Cheatsheets: MongoDB Cheatsheet — Query Quick Reference

MongoDB Cheatsheet — Query Quick Reference

DodaTech 3 min read

MongoDB CRUD operations, query operators, aggregation pipeline, indexes, and database commands — a dense reference for daily NoSQL database work.

Database & Collection Commands

use mydb                    // switch/create database
db                          // current db
show dbs                    // list databases
show collections            // list collections
db.createCollection("users")
db.users.drop()
db.dropDatabase()

CRUD — Create

db.users.insertOne({ name: "Alice", age: 30, city: "NYC" });
db.users.insertMany([
  { name: "Bob", age: 25 },
  { name: "Charlie", age: 35 }
]);

CRUD — Read

db.users.find()                         // all documents
db.users.find({ age: { $gt: 25 } })    // filtered
db.users.findOne({ name: "Alice" })     // first match
db.users.find({}, { name: 1, _id: 0 }) // projection (only name)

Query Operators

Operator	What it does
`$eq`, `$ne`	Equal, not equal
`$gt`, `$gte`	Greater than (or equal)
`$lt`, `$lte`	Less than (or equal)
`$in`, `$nin`	In array, not in array
`$regex`	Pattern match
`$exists`	Field exists
`$and`, `$or`	Logical AND/OR

db.users.find({ age: { $gte: 18, $lte: 65 } });
db.users.find({ name: { $regex: "^A", $options: "i" } });
db.users.find({ $or: [{ city: "NYC" }, { age: { $lt: 20 } }] });
db.users.find({ "address.zip": "10001" });  // nested field

CRUD — Update

db.users.updateOne(
  { name: "Alice" },
  { $set: { age: 31 } }
);
db.users.updateMany(
  { city: "NYC" },
  { $inc: { visits: 1 } }
);
db.users.replaceOne(
  { name: "Bob" },
  { name: "Bob", age: 26, city: "LA" }
);

Update operators: $set, $unset, $inc, $push, $pull, $addToSet, $rename.

CRUD — Delete

db.users.deleteOne({ name: "Charlie" });
db.users.deleteMany({ age: { $lt: 18 } });
db.users.deleteMany({});                     // all documents

Aggregation Pipeline

db.orders.aggregate([
  { $match: { status: "completed" } },
  { $group: { _id: "$customer_id", total: { $sum: "$amount" } } },
  { $sort: { total: -1 } },
  { $limit: 10 },
  { $project: { customer_id: 1, total: 1, _id: 0 } }
]);

Stage	Purpose
`$match`	Filter documents
`$group`	Group by field, compute aggregations
`$sort`	Sort documents
`$project`	Reshape fields
`$limit`	Limit results
`$lookup`	Left outer join with another collection
`$unwind`	Deconstruct array
`$count`	Count documents

Aggregation operators: $sum, $avg, $min, $max, $first, $last, $push, $addToSet.

Indexes

db.users.createIndex({ email: 1 });              // ascending
db.users.createIndex({ city: 1, age: -1 });     // compound
db.users.createIndex({ name: "text" });          // text index
db.users.createIndex({ created_at: 1 }, { expireAfterSeconds: 86400 });  // TTL
db.users.getIndexes();
db.users.dropIndex("email_1");

Useful Methods

db.users.countDocuments({ age: { $gt: 30 } });
db.users.distinct("city");
db.users.find().sort({ age: -1 }).skip(10).limit(5);
db.users.find().explain("executionStats");      // query performance

What is the difference between SQL and MongoDB?

MongoDB is a NoSQL document database — data is stored as JSON-like documents (BSON) with a flexible schema. Unlike SQL tables with fixed columns, each document in a MongoDB collection can have different fields. MongoDB trades strict consistency and joins (handled via $lookup) for scalability and developer flexibility.

What is the MongoDB aggregation pipeline?

The aggregation pipeline processes documents through a sequence of stages. Each stage transforms the data and passes it to the next stage. Common stages include $match (filter), $group (aggregate), $sort (order), and $lookup (join with another collection). It’s MongoDB’s equivalent of SQL’s GROUP BY and JOIN operations.

How do indexes work in MongoDB?

Indexes store a sorted reference to a field, allowing MongoDB to find documents without scanning the entire collection. A single-field index on email speeds up queries filtering by email. A compound index on {city: 1, age: -1} supports queries filtering by city, then sorting or filtering by age. Indexes consume write overhead and disk space.

See the full MongoDB tutorials for advanced pipelines.

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