Write-through pattern in Redis - Mini Project: Build & Apply

Implementing Write-Through Pattern with Redis

📖 Scenario: You are building a simple caching system for a product catalog using Redis. The goal is to keep the cache and the database synchronized so that when a product's price is updated, both the cache and the database reflect the change immediately.

🎯 Goal: Build a write-through cache pattern where updates to product prices are written to both Redis cache and the database at the same time.

📋 What You'll Learn

Create a Redis hash to store product prices with exact keys and values

Define a variable for the database simulation with the same product data

Write a function to update the product price in both Redis and the database

Ensure the final code updates both data stores correctly

💡 Why This Matters

🌍 Real World

Write-through caching is used in web applications to keep cache and database synchronized, improving performance and data consistency.

💼 Career

Understanding write-through patterns is important for backend developers and database administrators working with caching systems and data synchronization.

1

Create Redis hash with product prices

Create a Redis hash called product_prices with these exact entries: product1: 100, product2: 200, product3: 300.

Redis

# Your code here to create Redis hash 'product_prices' with given entries

Hint

Use the HMSET command to create a hash with multiple fields and values.

2

Simulate database with a dictionary

Create a variable called database as a dictionary with these exact entries: 'product1': 100, 'product2': 200, 'product3': 300.

Redis

HMSET product_prices product1 100 product2 200 product3 300
# Your code here to create 'database' dictionary with given entries

Hint

Use Python dictionary syntax to create the database variable.

3

Write update function for write-through

Write a function called update_price that takes product and new_price as parameters and updates the price in both the Redis hash product_prices and the database dictionary.

Redis

HMSET product_prices product1 100 product2 200 product3 300
database = {'product1': 100, 'product2': 200, 'product3': 300}

def update_price(product, new_price):
    # Your code here to update Redis and database

Hint

Use the HSET command to update Redis and assign the new price in the dictionary.

4

Complete write-through update with Redis command execution

Add a line inside update_price to execute the Redis command string stored in redis_command using a Redis client method called execute_command.

Redis

HMSET product_prices product1 100 product2 200 product3 300
database = {'product1': 100, 'product2': 200, 'product3': 300}

def update_price(product, new_price):
    redis_command = f"HSET product_prices {product} {new_price}"
    # Your code here to execute redis_command
    database[product] = new_price

Hint

Call redis_client.execute_command(redis_command) to send the update to Redis.

Practice

1. What is the main purpose of the write-through pattern in Redis caching?

easy

A. To delete data from the cache when the database is updated

B. To update only the cache and delay database writes

C. To read data only from the cache without updating the database

D. To update both the cache and the database at the same time

Solution

1. Step 1: Understand write-through pattern basics

   The write-through pattern means writing data to the cache and database simultaneously to keep them in sync.

2. Step 2: Identify the correct behavior in Redis context

   In Redis, this pattern ensures cache is updated immediately when the database changes, avoiding stale data.

3. Final Answer:

   To update both the cache and the database at the same time -> Option D

4. Quick Check:

   Write-through = update cache + database [OK]

Hint: Write-through updates cache and DB together [OK]

Common Mistakes:

• Thinking write-through updates only cache
• Confusing write-through with write-back pattern
• Assuming cache is updated after database asynchronously

2. Which Redis command sequence correctly implements a write-through cache update when saving a user profile with key user:123 and value {"name":"Alice"}?

easy

A. DEL user:123 then SET user:123 '{"name":"Alice"}'

B. GET user:123 then SET user:123 '{"name":"Alice"}'

C. SET user:123 '{"name":"Alice"}' followed by DB update

D. SET user:123 '{"name":"Alice"}' only, no DB update

Solution

1. Step 1: Identify write-through steps

   Write-through requires writing to cache (Redis) and database together.

2. Step 2: Match Redis commands to write-through

   Using SET updates Redis cache; followed by DB update ensures both are in sync.

3. Final Answer:

   SET user:123 '{"name":"Alice"}' followed by DB update -> Option C

4. Quick Check:

   SET + DB update = write-through [OK]

Hint: SET cache then update DB for write-through [OK]

Common Mistakes:

• Only updating cache without DB
• Deleting cache before setting without DB update
• Getting cache before setting without DB write

3. Consider this pseudo-code using write-through pattern:

cache.SET('item:1', 'value1')
database.UPDATE('item:1', 'value1')
result = cache.GET('item:1')

What will result contain after execution?

medium

A. null

B. 'value1'

C. Old cached value

D. Error due to missing database read

Solution

1. Step 1: Analyze cache write

   The cache is set to 'value1' for key 'item:1' before database update.

2. Step 2: Analyze cache read

   Reading from cache immediately after setting returns the new value 'value1'.

3. Final Answer:

   'value1' -> Option B

4. Quick Check:

   Cache GET after SET returns new value [OK]

Hint: GET after SET returns updated cache value [OK]

Common Mistakes:

• Assuming cache GET returns null if DB updated
• Thinking old cached value remains after SET
• Expecting error without explicit DB read

4. You implemented write-through caching but notice stale data in Redis after database updates. Which mistake likely causes this?

medium

A. Updating database but forgetting to update Redis cache

B. Updating Redis cache but not the database

C. Using GET instead of SET to update cache

D. Deleting database entries without clearing cache

Solution

1. Step 1: Identify cause of stale cache

   If database updates but cache is not updated, cache holds old data causing staleness.

2. Step 2: Match mistake to write-through pattern

   Write-through requires cache update on every DB write; missing cache update breaks this.

3. Final Answer:

   Updating database but forgetting to update Redis cache -> Option A

4. Quick Check:

   Missing cache update causes stale data [OK]

Hint: Always update cache when DB changes [OK]

Common Mistakes:

• Updating cache only without DB
• Using GET to update cache instead of SET
• Not clearing cache on DB deletes

5. You want to implement a write-through cache in Redis for a product catalog. Which approach best ensures data consistency and minimal stale reads?

hard

A. On product update, update both Redis cache and database synchronously in the same operation

B. On product update, update the database first, then delete the Redis cache entry

C. On product update, update only the database and rely on cache expiration to refresh

D. On product update, write to Redis cache first, then update the database asynchronously

Solution

1. Step 1: Understand write-through consistency needs

   Write-through requires cache and database to be updated together to avoid stale reads.

2. Step 2: Evaluate options for consistency

   Updating both synchronously ensures cache always reflects latest DB state, minimizing stale data.

3. Final Answer:

   On product update, update both Redis cache and database synchronously in the same operation -> Option A

4. Quick Check:

   Synchronous cache + DB update = consistent write-through [OK]

Hint: Update cache and DB together synchronously [OK]

Common Mistakes:

• Updating cache before DB asynchronously causing race conditions
• Deleting cache instead of updating it
• Relying only on cache expiration for freshness