MongoDB Database Structure

 

MongoDB Database Structure

MongoDB, a popular NoSQL database, utilizes a flexible document-based structure to store data. Here's a breakdown of its key components:

1. Databases:

• A logical container for a set of collections.
• You can have multiple databases within a single MongoDB instance.
• Databases are typically named to reflect their purpose (e.g., "customer_db", "product_db").

2. Collections:

• Analogous to tables in relational databases.
• Store a set of documents.
• Can contain documents with varying structures within the same collection.

3. Documents:

• The fundamental unit of data storage in MongoDB.
• Similar to JSON objects, they consist of key-value pairs.
• Keys are strings, and values can be of various data types:
• Strings
• Numbers (integers, decimals)
• Booleans
• Arrays
• Embedded documents (objects within a document)
• Dates
• Binary data

 

Many of the components that go into the MongoDB data structure have their counterparts in relational database management systems based on the Structured Query Language (SQL).

Example:

JSON

{

  "_id": ObjectId("5f49c3d27007234567890abc"), // Unique identifier

  "name": "John Doe",

  "age": 30,

  "address": {

    "street": "123 Main St",

    "city": "Anytown",

    "state": "CA"

  },

  "hobbies": ["reading", "hiking", "coding"]

}

 

The document, which is comprised of field/value pairs, is at the heart of the MongoDB data structure. Most interactions with MongoDB occur at the document level.

A field can contain a single value, multiple fields, or multiple elements.

 A value made up of multiple fields is referred to as an embedded document and is assigned the Object data type (see field cars in the screenshot). When rendered in the JSON format, an embedded document is enclosed in curly braces and adheres to the same structure as the outer (main) document.

 

 MongoDB Data Types

A field can be one of these MongoDB data types:

Type	Alias
Double	“double”
String	“string”
Object	“object”
Array	“array”
Binary data	“binData”
Undefined	“undefined”
ObjectId	“objectId”
Boolean	“bool”
Date	“date”
Null	“null”
Regular Expression	“regex”
DBPointer	“dbPointer”
JavaScript	“javascript”
Symbol	“symbol”
JavaScript (with scope)	“javascriptWithScope”
32-bit integer	“int”
Timestamp	“timestamp”
64-bit integer	“long”
Decimal128	“decimal”
Min key	“minKey”
Max key	“maxKey”

Key Characteristics:

• Schema-less: You don't need to define a strict schema upfront. Documents within a collection can have different fields.
• Dynamic: The structure of documents can evolve over time as your application needs change.
• Flexible: Handles complex data structures easily due to the use of embedded documents and arrays.

Visual Representation:

MongoDB database structure with databases, collections, and documents

By understanding this structure, you can effectively model and store your data in MongoDB, leveraging its flexibility and scalability for various applications.

Code to connect MongoDB to Python and create a database and a collection in MongoDB

import pymongo # Replace the connection string with your MongoDB connection string mongo_uri = "mongodb://username:password@localhost:27017/mydatabase" # Connect to MongoDB client = pymongo.MongoClient(mongo_uri) # Create or access a database db = client.get_database("mydatabase") # Create or access a collection collection = db.get_collection("mycollection") # Insert a document into the collection data_to_insert = { "name": "John Doe", "email": "john@example.com", "age": 30 } inserted_doc = collection.insert_one(data_to_insert) print(f"Inserted document ID: {inserted_doc.inserted_id}") # Close the MongoDB connection (optional, but recommended) client.close()

Query the MongoDB database using the find() method

import pymongo # Replace the connection string with your MongoDB connection string mongo_uri = "mongodb://username:password@localhost:27017/mydatabase" # Connect to MongoDB client = pymongo.MongoClient(mongo_uri) # Access the database and collection db = client.get_database("mydatabase") collection = db.get_collection("mycollection") # Define a query filter query_filter = {"age": {"$gte": 30}} # Use the find() method with the query filter cursor = collection.find(query_filter) # Iterate through the results and print them print("Documents with age greater than or equal to 30:") for document in cursor: print(document) # Close the MongoDB connection (optional, but recommended) client.close()