Working With Date, Regex, And Arrays In MongoDB (GAURI GAWANDE)

Da

• Dates:

MongoDB uses the Date type to store date and time values. Dates are stored in ISODate format, which includes both date and time.

syntax:

db.collection.insertOne({

  name: "Gauri",

  createdAt: new Date()

})

db.collection.find({

  createdAt: { $gte: ISODate("2025-01-01"), $lt: ISODate("2025-12-31") }

})

    

Date operation:

$gte, $lte, $eq – Compare dates
$dateToString – Format date
$subtract, $add – Date arithmetic 

example: 

Imagine an application where you want to record every time a user logs in, including the exact time of the login.

• Inserting Login Records: When a user successfully logs in, you would insert a document into a login history collection. The document would include the user's ID and a login_time field, set to the current date and time using MongoDB's new Date() or ISODate() functionality.

•  Regex:

Regular expressions (regex) are patterns used to match character combinations in strings. 

They are commonly used for search, replace, and input validation tasks.

syntax:

db.users.insertMany([

  { name: "Gauri Gawande" },       

  { name: "Ashok Gawande" },          

  { name: "Gauri Ashok" }])

db.users.find({

  name: { $regex: "^Gauri", $options: "i" }

})

db.users.find({

  name: { $regex: "Gawande$", $options: "i" }

})

example:

To find documents where a specific field contains a particular substring, use the basic regex 

• Arrays:

arrays are a data type that can hold multiple values, including other documents, arrays, and a mixture of different data types. This versatility makes arrays a cornerstone feature for modeling real-world data.

syntax:

db.students.insertOne({

  name: "Gauri",

  subjects: ["Math", "English", "Computer"]

})

db.students.find({

  subjects: "Math"

})

db.students.find({

  subjects: { $all: ["Math", "English"] }

})

db.students.find({

  subjects: { $size: 3 }

})

example:

E-commerce Product Tags: A product document might have an array field called tags to store keywords associated with the product, such as ["electronics", "laptop", "gaming"]. This allows for easy searching and filtering based on multiple categories

• future scope/use cases:

Dates:

• Time-series databases (e.g., IoT, stock prices)
• Analytics and reporting
• Scheduling and calendar apps
• Expiry and TTL (Time-To-Live) data management

Regex:

• Search engines and autocomplete
• Data validation (e.g., email, phone numbers)
• Log filtering and pattern matching
• Text mining and NLP preprocessing

Arrays:

• Nested data modeling (e.g., orders with items)
• Tagging systems (e.g., blog tags, product categories)
• Recommendation engines (e.g., user preferences)
• Real-time analytics on multi-value fields