Master Caching Strategies: LRU, LFU, FIFO Explained for Interview Prep | High Level Design

Описание: Boost your software engineering knowledge with this in-depth video on caching strategies—essential for learners tackling system design and technical interviews! Discover how correct caching approaches can supercharge app performance, reduce database load, and help you ace interviews at top tech companies.

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What is Caching?
Caching is a technique for storing frequently accessed data in a temporary, fast-access memory (the cache), dramatically improving application response times and scalability. Whenever the cache becomes full, an eviction policy determines which item to remove. Choosing the right policy makes a huge difference in both real-world applications and interview scenarios.

Key Caching Strategies Covered
1. FIFO (First-In, First-Out)
How it Works: The oldest item (added first) is the first to be evicted.

Analogy: Like a queue of people getting on a bus; first in is first out.

Pros: Simple to implement.

Cons: Frequently accessed items might be evicted if they were added early—making it inefficient for popular data.

2. LRU (Least Recently Used)
How it Works: The item not used for the longest period gets evicted first.

Analogy: Like a pile of papers—oldest untouched papers are tossed first.

Implementation: Often uses a hash map for quick access and a doubly linked list for fast reordering/removal.

Pros: Generally very effective and popular; adapts well to real usage patterns.

Cons: A bit more complex to implement; can perform poorly if large scans pollute the cache with rarely used items.

3. LFU (Least Frequently Used)
How it Works: The item used least often is evicted first.

Analogy: Like a library discarding books that are rarely checked out.

Implementation: Each item has an access counter, the lowest count item gets evicted.

Pros: Great for non-recency based patterns; avoids prematurely evicting steady, accessed data.

Cons: Most complex to implement; can quickly evict useful brand-new items unless mitigated; old, rarely needed items might persist if they had many past accesses.

Choosing the Right Strategy:

FIFO: Use when simplicity is key, but beware eviction of hot data.

LRU: The industry standard for systems where recent usage predicts future use.

LFU: Best for steady, non-flashy access patterns, e.g., telecom or CDN metadata caches.

Summary Table:

Strategy Eviction Criteria Pros Cons
FIFO Oldest by insertion Simple implementation Evicts popular items
LRU Least recently used Effective, adapts to patterns Can be polluted by scans, complex
LFU Least frequently used Tracks usage, steady data kept Complex; new/old item problems
Why Watch This Video?
Core knowledge for system design interviews: Frequently asked at FAANG and top company interviews.

Real-world scenarios: Use cases from web servers, databases, content delivery networks (CDNs), and mobile apps.

Practical analogies & implementation tips: Helps you understand and easily explain concepts during interviews.

Optimal prep for coding and architecture rounds.

Keywords for Ranking
caching strategies, LRU cache, LFU cache, FIFO eviction, system design interview, caching interview questions, memory management, database performance, eviction policies, cache algorithms, backend engineering, software optimization, scalable architecture, high performance systems

Timestamps
0:00 – Introduction to Caching
2:15 – Why Caching Matters
5:10 – FIFO Strategy Explained
8:45 – LRU Cache in Practice
13:20 – LFU: When Frequency Matters
17:30 – Choosing the Right Policy
21:10 – Real-World Examples
25:00 – Interview Tips & Sample Questions

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Master caching algorithms for real-world and interview success—watch now!