MongoDB provides a wide range of operators for both simple queries and complex aggregation pipelines. The following comparison opertaors are frequently used with the find() method:

• $gt – greater than
• $lt – less then
• $gte – greater than or equal to
• $lte – less than or equal to
• $ne – not equal

Example: fetch products with a stock count above 50 and a price less then 100:

db.inventory.find({
  qty: { $gt: 50 },
  price: { $lt: 100 }
})

Aggregation pipelines unlock more advanced data processing. A common pipeline starts with $match (like find()) and then transforms the records with stages such as $project, $group, $limit, or $skip.

Assume a sales collection:

{ _id: 1, item: "widget", price: 12, quantity: 3, saleDate: ISODate("2024-01-15") }
{ _id: 2, item: "gadget", price: 25, quantity: 1, saleDate: ISODate("2024-02-10") }
{ _id: 3, item: "widget", price: 12, quantity: 8, saleDate: ISODate("2024-02-20") }

$project – Reshape Documents

$project selects specific fields, creates computed fields, and can suppress the _id. To show item, price, and a new field revenue:

db.sales.aggregate([
  { $project: { _id: 0, item: 1, price: 1, revenue: { $multiply: ["$price", "$quantity"] } } }
])

$match – Filter before Aggregating

Place $match early to reduce later processing. Retrieve all sales where the quantity is at least 3:

db.sales.aggregate([
  { $match: { quantity: { $gte: 3 } } },
  { $group: { _id: "$item", totalQty: { $sum: "$quantity" } } }
])

$limit and $skip

For pagination, $skip ignores the first N documents and $limit restricts the output size.

db.sales.aggregate([
  { $skip: 10 },
  { $limit: 5 }
])

$cond – Conditional Expressions

$cond mimics an if-then-else logic inside stage expressions. Below, a discount factor is applied based on quantity:

db.sales.aggregate([
  { $project: {
      item: 1,
      price: 1,
      factor: {
        $cond: { if: { $gte: ["$quantity", 5] }, then: 0.85, else: 0.95 }
      }
  }}
])

$sum – Counting and Totaling

Within a $group stage, $sum serves two purposes:

• { $sum: 1 } counts the Number of documents per group.
• { $sum: "$field" } calculates the sum of a numeric field.

Collection orders:

{ _id: 1, customer: "A", amount: 150 }
{ _id: 2, customer: "B", amount: 200 }
{ _id: 3, customer: "A", amount: 100 }

To get the total spent and order count for each customer:

db.orders.aggregate([
  { $group: { _id: "$customer", totalSpent: { $sum: "$amount" }, orderCount: { $sum: 1 } } }
])

These operators form the backbone of everyday MongoDB work, from basic filtering to sophisticated analytical queries.