `countDocuments()` method when you need to apply filters to count documents, while `estimatedDocumentCount()` should be used when an approximate count is sufficient and you don't need to apply any filters.

src/data/roadmaps/mongodb/content/102-collections/101-insert-methods.md

@@ -1 +1,74 @@
-# Insert methods
+# insert() and relevant
+
+In MongoDB, collections are used to store documents. To add data into these collections, MongoDB provides two primary insertion methods: `insertOne()` and `insertMany()`. In this section, we'll explore the usage and syntax of these methods, along with their options and some basic examples.
+
+##  insertOne()
+
+The `insertOne()` method is used to insert a single document into a collection. This method returns an `InsertOneResult` object, that shows the outcome of the operation.
+
+**Syntax:**
+
+```javascript
+db.collection.insertOne(
+   <document>,
+   {
+      writeConcern: <document>,
+      ordered: <boolean>,
+      bypassDocumentValidation: <boolean>,
+      comment: <any>
+   }
+)
+```
+
+**Options:**
+
+- `writeConcern:` An optional document specifying the level of acknowledgment requested from MongoDB for the write operation.
+- `ordered:` An optional boolean flag. When set to `true`, MongoDB will return an error if it encounters a duplicate document in the operation. Default is also `true`.
+- `bypassDocumentValidation:` Optional boolean flag. To validate or not to validate the document against the collection's validation rules. Default is `false`.
+- `comment:` An optional string or BSON that can be used for descriptive purposes when profiling operations.
+
+**Example:**
+
+```javascript
+db.inventory.insertOne({
+   item: "book",
+   qty: 1
+})
+```
+
+##  insertMany()
+
+The `insertMany()` method is used to insert multiple documents into a collection at once. It returns an `InsertManyResult` object, displaying the status of the operation.
+
+**Syntax:**
+
+```javascript
+db.collection.insertMany(
+   [ <document_1>, <document_2>, ... ],
+   {
+      writeConcern: <document>,
+      ordered: <boolean>,
+      bypassDocumentValidation: <boolean>,
+      comment: <any>
+   }
+)
+```
+
+**Options:**
+
+- `writeConcern:` Same as mentioned in `insertOne()` method.
+- `ordered:` Same as mentioned in `insertOne()` method. When set to `true`, MongoDB will insert the documents in the array's order. If a fail occurs, it will stop further processing of the documents. Default is `true`.
+- `bypassDocumentValidation:` Same as mentioned in `insertOne()` method.
+- `comment:` Same as mentioned in `insertOne()` method.
+
+**Example:**
+
+```javascript
+db.inventory.insertMany([
+   { item: "pen", qty: 5 },
+   { item: "pencil", qty: 10 },
+   { item: "notebook", qty: 25 }
+])
+```
+
+In conclusion, insert methods in MongoDB allow users to add documents to a collection with a few simple commands. By understanding the syntax and options available for `insertOne()` and `insertMany()`, we can efficiently store and manage data within MongoDB collections.

src/data/roadmaps/mongodb/content/102-collections/102-find-methods.md

@@ -1 +1,73 @@
-# Find methods
+# find() and relevant
+
+In MongoDB, the `find()` method is an essential aspect of working with collections. It enables you to search for specific documents within a collection by providing query parameters. In this section, we'll explore various `find` methods and how to filter, sort, and limit the search results.
+
+## Basic Find Method
+
+The basic `find()` method is used to fetch all documents within a collection. To use it, you'll simply call the `find()` method on a collection.
+
+```javascript
+db.collection_name.find()
+```
+
+For example, to fetch all documents from a collection named `users`:
+
+```javascript
+db.users.find()
+```
+
+## Query Filters
+
+To search for specific documents, you would need to supply query parameters as a filter within the `find()` method. Filters are passed as JSON objects containing key-value pairs that the documents must match.
+
+For example, to fetch documents from the `users` collection with the `age` field set to `25`:
+
+```javascript
+db.users.find({ "age": 25 })
+```
+
+## Logical Operators
+
+MongoDB provides multiple logical operators for more advanced filtering, including `$and`, `$or`, and `$not`. To use logical operators, you pass an array of conditions.
+
+For example, to find users with an age of `25` and a first name of `John`:
+
+```javascript
+db.users.find({ "$and": [{"age": 25}, {"first_name": "John"}]})
+```
+
+## Projection
+
+Projection is used to control which fields are returned in the search results. By specifying a projection, you can choose to include or exclude specific fields in the output.
+
+To only include the `first_name` and `age` fields of the matching documents:
+
+```javascript
+db.users.find({ "age": 25 }, { "first_name": 1, "age": 1 })
+```
+
+## Sorting
+
+You can also sort the results of the `find()` method using the `sort()` function. To sort the results by one or multiple fields, pass a JSON object indicating the order.
+
+For example, to sort users by their age in ascending order:
+
+```javascript
+db.users.find().sort({ "age": 1 })
+```
+
+## Limit and Skip
+
+To limit the results of the `find()` method, use the `limit()` function. For instance, to fetch only the first `5` users:
+
+```javascript
+db.users.find().limit(5)
+```
+
+Additionally, use the `skip()` function to start fetching records after a specific number of rows:
+
+```javascript
+db.users.find().skip(10)
+```
+
+All these `find` methods combined provide powerful ways to query your MongoDB collections, allowing you to filter, sort, and retrieve the desired documents.

src/data/roadmaps/mongodb/content/102-collections/103-update-methods.md

@@ -1 +1,48 @@
-# Update methods
+# update() and relevant
+
+In MongoDB, update methods are used to modify the existing documents of a collection. They allow you to perform updates on specific fields or the entire document, depending on the query criteria provided. Here is a summary of the most commonly used update methods in MongoDB:
+
+- **updateOne()**: This method updates the first document that matches the query criteria provided. The syntax for updateOne is:
+   ```javascript
+   db.collection.updateOne(<filter>, <update>, <options>)
+   ```
+   - `<filter>`: Specifies the criteria for selecting the document to update.
+   - `<update>`: Specifies the modifications to apply to the selected document.
+   - `<options>`: (Optional) Additional options to configure the behavior of the update operation.
+
+- **updateMany()**: This method updates multiple documents that match the query criteria provided. The syntax for updateMany is:
+   ```javascript
+   db.collection.updateMany(<filter>, <update>, <options>)
+   ```
+   - `<filter>`: Specifies the criteria for selecting the documents to update.
+   - `<update>`: Specifies the modifications to apply to the selected documents.
+   - `<options>`: (Optional) Additional options to configure the behavior of the update operation.
+
+- **replaceOne()**: This method replaces a document that matches the query criteria with a new document. The syntax for replaceOne is:
+   ```javascript
+   db.collection.replaceOne(<filter>, <replacement>, <options>)
+   ```
+   - `<filter>`: Specifies the criteria for selecting the document to replace.
+   - `<replacement>`: The new document that will replace the matched document.
+   - `<options>`: (Optional) Additional options to configure the behavior of the replace operation.
+
+## Update Operators
+
+MongoDB provides additional update operators to specify the modifications like `$set`, `$unset`, `$inc`, `$push`, `$pull`, and more. Here are a few examples:
+
+- Use `$set` operator to update the value of a field:
+  ```javascript
+  db.collection.updateOne({name: "John Doe"}, {$set: {age: 30}})
+  ```
+
+- Use `$inc` operator to increment the value of a field:
+  ```javascript
+  db.collection.updateMany({status: "new"}, {$inc: {views: 1}})
+  ```
+
+- Use `$push` operator to add an item to an array field:
+  ```javascript
+  db.collection.updateOne({name: "Jane Doe"}, {$push: {tags: "mongodb"}})
+  ```
+
+Remember to thoroughly test your update operations to ensure the modifications are done correctly, and always backup your data before making any substantial changes to your documents.

src/data/roadmaps/mongodb/content/102-collections/104-delete-methods.md

@@ -1 +1,53 @@
-# Delete methods
+# deleteOne() and others
+
+When working with MongoDB, you will often need to delete documents or even entire collections to manage and maintain your database effectively. MongoDB provides several methods to remove documents from a collection, allowing for flexibility in how you choose to manage your data. In this section, we will explore key delete methods in MongoDB and provide examples for each.
+
+## db.collection.deleteOne()
+
+The `deleteOne()` method is used to delete a single document from a collection. It requires specifying a filter that selects the document(s) to be deleted. If multiple documents match the provided filter, only the first one (by natural order) will be deleted.
+
+Syntax: `db.collection.deleteOne(FILTER)`
+
+Example:
+```javascript
+db.users.deleteOne({"firstName": "John"})
+```
+This command will delete the first `users` document found with a `firstName` field equal to `"John"`.
+
+## db.collection.deleteMany()
+
+The `deleteMany()` method is used to remove multiple documents from a collection. Similar to `deleteOne()`, it requires specifying a filter to select the documents to be removed. The difference is that all documents matching the provided filter will be removed.
+
+Syntax: `db.collection.deleteMany(FILTER)`
+
+Example:
+```javascript
+db.users.deleteMany({"country": "Australia"})
+```
+This command will delete all `users` documents with a `country` field equal to `"Australia"`.
+
+## db.collection.remove()
+
+The `remove()` method can be used to delete documents in a more flexible way, as it takes both a filter and a `justOne` option. If `justOne` is set to true, only the first document (by natural order) that matches the filter will be removed. Otherwise, if `justOne` is set to false, all documents matching the filter will be deleted.
+
+Syntax: `db.collection.remove(FILTER, JUST_ONE)`
+
+Example:
+```javascript
+db.users.remove({"age": {"$lt": 18}}, true)
+```
+This command would delete a single user document with an `age` field value less than 18.
+
+## db.collection.drop()
+
+In cases where you want to remove an entire collection, including the documents and the metadata, you can use the `drop()` method. This command does not require a filter, as it removes everything in the specified collection.
+
+Syntax: `db.collection.drop()`
+
+Example:
+```javascript
+db.users.drop()
+```
+This command would delete the entire `users` collection and all related data.
+
+It's important to note that these methods will remove the affected documents permanently from the database, so use caution when executing delete commands. Keep in mind to keep backups or use version control to maintain data integrity throughout the lifecycle of your MongoDB database.

src/data/roadmaps/mongodb/content/102-collections/105-bulk-write.md

@@ -1 +1,43 @@
-# Bulk write
+# bulkWrite() and others
+
+Bulk write operations allow you to perform multiple create, update, and delete operations in a single command, which can significantly improve the performance of your application. MongoDB provides two types of bulk write operations:
+
+- **Ordered Bulk Write**: In this type of bulk operation, MongoDB executes the write operations in the order you provide. If a write operation fails, MongoDB returns an error and does not proceed with the remaining operations.
+
+- **Unordered Bulk Write**: In this type of bulk operation, MongoDB can execute the write operations in any order. If a write operation fails, MongoDB will continue to process the remaining write operations.
+
+To perform a bulk write operation, use the `initializeOrderedBulkOp()` or `initializeUnorderedBulkOp()` methods to create a bulk write object.
+
+## Example: Ordered Bulk Write
+
+Here's an example of an ordered bulk write operation:
+
+```javascript
+const orderedBulk = db.collection('mycollection').initializeOrderedBulkOp();
+
+orderedBulk.insert({ _id: 1, name: 'John Doe' });
+orderedBulk.find({ _id: 2 }).updateOne({ $set: { name: 'Jane Doe' } });
+orderedBulk.find({ _id: 3 }).remove();
+
+orderedBulk.execute((err, result) => {
+  // Handle error or result
+});
+```
+
+## Example: Unordered Bulk Write
+
+Here's an example of an unordered bulk write operation:
+
+```javascript
+const unorderedBulk = db.collection('mycollection').initializeUnorderedBulkOp();
+
+unorderedBulk.insert({ _id: 1, name: 'John Doe' });
+unorderedBulk.find({ _id: 2 }).updateOne({ $set: { name: 'Jane Doe' } });
+unorderedBulk.find({ _id: 3 }).remove();
+
+unorderedBulk.execute((err, result) => {
+  // Handle error or result
+});
+```
+
+Remember that using bulk write operations can greatly improve the performance of your MongoDB queries, but make sure to choose the right type (ordered or unordered) based on your application requirements.

src/data/roadmaps/mongodb/content/102-collections/106-validate.md

@@ -1 +1,52 @@
-# Validate
+# validate()
+
+The `validate` command is used to examine a MongoDB collection to verify and report on the correctness of its internal structures, such as indexes, namespace details, or documents. This command can also return statistics about the storage and distribution of data within a collection.
+
+## Usage
+
+The basic syntax of the `validate` command is as follows:
+```javascript
+db.runCommand({validate: "<collection_name>", options...})
+```
+
+`<collection_name>` is the name of the collection to be validated.
+
+## Options
+
+* `full`: (default: false) When set to true, the `validate` command conducts a more thorough inspection of the collection, looking through all its extents, which are contiguous sections of the collection's data on disk. This option should be used with caution as it may impact read and write performance.
+
+* `background`: (default: false) When set to true, the `validate` command runs in the background, allowing other read and write operations on the collection to proceed concurrently. This option is beneficial for large collections, as it minimizes the impact on system performance.
+
+## Example
+
+Validate a collection named "products":
+```javascript
+db.runCommand({validate: "products"})
+```
+
+Validate the collection and perform a background and full check:
+```javascript
+db.runCommand({validate: "products", background: true, full: true})
+```
+
+## Output
+
+The `validate` command returns an object that contains information about the validation process and its results.
+
+```javascript
+{
+    "ns": <string>, // Namespace of the validated collection
+    "nIndexes": <number>, // Number of indexes in the collection
+    "keysPerIndex": {
+        <index_name>: <number> // Number of keys per index
+    },
+    "valid": <boolean>, // If true, the collection is valid
+    "errors": [<string>, ...], // Array of error messages, if any
+    "warnings": [<string>, ...], // Array of warning messages, if any
+    "ok": <number> // If 1, the validation command executed successfully
+}
+```
+
+Keep in mind that the `validate` command should be used mainly for diagnostics and troubleshooting purposes, as it can impact system performance when validating large collections or when using the `full` flag. Use it when you suspect that there might be corruption or discrepancies within the collection's data or internal structures.
+
+That's all about the `validate` command. Now you know how to check the correctness of your MongoDB collections and gather important statistics about their internal structures.

src/data/roadmaps/mongodb/content/102-collections/index.md

@@ -1 +1,60 @@
-# Collections
+# Collections and Methods
+
+In MongoDB, **collections** are used to organize documents. A collection can be thought of as a container or group used to store documents of similar structure, like a table in relational databases. However, unlike tables, collections don't enforce a strict schema, offering more flexibility in managing your data.
+
+## Key Features
+
+- **Flexible Schema**: A collection can contain multiple documents with different structures or fields, allowing you to store unstructured or semi-structured data.
+- **Dynamic**: Collections can be created implicitly or explicitly, and documents can be added or removed easily without affecting others in the collection.
+
+## Creating Collections
+
+To create a collection in MongoDB, you can choose from two methods:
+
+- **Implicit Creation**: When you insert a document without specifying an existing collection, MongoDB automatically creates the collection for you.
+   ```javascript
+   db.createCollection("users")
+   ```
+
+- **Explicit Creation**: Use the `db.createCollection(name, options)` method to create a collection with specific options:
+   ```javascript
+   db.createCollection("users", { capped: true, size: 100000, max: 5000 })
+   ```
+
+## Managing Collections
+
+- **Insert Documents**: To insert a document into a collection, use the `insertOne()` or `insertMany()` methods.
+   ```javascript
+   db.users.insertOne({ name: "John Doe", age: 30, email: "john@example.com" })
+
+   db.users.insertMany([
+      { name: "Jane Doe", age: 28, email: "jane@example.com" },
+      { name: "Mary Jane", age: 32, email: "mary@example.com" }
+   ])
+   ```
+
+- **Find Documents**: Use the `find()` method to query documents in a collection.
+   ```javascript
+   db.users.find({ age: { $gt: 30 } })
+   ```
+
+- **Update Documents**: Use the `updateOne()`, `updateMany()`, or `replaceOne()` methods to modify documents in a collection.
+   ```javascript
+   db.users.updateOne({ name: "John Doe" }, { $set: { age: 31 } })
+
+   db.users.updateMany({ age: { $gt: 30 } }, { $inc: { age: 1 } })
+   ```
+
+- **Delete Documents**: Use the `deleteOne()` or `deleteMany()` methods to remove documents from a collection.
+   ```javascript
+   db.users.deleteOne({ name: "John Doe" })
+
+   db.users.deleteMany({ age: { $lt: 30 } })
+   ```
+
+- **Drop Collection**: To delete the entire collection, use the `drop()` method.
+   ```javascript
+   db.users.drop()
+   ```
+
+In summary, collections are an essential part of MongoDB that enable you to efficiently manage and store documents with varying structures. Their flexible schema and dynamic nature make them perfect for handling both unstructured and semi-structured data.

src/data/roadmaps/mongodb/content/102-useful-concepts/100-read-write-concerns.md

@@ -1 +1,29 @@
-# Read write concerns
+# Read / Write Concerns
+
+_Read and write concerns_ are crucial aspects of data consistency and reliability in MongoDB. They determine the level of acknowledgement required by the database for read and write operations. Understanding these concerns can help you balance performance and data durability based on your application needs.
+
+## Read Concern
+
+A _read concern_ determines the consistency level of the data returned by a query. It specifies the version of data that a query should return. MongoDB supports different read concern levels:
+
+- `local` (default): Returns the most recent data available on the primary node at the time of query execution. It does not guarantee consistency across replica sets.
+- `available`: The query returns the most recent data available on the queried node. This level is only applicable to sharded clusters.
+- `majority`: The query returns data that has been acknowledged by a majority of replica set members. It provides a higher level of consistency but may have higher latency.
+- `linearizable`: Ensures reading the most recent data that has been acknowledged by a majority of replica sets. This level guarantees the highest consistency but can be the slowest among all levels.
+- `snapshot`: Returns the data from a specific snapshot timestamp. This level is useful for read transactions with snapshot isolation.
+
+## Write Concern
+
+A _write concern_ indicates the level of acknowledgment MongoDB should provide when writing data to the database. It ensures that the data has been successfully written and replicated before acknowledging the write operation. The different write concern levels are:
+
+- `w: 0`: The write operation is unacknowledged, which means MongoDB does not send any acknowledgment. This level provides the lowest latency but carries the risk of losing data.
+- `w: 1` (default): The write operation is acknowledged after being successfully written to the primary node. It does not guarantee replication to other replica set members.
+- `w: majority`: The write operation is acknowledged after being written and replicated to a majority of replica set members. This level provides better data durability but may have increased latency.
+- `w: <number>`: The write operation is acknowledged after being replicated to the specified number of replica set members. This level provides a custom level of data durability.
+
+Additionally, the `j` and `wtimeout` options can be used to fine-tune the write concern:
+
+- `j: true/false`: Specifies whether the write operation must be written to the journal before acknowledgment. Setting `j: true` ensures the data is committed to the journal and provides increased durability.
+- `wtimeout: <ms>`: Specifies a time limit in milliseconds for write operations to be acknowledged. If the acknowledgment is not received within the specified time, the operation returns a timeout error. However, this does not mean the write operation failed; it may still be successful at a later point in time.
+
+By configuring read and write concerns appropriately, you can manage the consistency and durability of your MongoDB database according to your application requirements.

src/data/roadmaps/mongodb/content/102-useful-concepts/101-cursors.md

@@ -1 +1,56 @@
-# Cursors
+# Cursors
+
+In MongoDB, a **cursor** is an object that enables you to iterate over and retrieve documents from a query result. When you execute a query to fetch documents from a database, MongoDB returns a pointer to the result set, known as a cursor. The cursor automatically takes care of batch processing of the result documents, providing an efficient way to handle large amounts of data.
+
+Cursors play a vital role in managing database operations, particularly when working with large datasets. They can help improve the performance and reduce the memory footprint of your application.
+
+## Basic Usage
+
+When you execute a query, MongoDB implicitly creates a cursor. For example, using the `find()` method on a collection returns a cursor object:
+
+```javascript
+const cursor = db.collection('myCollection').find();
+```
+
+You can then iterate over the documents in the result set using the cursor's `forEach` method or other methods like `toArray()` or `next()`:
+
+```javascript
+cursor.forEach(doc => {
+  console.log(doc);
+});
+```
+
+## Cursor Methods
+
+Cursors provide several methods that allow you to manipulate the result set and control the query execution. Some key methods include:
+
+- **`count()`**: Returns the total number of documents in the result set.
+- **`limit(n)`**: Limits the number of documents retrieved to `n`.
+- **`skip(n)`**: Skips the first `n` documents in the result set.
+- **`sort(field, order)`**: Sorts the documents based on the specified field and order (1 for ascending, -1 for descending).
+- **`project(field)`**: Specifies the fields to include or exclude from the result documents.
+
+You can chain these methods together to build complex queries:
+
+```javascript
+const cursor = db.collection('myCollection')
+  .find({ age: { $gt: 25 } })
+  .sort('name', 1)
+  .limit(10)
+  .skip(20)
+  .project({ name: 1, _id: 0 });
+```
+
+In this example, the cursor retrieves the first ten documents of people older than 25 years, sorts them by name in ascending order, skips the first twenty documents, and returns only the `name` field.
+
+## Closing Cursors
+
+Cursors automatically close when all documents in the result set have been retrieved or after 10 minutes of inactivity. However, in some cases, you may want to manually close a cursor. To do this, you can use the `close()` method:
+
+```javascript
+cursor.close();
+```
+
+This method is particularly useful when working with large result sets or when you want to explicitly manage resources.
+
+In summary, cursors are essential tools for working with MongoDB, as they provide an efficient way to handle large volumes of data by iterating through documents in batches. Leveraging cursor methods can help you optimize the performance and resource usage of your application.

src/data/roadmaps/mongodb/content/102-useful-concepts/102-retryable-reads-writes.md

@@ -1 +1,44 @@
-# Retryable reads writes
+# Retryable Reads / Writes
+
+Retryable reads and writes are an essential feature in MongoDB that provides the ability to automatically retry certain read and write operations, ensuring data consistency and improving the fault tolerance of your applications. This feature is especially useful in case of transient network errors or replica set elections that may cause operations to fail temporarily.
+
+## Retryable Reads
+
+Retryable reads allow MongoDB to automatically retry eligible read operations if they fail due to a transient error. This ensures that the application can continue to perform read operations seamlessly without throwing errors at users due to temporary issues.
+
+Examples of retryable read operations include:
+
+- `find()`
+- `aggregate()`
+- `distinct()`
+
+To enable retryable reads, use the following option in your client settings:
+
+```javascript
+{retryReads: true}
+```
+
+By default, newer versions of MongoDB (since v3.6) have retryable reads enabled.
+
+## Retryable Writes
+
+Similar to retryable reads, retryable writes allow MongoDB to automatically retry specific write operations that fail due to transient errors. This helps maintain data consistency and reduces the chances of data loss or duplicate writes.
+
+Examples of retryable write operations include:
+
+- `insertOne()`
+- `updateOne()`
+- `deleteOne()`
+- `findOneAndUpdate()`
+
+To enable retryable writes, use the following option in your client settings:
+
+```javascript
+{retryWrites: true}
+```
+
+By default, MongoDB has retryable writes enabled for replica sets and sharded clusters (since v4.0).
+
+**Note**: It's important to ensure that you're using a compatible version of the MongoDB server and drivers to take full advantage of retryable reads and writes features. Additionally, these features are not supported in standalone configurations.
+
+For more information, check the official [MongoDB documentation on retryable reads](https://docs.mongodb.com/manual/core/retryable-reads/) and [retryable writes](https://docs.mongodb.com/manual/core/retryable-writes/).

src/data/roadmaps/mongodb/content/102-useful-concepts/index.md

@@ -1 +1,37 @@
-# Useful concepts
+# Useful Concepts
+
+In this section, we will cover some of the most useful concepts you should be familiar with while working with MongoDB. As a flexible, document-based, and scalable database, MongoDB offers a wide range of possibilities for developers and administrators. Understanding these key concepts will help you leverage the benefits of MongoDB to their fullest extent.
+
+##  Documents and Collections
+
+* **Document:**
+  A single record in MongoDB is referred to as a document. Documents consist of key-value pairs and are stored in the JSON-like format BSON(Binary-JSON). This structure makes it flexible, extensible, and easy to work with.
+
+* **Collection:**
+  A group of MongoDB documents is referred to as a collection. Collections are analogous to tables in traditional relational databases, but unlike tables, they do not require a fixed schema. This allows for documents within a collection to have a variety of different fields and structures.
+
+##  MongoDB Query Language (MQL)
+
+MQL is the syntax used for querying MongoDB databases, performing CRUD operations (Create, Read, Update, and Delete), and managing database administration tasks. MQL is concise, powerful, and easy to use.
+
+##  Indexing
+
+Indexing is crucial for optimizing database performance. MongoDB supports various types of indexes, including single-field, compound, and text indexes. Proper indexing can significantly improve query performance by reducing the amount of work the database has to perform in order to find relevant data.
+
+##  Aggregation Framework
+
+MongoDB offers a robust aggregation framework that allows you to transform, manipulate, and analyze data in your collections. With the aggregation framework, you can perform complex data analysis tasks, such as filtering, grouping, and computing averages, efficiently and with ease.
+
+##  Replication and Sharding
+
+* **Replication:**
+  MongoDB offers high availability by allowing data replication across multiple servers. The replication feature ensures that if one server becomes unavailable, the others can continue to function without data loss. Replicated data is managed in replica sets, which consist of multiple MongoDB instances.
+
+* **Sharding:**
+  One of MongoDB's strengths is its ability to scale horizontally through sharding, the process of splitting and distributing data across multiple servers or clusters. This helps to distribute load, ensure better performance, and maintain availability as the size of the dataset grows.
+
+##  MongoDB Atlas
+
+MongoDB Atlas is a fully managed, global cloud database service provided by MongoDB. It offers features such as automatic backup and scaling, as well as advanced security for your MongoDB data. Atlas makes it easy to deploy, manage, and optimize your MongoDB databases in the cloud.
+
+By familiarizing yourself with these useful concepts in MongoDB, you will be well-equipped to build and manage efficient, powerful, and scalable applications. Happy coding!

src/data/roadmaps/mongodb/content/105-query-operators/100-indexes.md

@@ -1 +1,68 @@
-# Indexes
+# Creating Indexes
+
+Indexes are a powerful feature in MongoDB that help improve the performance of read operations (queries) in your database. They work similarly to the indexes found in a book, where you can quickly locate specific information rather than scanning through the entire content. In this section, we will discuss the basics of MongoDB indexes and their usage.
+
+## Overview of Indexes
+
+Basically, an index in MongoDB is a data structure that holds a smaller version of the data in our documents, along with a reference to the original document. This smaller version is stored in an efficient manner, making it easier and faster to locate specific documents based on the indexed field(s).
+
+Indexes can be created on one or more fields in a MongoDB collection. The default index that exists in every collection is the `_id` index, which ensures unique values for the `_id` field.
+
+## Types of Indexes
+
+There are several types of indexes in MongoDB, including:
+
+- **Single Field Index:** Index based on a single field in the documents.
+- **Compound Index:** Index based on multiple fields in the documents.
+- **Multikey Index:** Index used when the indexed field contains an array of values.
+- **Text Index:** Index used to support text search queries on string content.
+- **2dsphere Index:** Index used to support geospatial queries on spherical data.
+- **2d Index:** Index used to support geospatial queries on planar data.
+
+It's important to choose the right type of index for the queries you will be running on your MongoDB collection.
+
+## Creating Indexes
+
+To create an index on a field or fields, you can use the `createIndex()` method. Here's an example of creating an index on the "username" field in the "users" collection:
+
+```javascript
+db.users.createIndex({ username: 1 });
+```
+
+The `1` indicates that the index uses ascending order on the "username" field. You can also create a descending order index using `-1` as the value.
+
+For compound indexes, you can specify multiple fields like this:
+
+```javascript
+db.users.createIndex({ username: 1, email: 1 });
+```
+
+## Using Indexes
+
+Once you have created an index on a field or fields, MongoDB will automatically use the appropriate index when you perform queries on the collection, optimizing the query execution.
+
+To see which index is being used for a specific query, you can use the `explain()` method. For example, to see the index used for a query on the "username" field:
+
+```javascript
+db.users.find({ username: 'John' }).explain();
+```
+
+This will give you detailed information about the query execution, including the index used.
+
+## Managing Indexes
+
+To manage indexes, you can:
+
+- List all the indexes in a collection: `db.COLLECTION_NAME.getIndexes()`.
+- Remove an index: `db.COLLECTION_NAME.dropIndex(INDEX_NAME)`.
+- Remove all indexes: `db.COLLECTION_NAME.dropIndexes()`.
+
+## Limitations and Considerations
+
+While indexes are an amazing tool, they can have some caveats:
+
+- They consume storage space, so creating a large number of indexes may affect the storage capacity.
+- They can slow down write operations, as indexes should be updated whenever write operations occur on the indexed fields.
+- Indexes should be chosen wisely, considering the queries that will run on the collection.
+
+In conclusion, MongoDB indexes are a vital aspect of optimizing query performance in your database. By understanding the different types of indexes and using them effectively, you can significantly improve the performance and efficiency of your MongoDB applications.

src/data/roadmaps/mongodb/content/105-query-operators/100-projection-operators/100-project.md

@@ -1 +1,71 @@
-# Project
+# $project
+
+The `$project` is a projection operator in MongoDB, which is used during the aggregation process to reshape/output a document by specifying the fields to include or exclude. This is particularly helpful when you need to limit the amount of data retrieved from the database or modify the structure of the result.
+
+## Using `$project`
+
+The general syntax for the `$project` operator is:
+
+```json
+{ $project: { field1: expression1, field2: expression2, ... } }
+```
+
+The key-value pairs within the `$project` operator specify the field names to be included in the final result, and their corresponding expressions help define how the output value would be computed.
+
+## Example
+
+Let's assume we have a "users" collection with documents that look like this:
+
+```json
+{
+    "_id": 1,
+    "name": "John Doe",
+    "posts": [
+        { "title": "Sample Post 1", "views": 43 },
+        { "title": "Sample Post 2", "views": 89 }
+    ]
+}
+```
+
+If you want to retrieve only the name and the total number of posts for each user, you can execute the following aggregate query with a `$project` operator:
+
+```javascript
+db.users.aggregate([
+    {
+        $project: {
+            name: 1,
+            totalPosts: { $size: "$posts" }
+        }
+    }
+])
+```
+
+Here, we are including the `name` field and calculating the `totalPosts` value with the `$size` operator. The output will look like this:
+
+```json
+{
+    "_id": 1,
+    "name": "John Doe",
+    "totalPosts": 2
+}
+```
+
+## Excluding Fields
+
+By default, using a field with a value of 0 (zero) within the `$project` operator will exclude all the other fields except for the specified ones. It's important to note that the `_id` field is always included in the output unless explicitly excluded by specifying `_id: 0`.
+
+For example, if you only want to exclude the "posts" field, you can do that as follows:
+
+```javascript
+db.users.aggregate([
+    {
+        $project: {
+            posts: 0
+        }
+    }
+])
+```
+
+## Conclusion
+
+The `$project` operator is a powerful tool in MongoDB's aggregation framework that helps you manage the shape and size of the output documents. By understanding and leveraging its capabilities, you can effectively optimize your queries and reduce the amount of unnecessary data transfer in your application.

src/data/roadmaps/mongodb/content/105-query-operators/100-projection-operators/101-include.md

@@ -1 +1,71 @@
-# Include
+# $include
+
+The `$include` projection operator is used in queries to specify the fields that should be returned in the result documents. By using `$include`, you can choose to retrieve only fields of interest, making your query more efficient by minimizing the amount of data returned.
+
+The syntax for `$include` is as follows:
+
+```javascript
+{ field: 1 }
+```
+
+Here, `field` is the name of the field to include, and `1` indicates that you want the field included in the result documents. You can include multiple fields by specifying them in a comma-separated list:
+
+```javascript
+{ field1: 1, field2: 1, field3: 1 }
+```
+
+## Example
+
+Suppose we have a collection called `books` with the following documents:
+
+```javascript
+[
+  {
+    "title": "The Catcher in the Rye",
+    "author": "J.D. Salinger",
+    "year": 1951,
+    "genre": "Literary fiction"
+  },
+  {
+    "title": "To Kill a Mockingbird",
+    "author": "Harper Lee",
+    "year": 1960,
+    "genre": "Southern Gothic"
+  },
+  {
+    "title": "Of Mice and Men",
+    "author": "John Steinbeck",
+    "year": 1937,
+    "genre": "Novella"
+  }
+]
+```
+
+If you want to retrieve only the `title` and `author` fields from the documents in the `books` collection, you can use the `$include` projection operator as follows:
+
+```javascript
+db.books.find({}, { title: 1, author: 1, _id: 0 })
+```
+
+The result will be:
+
+```javascript
+[
+  {
+    "title": "The Catcher in the Rye",
+    "author": "J.D. Salinger",
+  },
+  {
+    "title": "To Kill a Mockingbird",
+    "author": "Harper Lee",
+  },
+  {
+    "title": "Of Mice and Men",
+    "author": "John Steinbeck",
+  }
+]
+```
+
+Note that we have also excluded the `_id` field (which is included by default) by setting it to `0`.
+
+Keep in mind that you cannot combine `$include` and `$exclude` (or `1` and `0`) in the same query, except for the `_id` field, which can be excluded even when other fields are being included.

src/data/roadmaps/mongodb/content/105-query-operators/100-projection-operators/102-exclude.md

@@ -1 +1,80 @@
-# Exclude
+# $exclude
+
+In MongoDB, the projection operators help you to specify the fields you want to include or exclude in the query result. The `exclude` operator, as the name suggests, helps you to exclude certain fields from the result.
+
+To exclude a field from the query result, you need to set its value to `0` in the projection document. Let's understand it better with an example.
+
+## Syntax
+
+```javascript
+{
+    $project: {
+        field1: 0,
+        field2: 0
+        ...
+    }
+}
+```
+
+Here, we're specifying that the fields `field1` and `field2` should be excluded from the result.
+
+## Example
+
+Suppose we have a collection called `students` with the following documents:
+
+```javascript
+{
+    "_id": 1,
+    "name": "John Doe",
+    "age": 20,
+    "course": "Software Engineering"
+},
+{
+    "_id": 2,
+    "name": "Jane Smith",
+    "age": 22,
+    "course": "Computer Science"
+},
+{
+    "_id": 3,
+    "name": "Richard Roe",
+    "age": 21,
+    "course": "Information Technology"
+}
+```
+
+Now, let's say we want to fetch all the students but exclude the `age` field from the result. We can achieve this using the following command:
+
+```javascript
+db.students.aggregate([
+    {
+        $project: {
+            age: 0
+        }
+    }
+])
+```
+
+This command will return the following result:
+
+```javascript
+{
+    "_id": 1,
+    "name": "John Doe",
+    "course": "Software Engineering"
+},
+{
+    "_id": 2,
+    "name": "Jane Smith",
+    "course": "Computer Science"
+},
+{
+    "_id": 3,
+    "name": "Richard Roe",
+    "course": "Information Technology"
+}
+```
+
+As you can see, the `age` field is excluded from the result.
+
+Note: You cannot use the `exclude` operator (0) for a field that is explicitly included with the `include` operator (1) in the same document, except for the `_id` field. The `_id` field is the only field that can have both exclude (0) and include (1) options in the same document.

src/data/roadmaps/mongodb/content/105-query-operators/100-projection-operators/103-slice.md

@@ -1 +1,48 @@
-# Slice
+# $slice
+
+The `$slice` projection operator is a MongoDB feature that allows you to limit the number of elements returned for an array field within the documents. This is particularly useful when you have large arrays in your documents, and you only need to work with a specific portion of them. By applying the `$slice` operator, you can optimize your queries and minimize memory usage.
+
+## Usage
+
+The `$slice` operator can be used in two forms:
+
+- Limit the number of array elements returned, starting from the beginning of the array.
+- Limit the number of array elements returned, starting from a specific position in the array.
+
+### Syntax
+
+The basic syntax for the `$slice` operator is as follows:
+
+```javascript
+{ field: { $slice: <number> } }
+```
+
+For the advanced usage, supplying a specific starting position:
+
+```javascript
+{ field: { $slice: [<skip (optional)>, <limit>] } }
+```
+
+### Examples
+
+- Limit the number of elements returned:
+
+To return only the first 3 elements of the `tags` field, use the following projection:
+
+```javascript
+db.collection.find({}, { tags: { $slice: 3 } })
+```
+
+- Define a specific starting position:
+
+To return 3 elements of the `tags` field starting from the 5th element, use the following projection:
+
+```javascript
+db.collection.find({}, { tags: { $slice: [4, 3] } })
+```
+
+Keep in mind that the starting position uses a zero-based index, so the value '4' in the example above refers to the 5th element in the array.
+
+## Conclusion
+
+In this section, we learned how to use the `$slice` projection operator to limit the number of array elements returned in our MongoDB queries. This can be a powerful tool for optimizing query performance and managing memory usage when working with large arrays.

src/data/roadmaps/mongodb/content/105-query-operators/100-projection-operators/index.md

@@ -1 +1,71 @@
-# Projection operators
+# Projection Operators
+
+Projection operators in MongoDB are used in the queries to control the fields that should be included or excluded from the result set. They can either limit the fields to be returned or specify the fields to be excluded from the results. In this section, we will look at some common projection operators available in MongoDB, such as `$`, `$elemMatch`, and `$slice`.
+
+## 1. `$`
+
+The `$` operator is used to project the first element in an array that matches the specified condition. It is especially useful when dealing with large arrays, and you only need the first element matching a given condition.
+
+Syntax:
+
+```javascript
+{ <field>: { $elemMatch: { <query1>, <query2>, ... } } }
+```
+
+Usage example:
+
+```javascript
+db.collection.find({ "grades": { "$gte": 80 }}, { "name": 1, "grades.$": 1 })
+```
+
+This will return only the first `grades` element greater than or equal to 80 along with the `name` field.
+
+## 2. `$elemMatch`
+
+The `$elemMatch` operator matches documents in a collection that contain an array field with at least one element that satisfies multiple given conditions.
+
+Syntax:
+
+```javascript
+{ <field>: { $elemMatch: { <query1>, <query2>, ... } } }
+```
+
+Usage example:
+
+```javascript
+db.collection.find({ "subjects": { "$elemMatch": { "score": { "$gte": 80 }, "type": "exam" } } })
+```
+
+This will return documents that have at least one `subjects` element with a `score` greater than or equal to 80 and a `type` of "exam".
+
+## 3. `$slice`
+
+The `$slice` operator is used to limit the number of elements projected from an array. It can either return the first N elements, skip the first N elements, or return elements after skipping N elements.
+
+Syntax:
+
+```javascript
+{ <field>: { $slice: <num_elements> } }
+```
+
+or
+
+```javascript
+{ <field>: { $slice: [ <skip_count>, <num_elements> ] } }
+```
+
+Usage example:
+
+```javascript
+db.collection.find({}, { "name": 1, "grades": { "$slice": 3 } })
+```
+
+This will return the `name` field and the first 3 `grades` elements for all documents in the collection.
+
+```javascript
+db.collection.find({}, { "name": 1, "grades": { "$slice": [ 1, 2 ] } })
+```
+
+This will return the `name` field and the 2 `grades` elements after skipping the first element for all documents in the collection.
+
+In summary, projection operators play a crucial role in retrieving specific data from MongoDB collections as they allow you to get the desired output. Using the appropriate operator for your query can help optimize the performance and efficiency of your MongoDB queries.

src/data/roadmaps/mongodb/content/105-query-operators/101-atlas-search-indexes.md

@@ -1 +1,67 @@
-# Atlas search indexes
+# Atlas Search indexes
+
+Atlas Search Indexes are a powerful feature of MongoDB Atlas that allows you to create indexes on your dataset for advanced text searching and filtering functionalities. These indexes are built using the open-source search engine "Apache Lucene" to provide robust search capabilities directly within your MongoDB environment, enabling you to perform full-text search, filter, and scoring operations.
+
+## Benefits of Atlas Search Indexes
+
+- **Advanced Text Search:** Enhance search experience with support for multi-language text search, scoring, and relevancy rankings.
+
+- **Versatile Querying:** Perform advanced queries using a wide array of search operators like range, wildcard, and fuzzy queries.
+
+- **Dynamic Field Mapping:** Auto-map fields in your collection for seamless indexing without requiring a strict schema.
+
+- **Real-time Indexing:** Keep your search indexes up-to-date by updating them with database changes in near real-time.
+
+## Key Components
+
+Here are a few essential components you should know when working with Atlas Search Indexes:
+
+- **Index Definitions**: Index Definitions specify which fields in your collection to index and the analyzer to use for processing text. They ensure that your search queries are fast and efficient.
+
+```json
+{
+  "mappings": {
+    "dynamic": false,
+    "fields": {
+      "title": {
+        "type": "string",
+        "analyzer": "lucene.standard"
+      },
+      "description": {
+        "type": "string",
+        "analyzer": "lucene.english"
+      }
+    }
+  }
+}
+```
+
+- **Search Operators**: These are the query operators that allow you to perform advanced search operations on your indexed data. Some common search operators are:
+
+- `$search`: The primary search operator for Atlas Search queries.
+- `$compound`: Combines multiple queries using logical operators (`must`, `should`, `mustNot`).
+- `$text`: Performs text search queries.
+- `$range`: Performs range queries on the indexed data.
+
+- **Analyzers**: Analyzers process text input for indexing and search operations. They are responsible for tokenizing text, creating tokens, and processing filter conditions. MongoDB Atlas provides a range of Lucene analyzer options for handling different languages and use cases.
+
+## Usage
+
+To use Atlas Search Indexes in your queries, you will need to create an index definition for the required fields and use `$search` operator along with other search operators depending on your requirements.
+
+Here's an example of an Atlas Search Index query:
+
+```javascript
+db.collection.find({
+  $search: {
+    "text": {
+      "query": "mongodb atlas search",
+      "path": "title"
+    }
+  }
+})
+```
+
+In this example, we perform a text search query on the "title" field in the given collection.
+
+In summary, Atlas Search Indexes provide you with advanced search and filtering capabilities, rich text processing, and improved query performance. By working with Index Definitions, Search Operators, and Analyzers, you can run advanced text search queries within your MongoDB Atlas environment.

src/data/roadmaps/mongodb/content/105-query-operators/101-comparison-operators/100-eq.md

@@ -1 +1,39 @@
-# Eq
+# $eq
+
+The `$eq` (equal) operator in MongoDB is used for comparison operations. It compares two values, and if they are equal, the result is `true`. Otherwise, the result is `false`. 
+
+The `$eq` operator can be used in queries to filter documents based on a specific field's value. It can also be used in aggregations where you can determine whether two fields' values or expressions are equal.
+
+## Usage
+
+In a query, the `$eq` operator can be used as follows:
+
+```javascript
+db.collection.find({ field: { $eq: value } })
+```
+
+For example, if you have a collection named `products` and you want to find all documents where the `price` field is equal to `100`, you can use the `$eq` operator like this:
+
+```javascript
+db.products.find({ price: { $eq: 100 } })
+```
+
+### Usage in Aggregations
+
+In an aggregation pipeline, the `$eq` operator can be used within the `$project`, `$match`, `$addFields`, and other stages with expressions. For example, if you want to add a field "discounted" to the documents based on whether the `price` field is equal to `50`, you can use the `$eq` operator like this:
+
+```javascript
+db.products.aggregate([
+  {
+    $addFields: {
+      discounted: {
+        $eq: ["$price", 50]
+      }
+    }
+  }
+])
+```
+
+This will add a new field named "discounted" with a `true` or `false` value based on whether the `price` field is equal to `50`.
+
+In conclusion, the `$eq` operator is a helpful tool in MongoDB for performing equality checks and filtering documents based on matching values in queries and aggregations.

src/data/roadmaps/mongodb/content/105-query-operators/101-comparison-operators/101-gt.md

@@ -1 +1,25 @@
-# Gt
+# $gt
+
+The `$gt` operator in MongoDB is used to filter documents based on the values of a particular field being *greater than* the specified value. This operator is handy when you want to retrieve documents that fulfill a condition where a field's value is more than a given value.
+
+The general syntax for querying using the `$gt` operator is:
+
+```javascript
+{ field: { $gt: value } }
+```
+
+Here, we need to replace the `field` with the actual field name in the document, and `value` with the desired value you want to compare against.
+
+## Example
+
+Consider a `students` collection where each document contains information about a student, including their `first_name`, `last_name`, and `age`.
+
+If you want to find all the students whose ages are greater than 21, you would use a query with the `$gt` operator as follows:
+
+```javascript
+db.students.find({ age: { $gt: 21 } });
+```
+
+This query will return all the documents in the `students` collection where the `age` field has a value greater than 21.
+
+Keep in mind that the `$gt` operator can also be used with non-numeric data types, such as date values. The comparison will be made based on their natural order.

src/data/roadmaps/mongodb/content/105-query-operators/101-comparison-operators/102-lt.md

@@ -1 +1,41 @@
-# Lt
+# $lt
+
+In MongoDB, the `$lt` operator is used to filter documents where the value of a specified field is less than the provided value. This operator compares the specified field value with the provided one and returns documents that satisfy the "less than" condition. The `$lt` operator can be used with various data types like numbers, strings, and dates.
+
+Here's a brief description of the syntax and usage of the `$lt` operator:
+
+## Syntax
+
+```javascript
+{ field: { $lt: value } }
+```
+
+## Usage
+
+For instance, let's assume you have a collection named `products` with the following documents:
+
+```javascript
+[
+  { _id: 1, name: "Laptop", price: 1000 },
+  { _id: 2, name: "Smartphone", price: 600 },
+  { _id: 3, name: "Tablet", price: 300 },
+  { _id: 4, name: "Smartwatch", price: 200 }
+]
+```
+
+To find all products with a price less than 500, you can use the following query:
+
+```javascript
+db.products.find({ price: { $lt: 500 } })
+```
+
+This query will return the following documents:
+
+```javascript
+[
+  { "_id" : 3, "name" : "Tablet", "price" : 300 },
+  { "_id" : 4, "name" : "Smartwatch", "price" : 200 }
+]
+```
+
+In this example, the query checks for documents where the `price` field has a value less than 500 and returns the matching documents from the `products` collection.

src/data/roadmaps/mongodb/content/105-query-operators/101-comparison-operators/103-lte.md

@@ -1 +1,43 @@
-# Lte
+# $lte
+
+The `$lte` comparison operator matches values that are less than or equal to the specified value. It can be used in queries to filter documents based on the values of a specific field.
+
+### Syntax
+
+To use the `$lte` operator, specify it in the query filter using the following syntax:
+
+```javascript
+{ field: { $lte: value } }
+```
+
+### Example
+
+Consider a collection `products` with the following documents:
+
+```json
+[
+  { "_id": 1, "name": "Product A", "price": 10 },
+  { "_id": 2, "name": "Product B", "price": 15 },
+  { "_id": 3, "name": "Product C", "price": 20 },
+  { "_id": 4, "name": "Product D", "price": 25 }
+]
+```
+
+To query for products with a price of **15 or less**, use the `$lte` operator as shown below:
+
+```javascript
+db.products.find( { price: { $lte: 15 } } )
+```
+
+This query will return the following documents:
+
+```json
+[
+  { "_id": 1, "name": "Product A", "price": 10 },
+  { "_id": 2, "name": "Product B", "price": 15 }
+]
+```
+
+Using the `$lte` operator, you can easily filter documents based on numeric, date, or string values. Remember that string comparisons are done based on Unicode code points.
+
+Keep in mind that when comparing different data types, MongoDB uses a type hierarchy for comparisons. You can find more about it in the official documentation: [MongoDB Type Comparison Order](https://docs.mongodb.com/manual/reference/bson-type-comparison-order/).

src/data/roadmaps/mongodb/content/105-query-operators/101-comparison-operators/104-gte.md

@@ -1 +1,48 @@
-# Gte
+# $gte
+
+The Greater Than or Equal To Operator (`$gte`) in MongoDB is an essential comparison operator. It compares two values and returns `true` if the first value is greater than or equal to the second value. It is highly useful for filtering documents based on specific criteria in your queries.
+
+## Syntax
+
+The syntax for using the `$gte` operator is:
+
+```javascript
+{
+  field: { $gte: value }
+}
+```
+
+Where `field` is the name of the field being compared, and `value` is the comparison value.
+
+## Example
+
+Let's explore an example using the `$gte` operator. Assume we have a collection `products` with the following documents:
+
+```javascript
+[
+  { _id: 1, product: "A", price: 10 },
+  { _id: 2, product: "B", price: 20 },
+  { _id: 3, product: "C", price: 30 },
+  { _id: 4, product: "D", price: 40 },
+  { _id: 5, product: "E", price: 50 }
+]
+```
+
+To find all documents where `price` is greater than or equal to `20`, you can use the following query:
+
+```javascript
+db.products.find({ price: { $gte: 20 } })
+```
+
+The output will be:
+
+```javascript
+[
+  { "_id" : 2, "product" : "B", "price" : 20 },
+  { "_id" : 3, "product" : "C", "price" : 30 },
+  { "_id" : 4, "product" : "D", "price" : 40 },
+  { "_id" : 5, "product" : "E", "price" : 50 }
+]
+```
+
+As we can see, the `$gte` operator successfully filtered the documents based on the specified criteria. This operator is extremely helpful when you need to narrow down your search or filter documents depending on certain conditions, making it a valuable addition to the toolbox of any MongoDB developer.

src/data/roadmaps/mongodb/content/105-query-operators/101-comparison-operators/105-ne.md

@@ -1 +1,43 @@
-# Ne
+# $ne
+
+In MongoDB, the `$ne` operator is used to filter documents where the value of a specified field is _not equal_ to a specified value.
+
+## Usage
+
+To use the `$ne` comparison operator, include it within the query document as:
+
+```javascript
+{ field: { $ne: value } }
+```
+
+- `field` : The field that you want to apply the `$ne` operator on.
+- `value` : The value that you want to filter out from the results.
+
+## Example
+
+Let's say you have a collection called `products` with documents like:
+
+```javascript
+{ _id: 1, name: "Apple", category: "Fruits" }
+{ _id: 2, name: "Banana", category: "Fruits" }
+{ _id: 3, name: "Carrot", category: "Vegetables" }
+```
+
+If you want to query all documents where the category is _not_ "Fruits", you would execute:
+
+```javascript
+db.products.find({ category: { $ne: "Fruits" } })
+```
+
+The result would be:
+
+```javascript
+{ "_id" : 3, "name" : "Carrot", "category" : "Vegetables" }
+```
+
+## Additional Notes
+
+- The `$ne` operator also works with compound conditions.
+- You can compare values of different types (e.g., a string and a number), but remember that MongoDB uses BSON's comparison rules for different data types.
+
+And that's a brief summary of the `$ne` operator. Use it when you want to filter documents where a specified field's value is not equal to another specified value. Happy querying!

src/data/roadmaps/mongodb/content/105-query-operators/101-comparison-operators/index.md

@@ -1 +1,101 @@
-# Comparison operators
+# Comparison Operators
+
+Comparison operators are used to performing various operations like comparing values or selecting documents based on the comparison. In this section, we'll discuss some of the most commonly used comparison operators in MongoDB.
+
+## `$eq`
+
+The `$eq` operator is used to match documents where the value of a field equals the specified value. The syntax for `$eq` is:
+
+```javascript
+{ <field>: { $eq: <value> } }
+```
+
+Example:
+
+```javascript
+db.collection.find({ age: { $eq: 25 } })
+```
+
+This query will return all documents where the `age` field is equal to 25.
+
+## `$ne`
+
+The `$ne` operator is used to match documents where the value of a field is not equal to the specified value. The syntax for `$ne` is:
+
+```javascript
+{ <field>: { $ne: <value> } }
+```
+
+Example:
+
+```javascript
+db.collection.find({ age: { $ne: 25 } })
+```
+
+This query will return all documents where the `age` field is not equal to 25.
+
+## `$gt`
+
+The `$gt` operator is used to match documents where the value of a field is greater than the specified value. The syntax for `$gt` is:
+
+```javascript
+{ <field>: { $gt: <value> } }
+```
+
+Example:
+
+```javascript
+db.collection.find({ age: { $gt: 25 } })
+```
+
+This query will return all documents where the `age` field is greater than 25.
+
+## `$gte`
+
+The `$gte` operator is used to match documents where the value of a field is greater than or equal to the specified value. The syntax for `$gte` is:
+
+```javascript
+{ <field>: { $gte: <value> } }
+```
+
+Example:
+
+```javascript
+db.collection.find({ age: { $gte: 25 } })
+```
+
+This query will return all documents where the `age` field is greater than or equal to 25.
+
+## `$lt`
+
+The `$lt` operator is used to match documents where the value of a field is less than the specified value. The syntax for `$lt` is:
+
+```javascript
+{ <field>: { $lt: <value> } }
+```
+
+Example:
+
+```javascript
+db.collection.find({ age: { $lt: 25 } })
+```
+
+This query will return all documents where the `age` field is less than 25.
+
+## `$lte`
+
+The `$lte` operator is used to match documents where the value of a field is less than or equal to the specified value. The syntax for `$lte` is:
+
+```javascript
+{ <field>: { $lte: <value> } }
+```
+
+Example:
+
+```javascript
+db.collection.find({ age: { $lte: 25 } })
+```
+
+This query will return all documents where the `age` field is less than or equal to 25.
+
+These comparison operators can help query your data more efficiently and effectively. You can combine them to create complex queries to meet your specific requirements.

src/data/roadmaps/mongodb/content/105-query-operators/102-array-operators/100-in.md

@@ -1 +1,41 @@
-# In
+# $in
+
+The `$in` operator in MongoDB is used to match any one of the values specified in an array. It can be used with a field that contains an array or with a field that holds a scalar value. This operator is handy when you want to filter documents based on multiple possible values for a specific field.
+
+## Syntax
+
+Here's the general structure of a query using the `$in` operator:
+
+```javascript
+{ field: { $in: [<value1>, <value2>, ...] } }
+```
+
+## Example
+
+Consider a collection `articles` with the following documents:
+
+```javascript
+[
+  { "_id": 1, "title": "MongoDB", "tags": ["database", "NoSQL"] },
+  { "_id": 2, "title": "Node.js", "tags": ["javascript", "runtime"] },
+  { "_id": 3, "title": "React", "tags": ["library", "javascript"] }
+]
+```
+
+Let's say you want to find all articles that have either the "NoSQL" or "javascript" tag. You can use the `$in` operator like so:
+
+```javascript
+db.articles.find({ "tags": { $in: ["NoSQL", "javascript"] } })
+```
+
+This will return the following documents:
+
+```javascript
+[
+  { "_id": 1, "title": "MongoDB", "tags": ["database", "NoSQL"] },
+  { "_id": 2, "title": "Node.js", "tags": ["javascript", "runtime"] },
+  { "_id": 3, "title": "React", "tags": ["library", "javascript"] }
+]
+```
+
+In conclusion, the `$in` operator allows you to specify an array of values and filter documents based on whether their field value exists within that array.

src/data/roadmaps/mongodb/content/105-query-operators/102-array-operators/101-nin.md

@@ -1 +1,27 @@
-# Nin
+# $nin
+
+The `$nin` (Not In) operator is used to filter documents where the value of a field is not in a specified array. It selects the documents where the field value is either not in the specified array or the field does not exist.
+
+## Syntax
+
+To use the `$nin` operator in a query, you can use the following syntax:
+
+```javascript
+{ field: { $nin: [<value1>, <value2>, ..., <valueN>] } }
+```
+
+`<field>` is the name of the field you want to apply the `$nin` condition on, and `<value1>, <value2>, ..., <valueN>` are the values that the field should not have.
+
+## Example
+
+Suppose you have a `books` collection with documents containing `title` and `genre` fields, and you want to find books that are **not** in the genres 'Mystery', 'Sci-Fi', or 'Thriller'. You can use the `$nin` operator like this:
+
+```javascript
+db.books.find({ genre: { $nin: ['Mystery', 'Sci-Fi', 'Thriller'] }})
+```
+
+This query will return all documents where the `genre` field is not one of the specified values or the field does not exist.
+
+## Conclusion
+
+In summary, the `$nin` operator is a powerful tool that allows you to filter documents based on the absence of specific values in an array. By incorporating `$nin` into your MongoDB queries, you can effectively narrow down your search and retrieve the desired documents more efficiently.

src/data/roadmaps/mongodb/content/105-query-operators/102-array-operators/102-all.md

@@ -1 +1,43 @@
-# All
+# $all
+
+The `$all` operator is used to match arrays that contain all specified elements. This allows you to filter documents based on multiple values in a single array field.
+
+## Syntax
+
+The basic syntax for using the `$all` operator is:
+
+```javascript
+{
+  <field>: {
+    $all: [<value1>, <value2>, ..., <valueN>]
+  }
+}
+```
+
+Here, `<field>` refers to the name of the array field that should be queried, and `<value1>, <value2>, ..., <valueN>` are the values that you want to match against.
+
+## Example
+
+Let's assume we have a collection `movies` with documents containing the following fields: `_id`, `title`, and `tags`. The `tags` field is an array of string values.
+
+Here is an example document from the `movies` collection:
+
+```javascript
+{
+  _id: 1,
+  title: "The Matrix",
+  tags: ["action", "sci-fi", "cyberpunk"]
+}
+```
+
+If you want to find all movies with the tags "action" and "sci-fi", you can use the `$all` operator as shown below:
+
+```javascript
+db.movies.find({tags: {$all: ["action", "sci-fi"]}});
+```
+
+This query would return all documents where the `tags` array contains **both** "action" and "sci-fi" values.
+
+## Summary
+
+The `$all` operator allows you to match documents based on the presence of multiple values in an array field. It provides a simple and powerful way to query for documents that meet specific criteria within arrays.

src/data/roadmaps/mongodb/content/105-query-operators/102-array-operators/103-elem-match.md

@@ -1 +1,55 @@
-# Elem match
+# $elemMatch
+
+`$elemMatch` is an array operator in MongoDB that is used to select documents that contain an array field with at least one element matching the specified query criteria. This is useful in situations when you need to match multiple criteria within the same array element.
+
+## Usage
+
+To use `$elemMatch`, you need to include it in your query with the syntax `{ <field>: { $elemMatch: { <query> } } }`.
+
+* `<field>`: The name of the array field for which you want to apply the `$elemMatch` operator.
+* `<query>`: A document containing the query conditions to be matched against the elements in the array.
+
+## Example
+
+Let's say you have a collection named `courseRecords` containing the following documents:
+
+```json
+{
+  "_id": 1,
+  "student": "Mary",
+  "grades": [ { "subject": "Math", "score": 80 }, { "subject": "English", "score": 75 } ]
+}
+
+{
+  "_id": 2,
+  "student": "Tom",
+  "grades": [ { "subject": "Math", "score": 90 }, { "subject": "English", "score": 80 } ]
+}
+
+{
+  "_id": 3,
+  "student": "John",
+  "grades": [ { "subject": "Math", "score": 85 }, { "subject": "English", "score": 65 } ]
+}
+```
+
+If you want to find all the students who have scored 80 or above in Math and 70 or above in English, you can use `$elemMatch` as follows:
+
+```javascript
+db.courseRecords.find( {
+  "grades": {
+    $elemMatch: {
+      "subject": "Math",
+      "score": { $gte: 80 },
+      "subject": "English",
+      "score": { $gte: 70 }
+    }
+  }
+} )
+```
+
+This would return the records for Mary and Tom.
+
+## Further Reading
+
+For more advanced uses of `$elemMatch` and additional examples, you can refer to the [official MongoDB documentation](https://docs.mongodb.com/manual/reference/operator/query/elemMatch/).

src/data/roadmaps/mongodb/content/105-query-operators/102-array-operators/104-size.md

@@ -1 +1,29 @@
-# Size
+# $size
+
+The `$size` operator in MongoDB is a powerful tool for querying and filtering documents based on the size of an array field. This operator lets you find documents with array fields containing an exact number of elements. It is used within the `$elemMatch` operator, which allows for matching documents where an array field contains elements that satisfy a set of specified conditions.
+
+Here's a brief summary of how to work with the `$size` operator:
+
+**Syntax:**
+
+```javascript
+{ "<array_field>": { "$size": <numer_of_elements> } }
+```
+
+**Example:**
+
+Assume we have a collection called `products` with documents containing an attribute `colors` which is an array type.
+
+```javascript
+db.products.find( { "colors": { "$size": 5 } } )
+```
+
+This query will return all documents in the `products` collection that have exactly 5 elements in the `colors` array field.
+
+**Important notes:**
+
+- Keep in mind that the `$size` operator only matches exact array sizes. If you need more flexible array length comparison, you may consider using `$expr` with `$size` in the aggregation framework.
+
+- The `$size` operator does not require the creation of an additional index to work efficiently. It can leverage existing indexes on an array field.
+
+For more information and examples, refer to the [MongoDB documentation on `$size`.](https://docs.mongodb.com/manual/reference/operator/query/size/)

src/data/roadmaps/mongodb/content/105-query-operators/102-array-operators/index.md

@@ -1 +1,65 @@
-# Array operators
+# Array Operators
+
+In MongoDB, array operators allow you to perform various operations on arrays within documents. These operators help you query and manipulate the elements in the array fields of your collections. Let's go through some of the most commonly used array operators:
+
+##  `$elemMatch`
+
+The `$elemMatch` operator is used to match one or more array elements that satisfy the given query condition(s). It returns the documents where the array field has at least one matching element.
+
+**Example:**
+
+```javascript
+db.collection.find({ "scores": { "$elemMatch": { "$gte": 80, "$lt": 90 } } })
+```
+
+This query returns all documents where the `scores` array has at least one element between 80 and 90.
+
+##  `$all`
+
+The `$all` operator is used to match arrays that contain all the specified query elements. It returns documents where the array field has all the given elements, irrespective of their order.
+
+**Example:**
+
+```javascript
+db.collection.find({ "tags": { "$all": ["mongodb", "database"] } })
+```
+
+This query returns all documents where the `tags` array contains both "mongodb" and "database".
+
+##  `$size`
+
+The `$size` operator is used to match arrays that have the specified number of elements. It returns documents where the array field has the given size.
+
+**Example:**
+
+```javascript
+db.collection.find({ "comments": { "$size": 3 } })
+```
+
+This query returns all documents where the `comments` array contains exactly 3 elements.
+
+##  `$addToSet`
+
+The `$addToSet` operator is used to add unique values to an array field. If the value doesn't exist in the array, it will be added; otherwise, the array remains unchanged.
+
+**Example:**
+
+```javascript
+db.collection.updateOne({ "_id": 1 }, { "$addToSet": { "colors": "green" } })
+```
+
+This query adds "green" to the `colors` array in the document with `_id` equal to 1, only if it's not already present.
+
+##  `$push`
+
+The `$push` operator is used to add values to an array field. It adds the value to the array, even if it exists already.
+
+**Example:**
+
+```javascript
+db.collection.updateOne({ "_id": 1 }, { "$push": { "comments": "Great article!" } })
+```
+
+This query adds "Great article!" to the `comments` array in the document with `_id` equal to 1.
+
+Remember that there are several other array operators available in MongoDB, but the ones mentioned above are the most commonly used. You can always refer to the [MongoDB documentation](https://docs.mongodb.com/manual/reference/operator/query-array/) for more information on array operators.

src/data/roadmaps/mongodb/content/105-query-operators/102-query-optimization.md

@@ -1 +1,59 @@
-# Query optimization
+# Query Optimization
+
+In MongoDB, query optimization is a crucial aspect to ensure efficient and fast retrieval of data. The query optimizer helps in the selection of the appropriate query plan, enabling MongoDB to execute queries efficiently. The query optimizer's primary goal is to minimize the number of documents to be read or scanned, consequently reducing the overall execution time.
+
+In this section, we'll discuss some essential aspects of query optimization in MongoDB:
+
+##  Indexing
+
+One of the most important techniques for optimizing query performance in MongoDB is the use of indexes. In MongoDB, indexes are created on specific fields of a collection, enabling faster search results. They improve query performance by minimizing the number of documents to be scanned, thus reducing the overall execution time.
+
+To create an index, use the `createIndex()` method:
+
+```javascript
+db.collection.createIndex({ field1: 1, field2: -1 })
+```
+
+##  Explain
+
+MongoDB provides the `explain()` method, which is an essential tool for understanding the behavior and performance of your queries. By using `explain()`, you can identify the query plan used, evaluate the effectiveness of an index, and debug queries.
+
+Example usage:
+
+```javascript
+db.collection.find({ field: value }).explain("executionStats")
+```
+
+##  Profiling
+
+The MongoDB database profiler helps you analyze and diagnose the performance issues of your queries. By monitoring the executed operations on the database, the profiler can provide valuable insights for query optimization.
+
+To enable the database profiler with system log level:
+
+```javascript
+db.setProfilingLevel(1)
+```
+
+To query the `system.profile` collection:
+
+```javascript
+db.system.profile.find().pretty()
+```
+
+##  Schema Design
+
+A well-designed data schema can have a significant impact on the query performance. Design your schema by considering the common query patterns and use cases of your application. Make use of embedded documents and store related data in the same documents to enable faster data retrieval.
+
+##  Query Limits and Projections
+
+To optimize queries, you can apply limits and use projections in your queries. Limits allow you to restrict the number of documents returned during a query, which eventually reduces the amount of data transferred between the server and your application.
+
+Projections, on the other hand, allow you to specify the fields to return in the query results. This means that only the required fields are retrieved, thus reducing the overall document size and improving the query performance.
+
+Example usage:
+
+```javascript
+db.collection.find({ field: value }, { projectionField: 1 }).limit(10)
+```
+
+In conclusion, MongoDB offers several features and methodologies to optimize the performance of your queries. By making wise use of indexing, understanding query plans with `explain()`, leveraging the database profiler, designing efficient schema, and using limits and projections, you can ensure a performant and optimally functioning MongoDB database.

src/data/roadmaps/mongodb/content/105-query-operators/103-element-operators/100-exists.md

@@ -1 +1,48 @@
-# Exists
+# $exists
+
+The `$exists` operator in MongoDB is one of the essential element operators used to filter documents in queries. This operator allows you to search documents in a collection based on the presence or absence of a field, regardless of its value.
+
+## Syntax
+
+```javascript
+{ field: { $exists: <boolean> } }
+```
+
+Here, `<boolean>` can be either `true` or `false`. If `true`, then it filters the documents containing the specified field, and if `false`, it filters the documents not containing the specified field.
+
+## Examples
+
+- Find all documents where the field "author" exists:
+```javascript
+db.books.find( { author: { $exists: true } } )
+```
+
+- Find all documents where the field "publisher" does not exist:
+```javascript
+db.books.find( { publisher: { $exists: false } } )
+```
+
+## Usage with Embedded Documents
+
+`$exists` also works perfectly with embedded documents or arrays when searching for the presence or absence of specific fields.
+
+**Example:**
+
+Find all documents where the field "address.city" is present.
+
+```javascript
+db.users.find( { "address.city": { $exists: true } } )
+```
+
+## Note
+Keep in mind that `$exists` checks for both the presence of a field and `null` values since they represent the existence of a field with no value. If you want to search for fields with non-null values, you can use a combination of `$exists` and `$ne` (not equal to) operator.
+
+**Example:**
+
+Find all documents where the field "edition" exists and has a non-null value.
+
+```javascript
+db.books.find( { edition: { $exists: true, $ne: null } } )
+```
+
+That's all you need to know about `$exists` in MongoDB! Happy querying!

src/data/roadmaps/mongodb/content/105-query-operators/103-element-operators/101-type.md

@@ -1 +1,51 @@
-# Type
+# $type
+
+The `$type` operator is an element query operator in MongoDB that allows you to select documents based on data types of their fields. This can be useful when you want to perform operations only on those documents that have specific data types for certain fields. 
+
+## Syntax
+
+The basic syntax for using the `$type` operator is:
+
+```javascript
+{ fieldName: { $type: dataType } }
+```
+
+Here, `fieldName` is the name of the field whose data type you want to check, and `dataType` is the BSON data type or its corresponding alias.
+
+## BSON Data Types and Aliases
+
+MongoDB supports various data types for fields, such as `String`, `Number`, `Date`, etc. Some of the common BSON data types and their corresponding aliases are:
+
+- `Double`: 1 or 'double'
+- `String`: 2 or 'string'
+- `Object`: 3 or 'object'
+- `Array`: 4 or 'array'
+- `Binary`: 5 or 'binData'
+- `ObjectId`: 7 or 'objectId'
+- `Boolean`: 8 or 'bool'
+- `Date`: 9 or 'date'
+- `Null`: 10 or 'null'
+- `Regex`: 11 or 'regex'
+- `Int32`: 16 or 'int'
+- `Int64`: 18 or 'long'
+- `Decimal128`: 19 or 'decimal'
+
+Refer to the [MongoDB documentation](https://docs.mongodb.com/manual/reference/bson-types/) for a comprehensive list of supported BSON data types and their aliases.
+
+## Example
+
+Suppose you have a collection named `products` with different fields like `name`, `price`, and `discount`. You want to find documents that have a `price` field of type `Double`. You can use the `$type` operator like this:
+
+```javascript
+db.products.find( { price: { $type: "double" } } )
+```
+
+Or use the BSON data type instead of alias:
+
+```javascript
+db.products.find( { price: { $type: 1 } } )
+```
+
+Keep in mind that the `$type` operator will only match documents with the exact data type specified for the field. So, if the field has an integer value, using `$type` with `Double` will not match those documents.
+
+In summary, the `$type` element operator is a useful query tool for selecting documents based on the data types of their fields in MongoDB. By understanding and utilizing the BSON data types and aliases, you can effectively filter documents in your queries based on specific fields' data types.

src/data/roadmaps/mongodb/content/105-query-operators/103-element-operators/102-regex.md

@@ -1 +1,45 @@
-# Regex
+# $regex
+
+The `$regex` operator in MongoDB is a powerful and versatile tool for searching and querying text-based fields in your documents. It allows you to search for strings that match a specific pattern, which is defined using Regular Expressions (regex).
+
+Regular Expressions are a sequence of characters that define a search pattern. These patterns can be used to perform powerful searches, like matching specific words, phrases, or even complex combinations of characters.
+
+In this section, we'll explore the usage of the `$regex` operator and see how it can be an invaluable tool in your MongoDB queries.
+
+## Using `$regex` Operator
+
+The `$regex` operator can be used in the `find()` method, when searching through a collection of documents. It takes a pattern and searches for any documents that match the provided pattern. Here's a basic example:
+
+```javascript
+db.collection.find({ fieldName: { $regex: "your-pattern" } });
+```
+
+Replace `fieldName` with the name of the field you want to search and `your-pattern` with the regular expression pattern you want to match. This query will return any documents that contain the matching pattern in the specified field.
+
+## Case Insensitive Searches
+
+By default, the `$regex` operator is case-sensitive. If you want to perform a case-insensitive search, use the `$options` parameter with the `$regex` operator. To make the search case-insensitive, add the option `i`.
+
+Here's an example:
+
+```javascript
+db.collection.find({ fieldName: { $regex: "your-pattern", $options: "i" } });
+```
+
+In this example, the query will return any documents that contain the matching pattern in the specified field, regardless of the text case.
+
+## Using Special Characters
+
+In Regular Expressions, some characters have special meanings, such as the period (`.`), asterisk (`*`), and plus sign (`+`). To search for these characters in your documents, you need to escape them with a backslash (`\`). For example, if you want to find documents that have a `+` sign in a field, you can use the following pattern:
+
+```javascript
+db.collection.find({ fieldName: { $regex: "\\+" } });
+```
+
+In this example, the backslash escapes the `+` sign, telling the `$regex` operator to search for the literal character `+` in the documents.
+
+## Conclusion
+
+The `$regex` operator allows you to flexibly search through text-based fields in your MongoDB documents by using powerful Regular Expressions. Remember to use the appropriate `$options` when necessary, and be mindful of special characters that require escaping.
+
+Learning and mastering Regular Expressions can greatly improve the searching capabilities of your MongoDB queries, making use of the `$regex` operator a valuable skill.

src/data/roadmaps/mongodb/content/105-query-operators/103-element-operators/index.md

@@ -1 +1,40 @@
-# Element operators
+# Element Operators
+
+Element operators in MongoDB are used to query documents based on the presence, type, or absence of a field and its value. These operators offer a flexible approach to querying the data and allow you to manipulate elements at a granular level.
+
+Here's a brief summary of different element operators available in MongoDB.
+
+## $exists
+
+The `$exists` operator checks if a field is present or not in a document. Use this operator when you want to filter documents based on the existence of a specific field, regardless of the field's value.
+
+## Example
+To query all documents where the field "age" exists:
+
+```javascript
+db.collection.find({ "age": { "$exists": true } })
+```
+
+## $type
+
+The `$type` operator filters documents based on the data type of a field's value. This operator can be handy when you need to retrieve documents with value types such as String, Number, Date, Object, and Array.
+
+## Example
+To query all documents where the field "age" is of type "number":
+
+```javascript
+db.collection.find({ "age": { "$type": "number" } })
+```
+
+## Combining Element Operators
+
+You can combine multiple element operators to create more specific queries.
+
+## Example
+To query all documents where the field "age" exists and its value type is "number":
+
+```javascript
+db.collection.find({ "age": { "$exists": true, "$type": "number" } })
+```
+
+In summary, element operators in MongoDB provide a way to query documents based on their field properties. By using `$exists`, `$type`, and other similar operators, you can create complex and expressive queries to extract the exact data you need from your collections.

src/data/roadmaps/mongodb/content/105-query-operators/104-logical-operators/100-and.md

@@ -1 +1,42 @@
-# And
+# $and
+
+The `$and` operator is a logical operator in MongoDB that allows you to combine multiple query statements and returns a result only when all of those conditions are met. With `$and`, you can join together as many query conditions as necessary.
+
+## Syntax
+
+Here's the basic syntax for using the `$and` operator:
+
+```javascript
+{ $and: [{ expression1 }, { expression2 }, ... ] }
+```
+
+## Example
+
+Suppose we have a collection named `orders` with the following documents:
+
+```json
+{ "_id": 1, "item": "apple", "price": 1, "quantity": 5 }
+{ "_id": 2, "item": "banana", "price": 1, "quantity": 10 }
+{ "_id": 3, "item": "orange", "price": 2, "quantity": 5 }
+{ "_id": 4, "item": "mango", "price": 3, "quantity": 15 }
+```
+
+If we want to find all the documents with a `price` greater than 1 and `quantity` less than 10, we use the `$and` operator as follows:
+
+```javascript
+db.orders.find({ $and: [{ "price": { $gt: 1 } }, { "quantity": { $lt: 10 } }]})
+```
+
+This query returns the following result:
+
+```json
+{ "_id": 3, "item": "orange", "price": 2, "quantity": 5 }
+```
+
+Keep in mind that using `$and` is only necessary when you have multiple conditions on the same field or you want to enforce a specific order for applying the conditions. Otherwise, you can use the standard query syntax like the following:
+
+```javascript
+db.orders.find({ "price": { $gt: 1 }, "quantity": { $lt: 10 } })
+```
+
+This query will also return the same result as the `$and` example above.

src/data/roadmaps/mongodb/content/105-query-operators/104-logical-operators/101-or.md

@@ -1 +1,83 @@
-# Or
+# $or
+
+The `$or` operator in MongoDB is a logical operator that allows you to perform queries on multiple fields, and return documents that satisfy any of the specified conditions. It is useful when you need to filter data based on one or more criteria.
+
+## Syntax
+
+The syntax for using the `$or` operator is as follows:
+
+```javascript
+{
+  $or: [
+    { condition1 },
+    { condition2 },
+    // ...,
+    { conditionN }
+  ]
+}
+```
+
+## Usage
+
+To use the `$or` operator, you need to specify the conditions inside the `$or` array. Each condition should be an object containing one or more field-value pairs to be matched.
+
+Let's consider a collection named `products` with the following documents:
+
+```javascript
+[
+  { _id: 1, category: "Fruits", price: 20 },
+  { _id: 2, category: "Fruits", price: 30 },
+  { _id: 3, category: "Vegetables", price: 10 },
+  { _id: 4, category: "Vegetables", price: 15 }
+]
+```
+
+If you want to find all the documents where the `category` is "Fruits" or the `price` is less than or equal to `15`, you can use the `$or` operator as shown below:
+
+```javascript
+db.products.find({
+  $or: [
+    { category: "Fruits" },
+    { price: { $lte: 15 } }
+  ]
+})
+```
+
+The result will include the documents that match either of the conditions:
+
+```javascript
+[
+  { _id: 1, category: "Fruits", price: 20 },
+  { _id: 2, category: "Fruits", price: 30 },
+  { _id: 3, category: "Vegetables", price: 10 },
+  { _id: 4, category: "Vegetables", price: 15 }
+]
+```
+
+## Combination with Other Operators
+
+The `$or` operator can be combined with other MongoDB operators to build more complex queries. For example, if you want to find all the documents where the `category` is "Fruits" and the `price` is either less than `20` or greater than `25`, you can use the `$and` and `$or` operators together:
+
+```javascript
+db.products.find({
+  $and: [
+    { category: "Fruits" },
+    {
+      $or: [
+        { price: { $lt: 20 } },
+        { price: { $gt: 25 } }
+      ]
+    }
+  ]
+})
+```
+
+The result will include the documents that match the specified conditions:
+
+```javascript
+[
+  { _id: 2, category: "Fruits", price: 30 },
+]
+```
+
+And that's an overview of the `$or` logical operator in MongoDB! It enables you to create more flexible queries and fetch the desired documents based on multiple conditions. Use it wisely in conjunction with other operators to get the most out of your MongoDB queries.

src/data/roadmaps/mongodb/content/105-query-operators/104-logical-operators/102-not.md

@@ -1 +1,52 @@
-# Not
+# $not
+
+In this section, we'll explore the `$not` operator in MongoDB. This handy operator allows us to negate the logical expression or condition applied in a query. It can be especially useful when we want to find documents that don't match a given condition.
+
+## Syntax
+
+Here's the general structure of a query that includes the `$not` operator:
+
+```javascript
+{
+  field: { $not: { <operator-expression> } }
+}
+```
+
+The `$not` operator must be associated with a field, followed by the desired operator expression or condition.
+
+## Examples
+
+Let's dive into some examples to better understand how to use the `$not` operator. Suppose we have a collection called `products` containing documents with information about various products.
+
+## Example 1: Simple Usage
+
+```javascript
+db.products.find({ price: { $not: { $gt: 100 } } })
+```
+
+In this example, we're looking for all products that are **not** greater (`$gt`) than 100 in price. In other words, we want products that have a price of 100 or less.
+
+## Example 2: Combining with Other Operators
+
+```javascript
+db.products.find({
+  $and: [
+    { category: 'Electronics' },
+    { price: { $not: { $lt: 50, $gt: 200 } } },
+  ],
+})
+```
+
+This time, we want to find all electronics products (`category: 'Electronics'`) whose price is **not** less than 50 **and** greater than 200. Essentially, this query will return products with a price between 50 and 200.
+
+## Example 3: Using Regular Expressions
+
+```javascript
+db.products.find({ name: { $not: /^apple/i } })
+```
+
+In our final example, we want to find all products whose name does **not** start with "apple" (case-insensitive). To achieve this, we use `$not` in conjunction with a regular expression (`/^apple/i`).
+
+## Conclusion
+
+Using the `$not` operator in your MongoDB queries can help filter for documents that don't meet specific conditions. Mastery of this powerful operator will allow you to further refine and narrow down your searches, providing better results when working with collections.

src/data/roadmaps/mongodb/content/105-query-operators/104-logical-operators/103-nor.md

@@ -1 +1,54 @@
-# Nor
+# $nor
+
+The `$nor` operator in MongoDB is a logical operator used as a filter in queries. It performs a logical NOR operation on an array of one or more filter expressions and returns the documents that fail to match any of the conditions specified in the array. In simple terms, `$nor` selects the documents that do not match the given conditions. 
+
+## Syntax
+
+The basic syntax for the `$nor` operator is as follows:
+
+```javascript
+{ $nor: [ { <expression1> }, { <expression2> }, ...  { <expressionN> } ] }
+```
+
+## Usage
+
+To use the `$nor` operator, you need to specify an array of expressions as its value. Documents that don't satisfy any of these expressions will be returned from the query.
+
+Here's an example:
+
+Suppose you have a `students` collection with the following documents:
+
+```javascript
+[
+  { "_id": 1, "name": "Alice", "age": 30, "subjects": ["math", "science"] },
+  { "_id": 2, "name": "Bob", "age": 25, "subjects": ["history"] },
+  { "_id": 3, "name": "Cathy", "age": 35, "subjects": ["math", "history"] },
+  { "_id": 4, "name": "David", "age": 28, "subjects": ["science"] }
+]
+```
+
+Now, if you want to find the students that are not older than 30 and not studying math, you would use the following query with `$nor`:
+
+```javascript
+db.students.find({
+  $nor: [
+    { age: { $gt: 30 } },
+    { subjects: "math" }
+  ]
+})
+```
+
+This will return the following documents:
+
+```javascript
+[
+  { "_id": 2, "name": "Bob", "age": 25, "subjects": ["history"] },
+  { "_id": 4, "name": "David", "age": 28, "subjects": ["science"] }
+]
+```
+
+As you can see, the query returned only the documents that don't match any of the conditions specified in the `$nor` array.
+
+Keep in mind that only one expression needs to be true for a document to be excluded from the result set. Also, when using the `$nor` operator, it is important to ensure that the array contains at least one filter expression.
+
+Now you know how to use the `$nor` operator in MongoDB to filter documents based on multiple negated conditions. Remember to use it wisely, as it can help you fetch refined data from your collections.

src/data/roadmaps/mongodb/content/105-query-operators/104-logical-operators/index.md

@@ -1 +1,74 @@
-# Logical operators
+# Logical Operators
+
+In MongoDB, logical operators are used to filter the results of queries based on multiple conditions. These operators provide flexibility to perform complex comparisons and create more sophisticated queries. The key logical operators in MongoDB are:
+
+- `$and`: Matches for documents where all the specified conditions are true.
+- `$or`: Matches for documents where at least one of the specified conditions is true.
+- `$not`: Matches for documents where the specified condition is false.
+- `$nor`: Matches for documents where none of the specified conditions are true.
+
+Below is a brief explanation of each operator along with examples.
+
+## $and
+
+The `$and` operator is used to combine multiple conditions in a query, and will only return documents where all the conditions are met. The syntax is as follows:
+
+```javascript
+{ $and: [ { condition1 }, { condition2 }, ... ] }
+```
+
+**Example:**
+
+```javascript
+db.collection_name.find({$and: [{key1: value1}, {key2: value2}]})
+```
+
+In this example, only documents that have both `key1` as `value1` and `key2` as `value2` would be returned.
+
+## $or
+
+The `$or` operator is used to return documents where at least one of the specified conditions is true. The syntax is as follows:
+
+```javascript
+{ $or: [ { condition1 }, { condition2 }, ... ] }
+```
+
+**Example:**
+
+```javascript
+db.collection_name.find({$or: [{key1: value1}, {key2: value2}]})
+```
+
+In this example, documents that have either `key1` as `value1` or `key2` as `value2` would be returned.
+
+## $not
+
+The `$not` operator is used to negate a condition, so only documents where the specified condition is not true will be returned. The syntax is as follows:
+
+```javascript
+{ key: { $not: { operator_expression } } }
+```
+
+**Example:**
+
+```javascript
+db.collection_name.find({key1: { $not: { $eq: value1 }}})
+```
+
+In this example, only documents where `key1` is not equal to `value1` would be returned.
+
+## $nor
+
+The `$nor` operator is used to return documents where none of the specified conditions are true. The syntax is as follows:
+
+```javascript
+{ $nor: [ { condition1 }, { condition2 }, ... ] }
+```
+
+**Example:**
+
+```javascript
+db.collection_name.find({$nor: [{key1: value1}, {key2: value2}]})
+```
+
+In this example, only documents where `key1` is not equal to `value1` and `key2` is not equal to `value2` would be returned.

src/data/roadmaps/mongodb/content/105-query-operators/index-types/100-expiring.md

@@ -1 +1,23 @@
-# Expiring
+# Expiring
+
+Expiring indexes are a specific index type in MongoDB that allows you to automatically remove documents from a collection after a certain time period or at a specific expiration date. These indexes are particularly useful for managing time-sensitive data, such as session data, cached data, or logs, where the information becomes irrelevant or less valuable after a certain period of time.
+
+To create an expiring index, you can use the `createIndex()` method along with the `expireAfterSeconds` option. This option takes a number of seconds as its value, which represents the duration after which the document should be removed automatically.
+
+Here's an example of creating an expiring index on a `createdAt` field with a time-to-live (TTL) of 3600 seconds (1 hour):
+
+```javascript
+db.collection.createIndex({createdAt: 1}, {expireAfterSeconds: 3600})
+```
+
+When using expiring indexes, it's essential to note the following points:
+
+- The field used for the expiring index must be a date or an array of date values. If the field holds an array of dates, MongoDB will expire the document when the earliest date in the array has passed the specified TTL.
+
+- Expiring indexes have no effect on capped collections, as MongoDB does not support the removal of documents in a capped collection.
+
+- The background task that removes expired documents runs every 60 seconds. As a result, there may be a slight delay between the document's expiration time and its actual deletion from the database.
+
+- Expiring indexes can only be single-field indexes, i.e., they cannot be created as part of a compound index or a multi-key index.
+
+In summary, expiring indexes provide an efficient way to manage time-sensitive data in MongoDB by automatically removing documents that have passed a specified time-to-live. This can help to keep your database clean and ensure that irrelevant or outdated data are not retained longer than necessary.

src/data/roadmaps/mongodb/content/105-query-operators/index-types/101-geospatial.md

@@ -1 +1,44 @@
-# Geospatial
+# Geospatial
+
+Geospatial indexes are used for querying geospatial coordinate data in MongoDB. These indexes are helpful when you want to store information related to spatial or geographical objects, like the location of restaurants, hotels, or landmarks, and perform queries based on proximity or containment.
+
+MongoDB supports two types of geospatial indexes: **2dsphere** and **2d**.
+
+##  2dsphere Index
+
+The `2dsphere` index supports queries on the surface of a sphere or a round object, like Earth. It uses the GeoJSON format for storing the geospatial data such as `Point`, `LineString`, and `Polygon`.
+
+To create a 2dsphere index, you can use the `createIndex` method:
+
+```javascript
+db.collection.createIndex({ location: "2dsphere" })
+```
+
+In this example, the `location` field contains the GeoJSON representation of the geospatial data.
+
+Some common queries using the 2dsphere index include:
+
+* `$geoIntersects`: Find geometries that intersect with the specified GeoJSON geometry.
+* `$geoWithin`: Find geometries contained within the specified GeoJSON geometry.
+* `$nearSphere`: Find geometries that are near a given point.
+
+##  2d Index
+
+The `2d` index supports queries on a flat Cartesian plane, which can be useful for simpler cases when dealing with small scale data. It stores geospatial data as legacy coordinate pairs.
+
+To create a 2d index, you can use the `createIndex` method:
+
+```javascript
+db.collection.createIndex({ location: "2d" })
+```
+
+In this example, the `location` field contains a coordinate pair [x, y].
+
+Some common queries using the 2d index include:
+
+* `$geoWithin`: Find geometries contained within the specified boundary.
+* `$center`: Find geometries that are near a given point within a given radius.
+
+Keep in mind that the 2d index has some limitations. It doesn't support queries for data spanning the 180-degree meridian (e.g., data wrapping around the Earth), and it cannot handle queries involving measurements or distances in greater depths.
+
+To sum up, geospatial indexes in MongoDB are essential for querying and analyzing geospatial coordinate data. By choosing the correct index type and querying methods, you can efficiently perform location-based queries in your application.

src/data/roadmaps/mongodb/content/105-query-operators/index-types/102-text.md

@@ -1 +1,39 @@
-# Text
+# Text
+
+MongoDB provides a powerful feature called "Text Indexes" to enable searching for string content within documents. This comes in handy when you need to perform text searching and analysis in your MongoDB collections. A Text Index allows you to search for words, phrases or even complex query expressions with ease.
+
+## Creating a Text Index
+
+To create a Text Index, use the `db.collection.createIndex()` method along with the special index type: `{ fieldName: "text" }`. For example, to create a Text Index on the `title` field in a books collection, execute the following command:
+
+```javascript
+db.books.createIndex({ title: "text" });
+```
+
+## Perform Text Searches
+
+After creating the Text Index, you can perform text searches on your documents using the `$text` operator in your queries inside `db.collection.find()`. For example, to find all books with a title matching the words "mongodb" or "guide", execute the following command:
+
+```javascript
+db.books.find({ $text: { $search: "mongodb guide" } });
+```
+
+## Advanced search options
+
+MongoDB provides several advanced options to refine your text search:
+
+- **$language**: Specify the language for your search query. Useful for stemming and ignoring stop words.
+
+- **$caseSensitive**: Enable or disable case-sensitive search (false by default).
+
+- **$diacriticSensitive**: Enable or disable diacritic-sensitive search (false by default).
+
+## Dropping a Text Index
+
+If you no longer need the Text Index, you can drop it using the `db.collection.dropIndex()` method. You'll need to provide the index name as the parameter:
+
+```javascript
+db.books.dropIndex("title_text");
+```
+
+Text Indexes provide an efficient way to search for content within your MongoDB documents, making it easier to analyze and locate specific information. However, it's important to keep in mind that Text Indexes can slow down your write performance, so use them judiciously!

src/data/roadmaps/mongodb/content/105-query-operators/index-types/103-compound.md

@@ -1 +1,64 @@
-# Compound
+# Compound
+
+A compound index is a type of index in MongoDB that allows you to specify multiple fields in a single index, effectively creating an index on the combined values of those fields. This type of index can support queries that involve multiple fields, allowing you to optimize the performance of your queries and efficiently search through large datasets.
+
+## Structure
+
+To create a compound index, you specify each field and its corresponding sort order (ascending/descending) as an object:
+
+```javascript
+{ 
+    field1: <sort order>, 
+    field2: <sort order>, 
+    ... 
+}
+```
+
+For example, to create a compound index on the `author` field in ascending order and the `title` field in descending order, you would use:
+
+```javascript
+{ 
+  author: 1,
+  title: -1 
+}
+```
+
+## Usage
+
+When using a compound index, consider the following:
+
+- **Prefixes**: A compound index can support queries on any of its "prefixes", which are subsets of its fields starting from the left. For example, if a collection has a compound index `{ author: 1, title: -1 }`, it can support queries on both the `author` field and the combined `author` and `title` fields.
+
+- **Sort Order**: The sort order of fields in the compound index can affect query performance. In general, choose the sort order based on your application's query patterns.
+
+- **Covered Queries**: A compound index can also be used to perform "covered queries", where all the fields of the query are part of the index. In such a case, MongoDB can satisfy the query using only the index, without the need to access the actual documents, resulting in improved performance.
+
+## Example
+
+Suppose you have a collection named `books` with the following documents:
+
+```javascript
+{ "_id" : ObjectId("..."), "author" : "John Smith", "title" : "Introduction to MongoDB", "year" : 2020 }
+{ "_id" : ObjectId("..."), "author" : "Jane Doe", "title" : "Advanced MongoDB", "year" : 2021 }
+{ "_id" : ObjectId("..."), "author" : "John Smith", "title" : "MongoDB for Experts", "year" : 2021 }
+```
+
+You can create a compound index on the `author` and `title` fields using the following command:
+
+```javascript
+db.books.createIndex({ author: 1, title: 1 })
+```
+
+With the compound index in place, MongoDB can efficiently execute queries involving both the `author` and `title` fields. For example, the following query would benefit from the compound index:
+
+```javascript
+db.books.find({ author: "John Smith", title: "Introduction to MongoDB" })
+```
+
+In addition, the query could use the index for sorting results:
+
+```javascript
+db.books.find({ author: "John Smith" }).sort({ title: 1 })
+```
+
+In summary, compound indexes provide a powerful optimization tool for queries involving multiple fields, allowing MongoDB to execute searches more efficiently and improve overall performance.

src/data/roadmaps/mongodb/content/105-query-operators/index-types/104-single-field.md

@@ -1 +1,37 @@
-# Single field
+# Single Field
+
+In MongoDB, a single field index is an index that sorts and organizes the data based on a single field inside your documents. It can be either on a top-level field or on a nested field (sub-field in an embedded document). Single field indexes are useful to improve the performance of read operations, making it faster to search for documents containing a specific field value.
+
+## Creating a Single Field Index
+
+To create a single field index, you can use the `db.collection.createIndex()` function, specifying the field name and the sorting order (1 for ascending or -1 for descending order). For example:
+
+```javascript
+db.users.createIndex({ "name": 1 });
+```
+
+This command creates an ascending index on the `name` field of the `users` collection.
+
+## Unique Single Field Index
+
+You can create a unique single-field index to prevent the insertion of duplicate values for a specific field. To create a unique index, include the `unique` option and set its value to `true`:
+
+```javascript
+db.users.createIndex({ "email": 1 }, { unique: true });
+```
+
+This command ensures each document in the collection has a unique email value.
+
+## Sparse Single Field Index
+
+A sparse single field index is an index that only considers the documents with the indexed field. This type of index might not index all the documents in a collection, resulting in reduced index size and better performance. To create a sparse index, include the `sparse` option and set its value to `true`:
+
+```javascript
+db.customers.createIndex({ "address.zipcode": 1 }, { sparse: true });
+```
+
+This command creates a sparse index on the `zipcode` field of the `address` sub-document.
+
+## Use cases
+
+A single field index is well-suited in use cases where you commonly search, sort, or filter documents based on a specific field value. Examples include finding all documents with a particular age, sorting blog posts by title, or looking up users by their email addresses. By utilizing single-field indexes, you can significantly boost the performance of these common operations.

src/data/roadmaps/mongodb/content/105-query-operators/index.md

@@ -1 +1,69 @@
-# Query operators
+# Query Operators
+
+In this section, we'll be exploring **query operators** in MongoDB. Query operators provide powerful ways to search and manipulate documents in a MongoDB collection. There are several types of query operators, including:
+
+- Comparison Operators
+- Logical Operators
+- Element Operators
+- Evaluation Operators
+- Array Operators
+- Bitwise Operators
+
+Let's explore each category in more detail.
+
+##  Comparison Operators
+
+Comparison operators allow you to compare the value of a field with specified values. Some common comparison operators are:
+
+- `$eq`: Matches values that are equal to the specified value.
+- `$gt`: Matches values that are greater than the specified value.
+- `$gte`: Matches values that are greater than or equal to the specified value.
+- `$lt`: Matches values that are less than the specified value.
+- `$lte`: Matches values that are less than or equal to the specified value.
+- `$ne`: Matches values that are not equal to the specified value.
+- `$in`: Matches values that are in the specified array.
+- `$nin`: Matches values that are not in the specified array.
+
+##  Logical Operators
+
+Logical operators provide ways to combine multiple query conditions. Some common logical operators include:
+
+- `$and`: Matches documents where all the specified conditions are true.
+- `$or`: Matches documents where at least one of the specified conditions is true.
+- `$not`: Matches documents where the specified condition is not true.
+- `$nor`: Matches documents where none of the specified conditions are true.
+
+##  Element Operators
+
+Element operators target specific elements within documents, including:
+
+- `$exists`: Matches documents that have the specified field.
+- `$type`: Matches documents where the specified field is of the specified BSON type.
+
+##  Evaluation Operators
+
+Evaluation operators perform operations on specific fields and values, such as regular expression searches or checking the size of arrays. Some examples include:
+
+- `$expr`: Allows the use of aggregation expressions within query language.
+- `$jsonSchema`: Matches documents that fulfill the specified JSON Schema.
+- `$mod`: Matches documents where the specified field has a value divisible by a divisor and equal to a remainder.
+- `$regex`: Matches documents where the specified field contains a string that matches the provided regular expression pattern.
+- `$text`: Performs text search on the content of indexed fields in the documents.
+- `$where`: Matches documents that satisfy a JavaScript expression.
+
+##  Array Operators
+
+Array operators are used to query or manipulate documents that contain arrays. Some common array operators include:
+
+- `$all`: Matches documents where an array field contains all specified values.
+- `$elemMatch`: Matches documents where an array field contains at least one element that matches the specified conditions.
+- `$size`: Matches documents where an array field contains a specified number of elements.
+
+##  Bitwise Operators
+
+Bitwise operators allow you to perform bit manipulation on integer values. Some examples are:
+
+- `$bitsAllClear`: Matches documents where all bits of the specified field are clear (0) in the specified bitmask.
+- `$bitsAllSet`: Matches documents where all bits of the specified field are set (1) in the specified bitmask.
+- `$bitsAnyClear`: Matches documents where any bits of the specified field are clear (0) in the specified bitmask.
+- `$bitsAnySet`: Matches documents where any bits of the specified field are set (1) in the specified bitmask.

src/data/roadmaps/mongodb/content/106-mongodb-aggregation.md

@@ -1 +1,48 @@
-# Mongodb aggregation
+# MongoDB Aggregation
+
+MongoDB Aggregation framework provides a way to process and transform data that is stored in our MongoDB collections. It allows you to perform calculations and return the calculated results using various data aggregation tools such as aggregation pipelines, map-reduce functions, or single-purpose aggregation methods.
+
+**Here is a brief summary of MongoDB Aggregation:**
+
+## Aggregation Pipeline
+
+The aggregation pipeline is a framework in MongoDB that enables the developers to execute a series of data transformations on the documents in a collection. The pipeline consists of multiple stages, and each stage applies a specific operation on the input documents. Among these operations, you can find features like filtering, sorting, projecting, and grouping.
+
+Example of a simple aggregation pipeline:
+
+```javascript
+db.collection.aggregate([
+   { $match: { status: "A" } },
+   { $group: { _id: "$cust_id", total: { $sum: "$amount" } } },
+   { $sort: { total: -1 } }
+])
+```
+
+## Map-Reduce
+
+Map-Reduce is another method to aggregate data in MongoDB. It involves defining a map function to extract data from the input documents, which emits key-value pairs. A reduce function combines the emitted data by keys and optionally a finalize function to further process the results.
+
+Example of a simple map-reduce function:
+
+```javascript
+db.collection.mapReduce(
+   function() { emit(this.cust_id, this.amount); },
+   function(key, values) { return Array.sum(values) },
+   {
+     query: { status: "A" },
+     out: "order_totals"
+   }
+)
+```
+
+## Single-Purpose Aggregation
+
+MongoDB also supports single-purpose aggregation methods, such as `db.collection.count()`, `db.collection.distinct()`, and `db.collection.group()` etc. These methods offer a faster and more convenient way to perform simple aggregations directly.
+
+Example of db.collection.count():
+
+```javascript
+db.collection.count({ status: "A" })
+```
+
+In conclusion, MongoDB Aggregation is a powerful feature that helps you extract, manipulate and aggregate data from your collections. By using aggregation pipelines, map-reduce functions or single-purpose aggregation methods, you can perform various data analysis tasks efficiently on your MongoDB dataset.

src/data/roadmaps/mongodb/content/107-transactions.md

@@ -1 +1,59 @@
-# Transactions
+# Transactions
+
+**Transactions** play a vital role in maintaining data consistency and integrity within a database. They represent a single unit of work that consists of multiple operations executed in a sequence. In this section, we'll discuss the concept of transactions in MongoDB, their usage, and how they help in accomplishing various operations.
+
+## Overview
+
+MongoDB supports multi-document transactions, enabling you to perform multiple read and write operations across several documents within a single, atomic transaction. A transaction might involve several operations, for instance:
+
+- Creating a new document
+- Updating an existing document
+- Deleting a document
+- Reading documents
+
+The fundamental purpose of a transaction is to either execute **all** or **none** of its operations. This means that, in case any operation within the transaction fails, the entire transaction will be aborted, and the database will return to its initial state, thus ensuring data consistency.
+
+Transactions in MongoDB are essential to achieve the following **ACID** properties:
+- **Atomicity**: Ensures that either all the operations in the transaction are executed, or none are.
+- **Consistency**: Guarantees that, upon completing a transaction, the database remains in a consistent state.
+- **Isolation**: Secures that the operations within the transaction are isolated from other transactions being executed simultaneously.
+- **Durability**: Warrants that once a transaction is successfully completed, its effects will be stored persistently in the database.
+
+## Usage
+
+To begin a transaction in MongoDB, you'll need to obtain a session and then start the transaction using the `startTransaction()` method. After performing the necessary operations, you may `commit` the transaction to apply the changes to the database, or `abort` to discard the changes.
+
+Here's an example to illustrate transactions:
+
+```javascript
+// Start a session
+const session = client.startSession();
+
+// Start a transaction within the session
+session.startTransaction();
+
+try {
+  // Perform various operations within the transaction
+  const operation1 = await collection1.insertOne(doc1, {session});
+  const operation2 = await collection2.updateOne(condition, update, {session});
+  const operation3 = await collection3.deleteOne(doc3, {session});
+
+  // Commit the transaction
+  await session.commitTransaction();
+} catch (error) {
+  // If any operation fails, abort the transaction
+  await session.abortTransaction();
+} finally {
+  // End the session
+  session.endSession();
+}
+```
+
+## Limitations
+
+While transactions provide immense benefits regarding data consistency and integrity, it is vital to be aware of some of its limitations:
+- They are available only in MongoDB versions 4.0 and above.
+- They can cause performance overhead, especially for write-heavy workloads.
+- In MongoDB clusters, transactions only support a maximum duration of 60 seconds.
+
+In summary, transactions are a powerful feature of MongoDB, ensuring data integrity, and consistency in the database. By understanding their usage and implications, you can effectively utilize them in your application according to your specific requirements.

src/data/roadmaps/mongodb/content/108-developer-tools/100-language-drivers.md

@@ -1 +1,22 @@
-# Language drivers
+# Language Drivers
+
+Language drivers are essential tools for developers to interface with MongoDB. They are libraries provided by MongoDB to help developers work with MongoDB in their choice of programming language. With language drivers, you can perform various CRUD operations, manage authentication, and handle connections with the database effectively without worrying about low-level details. 
+
+MongoDB supports a wide range of languages, and some of the most popular drivers are:
+
+- [C Driver](http://mongoc.org/)
+- [C++ Driver](https://github.com/mongodb/mongo-cxx-driver)
+- [C# and .NET Driver](https://docs.mongodb.com/drivers/csharp/)
+- [Go Driver](https://docs.mongodb.com/drivers/go/)
+- [Java Driver](https://docs.mongodb.com/drivers/java/)
+- [Node.js Driver](https://docs.mongodb.com/drivers/node/)
+- [PHP Driver](https://docs.mongodb.com/drivers/php/)
+- [Python Driver (PyMongo)](https://docs.mongodb.com/drivers/pymongo/)
+- [Ruby Driver](https://docs.mongodb.com/drivers/ruby/)
+- [Rust Driver](https://docs.rs/mongodb/1.2.0/mongodb/)
+
+With a suitable driver installed, you can interact with MongoDB using the idiomatic style of your programming language. The driver simplifies your code and boosts productivity, as it handles the communication between your application and the MongoDB server.
+
+To get started with the language driver of your choice, visit the respective documentation linked above. The documentation will help you set up the driver, establish a connection, and perform various database operations in your preferred programming language.
+
+Remember to always use the latest version of language drivers to ensure compatibility with new MongoDB features and improve overall performance.

src/data/roadmaps/mongodb/content/108-developer-tools/101-mongodb-connectors/100-kafka.md

@@ -1 +1,35 @@
-# Kafka
+# Kafka
+
+Apache Kafka is a popular open-source distributed streaming platform for building real-time data pipelines and high-throughput applications in a fault-tolerant and scalable manner. This section of our guide will provide you with a summary of the Kafka Connectors related to MongoDB, which helps you to effectively stream data between Kafka and MongoDB.
+
+## Overview
+
+Kafka Connect is a powerful framework, part of Apache Kafka, for integrating with external systems like databases, key-value stores, or search indexes through connectors. MongoDB Kafka Connectors allow you to transfer data between the MongoDB Atlas or self-managed MongoDB clusters and Kafka clusters seamlessly.
+
+## MongoDB Source Connector
+
+The MongoDB Source Connector streams the data changes (inserts, updates, deletes, and replacements) within the MongoDB cluster into Kafka in real-time. This is particularly useful when you want to process, analyze, or distribute the updates happening within your MongoDB cluster to different Kafka consumers.
+
+## MongoDB Sink Connector
+
+The MongoDB Sink Connector enables the transfer of data from a Kafka topic to MongoDB by consuming Kafka records and inserting them into the specified MongoDB collection. This can be used to store the result of stream processing or any other transformations applied to the data coming from Kafka into MongoDB, serving as the final data persistence layer.
+
+## Key Features
+
+- **Change Data Capture (CDC)**: Kafka Connectors for MongoDB enable change data capture by capturing and streaming database events and changes in real-time.
+- **Schema Evolution**: Connectors automatically handle schema changes and support using Kafka schema registry to manage schema evolution.
+- **Ease of setup**: High-level abstraction of the connector framework simplifies setup and configuration.
+- **Scalability**: Built on top of the Kafka framework, you can scale up to handle massive data streams.
+
+## Getting Started
+
+To get started with MongoDB Kafka connectors, you can follow these steps:
+
+- Download and install [Apache Kafka](https://kafka.apache.org/downloads) and [MongoDB Kafka Connector](https://www.confluent.io/hub/mongodb/mongo-kafka-connect).
+- Configure your source/sink connector properties.
+- Start the Kafka connect runtime with the MongoDB connector.
+- Verify that your data is being transferred between Kafka and MongoDB as per your requirement.
+
+For a complete tutorial and detailed configuration options, refer to the [official documentation](https://docs.mongodb.com/kafka-connector/current/kafka-source/).
+
+In conclusion, MongoDB Kafka Connectors allow you to integrate MongoDB and Kafka seamlessly, enabling real-time data streaming and processing. By using these connectors, you can effectively build scalable, fault-tolerant, and resilient data pipelines between the two technologies.

src/data/roadmaps/mongodb/content/108-developer-tools/101-mongodb-connectors/101-spark.md

@@ -1 +1,65 @@
-# Spark
+# Spark
+
+The [Spark Connector](https://docs.mongodb.com/spark-connector/current/) is a powerful integration tool that allows you to use MongoDB as a data source for your Spark applications. This connector provides seamless integration of the robustness and scalability of MongoDB with the computational power of the Apache Spark framework, allowing you to process large volumes of data quickly and efficiently.
+
+## Key Features
+
+- **MongoDB as Data Source**: The connector enables loading data from MongoDB into Spark data structures like DataFrames and Datasets.
+- **Filter Pushdown**: It optimizes performance by pushing down supported filters to execute directly on MongoDB, returning only the relevant data to Spark.
+- **Aggregation Pipeline**: The connector allows you to execute MongoDB's aggregation pipeline within Spark, for efficient and powerful transformations.
+
+## Installation
+
+To start using the Spark Connector for MongoDB, you simply need to add the Maven dependency to your `build.sbt` or `pom.xml` file:
+
+For SBT:
+
+```scala
+libraryDependencies += "org.mongodb.spark" %% "mongo-spark-connector" % "3.0.1"
+```
+
+For Maven:
+
+```xml
+<dependency>
+  <groupId>org.mongodb.spark</groupId>
+  <artifactId>mongo-spark-connector_2.12</artifactId>
+  <version>3.0.1</version>
+</dependency>
+```
+
+## Usage
+
+Here's a basic example of how to work with the MongoDB Spark Connector:
+
+```scala
+import org.apache.spark.sql.SparkSession
+import com.mongodb.spark.MongoSpark
+
+object MongoDBwithSpark {
+  def main(args: Array[String]): Unit = {
+    val spark = SparkSession.builder()
+      .master("local")
+      .appName("MongoDB Integration")
+      .config("spark.mongodb.input.uri", "mongodb://username:password@host/database.collection")
+      .config("spark.mongodb.output.uri", "mongodb://username:password@host/database.collection")
+      .getOrCreate()
+    
+    // Load data from MongoDB into a DataFrame
+    val df = MongoSpark.load(spark)
+    
+    // Perform operations on DataFrame
+    // ...
+    
+    // Write the DataFrame back to MongoDB
+    MongoSpark.save(df.write.mode("overwrite"))
+    
+    // Stop the Spark session
+    spark.stop()
+  }
+}
+```
+
+With the MongoDB Spark Connector, you can leverage the power of Apache Spark to analyze and process your data, making it easier to develop analytics solutions and handle complex data processing tasks.
+
+For more details, check the [official documentation](https://docs.mongodb.com/spark-connector/current/).

src/data/roadmaps/mongodb/content/108-developer-tools/101-mongodb-connectors/102-elastic-search.md

@@ -1 +1,25 @@
-# Elastic search
+# Elastic Search
+
+Elasticsearch is a powerful open-source search and analytics engine that allows you to store, search, and analyze your data in near real-time. It operates on distributed architecture, making it scalable and highly available for dealing with large volumes of data. Elasticsearch is built on top of Apache Lucene, which provides the foundational search capabilities.
+
+## Why Elasticsearch?
+
+Some of the key benefits of Elasticsearch include:
+
+- **Real-time search:** Elasticsearch indexes data in real-time, allowing you to receive up-to-date search results.
+- **Scalability:** Elasticsearch can scale horizontally by adding new nodes to the cluster as your data grows.
+- **Distributed architecture:** The data stored in Elasticsearch is automatically distributed across multiple nodes, providing redundancy and high availability.
+- **Robust API:** Elasticsearch provides a comprehensive REST API for managing and querying your data.
+- **Integration with MongoDB**: Elasticsearch can be used in conjunction with MongoDB to provide full-text search capabilities and powerful analytics on MongoDB data.
+
+## MongoDB Connector for Elasticsearch
+
+If you're using MongoDB and wish to integrate Elasticsearch for enhanced search and analytics capabilities, you can use the MongoDB Connector for Elasticsearch. This connector is a plugin that enables you to synchronize your MongoDB data with Elasticsearch in real-time, allowing you to take advantage of Elasticsearch's powerful search capabilities on your MongoDB data.
+
+## Key features:
+
+- **Real-time synchronization:** The MongoDB Connector for Elasticsearch synchronizes the data in real-time, ensuring that your Elasticsearch cluster is always up-to-date with the latest data from MongoDB.
+- **Flexible configuration:** You can configure the connector to sync specific fields, collections, and databases, and to apply custom transformations to the data before indexing it in Elasticsearch.
+- **Resilient:** The connector maintains a checkpoint of the last synced MongoDB operation, so in case of a failure or restart, it can resume the synchronization from the last checkpoint.
+
+To get started with the MongoDB Connector for Elasticsearch, you can refer to the [official documentation](https://docs.mongodb.com/kafka-connector/current/kafka-elasticsearch-sink/) for installation and configuration instructions.

src/data/roadmaps/mongodb/content/108-developer-tools/101-mongodb-connectors/index.md

@@ -1 +1,47 @@
-# Mongodb connectors
+# MongoDB Connectors
+
+MongoDB Connectors provide the integration between your application and the MongoDB database, allowing your applications to interact with the data stored in MongoDB. These connectors enable you to use your preferred language, framework, or platform to communicate with MongoDB using native APIs or drivers.
+
+In this section, we'll discuss some commonly used MongoDB Connectors and their main features.
+
+##  MongoDB BI Connector
+
+The MongoDB BI (Business Intelligence) Connector allows you to connect MongoDB to third-party tools like Tableau or PowerBI, enabling users to create visualizations, reports, and dashboards using data stored in MongoDB. It translates incoming SQL queries into equivalent MongoDB queries, providing a seamless experience when working with your data.
+
+Key Features:
+
+- Directly connects to your MongoDB data
+- Provides integration with popular BI tools
+- Supports various SQL-compatible clients
+
+##  MongoDB Kafka Connector
+
+The MongoDB Kafka Connector lets you stream data between Apache Kafka and MongoDB, enabling you to build real-time, event-driven data pipelines that can process and analyze large volumes of data quickly. With this connector, you can use Kafka as the central event bus for your system and automatically persist the events in MongoDB as required.
+
+Key Features:
+
+- Support for Kafka Connect API
+- Flexible and customizable pipelines
+- Scalable and fault-tolerant design
+
+##  MongoDB Connector for Spark
+
+The MongoDB Connector for Spark enables you to use MongoDB as a data source or destination for Apache Spark, a powerful analytics engine designed for large-scale data processing. With this connector, you can leverage Spark's advanced capabilities like machine learning and graph processing on your MongoDB data.
+
+Key Features:
+
+- Integration with Spark APIs
+- Support for various Spark libraries like MLlib and GraphX
+- Parallel data processing for faster analytics
+
+##  MongoDB Language Drivers
+
+MongoDB provides a range of official and community-supported language drivers that allow developers to interact with MongoDB using their preferred programming language. Officially supported drivers include C, C++, C#, Go, Java, Node.js, PHP, Python, Ruby, Rust, Scala, and Swift. There are also many community-supported drivers for other languages and frameworks.
+
+Key Features:
+
+- Native APIs for your preferred language
+- Connection pooling and efficient use of system resources
+- Strong consistency and safety with MongoDB's features
+
+That's an overview of MongoDB Connectors. Remember that each connector has specific setup and configuration steps, so be sure to check the official MongoDB documentation for detailed instructions. Now, you should have a better understanding of how to use MongoDB with your preferred tools and platforms to build powerful applications and perform insightful analysis on your data.

src/data/roadmaps/mongodb/content/108-developer-tools/102-developer-tools/100-vs-code-extension.md

@@ -1 +1,25 @@
-# Vs code extension
+# VS Code Extension
+
+Visual Studio Code (VS Code) offers an extension for MongoDB that provides a convenient and powerful way to work with MongoDB databases directly from VS Code. This extension allows you to effortlessly manage your MongoDB databases, perform CRUD operations, and visualize your data schema within the VS Code environment. Let's explore the key features of the MongoDB VS Code extension:
+
+## Features
+
+* __Explorer Integration__: This extension integrates directly with the VS Code Explorer – allowing you to efficiently browse your databases, collections, and documents sans the need to leave the comfort of your code editor.
+
+* __Query Execution__: Write and execute MongoDB queries in your editor using the built-in MongoDB Query Playground. Easily execute commands, one by one or in groups, making your data manipulation experience more efficient.
+
+* __CRUD Operations__: Perform Create, Read, Update, Delete (CRUD) operations on your documents right from the VS Code, without needing to switch between applications, keeping you focused and productive.
+
+* __Schema Visualization__: Get insights into your data schema by simply hovering over a field or a document. This feature enables you to have a more profound understanding of your data, helping you make better decisions while developing your application.
+
+* __Snippet Support__: The extension provides template snippets for the most common MongoDB commands, such as find, update or aggregation, to help you write queries faster and adhere to best practices.
+
+* __Error Monitoring__: Get real-time feedback on syntax or runtime errors while writing MongoDB queries or manipulating documents. This feature helps you identify any potential issues or inconsistencies in your MongoDB operations.
+
+## Getting Started
+
+To get started, simply install the [MongoDB for VS Code extension](https://marketplace.visualstudio.com/items?itemName=mongodb.mongodb-vscode) from the Visual Studio Marketplace.
+
+After installation, you can connect to your MongoDB instance by clicking on the MongoDB icon on the Activity Bar (the vertical bar on the side of the window). From there, you can add your connection string, choose which databases and collections to explore, and interact with your MongoDB data using the extension features described above.
+
+In conclusion, the MongoDB VS Code extension enhances your productivity as a developer by enabling you to seamlessly work with MongoDB databases directly in your code editor. If you haven't tried it yet, we recommend installing it and exploring its rich set of features.

src/data/roadmaps/mongodb/content/108-developer-tools/102-developer-tools/101-vs-analyzer.md

@@ -1 +1,33 @@
-# Vs analyzer
+# Analyzer
+
+The Visual Studio (VS) Analyzer for MongoDB is a powerful development tool that helps you work with MongoDB by providing an integrated environment within your Visual Studio IDE. This add-on enhances your productivity and efficiency when developing applications with MongoDB, as it offers several benefits such as code assistance, syntax highlighting, IntelliSense support, and more.
+
+## Key Features
+
+- **Syntax Highlighting**: The VS Analyzer provides syntax highlighting to help you quickly identify and understand different elements in your code, such as variables, operators, and functions.
+
+- **IntelliSense Support**: IntelliSense is an intelligent code completion feature that predicts and suggests likely entries based on the context. It makes it easier to write queries by providing contextual suggestions based on your input.
+
+- **Code Snippets**: This feature allows you to insert common MongoDB code patterns and functionalities directly into your code editor with just a few clicks. This can save you time and help maintain a consistent coding style across your project.
+
+- **Query Profiling**: The VS Analyzer allows you to profile and optimize MongoDB queries. By analyzing query performance, you can identify slow or problematic queries and make appropriate improvements to ensure better performance.
+
+- **Debugging**: The Analyzer offers debugging support to help you identify and fix issues in your MongoDB queries and scripts, improving the overall reliability of your application.
+
+- **Integrated Shell**: VS Analyzer offers an integrated shell within Visual Studio that allows you to run MongoDB commands and queries directly within the IDE. This makes it more convenient to interact with your MongoDB instances and perform various tasks without switching between different tools.
+
+## Getting Started
+
+To start using the VS Analyzer for MongoDB, follow these steps:
+
+- Download and install the [Visual Studio MongoDB Extension](https://marketplace.visualstudio.com/items?itemName=ms-ossdata.vscode-mongodb) from the Visual Studio Marketplace.
+
+- Open your Visual Studio IDE and create a new project or open an existing one.
+
+- Add a reference to the MongoDB extension in your project by right-clicking on `References` and selecting `Add Package`.
+
+- Search for `MongoDB` in the package manager window, and install the relevant packages for your project.
+
+- Once the extension is installed, you can access the MongoDB features through the `Extensions` menu in Visual Studio.
+
+With the VS Analyzer for MongoDB, you'll be able to write cleaner, faster, and more efficient code, making it an essential tool for any MongoDB developer.

src/data/roadmaps/mongodb/content/108-developer-tools/102-developer-tools/index.md

@@ -1 +1,50 @@
-# Developer tools
+# Developer Tools
+
+In this chapter, we will discuss the various developer tools available for MongoDB. These tools are essential for developers to work efficiently with MongoDB, as they help in areas such as database management, data validation, and data visualization.
+
+## MongoDB Compass
+
+[MongoDB Compass](https://www.mongodb.com/products/compass) is a GUI that allows you to visually explore and interact with your data. It provides an intuitive interface for tasks such as creating, reading, updating, and deleting (CRUD) documents, optimizing queries, and managing indexes.
+
+Key Features:
+
+- Schema visualization to understand the structure of your data.
+- Index management for performance optimization.
+- Aggregation pipeline builder for building complex queries.
+- Real-time server stats and other metrics.
+
+## MongoDB Shell
+
+[The MongoDB Shell](https://www.mongodb.com/products/shell) is an interactive JavaScript interface to MongoDB. It offers an easy way to query and manage your MongoDB databases through a command-line interface.
+
+Key Features:
+
+- Create, read, update, and delete documents.
+- Perform administrative tasks such as data import/export, index creation, and setting up replication.
+- Write JavaScript functions to automate complex tasks.
+- Test queries and pipelines before deploying them to your applications.
+
+## MongoDB Extensions for Visual Studio Code
+
+[The MongoDB extension for Visual Studio Code](https://marketplace.visualstudio.com/items?itemName=mongodb.mongodb-vscode) allows you to work with MongoDB directly from your code editor. This extension enables you to create and execute MongoDB queries, manage connections, and create Playgrounds to prototype queries and manipulate data.
+
+Key Features:
+
+- Connect to MongoDB instances (local or remote) with ease.
+- Run MongoDB commands in the built-in terminal.
+- Explore databases, collections, and documents.
+- Create, read, update, and delete documents from within the editor.
+- Compact and lint your queries for readability and maintainability.
+
+## MongoDB Drivers
+
+MongoDB provides [drivers](https://www.mongodb.com/drivers) for various programming languages, allowing developers to create applications that interact with MongoDB databases easily. Officially supported languages include Java, Python, Node.js, C#, and many others.
+
+Key Features:
+
+- CRUD operations to manage documents in the database.
+- Support for advanced queries such as aggregations, text search, and geospatial queries.
+- Connection pooling for efficient resource utilization.
+- Tuned for performance and optimized for MongoDB specific features.
+
+With these developer tools, you can increase your productivity while working with MongoDB and create efficient and maintainable applications. Whether you prefer a visual interface, a command-line environment, or even your favorite coding editor, there is a tool available to make your MongoDB development experience smooth and efficient.

src/data/roadmaps/mongodb/content/108-developer-tools/backup-recovery/100-mongodump.md

@@ -1 +1,42 @@
-# Mongodump
+# mongodump
+
+**Mongodump** is a utility tool that comes with MongoDB, which is used to create a backup of your data by capturing the BSON output from your MongoDB database. It is especially useful when you want to export data from MongoDB instances, clusters or replica sets for either backup purposes or to migrate data from one environment to another.
+
+## How it works
+
+Mongodump connects to a running `mongod` or `mongos` process and extracts the BSON data from the database, which includes collections, their documents, and indexes. The tool stores the exported data in a binary format in a directory named `dump` by default, with each collection's data placed inside a separate BSON file.
+
+## Usage
+
+Here's a basic example of using `mongodump`:
+
+```bash
+mongodump --uri "mongodb://username:password@host:port/database" --out /path/to/output/dir
+```
+
+Replace the values for `username`, `password`, `host`, `port`, and `database` with your actual MongoDB credentials and target database. This command will create a backup of your specified database and will store it in the specified output directory.
+
+## Options
+
+Mongodump offers a variety of options to customize your backups:
+
+- `--uri`: The MongoDB connection string with authentication details.
+- `--out`: The path to save the output data.
+- `--db`: The specific database to backup.
+- `--collection`: The specific collection to backup.
+- `--query`: An optional query to export only matching documents.
+- `--oplog`: Include oplog data for a consistent point-in-time snapshot.
+- `--gzip`: Compress the backup files using gzip.
+- `--archive`: Write the output to a single archive file instead of individual files.
+
+## Restoring data with `mongorestore`
+
+To restore data from a `mongodump` backup, you can use the `mongorestore` tool, which comes with MongoDB as well. Here's a basic example of using `mongorestore`:
+
+```bash
+mongorestore --uri "mongodb://username:password@host:port/database" --drop /path/to/backup/dir
+```
+
+This command will restore the specified database from the backup directory, and the `--drop` flag will remove any existing data in the target database before restoring the data.
+
+In summary, `mongodump` is a powerful utility for creating backups of your MongoDB data. Used in conjunction with `mongorestore`, you can easily create, store, and restore data backups as needed.

src/data/roadmaps/mongodb/content/108-developer-tools/backup-recovery/101-mongorestore.md

@@ -1 +1,62 @@
-# Mongorestore
+# mongorestore
+
+`mongorestore` is a utility tool that comes with MongoDB and is used to restore a binary database dump from `mongodump`. It is particularly helpful in scenarios where you need to recover your database, migrate data between MongoDB instances, or manage your data backup strategy.
+
+## Features
+
+- Restores BSON data from a `mongodump` output
+- Supports multiple formats, such as gzip
+- Allows filtering documents during restore
+- Can restore data to a new MongoDB instance, or into an existing database and collection
+
+## Usage
+
+Here's a basic usage of `mongorestore`:
+
+```bash
+mongorestore /path/to/your/dump/folder
+```
+
+This command will restore the dump in the specified folder.
+
+## Common Options
+
+- `--host`: Specifies the target MongoDB instance (default: `localhost`).
+- `--port`: Specifies the port number of the target MongoDB instance (default: `27017`).
+- `--username`: Specifies the username for authentication (if needed).
+- `--password`: Specifies the password for authentication (if needed).
+- `--authenticationDatabase`: Specifies the database that holds the user's credentials (default: `admin`).
+- `--db`: Specifies a single database to restore (default: all databases in the dump folder).
+- `--collection`: Specifies a single collection to restore (default: all collections in the dump folder).
+- `--drop`: Drops the database or collection before importing data.
+- `--gzip`: Decompresses the input BSON files before importing (use with compressed dumps).
+- `--archive`: Reads/writes the database dump as an archive file.
+- `--nsExclude`: Exclude namespaces with the specified pattern from the restore.
+
+## Examples
+
+Restore only a specific database:
+
+```bash
+mongorestore --db=mydatabase /path/to/your/dump/folder
+```
+
+Restore using gzip format:
+
+```bash
+mongorestore --gzip /path/to/your/compressed/dump/folder
+```
+
+Restore with authentication:
+
+```bash
+mongorestore --username=myUser --password=myPassword /path/to/your/dump/folder
+```
+
+Restore to a remote MongoDB instance:
+
+```bash
+mongorestore --host=remoteHost --port=27017 /path/to/your/dump/folder
+```
+
+**Important**: Ensure you have proper backups of your data, and test the restore process periodically to validate your backup strategy.

src/data/roadmaps/mongodb/content/108-developer-tools/index.md

@@ -1 +1,38 @@
-# Developer tools
+# Developer Tools
+
+This section explores the essential developer tools you need when working with MongoDB. These developer tools aim to help you manage, interact, and visualize your data to make development tasks quicker and easier.
+
+## MongoDB Shell (mongo)
+
+MongoDB Shell, also known as `mongo`, is a command-line interface that allows you to interact with a MongoDB instance. You can use the `mongo` shell to perform CRUD operations, administrative tasks, and manage your databases.
+
+```bash
+mongo [options] [db address]
+```
+
+## MongoDB Compass
+
+[MongoDB Compass](https://www.mongodb.com/products/compass) is a graphical user interface (GUI) that simplifies the process of managing your MongoDB data. With Compass, you can visually explore and interact with your data, modify and sort documents, create indexes, and validate data schemas for better data governance.
+
+## MongoDB Atlas
+
+[MongoDB Atlas](https://www.mongodb.com/cloud/atlas) is a fully-managed cloud-based database platform offering the best of MongoDB. Its intuitive interface provides an effortless deployment experience, automated backups, self-healing recovery, and many other features that make it an ideal choice for database management. 
+
+## MongoDB APIs and Drivers
+
+MongoDB offers a variety of APIs and native [drivers](https://docs.mongodb.com/drivers/) for numerous programming languages, enabling developers to build applications using their preferred languages. The most popular of these include:
+
+- [Node.js Driver](https://docs.mongodb.com/drivers/node/)
+- [Python Driver (Pymongo)](https://docs.mongodb.com/drivers/pymongo/)
+- [C# Driver](https://docs.mongodb.com/drivers/csharp/)
+- [Java Driver](https://docs.mongodb.com/drivers/java/)
+
+These drivers provide a high-level API for connecting to MongoDB and performing CRUD operations.
+
+## Robo 3T / Studio 3T
+
+[Robo 3T](https://robomongo.org/) (formerly Robomongo) is a lightweight, open-source MongoDB management tool. It provides basic features like connecting to a MongoDB instance, managing databases, collections, and performing CRUD operations.
+
+[Studio 3T](https://studio3t.com/) is a powerful, feature-rich MongoDB management tool that provides a comprehensive set of tools and features for MongoDB management and development. Studio 3T offers advanced features such as IntelliShell, Query Code, and SQL Migration.
+
+Choosing the right developer tool depends upon your specific requirements, but being familiar with these tools will offer you a range of options for a faster and more efficient development process.

src/data/roadmaps/mongodb/content/109-scaling-mongodb.md

@@ -1 +1,33 @@
-# Scaling mongodb
+# Scaling MongoDB
+
+Scaling MongoDB is crucial for maintaining high performance and availability of your database, especially as your application and its data grow. There are two main methods for scaling MongoDB: *horizontal scaling* and *vertical scaling*. In this section, we'll discuss the differences between the two methods, the scenarios in which each method is suitable, and the tools and techniques used to scale a MongoDB deployment.
+
+## Horizontal Scaling
+
+Horizontal scaling refers to the process of adding more servers to a system to share the workload evenly. In MongoDB, horizontal scaling is achieved through sharding.
+
+## Sharding
+
+Sharding is a method of spreading data across multiple servers, allowing MongoDB to scale out and manage large amounts of data. Sharding enables you to partition your data and distribute it across several machines, ensuring that no single machine is overwhelmed with data or queries. With the use of a `shard key`, MongoDB automatically distributes data across the multiple machines.
+
+## Components of Sharding
+
+- *Shard*: A single server or a replica set that stores a portion of the sharded data.
+- *Config Server*: A server or a replica set that stores metadata about the sharded clusters. The config server tracks which data is stored on which shard.
+- *Query Router (mongos)*: A server that routes the application queries to the appropriate shard based on the metadata obtained from the config server.
+
+## Vertical Scaling
+
+Vertical scaling involves increasing the resources available on individual servers, such as CPU, memory, and storage. This can be done by adding more resources to existing servers or by upgrading to more powerful servers.
+
+## Replica Sets
+
+Although not exclusively a vertical scaling method, using replica sets can also help increase the performance and availability of your MongoDB deployment. A replica set is a group of MongoDB servers that maintain the same data set, providing redundancy and increasing data availability.
+
+## Components of Replica Sets
+
+- *Primary Node*: The primary node processes all the write operations and can also process read operations.
+- *Secondary Nodes*: Secondary nodes replicate the data stored in the primary node and can serve read operations. They can be promoted to the primary role if the primary node experiences a failure.
+- *Arbiter Nodes* (optional): Arbiter nodes do not store any data but participate in the election process for primary node selection, preventing split-brain scenarios.
+
+In conclusion, scaling MongoDB can be achieved by using a combination of horizontal and vertical scaling methods. Additionally, managing replica sets improves the overall performance and availability of your system. Accurate planning and consideration of your application requirements will help you decide which scaling methods to apply for your MongoDB deployment.

src/data/roadmaps/mongodb/content/110-mongodb-security/100-role-based-access-control.md

@@ -1 +1,59 @@
-# Role based access control
+# Role-based access control
+
+Role-Based Access Control (RBAC) is an approach to restricting the access of users to perform certain tasks, view data, or execute specific commands. In MongoDB, RBAC is an essential aspect to ensure security within the database.
+
+Each role in MongoDB consists of a set of privileges that determine the user's abilities within the system. MongoDB has several built-in roles, and you also have the option to create custom roles as needed. By assigning the appropriate roles to users, you can limit their access to various parts of the database and protect sensitive information.
+
+## Built-in Roles
+
+MongoDB provides several built-in roles that have predefined sets of privileges:
+
+- **Read**: Allows read operations on the specified database.
+- **ReadWrite**: Allows read and write operations on the specified database.
+- **dbAdmin**: Allows administrative tasks, such as managing indexes and user-defined roles, for the specified database.
+- **userAdmin**: Allows managing user access for the specified database.
+- **ClusterAdmin**: Allows administrative tasks for the entire cluster, such as configuring replica sets and sharding.
+- **ReadAnyDatabase**: Allows read operations on all databases except for the `local` and `config` databases.
+- **ReadWriteAnyDatabase**: Allows read and write operations on all databases except for the `local` and `config` databases.
+- **userAdminAnyDatabase**: Allows managing user access for all databases except for the `local` and `config` databases.
+- **dbAdminAnyDatabase**: Allows administrative tasks for all databases except for the `local` and `config` databases.
+
+## Custom Roles
+
+In addition to the built-in roles, you can create custom roles to cater to specific requirements of your application. Custom roles can have any combination of built-in roles' privileges and user-defined actions.
+
+To create a custom role, you can use the `db.createRole()` method. Here's an example:
+
+```javascript
+db.createRole({
+  role: "customRole",
+  privileges: [
+    {
+      resource: {db: "exampleDB", collection: ""},
+      actions: ["find", "insert", "update", "remove"]
+    }
+  ],
+  roles: []
+})
+```
+
+In the example above, we created a custom role `customRole` with privileges that allow users with this role to perform `find`, `insert`, `update`, and `remove` operations on all collections within the `exampleDB` database.
+
+## Assigning Roles to Users
+
+To ensure that users have the appropriate level of access and permissions, you assign specific roles to them. When creating a new user, you can assign roles using the `db.createUser()` method. Here's an example:
+
+```javascript
+db.createUser({
+  user: "exampleUser",
+  pwd: "examplePassword",
+  roles: [
+    {role: "read", db: "exampleDB"},
+    {role: "customRole", db: "exampleDB"}
+  ]
+})
+```
+
+In this example, we created a new user `exampleUser` and assigned the built-in `read` role and a custom `customRole` role.
+
+By effectively using role-based access control, you can strengthen the security of your MongoDB database and protect sensitive data from unauthorized access.

src/data/roadmaps/mongodb/content/110-mongodb-security/101-x509-certificate-auth.md

@@ -1 +1,44 @@
-# X509 certificate auth
+# X.509 Certificate Auth
+
+X.509 certificate authentication is a crucial aspect of MongoDB security that enables clients to verify each other's authenticity using public key infrastructure (PKI). With X.509 certificate authentication, both the client and MongoDB server confirm the identity of the other party, ensuring secure communication and preventing unauthorized access.
+
+## Implementing X.509 Certificate Authentication
+
+To incorporate x.509 certificate authentication, follow these steps:
+
+- **Obtain Certificates**: Get an X.509 certificate for the server and each client that connects to the MongoDB server. The certificates must be issued by a single Certificate Authority (CA).
+
+- **Configure the MongoDB Server**: To enable X.509 authentication, you'll need to start MongoDB with the following options:
+
+    ```bash
+    mongod --tlsMode requireTLS --tlsCertificateKeyFile /path/to/server.pem --tlsCAFile /path/to/ca.pem --auth
+    ```
+    Replace `/path/to/server.pem` with the path to the MongoDB server certificate file and `/path/to/ca.pem` with the CA certificate file. Add `--auth` to require authentication for all connections.
+
+- **Create the User Administrator**: Use the following command on the `admin` database to create a user administrator with an X.509 certificate:
+
+    ```javascript
+    db.getSiblingDB("$external").runCommand(
+      {
+        createUser: "C=US,ST=New York,L=New York City,O=MongoDB,OU=kerneluser,CN=client@example.com",
+        roles: [
+           { role: "userAdminAnyDatabase", db: "admin" },
+           { role: "clusterAdmin", db: "admin" },
+           { role: "readWriteAnyDatabase", db: "admin" },
+           { role: "dbAdminAnyDatabase", db: "admin" },
+        ],
+        writeConcern: { w: "majority" , wtimeout: 5000 }
+      }
+    )
+    ```
+
+    Replace the `createUser` field with your X.509 certificate's subject.
+
+- **Authenticate with the Client Certificate**: To authenticate the client, use a `mongo` shell command that includes the client certificate and CA certificate files:
+
+    ```bash
+    mongo --tls --tlsCertificateKeyFile /path/to/client.pem --tlsCAFile /path/to/ca.pem --authenticationDatabase '$external' --authenticationMechanism 'MONGODB-X509' --host hostname.example.com
+    ```
+    Update `/path/to/client.pem` with the client certificate file path and `/path/to/ca.pem` with the CA certificate file. Replace `hostname.example.com` with your MongoDB server's hostname.
+
+After successfully implementing these steps, you will have enabled X.509 certificate authentication for your MongoDB environment, providing an added layer of security for client-server communications.

src/data/roadmaps/mongodb/content/110-mongodb-security/102-kerberos-authentication.md

@@ -1 +1,34 @@
-# Kerberos authentication
+# Kerberos Authentication
+
+Kerberos is a network authentication protocol that uses secret-key cryptography to provide strong authentication for client-server applications. In the context of MongoDB, it provides an additional layer of security by ensuring that the MongoDB server and clients can mutually identify each other, reducing the risk of unauthorized access.
+
+## How Kerberos Authentication Works
+
+Kerberos operates on the principle of issuing tickets to establish trust between entities, such as clients and servers. These tickets are encrypted and contain information about the user's credentials and rights. The Key Distribution Center (KDC) is the central authority responsible for authenticating the entities and issuing tickets.
+
+The process of Kerberos authentication involves the following steps:
+
+- **Client Authentication:** The client sends an authentication request to the KDC, which validates the client's identity and issues a Ticket Granting Ticket (TGT).
+- **Service Ticket Request:** The client requests a service ticket from the KDC, using the TGT as proof of authentication.
+- **Service Ticket Issuance:** The KDC verifies the TGT and issues a service ticket (ST) for the requested service (in this case, MongoDB).
+- **Service Authentication:** The client presents the ST to the MongoDB server, which verifies the ticket and allows access to the client.
+
+## Configuring MongoDB for Kerberos Authentication
+
+Setting MongoDB to use Kerberos authentication involves the following steps:
+
+- Set up a Kerberos environment, including the KDC, clients, and MongoDB server.
+- Create a MongoDB service principal within the Kerberos environment.
+- Set up a keytab file containing the service principal's key to be used by the MongoDB server.
+- Configure the MongoDB server to use Kerberos authentication by setting the `security.authenticationMechanisms` parameter to `GSSAPI`.
+- Start the MongoDB server with the `--keyFile` and `--setParameter` options, specifying the keytab file and service principal name.
+
+## Configuring MongoDB Clients for Kerberos Authentication
+
+MongoDB clients need to have valid tickets in their credentials cache to authenticate with the MongoDB server. This commonly involves the following steps:
+
+- Set up the client machine as part of the Kerberos realm.
+- Request a TGT from the KDC using the `kinit` command.
+- Configure the MongoDB client to use Kerberos authentication by passing a connection string that includes the GSSAPI mechanism.
+
+In summary, Kerberos authentication provides an additional layer of security in MongoDB, ensuring the mutual identification of the server and clients. By properly configuring the MongoDB server and clients, you can take advantage of this powerful authentication mechanism to protect your data.

src/data/roadmaps/mongodb/content/110-mongodb-security/103-ldap-proxy-auth.md

@@ -1 +1,21 @@
-# Ldap proxy auth
+# LDAP Proxy Auth
+
+**LDAP** (Lightweight Directory Access Protocol) is an application protocol used for accessing and managing distributed directory information services over a network. While MongoDB already supports LDAP in its Enterprise Edition, **LDAP Proxy Authentication** adds an additional layer of security and simplifies the user management process. It allows MongoDB to delegate the authentication process to an LDAP server without storing any user credentials in the MongoDB server.
+
+In this section, we'll take a closer look at LDAP Proxy Authentication and its benefits.
+
+## How does it work?
+
+- A client sends a request to MongoDB with their credentials.
+- MongoDB then forwards the credentials to the LDAP server.
+- The LDAP server checks if the provided credentials are valid and authenticates the user accordingly.
+- Once the user has been authenticated, MongoDB receives a response from the LDAP server confirming the user's identity and proceeds with executing the requested operation.
+
+## Advantages of using LDAP Proxy Authentication
+
+- **Single sign-on**: Users can use a single set of credentials across different servers and applications that are connected to the LDAP server. This simplifies the login process and reduces the need to remember multiple passwords.
+- **Centralized user management**: User information is stored in the LDAP server rather than multiple MongoDB servers. This makes it easier to manage users, as all the changes can be made in one place, and they're instantly applied across all applications using LDAP.
+- **Enhanced security**: MongoDB doesn't store any user credentials, which helps protect against unauthorized access in case of a MongoDB server compromise. Additionally, the LDAP server can enforce strong authentication and password policies.
+- **Reduced administrative overhead**: Managing users directly in MongoDB can be cumbersome, especially in large-scale deployments with multiple servers. LDAP Proxy Authentication simplifies the process by keeping user information centralized in the LDAP server.
+
+To implement LDAP Proxy Authentication in your MongoDB security setup, you can follow the [official MongoDB documentation](https://docs.mongodb.com/manual/security/authentication/) that provides comprehensive instructions on how to configure the feature depending on your LDAP server and MongoDB version.

src/data/roadmaps/mongodb/content/110-mongodb-security/104-mongodb-audit.md

@@ -1 +1,45 @@
-# Mongodb audit
+# MongoDB Audit
+
+Auditing is a critical aspect of maintaining the security and compliance of your database systems. MongoDB provides auditing capabilities to track and log various activities occurring within your MongoDB deployment. This information can be vital for identifying potential security risks, troubleshooting issues, and meeting regulatory compliance requirements such as HIPAA, GDPR, and PCI DSS.
+
+## How MongoDB Auditing Works
+
+MongoDB auditing enables you to capture detailed information about database events, such as user authentication, command execution, and changes to the database configuration. The audit log provides complete visibility into database operations, which can be analyzed and monitored in real-time or stored for future examination.
+
+## Enabling MongoDB Auditing
+
+To enable auditing in MongoDB, you must use MongoDB Enterprise Advanced or an equivalent Atlas tier. Once you have the required version, you can enable auditing by modifying your `mongod` or `mongos` configuration file to include the `auditLog` option, specifying the format, destination, and filter criteria for the audit events.
+
+Example:
+```yaml
+auditLog:
+  destination: file
+  format: JSON
+  path: "/path/to/audit/log/file.json"
+  filter: "{ atype: { $in: ['authenticate', 'createUser', 'dropUser', 'revokeRolesFromUser'] }}"
+```
+
+## Audit Log Formats
+
+MongoDB audit logs can be generated in two formats:
+
+- **BSON**: The default format for audit logs, BSON is space-efficient and simplifies parsing of audit events by MongoDB tools.
+
+- **JSON**: A human-readable representation of audit events, JSON format can be read and processed by most log management systems.
+
+## Filtering Audit Events
+
+To specify which events should be audited, you can provide a filter expression to the `auditLog.filter` configuration parameter.
+Example:
+```yaml
+auditLog:
+  filter: "{ atype: { $in: ['authCheck', 'createUser', 'dropUser', 'revokeRolesFromUser'] }}"
+```
+
+You can customize the filter criteria to suit your security requirements and avoid capturing unnecessary events.
+
+## Analyzing and Monitoring Audit Logs
+
+You can analyze and monitor MongoDB audit logs using various tools, including log analysis software, SIEM systems, or built-in MongoDB utilities like `mongoaudit`. Regular audits can help identify unusual activities, data breaches, or unauthorized access, therefore ensuring the continued security and integrity of your database environment.
+
+In conclusion, MongoDB's auditing feature is an essential component of a robust security strategy. By enabling audit logging and regularly analyzing the captured events, you can ensure the safety, performance, and compliance of your MongoDB deployment.

src/data/roadmaps/mongodb/content/110-mongodb-security/encryption/100-encryption-at-rest.md

@@ -1 +1,50 @@
-# Encryption at rest
+# Encryption at Rest
+
+Encryption at Rest refers to the process of encrypting data when it is stored within a database system such as MongoDB. The goal is to protect sensitive information from unauthorized access in cases like a security breach or if the database server is physically stolen.
+
+## Benefits
+
+* **Enhanced Security**: By encrypting the data, you make it more difficult for attackers to access sensitive information.
+* **Compliance**: Encryption at rest can help you meet various regulatory compliance requirements that mandate data protection.
+* **Reduced Risk**: If someone gains unauthorized access to the storage, they won't be able to read the encrypted data.
+
+## How it Works in MongoDB
+
+MongoDB Enterprise edition supports encryption at rest using WiredTiger, the default storage engine. It internally uses **libsodium** library to perform encryption and decryption operations. The encryption process has three major components:
+
+- **Encryption key management**: MongoDB uses symmetric encryption algorithms with keys that must be generated and securely stored. You can store the master keys in a secure external key management server or use locally managed external keys.
+
+- **Encryption algorithm**: MongoDB supports both AES-256-CBC and AES-256-GCM encryption algorithms for encrypting data at rest. You should select an algorithm suitable for your specific security needs.
+
+- **Encrypted Storage Engine**: WiredTiger storage engine uses the selected encryption algorithm to encrypt all database files, including indexes, journals, and log files.
+
+## Configuring Encryption at Rest
+
+To enable encryption at rest in MongoDB, you have to perform the following steps:
+
+- **Generate the encryption key**: Generate the symmetric encryption key and store it securely. You should use key management best practices to ensure secure key storage and rotation.
+
+- **Configure the key management**: In your `mongod.conf`, set the path to the encryption key and choose the method to manage the encryption key (`local` or `kmip`).
+
+- **Choose the encryption algorithm**: Specify the encryption algorithm (AES256-CBC or AES256-GCM) in your `mongod.conf`.
+
+- **Enable encryption**: Turn on the `encryptWith` parameter in the WiredTiger storage engine.
+
+Example `mongod.conf` file:
+```yml
+storage:
+  wiredTiger:
+    engineConfig:
+      encryptWith: "AES256-CBC"
+      encryptionKeyManager:
+        keyLocation: "/path/to/encryption/key"
+        keyManagement: "local"
+```
+
+Start MongoDB with:
+
+```bash
+mongod --config /etc/mongod.conf
+```
+
+By configuring encryption at rest, you are now providing an added layer of security to your MongoDB database, making it more difficult for unauthorized users to access sensitive information while ensuring compliance with regulatory requirements.

src/data/roadmaps/mongodb/content/110-mongodb-security/encryption/101-queryable-encryption.md

@@ -1 +1,31 @@
-# Queryable encryption
+# Queryable encryption
+
+Queryable encryption is a security feature offered by MongoDB, which allows the users to perform queries on encrypted data without decrypting it. This ensures data confidentiality while maintaining the ability to perform essential database operations. It is particularly useful in protecting sensitive data such as Personally Identifiable Information (PII), credit card numbers, or medical records.
+
+Here we discuss the following aspects of queryable encryption in MongoDB:
+
+##  Client-Side Field Level Encryption (FLE)
+
+Client-side FLE is a technique where data is encrypted on the client-side before it is sent to the MongoDB server. This ensures that only encrypted data is stored in the database, and sensitive fields remain confidential. With client-side FLE, the encryption keys are managed outside of the database server, granting even finer control over data access.
+
+##  Supported Algorithms
+
+MongoDB supports two types of encryption algorithms for queryable encryption:
+
+- **Deterministic Encryption**: This algorithm allows exact matches of the encrypted field values. It means, if two values are the same, their encrypted versions will also be the same. This kind of encryption allows performing operations like equality and `$in` queries, sorting, and more. However, deterministic encryption can leak some frequency details about the original data.
+
+- **Randomized Encryption**: This algorithm ensures that the same value will result in different encrypted values, providing a higher level of security. However, randomized encryption does not allow performing database operations like equality or sorting on the encrypted fields.
+
+##  Indexing Encrypted Fields
+
+One of the crucial benefits of queryable encryption in MongoDB is the support for indexing encrypted fields. You can create indexes on fields encrypted with deterministic encryption to improve query performance. However, indexing is not supported on fields encrypted with randomized encryption, as the encrypted values are not predictable.
+
+##  Supported Data Types
+
+Queryable encryption in MongoDB supports encrypting fields with various data types, including strings, numbers (integers, doubles, and decimals), dates, and binary data (including UUIDs and ObjectIDs). However, it does not support encrypting fields containing arrays, embedded documents, or other special data types.
+
+##  Encryption Performance
+
+Implementing queryable encryption may introduce some performance overhead while performing encryption and decryption operations on the client-side. It is essential to evaluate the impact of encryption on your application and consider optimizing the encryption settings or the database schema based on the use case.
+
+In summary, queryable encryption in MongoDB offers a powerful way to secure sensitive data while enabling necessary database operations like querying and indexing. By using client-side field level encryption and choosing appropriate encryption algorithms, you can strike the right balance between data confidentiality and performance.

src/data/roadmaps/mongodb/content/110-mongodb-security/encryption/103-client-side-field-level-encryption.md

@@ -1 +1,35 @@
-# Client side field level encryption
+# Client-Side Field Level
+
+Client-Side Field Level Encryption (CSFLE) in MongoDB provides enhanced security by encrypting specific fields of a document while they are stored in the database. With CSFLE, the data is encrypted and decrypted on the client-side, securing sensitive data from unauthorized access by malicious actors or even database administrators.
+
+## Key Features
+
+* **Field-level granularity**: Encrypt only the required fields in a document, ensuring optimal performance while maintaining security.
+* **Automatic encryption and decryption**: The MongoDB client library automatically encrypts and decrypts sensitive fields, without requiring any manual intervention.
+* **Separation of duties**: Client-Side Field Level Encryption separates the management of encryption keys and the encrypted data, allowing for a more secure infrastructure.
+
+## How It Works
+
+- **Define a JSON Schema**: Specify the fields to be encrypted in the JSON schema, along with the encryption type, algorithm, and key management options.
+- **Generate a Data Encryption Key**: Generate a data encryption key (DEK) using a secure source of randomness. This key will be used to encrypt and decrypt sensitive fields.
+- **Encrypt fields**: When inserting or updating documents, MongoDB will automatically encrypt the specified fields using the configured encryption options and DEK.
+- **Store encrypted data**: The encrypted data is stored in the database, securely protecting sensitive information from unauthorized access.
+- **Query and decrypt**: When querying the data, MongoDB decrypts the encrypted fields on the client side, allowing users to interact with the data seamlessly.
+
+## Supported Algorithms
+
+MongoDB supports the following encryption algorithms for CSFLE:
+
+* **Deterministic Encryption**: This encryption method allows for equality queries on encrypted fields. It uses the same encryption key and plaintext to generate the same encrypted data, ensuring that the same values will be encrypted the same way.
+* **Random Encryption**: This encryption method provides a higher level of security by using different values for each encryption, even with identical plaintext. It is suitable for fields that don't require searching or querying based on individual values.
+
+## Key Management
+
+CSFLE requires the use of a separate Key Management System (KMS) to store and maintain encryption keys. MongoDB supports the following KMS providers:
+
+* AWS Key Management Service (KMS)
+* Azure Key Vault
+* Google Cloud KMS
+* Local Key Management (using a local master key)
+
+By using CSFLE in MongoDB, you can significantly enhance the security of your sensitive data and comply with regulatory standards such as GDPR, HIPAA, and PCI-DSS.

src/data/roadmaps/mongodb/content/110-mongodb-security/index.md

@@ -1 +1,52 @@
-# Mongodb security
+# MongoDB Security
+
+In this section, we are going to learn about MongoDB security, its importance, and best practices to ensure a secure and robust MongoDB deployment. Security is crucial for protecting your data and keeping unauthorized access at bay. MongoDB provides several security mechanisms and features to help you safeguard your data.
+
+## Authentication
+
+Authentication is the process of verifying the identity of a user or client. MongoDB supports multiple authentication mechanisms, including:
+
+- **SCRAM**: Salted Challenge Response Authentication Mechanism (SCRAM) is the default authentication mechanism in MongoDB. It's a modern, secure, and password-based authentication method.
+- **x.509**: MongoDB supports x.509 certificate-based authentication for both clients and servers.
+- **LDAP**: MongoDB Enterprise Edition provides support for proxy authentication through a Lightweight Directory Access Protocol (LDAP) server.
+- **Kerberos**: MongoDB Enterprise Edition also supports Kerberos-based authentication.
+
+## Authorization
+
+Authorization is the process of granting access and privileges to authenticated users. MongoDB authorization model revolves around the concept of **Role-Based Access Control (RBAC)**. Roles grant privileges, and users are assigned one or more roles to define their access. MongoDB provides a set of built-in roles:
+
+- Read
+- ReadWrite
+- dbAdmin
+- userAdmin
+- clusterAdmin
+- backup
+- restore
+
+You can also create custom roles tailored to your specific needs.
+
+## Encryption
+
+Encryption plays a vital role in securing your data both at rest and in transit:
+
+- **Encryption at Rest**: MongoDB Enterprise Edition provides an encryption-at-rest feature using the WiredTiger storage engine. This feature encrypts all data files and logs with algorithms such as AES256-GCM.
+- **Encryption in Transit**: MongoDB supports [Transport Layer Security (TLS)]() and [Secure Socket Layers (SSL)]() to encrypt data during transfer between client and server.
+
+## Auditing
+
+Auditing consists of capturing and maintaining traceable records of system activities. It helps you gain insight into how your MongoDB deployment is being used and assists in meeting regulatory or compliance needs. MongoDB Enterprise Edition provides auditing capabilities that can be configured according to business requirements.
+
+## Other Security Best Practices
+
+Here are some additional best practices to ensure a secure MongoDB deployment:
+
+- Enable access control and disable anonymous access
+- Limit network exposure by binding to private IP addresses and using firewalls
+- Configure role-based authorization
+- Rotate X.509 certificates and limit their validity period
+- Use encryption for data at rest and during transit
+- Employ strong and unique passwords
+- Enable auditing and monitor logs
+- Regularly update and patch MongoDB
+
+In conclusion, MongoDB provides a comprehensive security framework to protect your data and applications from unauthorized access and attacks. By understanding and implementing various MongoDB security features, you can ensure the safety and integrity of your database systems.