Why caching reduces latency in HLD - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to show where cached data is stored.

HLD

cache = [1]()

Drag options to blanks, or click blank then click option'

Adatabase

Bnetwork

Cdisk

Din-memory

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing disk or database instead of in-memory storage.

Caching stores data in in-memory to allow faster access than disk or network.

2fill in blank

medium

Complete the code to check if data is in cache before fetching from source.

HLD

if key [1] cache:
    return cache[key]

Drag options to blanks, or click blank then click option'

Aequals

Bin

Cnot in

Dcontains

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'not in' which checks absence instead of presence.

We check if the key is in the cache to use cached data and reduce latency.

3fill in blank

hard

Fix the error in the code that updates the cache after fetching data.

HLD

data = fetch_from_source(key)
cache[[1]] = data

Drag options to blanks, or click blank then click option'

Akey

Bvalue

Cdata

Dsource

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'value' or 'data' as the cache key instead of 'key'.

The cache key must be the key used to fetch data, not the value or data itself.

4fill in blank

hard

Fill both blanks to complete the code that returns cached data or fetches and caches it.

HLD

if [1] in cache:
    return cache[[2]]

Drag options to blanks, or click blank then click option'

Akey

Bdata

Cvalue

Dsource

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variables for checking and accessing cache.

Both blanks must be the key to check and retrieve the cached data correctly.

5fill in blank

hard

Fill all three blanks to complete the caching logic with fetch and store.

HLD

if [1] not in cache:
    [2] = fetch_from_source([3])
    cache[key] = data

Drag options to blanks, or click blank then click option'

Akey

Bdata

Dsource

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing variable names or using undefined variables.

Check if key is missing, fetch data into data, and use key to store it in cache.

Practice

1. Why does caching reduce latency in a system?

easy

A. Because it stores frequently accessed data closer to the user, reducing access time.

B. Because it increases the number of servers in the system.

C. Because it compresses data to make it smaller.

D. Because it deletes old data from the database.

Solution

1. Step 1: Understand caching location

   Caching stores data near the user or application, such as in memory or edge servers.

2. Step 2: Effect on access time

   Accessing data from cache is faster than fetching from the main database or server, reducing waiting time.

3. Final Answer:

   Because it stores frequently accessed data closer to the user, reducing access time. -> Option A

4. Quick Check:

   Caching reduces latency by storing data close [OK]

Hint: Caching stores data near users to speed up access [OK]

Common Mistakes:

• Thinking caching increases servers
• Confusing caching with data compression
• Believing caching deletes data

2. Which of the following is the correct way to describe caching in system design?

easy

A. Caching permanently deletes old data to save space.

B. Caching is a process of storing data in a remote backup server.

C. Caching temporarily stores data in a fast-access location to reduce retrieval time.

D. Caching encrypts data to improve security.

Solution

1. Step 1: Define caching purpose

   Caching temporarily holds data in a fast-access place like memory or local storage.

2. Step 2: Identify correct description

   Only caching temporarily stores data in a fast-access location to reduce retrieval time correctly states caching reduces retrieval time by temporary storage.

3. Final Answer:

   Caching temporarily stores data in a fast-access location to reduce retrieval time. -> Option C

4. Quick Check:

   Caching = temporary fast storage [OK]

Hint: Caching is temporary fast storage, not permanent or remote [OK]

Common Mistakes:

• Confusing caching with backup storage
• Thinking caching deletes data
• Mixing caching with encryption

3. Consider a system where fetching data from the main database takes 100ms, and fetching from cache takes 10ms. If 80% of requests hit the cache, what is the average latency per request?

medium

A. 18 ms

B. 90 ms

C. 50 ms

D. 28 ms

Solution

1. Step 1: Calculate cache hit and miss times

   Cache hit latency = 10ms (80% requests), cache miss latency = 100ms (20% requests).

2. Step 2: Compute weighted average latency

   Average latency = (0.8 * 10) + (0.2 * 100) = 8 + 20 = 28 ms.

3. Final Answer:

   28 ms -> Option D

4. Quick Check:

   Average latency = hit%*hit_time + miss%*miss_time [OK]

Hint: Use weighted average: hit%*hit_time + miss%*miss_time [OK]

Common Mistakes:

• Ignoring cache miss latency
• Adding times instead of weighted average
• Using wrong percentages

4. A system uses caching but users still experience high latency. Which of the following is a likely cause?

medium

A. Cache encrypts data, increasing retrieval time.

B. Cache hit rate is very low, so most requests go to the slow database.

C. Cache size is too large, slowing down the system.

D. Cache stores data permanently, causing outdated results.

Solution

1. Step 1: Analyze cache hit rate impact

   If cache hit rate is low, most requests bypass cache and hit slow database, increasing latency.

2. Step 2: Evaluate other options

   Permanent storage or encryption are not typical causes of high latency; large cache size usually improves speed.

3. Final Answer:

   Cache hit rate is very low, so most requests go to the slow database. -> Option B

4. Quick Check:

   Low cache hit rate = high latency [OK]

Hint: Low cache hit rate means slow database access dominates [OK]

Common Mistakes:

• Assuming cache size slows system
• Confusing cache permanence with latency
• Thinking encryption is main latency cause

5. In a large-scale web application, which caching strategy best reduces latency while keeping data fresh?

hard

A. Use a cache with a short expiration time and update cache asynchronously on data changes.

B. Use a cache that never expires and manually clear it once a month.

C. Disable caching and always fetch fresh data from the database.

D. Cache only rarely accessed data to save memory.

Solution

1. Step 1: Understand freshness and latency tradeoff

   Short expiration keeps data fresh; asynchronous updates reduce latency by avoiding blocking calls.

2. Step 2: Evaluate other options

   Never expiring cache risks stale data; disabling cache increases latency; caching rarely accessed data wastes resources.

3. Final Answer:

   Use a cache with a short expiration time and update cache asynchronously on data changes. -> Option A

4. Quick Check:

   Short expiry + async update = fresh data + low latency [OK]

Hint: Short expiry plus async update balances freshness and speed [OK]

Common Mistakes:

• Choosing never expiring cache causing stale data
• Disabling cache increases latency
• Caching rarely accessed data wastes resources