Tasks
Implement Request Node Cache
Implement a local cache at each request-handling node. When a request comes in: 1. Check if the key exists in the local cache 2. If cache hit and not...
Build Global Cache
Implement a shared cache accessible by all nodes. Instead of each node maintaining its own cache, a dedicated cache server handles all cache operation...
Implement Distributed Cache with Consistent Hashing
Distribute cache entries across multiple cache nodes using consistent hashing. This scales cache capacity beyond a single node while maintaining effic...
Add Eviction Strategies (LRU, TTL)
Implement cache eviction policies to manage limited memory. When the cache is full and a new entry must be added, an eviction policy decides what to r...
Handle Cache Invalidation and Consistency
Implement cache invalidation strategies to maintain consistency between cache and database. When data changes, cached copies must be updated or remove...
Interview Prep
Common interview questions for Backend / Infrastructure Engineer roles that map directly to what you build in this track. Click any question to reveal the model answer.
Questions are representative of real interview patterns. Model answers are starting points — adapt them with your own experience and the specific context of the interview.
Common Mistakes
The top 5 mistakes builders make in this track — and exactly how to fix them. Click any mistake to see the root cause and the correct approach.
Comparison Mode
Side-by-side comparisons of the approaches, algorithms, and trade-offs you encounter in this track. Expand any comparison to see a detailed breakdown.
Concepts Covered
Prerequisites
It is recommended to complete the previous tracks before starting this one. Concepts build progressively throughout the curriculum.
Rabbit Holes
For when you want to go deeper. Curated papers, posts, and talks beyond what this track covers.
Scaling Memcache at Facebook
Nishtala et al., 2013. How Facebook evolved Memcached from a simple cache into a distributed system handling billions of requests per second. The lease mechanism for handling thundering herds and the regional replication design are both worth deep study.
Redis Persistence: How Redis Handles Durability
Redis's documentation on RDB snapshots vs AOF logging. The tradeoff between durability and performance is laid out clearly. This is the practical counterpart to the theoretical LRU/LFU discussion.
The ARC Replacement Algorithm (Nimrod Megiddo)
Adaptive Replacement Cache combines recency and frequency in a way that outperforms both LRU and LFU on real workloads. The IBM storage product that first used this drove the research.
Cache Consistency in Distributed Systems (Tanenbaum)
The cache coherence and consistency chapters from Tanenbaum's Distributed Systems are the most accessible treatment of why cache invalidation is the second hardest problem in computer science.