Embedding vs referencing in Redis - Practice Questions

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Embedding in Redis

In Redis, embedding data means storing related information together in a single key. Which of the following best describes a scenario where embedding is beneficial?

A Storing large binary files separately and referencing them by file IDs.

B Storing user profiles in one key and their orders in separate keys linked by user IDs.

C Using separate keys for each product and referencing them in a shopping cart key.

D Storing user profile details and their recent activity logs together in one hash for quick access.

💡 Hint

Think about when you want to access all related data quickly without multiple lookups.

Embedding groups related data in one place, which is efficient when you often need all that data together. Option D shows this by combining profile and recent activity in one hash.

🧠 Conceptual

intermediate

When to Use Referencing in Redis

Referencing in Redis means storing related data separately and linking them by keys or IDs. Which situation is best suited for referencing?

A Storing product details separately and referencing them in multiple shopping carts.

B Storing a blog post and all its comments together in one hash.

C Storing all user data in one large JSON string.

D Embedding all order details inside the user profile hash.

💡 Hint

Consider when data is shared or reused in multiple places.

Referencing is ideal when data is reused or shared, like product details referenced by many shopping carts. This avoids duplication and keeps data consistent.

❓ query_result

advanced

Result of Embedded Data Retrieval

Given a Redis hash key 'user:1000' storing embedded data: {"name":"Alice", "age":"30", "city":"NY"}, what is the output of the command: HGETALL user:1000 ?

Redis

HGETALL user:1000

A ["user:1000", "Alice", "age", "30", "city", "NY"]

B ["name", "Alice", "age", "30", "city", "NY"]

C ["name", "Alice", "age", 30, "city", "NY"]

D Error: key does not exist

💡 Hint

HGETALL returns all fields and values as strings in an array.

HGETALL returns all fields and values in the hash as an array of strings. Option B matches the exact output format.

❓ query_result

advanced

Output of Referencing Data Retrieval

Assume you have two Redis keys: 'product:200' with value '{"name":"Book", "price":"15"}' and 'cart:300' storing a list of product IDs ['200', '201']. What is the output of LRANGE cart:300 0 -1 ?

Redis

LRANGE cart:300 0 -1

A Error: wrong type operation on string

B [{"name":"Book", "price":"15"}, {"name":"Pen", "price":"5"}]

C ["200", "201"]

D []

💡 Hint

LRANGE returns elements of a list as stored, not the referenced data.

LRANGE returns the list elements as strings. It does not fetch referenced data automatically. So it returns the product IDs stored in the list.

❓ schema

expert

Designing Redis Schema for Mixed Embedding and Referencing

You want to design a Redis schema for an e-commerce app where user profiles embed contact info but reference orders separately. Which schema design below correctly follows this approach?

A User profile stored as hash 'user:ID' with fields 'name', 'email', 'phone'; orders stored as separate lists 'orders:ID' containing order IDs.

B User profile stored as JSON string with embedded orders array inside; orders stored inside user hash fields.

C Orders stored as hashes with user IDs embedded inside; user profiles stored as separate keys with no contact info.

D All user data and orders embedded in one large hash 'user:ID' with nested fields for orders.

💡 Hint

Embedding contact info means storing it directly in user profile; referencing orders means separate keys.

Option A correctly embeds contact info in user hash and references orders separately by storing order IDs in separate lists. This balances embedding and referencing.