NoSQL tutorial

nosql-0.md

📊 SQL vs. MongoDB: The Ultimate Cheat Sheet

This guide translates common SQL operations into MongoDB (NoSQL) syntax, highlighting the shift from tables and rows to collections and documents.

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📁 Sections

File	Topic
01-finding-data.md	Finding Data — SELECT vs .findOne()
02-conditional-queries.md	Conditional Queries — Operators & Filtering
03-projections.md	Projections — Selecting Specific Fields
04-arrays.md	The NoSQL Superpower: Arrays
05-joins-lookup.md	Relationships & Joins — $lookup
06-aggregation.md	Complex Aggregation — The "Assembly Line"
07-rules-of-thumb.md	Quick Rules of Thumb

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🗺️ Core Concept Map

SQL                         MongoDB
─────────────────────────────────────────
Database          →         Database
Table             →         Collection
Row               →         Document  (JSON-like)
Column            →         Field
JOIN              →         $lookup  (or embed!)
WHERE             →         filter object  { field: value }
SELECT col        →         projection  { col: 1 }
GROUP BY          →         $group  (aggregation stage)

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⚡ Key Mindset Shift

SQL Thinking	MongoDB Thinking
Normalize data into many tables	Embed related data in one document
Rows are flat	Documents can have nested objects & arrays
Joins are cheap	Joins ($lookup) are expensive — avoid when possible
Schema is rigid (defined upfront)	Schema is flexible (per-document)

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📊 SQL vs MongoDB — Ultimate Cheat Sheet [TLDR]

A quick guide translating common SQL operations into MongoDB syntax.

SQL uses tables & rows, while MongoDB uses collections & documents.

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1️⃣ Finding Data (The Basics)

Looking for one specific record, e.g., a user by email.

Feature	SQL (Relational)	MongoDB (Document)
Concept	SELECT * with LIMIT 1	findOne() method

SQL

SELECT * 
FROM Users 
WHERE email = 'jane@example.com'
LIMIT 1;

MongoDB

db.users.findOne({ email: "jane@example.com" });

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2️⃣ Conditional Queries (Find Many)

Filtering results based on conditions.

Logic

SQL uses math symbols

>, <, =, >=

MongoDB uses operators

$gt   Greater Than
$lt   Less Than
$gte  Greater Than or Equal
$lte  Less Than or Equal

Task

Find all active users older than 21

SQL

SELECT *
FROM Users
WHERE status = 'active'
AND age > 21;

MongoDB

db.users.find({
  status: "active",
  age: { $gt: 21 }
});

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3️⃣ Projections (Selecting Columns)

SQL selects columns directly.

MongoDB passes a projection object as the 2nd parameter.

Rules

1 = show field
0 = hide field

SQL

SELECT username, email
FROM Users
WHERE status = 'active';

MongoDB

db.users.find(
  { status: "active" },              // Filter (WHERE)
  { username: 1, email: 1, _id: 0 }  // Projection (SELECT)
);

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4️⃣ The NoSQL Superpower: Arrays

MongoDB makes querying arrays very easy.

Task

Find blog posts tagged "Tutorial"

SQL Approach

Requires join tables

Posts
Post_Tags
Tags

MongoDB

MongoDB automatically searches inside arrays.

db.posts.find({
  tags: "Tutorial"
});

Example document:

{
  "title": "Learn MongoDB",
  "tags": ["Database", "Tutorial", "Backend"]
}

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5️⃣ Relationships & Joins ($lookup)

MongoDB equivalent of LEFT OUTER JOIN.

Uses the Aggregation Pipeline.

Result Difference

SQL joins flatten data

Order + 3 products
= 3 rows

MongoDB keeps hierarchy

1 order document
+ array of 3 products

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SQL

SELECT *
FROM Orders
LEFT JOIN Products
ON Orders.product_id = Products.id;

MongoDB

db.orders.aggregate([
  {
    $lookup: {
      from: "products", // The collection to join with
      localField: "product_id", // The field in the 'Orders' collection
      foreignField: "_id", // The field in the 'Products' collection
      as: "product_details" // What to name the resulting array
    }
  }
])

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6️⃣ Aggregation Pipeline (Complex Queries)

MongoDB can chain operations like an assembly line.

Example: filter → join → calculate

db.orders.aggregate([

  // 1️⃣ WHERE
  { 
    $match: { customer: "Customer A" } 
  },

  // 2️⃣ JOIN
  { 
    $lookup: { 
      from: "products",
      localField: "product_id",
      foreignField: "_id",
      as: "product_details"
    }
  },

  // 3️⃣ GROUP BY
  { 
    $group: { 
      _id: "$customer",
      total: { $sum: "$price" }
    }
  }

])

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💡 Quick Rules of Thumb

Embed Data

Use when data belongs to a single parent

Example

Post
 └── Comments

Example document

{
  "title": "MongoDB Tips",
  "comments": [
    { "user": "John", "text": "Nice post!" }
  ]
}

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Reference Data

Use when data is shared by many documents

Example

Orders → Products

Use $lookup.

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🚩 Red Flag

If your MongoDB queries use $lookup everywhere, you are probably trying to build:

A SQL database inside MongoDB

MongoDB works best when data is embedded, not constantly joined.

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⚡ Mental Model

SQL	MongoDB
Database	Database
Table	Collection
Row	Document
Column	Field
JOIN	$lookup
GROUP BY	$group
WHERE	$match

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✅ Tip for SQL Developers

Think:

SQL = Normalize data
MongoDB = Denormalize data

MongoDB favors reading fast from one document instead of joining many tables.