Why Cache eviction policies (LRU, LFU, TTL) in HLD? - Purpose & Use Cases

The Big Idea

What if your system could decide on its own which data to keep or toss, making everything faster without your constant attention?

The Scenario

Imagine you have a small desk where you keep your most used books. When the desk is full, you have to decide which book to remove to make space for a new one. Without a clear rule, you might remove a book you still need soon, causing frustration and wasted time.

The Problem

Manually choosing which items to remove from a cache is slow and error-prone. It can lead to removing important data too early or keeping useless data too long, which slows down the system and wastes memory.

The Solution

Cache eviction policies like LRU (Least Recently Used), LFU (Least Frequently Used), and TTL (Time To Live) automate these decisions. They smartly decide which data to remove, keeping the cache efficient and fast without manual effort.

Before vs After

✗ Before

if cache_is_full:
    remove_random_item()
add_new_item()

✓ After

cache.put(key, value)  # Eviction policy handles removal automatically

What It Enables

It enables systems to keep the most useful data readily available, improving speed and saving resources effortlessly.

Real Life Example

Think of a streaming app that remembers your recently watched shows (LRU) or your favorite songs you play often (LFU), so it loads them quickly without searching every time.

Key Takeaways

Manual cache management is inefficient and error-prone.

Eviction policies automate smart removal of data.

This keeps systems fast and resource-friendly.

Practice

1. Which cache eviction policy removes the data that was used the longest time ago?

easy

A. Random Replacement

B. Least Frequently Used (LFU)

C. Time To Live (TTL)

D. Least Recently Used (LRU)

Solution

1. Step 1: Understand LRU policy

   LRU removes the cache entry that was accessed the longest time ago, prioritizing recent usage.

2. Step 2: Compare with other policies

   LFU removes least used items by frequency, TTL removes expired items by time, Random Replacement is arbitrary.

3. Final Answer:

   Least Recently Used (LRU) -> Option D

4. Quick Check:

   Oldest access time = LRU [OK]

Hint: LRU = remove oldest used data [OK]

Common Mistakes:

• Confusing LRU with LFU
• Thinking TTL removes based on usage
• Assuming random removal is LRU

2. Which of the following is a correct description of the TTL cache eviction policy?

easy

A. Removes data after a fixed expiration time

B. Removes the least recently used data

C. Removes the least frequently used data

D. Removes data randomly when cache is full

Solution

1. Step 1: Define TTL policy

   TTL (Time To Live) removes cache entries after their set expiration time passes.

2. Step 2: Contrast with other policies

   LRU removes oldest used, LFU removes least used by frequency, random is arbitrary.

3. Final Answer:

   Removes data after a fixed expiration time -> Option A

4. Quick Check:

   TTL = expiration time removal [OK]

Hint: TTL means data expires after set time [OK]

Common Mistakes:

• Mixing TTL with LRU or LFU
• Thinking TTL depends on usage frequency
• Assuming TTL removes randomly

3. Consider a cache with the following access pattern: keys A, B, A, C, B, A. Using LFU eviction, which key will be evicted when the cache is full and a new key D is added?

medium

A. Key C

B. Key A

C. Key B

D. Key D

Solution

1. Step 1: Count frequency of each key

   Key A accessed 3 times, B accessed 2 times, C accessed 1 time.

2. Step 2: Identify LFU eviction candidate

   LFU removes the least frequently used key, which is C with frequency 1.

3. Final Answer:

   Key C -> Option A

4. Quick Check:

   Lowest frequency = C [OK]

Hint: LFU evicts lowest frequency key [OK]

Common Mistakes:

• Evicting most recent key instead of least frequent
• Confusing frequency with recency
• Evicting key D which is new

4. A cache using TTL eviction is not removing expired entries as expected. Which of the following is the most likely cause?

medium

A. The TTL value is set too low

B. The cache uses LRU instead of TTL

C. The cache does not check expiration before access

D. The cache size is too large

Solution

1. Step 1: Understand TTL eviction mechanism

   TTL eviction requires checking if entries have expired before returning or during cleanup.

2. Step 2: Identify why expired entries remain

   If cache does not check expiration on access or cleanup, expired entries stay indefinitely.

3. Final Answer:

   The cache does not check expiration before access -> Option C

4. Quick Check:

   Expiration check missing = no TTL eviction [OK]

Hint: TTL needs expiration checks to remove entries [OK]

Common Mistakes:

• Assuming low TTL causes no eviction
• Confusing TTL with LRU behavior
• Thinking cache size affects TTL eviction

5. You are designing a cache for a news website where articles are frequently accessed shortly after publishing but rarely after a few days. Which eviction policy is best suited to keep fresh content while removing stale data efficiently?

hard

A. Least Recently Used (LRU)

B. Time To Live (TTL)

C. Random Replacement

D. Least Frequently Used (LFU)

Solution

1. Step 1: Analyze data usage pattern

   Articles are popular shortly after publishing, then become stale after days.

2. Step 2: Match eviction policy to pattern

   TTL removes data after expiration time, ideal for removing stale articles automatically.

3. Step 3: Compare with other policies

   LRU and LFU focus on usage, but TTL ensures freshness by time. Random is inefficient.

4. Final Answer:

   Time To Live (TTL) -> Option B

5. Quick Check:

   Freshness by expiration time = TTL [OK]

Hint: Use TTL for time-based freshness eviction [OK]

Common Mistakes:

• Choosing LRU or LFU ignoring data staleness
• Assuming random eviction is efficient
• Not considering time-based expiration