MongoDB Online Compiler

Run MongoDB queries online with our NoSQL database environment. Perfect for learning document databases, testing queries, and MongoDB development.

mongodb Online Compiler

// MongoDB Online Compiler
// Welcome to MongoDB query environment

// Connect to database (simulated)
use company_database;

print("Hello, World!");
print("Welcome to MongoDB Online Compiler");

// Insert documents
db.employees.insertMany([
    {
        name: "John Doe",
        department: "Engineering",
        salary: 75000,
        skills: ["JavaScript", "Python", "MongoDB"],
        hire_date: new Date("2023-01-15"),
        active: true
    },
    {
        name: "Jane Smith",
        department: "Marketing",
        salary: 65000,
        skills: ["Marketing", "Analytics", "Social Media"],
        hire_date: new Date("2023-02-20"),
        active: true
    },
    {
        name: "Mike Johnson",
        department: "Engineering",
        salary: 80000,
        skills: ["Java", "Spring", "Docker"],
        hire_date: new Date("2022-11-10"),
        active: true
    },
    {
        name: "Sarah Wilson",
        department: "HR",
        salary: 60000,
        skills: ["Recruitment", "Training", "Policy"],
        hire_date: new Date("2023-03-05"),
        active: false
    }
]);

print("Inserted employee documents");

// Basic queries
print("\n=== All Employees ===");
db.employees.find().pretty();

print("\n=== Engineering Department ===");
db.employees.find({department: "Engineering"}).pretty();

print("\n=== Active Employees Only ===");
db.employees.find({active: true}, {name: 1, department: 1, salary: 1}).pretty();

// Aggregation pipeline
print("\n=== Average Salary by Department ===");
db.employees.aggregate([
    {
        $group: {
            _id: "$department",
            avgSalary: { $avg: "$salary" },
            count: { $sum: 1 }
        }
    },
    {
        $sort: { avgSalary: -1 }
    }
]).pretty();

// Complex queries
print("\n=== Employees with JavaScript Skills ===");
db.employees.find({
    skills: { $in: ["JavaScript"] }
}, {
    name: 1,
    skills: 1
}).pretty();

print("\n=== High Salary Employees (>70k) ===");
db.employees.find({
    salary: { $gt: 70000 }
}, {
    name: 1,
    salary: 1,
    department: 1
}).pretty();

// Update operations
print("\n=== Updating Salaries ===");
db.employees.updateMany(
    { department: "Engineering" },
    { 
        $inc: { salary: 5000 },
        $set: { last_updated: new Date() }
    }
);

print("Updated Engineering salaries");

// Text search (simulated)
print("\n=== Text Search Example ===");
db.employees.find({
    $or: [
        { name: { $regex: "John", $options: "i" } },
        { skills: { $regex: "Java", $options: "i" } }
    ]
}).pretty();

// Array operations
print("\n=== Employees with Multiple Skills ===");
db.employees.find({
    skills: { $size: { $gte: 3 } }
}, {
    name: 1,
    skills: 1
}).pretty();

// Date queries
print("\n=== Recently Hired (2023) ===");
db.employees.find({
    hire_date: {
        $gte: new Date("2023-01-01"),
        $lt: new Date("2024-01-01")
    }
}, {
    name: 1,
    hire_date: 1
}).pretty();

// Create indexes for performance
print("\n=== Creating Indexes ===");
db.employees.createIndex({ department: 1 });
db.employees.createIndex({ salary: -1 });
db.employees.createIndex({ skills: 1 });

print("Created indexes on department, salary, and skills");

// Advanced aggregation
print("\n=== Skills Analysis ===");
db.employees.aggregate([
    { $unwind: "$skills" },
    {
        $group: {
            _id: "$skills",
            count: { $sum: 1 },
            employees: { $push: "$name" }
        }
    },
    { $sort: { count: -1 } }
]).pretty();

// MapReduce example (conceptual)
print("\n=== Department Statistics ===");
db.employees.aggregate([
    {
        $group: {
            _id: "$department",
            totalSalary: { $sum: "$salary" },
            avgSalary: { $avg: "$salary" },
            minSalary: { $min: "$salary" },
            maxSalary: { $max: "$salary" },
            employeeCount: { $sum: 1 }
        }
    }
]).pretty();

// Geospatial data example
db.offices.insertMany([
    {
        name: "New York Office",
        location: {
            type: "Point",
            coordinates: [-74.0059, 40.7128]
        },
        employees: 150
    },
    {
        name: "San Francisco Office",
        location: {
            type: "Point",
            coordinates: [-122.4194, 37.7749]
        },
        employees: 200
    }
]);

print("\n=== Office Locations ===");
db.offices.find().pretty();

// Cleanup and final operations
print("\n=== Final Statistics ===");
print("Total employees: " + db.employees.countDocuments());
print("Active employees: " + db.employees.countDocuments({active: true}));
print("Departments: " + db.employees.distinct("department").length);

print("\nMongoDB operations completed successfully!");

Output

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