Data denormalization strategies in Firebase - Mini Project: Build & Apply

Data Denormalization Strategies in Firebase

📖 Scenario: You are building a simple social app using Firebase Realtime Database. You want to store user profiles and their posts. To make data retrieval faster and simpler, you will denormalize the data by duplicating some user information inside each post.

🎯 Goal: Create a Firebase Realtime Database structure that stores users and posts separately, then denormalize by adding the user's name inside each post.

📋 What You'll Learn

Create a users node with two users and their names

Create a posts node with two posts referencing user IDs

Add a userName field inside each post to denormalize user data

Use exact keys and values as specified

💡 Why This Matters

🌍 Real World

Denormalization in Firebase is common to optimize app performance by reducing the number of database reads needed to display related data.

💼 Career

Understanding data denormalization is important for cloud developers and database engineers working with NoSQL databases like Firebase.

1

Create the initial Firebase data structure

Create a Firebase Realtime Database JSON structure with a users node containing two users: user1 with name "Alice" and user2 with name "Bob". Also create an empty posts node.

Firebase

{
  "users": {
    // Add user1 and user2 with their names here
  },
  "posts": {
    // Posts will be added here
  }
}

Hint

Use nested JSON objects for users and posts. Each user should have a name field.

2

Add posts referencing user IDs

Inside the posts node, add two posts: post1 referencing user1 with content "Hello world", and post2 referencing user2 with content "Good morning". Use the exact keys userId and content.

Firebase

{
  "users": {
    "user1": {"name": "Alice"},
    "user2": {"name": "Bob"}
  },
  "posts": {
    // Add post1 and post2 with userId and content here
  }
}

Hint

Each post should have userId and content fields.

3

Denormalize by adding userName inside posts

Add a userName field inside each post in the posts node. For post1, set userName to "Alice". For post2, set userName to "Bob".

Firebase

{
  "users": {
    "user1": {"name": "Alice"},
    "user2": {"name": "Bob"}
  },
  "posts": {
    "post1": {"userId": "user1", "content": "Hello world", /* Add userName here */},
    "post2": {"userId": "user2", "content": "Good morning", /* Add userName here */}
  }
}

Hint

Duplicate the user's name inside each post as userName.

4

Complete the Firebase data with denormalized structure

Ensure the Firebase Realtime Database JSON includes the users node with two users, and the posts node with two posts each containing userId, content, and userName fields exactly as specified.

Firebase

{
  "users": {
    "user1": {"name": "Alice"},
    "user2": {"name": "Bob"}
  },
  "posts": {
    "post1": {"userId": "user1", "content": "Hello world", "userName": "Alice"},
    "post2": {"userId": "user2", "content": "Good morning", "userName": "Bob"}
  }
}

// Finalize the JSON structure if needed

Hint

Review the entire JSON to confirm all required fields and nodes are present.

Practice

1. What is the main purpose of data denormalization in Firebase?

easy

A. To copy data for faster reading and fewer database calls

B. To reduce data storage by removing duplicates

C. To encrypt data for better security

D. To create backups automatically

Solution

1. Step 1: Understand denormalization concept

   Denormalization means copying data to make reading faster and reduce the number of database calls.

2. Step 2: Identify the main goal in Firebase context

   Firebase uses denormalization to speed up data access, not to reduce storage or encrypt data.

3. Final Answer:

   To copy data for faster reading and fewer database calls -> Option A

4. Quick Check:

   Denormalization = faster reads [OK]

Hint: Denormalization means copying data to speed up reads [OK]

Common Mistakes:

• Confusing denormalization with data encryption
• Thinking denormalization reduces storage space
• Assuming denormalization creates backups

2. Which Firebase data structure is best suited for denormalization?

easy

A. Nested arrays with repeated data

B. Encrypted blobs stored in Firestore

C. Flat JSON objects with duplicated fields

D. Single document with all data combined

Solution

1. Step 1: Review Firebase data structures

   Firebase uses JSON-like objects. Denormalization copies data into flat objects with duplicated fields for fast access.

2. Step 2: Compare options

   Nested arrays with repeated data are not ideal; single documents can be large and slow; encrypted blobs are unrelated to denormalization.

3. Final Answer:

   Flat JSON objects with duplicated fields -> Option C

4. Quick Check:

   Denormalization uses flat JSON with duplicates [OK]

Hint: Denormalization uses flat JSON with repeated fields [OK]

Common Mistakes:

• Choosing nested arrays which complicate queries
• Using single large documents that slow reads
• Confusing encryption with denormalization

3. Given this Firebase data snippet:

{
  "users": {
    "user1": {"name": "Anna", "city": "NY"},
    "user2": {"name": "Ben", "city": "LA"}
  },
  "posts": {
    "post1": {"author": "user1", "content": "Hello"},
    "post2": {"author": "user2", "content": "Hi"}
  }
}

Which denormalized structure speeds up reading posts with author names?

medium

A. { "users": { "user1": {"name": "Anna"}, "user2": {"name": "Ben"} }, "posts": {} }

B. { "posts": { "post1": {"author": "user1", "content": "Hello"}, "post2": {"author": "user2", "content": "Hi"} } }

C. { "posts": { "post1": {"authorId": "user1"}, "post2": {"authorId": "user2"} } }

D. { "posts": { "post1": {"authorName": "Anna", "content": "Hello"}, "post2": {"authorName": "Ben", "content": "Hi"} } }

Solution

1. Step 1: Understand denormalization for faster reads

   Copy author names directly into posts to avoid extra lookups when reading posts.

2. Step 2: Analyze options

   { "posts": { "post1": {"authorName": "Anna", "content": "Hello"}, "post2": {"authorName": "Ben", "content": "Hi"} } } copies author names into posts, speeding reads. Others keep references requiring extra calls.

3. Final Answer:

   { "posts": { "post1": {"authorName": "Anna", "content": "Hello"}, "post2": {"authorName": "Ben", "content": "Hi"} } } -> Option D

4. Quick Check:

   Copy data to avoid extra lookups = { "posts": { "post1": {"authorName": "Anna", "content": "Hello"}, "post2": {"authorName": "Ben", "content": "Hi"} } } [OK]

Hint: Copy related data into posts to speed reads [OK]

Common Mistakes:

• Keeping only IDs requiring extra database calls
• Removing posts data instead of denormalizing
• Not copying author names into posts

4. You denormalized user emails into multiple places in Firebase. What is a common error when updating user email?

medium

A. Forgetting to update all copies, causing inconsistent data

B. Deleting the original user record accidentally

C. Encrypting emails twice causing read errors

D. Using arrays instead of objects for user data

Solution

1. Step 1: Understand denormalization update challenges

   When data is copied, all copies must be updated to keep data consistent.

2. Step 2: Identify common mistake

   Forgetting to update all copies leads to inconsistent emails across the database.

3. Final Answer:

   Forgetting to update all copies, causing inconsistent data -> Option A

4. Quick Check:

   Missed updates cause inconsistency [OK]

Hint: Always update every copy of denormalized data [OK]

Common Mistakes:

• Updating only one copy of data
• Deleting original data by mistake
• Confusing encryption with denormalization

5. You want to denormalize product prices into order items in Firebase. Which approach best avoids stale prices when product prices change?

hard

A. Copy product price into each order item and update all order items on price change

B. Store only product IDs in order items and fetch price dynamically on read

C. Keep prices only in product records and never copy to orders

D. Use arrays to store prices inside product documents

Solution

1. Step 1: Understand denormalization trade-offs

   Copying prices into order items speeds reads but risks stale data if not updated.

2. Step 2: Choose best approach to avoid stale data

   Storing only product IDs and fetching prices dynamically on read avoids stale prices without complex updates.

3. Final Answer:

   Store only product IDs in order items and fetch price dynamically on read -> Option B

4. Quick Check:

   Fetch price dynamically to avoid stale data [OK]

Hint: Fetch prices dynamically to keep data fresh [OK]

Common Mistakes:

• Not updating copied prices causing stale data
• Copying prices without update causing stale data
• Using arrays which complicate updates