Data denormalization strategies in Firebase - Step-by-Step Execution

Process Flow - Data denormalization strategies

Identify data entities

↓

Decide relationships

↓

Choose denormalization strategy

↓

Duplicate data

↓

Update all copies

↓

Maintain consistency

↓

Query optimized

Start by identifying data entities and their relationships, then choose a denormalization strategy like duplicating or embedding data, update all copies or embedded data to keep consistency, resulting in optimized queries.

Execution Sample

Firebase

users/{userId} {
  name: "Alice",
  favoriteBook: {
    id: "book123",
    title: "Firebase Guide"
  }
}

This shows embedding book data inside a user document to avoid extra lookups.

Process Table

Step	Action	Data State	Result
1	Create user document with embedded book data	{"users/user1": {"name": "Alice", "favoriteBook": {"id": "book123", "title": "Firebase Guide"}}}	User data ready with embedded book info
2	Update book title in embedded data	{"users/user1": {"name": "Alice", "favoriteBook": {"id": "book123", "title": "Firebase Guide v2"}}}	Book title updated inside user document
3	Create separate book document	{"books/book123": {"title": "Firebase Guide v2"}}	Book data stored separately
4	Duplicate book title in user document	{"users/user1": {"name": "Alice", "favoriteBookTitle": "Firebase Guide v2"}}	Book title duplicated in user document
5	Update book title in book document only	{"books/book123": {"title": "Firebase Guide v3"}}	Book title updated in book document only
6	User document book title stale	{"users/user1": {"name": "Alice", "favoriteBookTitle": "Firebase Guide v2"}}	User data inconsistent, needs update
7	Update duplicated book title in user document	{"users/user1": {"name": "Alice", "favoriteBookTitle": "Firebase Guide v3"}}	User data consistent again
8	Query user document	{"users/user1": {"name": "Alice", "favoriteBookTitle": "Firebase Guide v3"}}	Fast query, no join needed
9	Query book document	{"books/book123": {"title": "Firebase Guide v3"}}	Separate book data available
10	End	N/A	Denormalization complete with consistency maintained

💡 Denormalization stops when data is consistent and queries are optimized.

Status Tracker

Variable	Start	After 1	After 2	After 3	After 4	After 5	After 6	After 7	Final
users/user1.favoriteBook.title	"Firebase Guide"	"Firebase Guide v2"	"Firebase Guide v2"	N/A	N/A	N/A	"Firebase Guide v2"	"Firebase Guide v3"	"Firebase Guide v3"
books/book123.title	N/A	N/A	"Firebase Guide v2"	"Firebase Guide v2"	"Firebase Guide v3"	"Firebase Guide v3"	"Firebase Guide v3"	"Firebase Guide v3"	"Firebase Guide v3"
users/user1.favoriteBookTitle	N/A	N/A	N/A	"Firebase Guide v2"	"Firebase Guide v2"	"Firebase Guide v2"	"Firebase Guide v2"	"Firebase Guide v3"	"Firebase Guide v3"

Key Moments - 3 Insights

Why does the user document's book title become inconsistent after updating only the book document?

Because the book title is duplicated in the user document, updating only the book document does not automatically update the user document. See execution_table rows 5 and 6 where the user data is stale until manually updated.

What is the benefit of embedding book data inside the user document?

Embedding avoids extra lookups when querying user data, making reads faster and simpler, as shown in execution_table step 1 and 8.

How do we maintain consistency when duplicating data?

We must update all copies of the duplicated data whenever one changes, as shown in execution_table steps 5 to 7.

Visual Quiz - 3 Questions

Look at the execution_table at step 6, what is the state of the user's book title?

A "Firebase Guide v2"

B "Firebase Guide v3"

C Undefined

D "Firebase Guide"

💡 Hint

Check the 'Data State' column at step 6 in the execution_table.

At which step is the book title duplicated into the user document?

A Step 3

B Step 5

C Step 4

D Step 2

💡 Hint

Look for the action mentioning duplication in the execution_table.

If we embed book data inside the user document, what is the main advantage?

A Less storage used

B Faster queries without extra lookups

C Automatic updates across all documents

D Simpler data model with no duplication

💡 Hint

Refer to the description in execution_sample and execution_table step 8.

Concept Snapshot

Data denormalization in Firebase means copying or embedding related data to speed up queries.
Two main strategies: duplicate data in multiple places or embed data inside documents.
Keep data consistent by updating all copies when changes happen.
Denormalization trades storage and update complexity for faster reads.
Choose strategy based on query patterns and update frequency.

Full Transcript

Data denormalization strategies in Firebase involve copying or embedding related data to optimize query speed. First, identify your data entities and their relationships. Then decide whether to duplicate data across documents or embed related data inside a document. For example, embedding book details inside a user document avoids extra lookups. However, when data is duplicated, you must update all copies to keep consistency. The execution steps show creating user and book documents, duplicating data, updating titles, and maintaining consistency. Denormalization improves read performance but requires careful update management. Choose the strategy that fits your app's query and update needs.

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