Caching is the one pattern that comes up in almost every system...
Puneet Patwari@system_monarch
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Aug 01, 2026
~5 min read
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Caching is the one pattern that comes up in almost every system design interview.
Most engineers know the high-level overview… but completely fall apart when the interviewer starts drilling into the deep-dives — cache stampede, thundering herds, stale data at scale, or smart invalidation strategies.
Let’s fix that today.
Interviewers have moved way beyond “Design Twitter”. They now hit you with the hard follow-ups:
“How do you handle cache stampede at 10x traffic?” or “What happens when your cache becomes inconsistent during peak load?”
These 15 must-know Caching Strategies & Pitfalls are exactly the ones that separate clean designs from production systems that actually stay fast and reliable at scale.
I turned them into this full detailed thread with clear explanations and hands-on ways to master each one.
Save this thread. Read till the end.
Your next interview and your on-call shifts will thank you.
Also checkout - puneetpatwari.in
Most engineers know the high-level overview… but completely fall apart when the interviewer starts drilling into the deep-dives — cache stampede, thundering herds, stale data at scale, or smart invalidation strategies.
Let’s fix that today.
Interviewers have moved way beyond “Design Twitter”. They now hit you with the hard follow-ups:
“How do you handle cache stampede at 10x traffic?” or “What happens when your cache becomes inconsistent during peak load?”
These 15 must-know Caching Strategies & Pitfalls are exactly the ones that separate clean designs from production systems that actually stay fast and reliable at scale.
I turned them into this full detailed thread with clear explanations and hands-on ways to master each one.
Save this thread. Read till the end.
Your next interview and your on-call shifts will thank you.
Also checkout - puneetpatwari.in
2
1) Cache-Aside (Lazy Loading)
Most popular pattern: Application checks cache first. On miss, fetch from DB, store in cache, then return.
Simple to implement but puts load on the DB during cold starts and cache misses.
How to learn it: Implement it in a Spring Boot or Node.js service using Redis. Run a Locust load test with 0% cache hit rate first, then 90%. Measure DB queries and p99 latency — you’ll immediately see the cold-start problem.
Most popular pattern: Application checks cache first. On miss, fetch from DB, store in cache, then return.
Simple to implement but puts load on the DB during cold starts and cache misses.
How to learn it: Implement it in a Spring Boot or Node.js service using Redis. Run a Locust load test with 0% cache hit rate first, then 90%. Measure DB queries and p99 latency — you’ll immediately see the cold-start problem.
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2) Read-Through Cache
Cache sits in front of the DB and automatically fetches missing data on behalf of the application.
Cleaner separation of concerns than Cache-Aside; the app just talks to the cache.
How to learn it: Use Redis with a read-through library (or custom Lua script) and compare it side-by-side with Cache-Aside under the same load. You’ll feel the difference in code cleanliness and DB pressure.
Cache sits in front of the DB and automatically fetches missing data on behalf of the application.
Cleaner separation of concerns than Cache-Aside; the app just talks to the cache.
How to learn it: Use Redis with a read-through library (or custom Lua script) and compare it side-by-side with Cache-Aside under the same load. You’ll feel the difference in code cleanliness and DB pressure.
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3) Write-Through Cache
Every write goes to both cache and DB at the same time. Guarantees cache and DB stay in sync.
Slower writes but eliminates stale reads completely.
How to learn it: Implement Write-Through using Redis transactions or a library. Run mixed read/write traffic and compare consistency + latency against Write-Behind.
Every write goes to both cache and DB at the same time. Guarantees cache and DB stay in sync.
Slower writes but eliminates stale reads completely.
How to learn it: Implement Write-Through using Redis transactions or a library. Run mixed read/write traffic and compare consistency + latency against Write-Behind.
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4) Write-Behind (Write-Back) Cache
Writes go to cache first and are asynchronously flushed to DB later. Super fast writes.
Risk of data loss if cache crashes before flush.
How to learn it: Code it with a background worker + Redis. Simulate high write traffic and then kill the cache node — watch what happens to data consistency.
Writes go to cache first and are asynchronously flushed to DB later. Super fast writes.
Risk of data loss if cache crashes before flush.
How to learn it: Code it with a background worker + Redis. Simulate high write traffic and then kill the cache node — watch what happens to data consistency.
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5) TTL (Time-To-Live) & Expiration
Automatically expire cache entries after a set time (e.g., 5 minutes, 1 hour).
The simplest way to keep data fresh without complex invalidation logic.
How to learn it: Set different TTLs on the same endpoint and run a long-running load test. Measure staleness vs DB load — you’ll discover the perfect TTL sweet spot for your use case.
Automatically expire cache entries after a set time (e.g., 5 minutes, 1 hour).
The simplest way to keep data fresh without complex invalidation logic.
How to learn it: Set different TTLs on the same endpoint and run a long-running load test. Measure staleness vs DB load — you’ll discover the perfect TTL sweet spot for your use case.
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6) Eviction Policies (LRU, LFU, ARC)
When cache is full, decide which items to kick out: Least Recently Used, Least Frequently Used, or Adaptive Replacement Cache.
Directly impacts hit rate under different access patterns.
How to learn it: Use Redis with maxmemory-policy set to allpolicy variants. Run trace-driven workloads (hot keys vs uniform) and compare hit rates using Redis INFO command.
When cache is full, decide which items to kick out: Least Recently Used, Least Frequently Used, or Adaptive Replacement Cache.
Directly impacts hit rate under different access patterns.
How to learn it: Use Redis with maxmemory-policy set to allpolicy variants. Run trace-driven workloads (hot keys vs uniform) and compare hit rates using Redis INFO command.
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7) Cache Invalidation Strategies
The hardest part of caching: knowing when to remove or update stale entries.
Options: Key-based, tag-based, or event-driven invalidation.
How to learn it: Build a service with both key-based and event-driven (Kafka/RabbitMQ) invalidation. Intentionally update data and measure how long stale data stays in cache.
The hardest part of caching: knowing when to remove or update stale entries.
Options: Key-based, tag-based, or event-driven invalidation.
How to learn it: Build a service with both key-based and event-driven (Kafka/RabbitMQ) invalidation. Intentionally update data and measure how long stale data stays in cache.
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8) Cache Stampede (Thundering Herd)
Hundreds of requests hit a cache miss at the exact same time → massive DB overload.
Classic pitfall when cache expires or cold-starts.
How to learn it: Simulate it by setting a short TTL and hitting the endpoint with 500 concurrent requests right after expiry. Then fix it with mutex or probabilistic early refresh and measure DB load drop.
Hundreds of requests hit a cache miss at the exact same time → massive DB overload.
Classic pitfall when cache expires or cold-starts.
How to learn it: Simulate it by setting a short TTL and hitting the endpoint with 500 concurrent requests right after expiry. Then fix it with mutex or probabilistic early refresh and measure DB load drop.
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9) Stale-While-Revalidate
Serve stale data immediately while asynchronously refreshing in background.
Great UX + performance with acceptable freshness trade-off.
How to learn it: Implement it using Redis + background job (or libraries like stale-while-revalidate). A/B test it against pure TTL and watch user-perceived latency and error rates.
Serve stale data immediately while asynchronously refreshing in background.
Great UX + performance with acceptable freshness trade-off.
How to learn it: Implement it using Redis + background job (or libraries like stale-while-revalidate). A/B test it against pure TTL and watch user-perceived latency and error rates.
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10) Distributed Caching & Consistent Hashing
Single Redis instance doesn’t scale. Use consistent hashing to add/remove nodes without massive rehashing.
Essential for horizontal scaling.
How to learn it: Set up a 3-node Redis Cluster with consistent hashing. Add/remove a node while running traffic and observe hit rate stability.
Single Redis instance doesn’t scale. Use consistent hashing to add/remove nodes without massive rehashing.
Essential for horizontal scaling.
How to learn it: Set up a 3-node Redis Cluster with consistent hashing. Add/remove a node while running traffic and observe hit rate stability.
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11) Multi-Level Caching (L1 + L2 + CDN)
Local in-memory cache (L1) → distributed cache (L2) → CDN.
Each level trades speed for consistency and scale.
How to learn it: Add Caffeine (L1) + Redis (L2) + CloudFront. Run realistic geo-distributed load tests and measure latency at each layer.
Local in-memory cache (L1) → distributed cache (L2) → CDN.
Each level trades speed for consistency and scale.
How to learn it: Add Caffeine (L1) + Redis (L2) + CloudFront. Run realistic geo-distributed load tests and measure latency at each layer.
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12) Negative Caching
Cache the fact that something does NOT exist (e.g., 404 responses).
Prevents repeated expensive DB lookups for missing items.
How to learn it: Implement negative caching with a short TTL for “not found” entries. Compare DB query count before and after under high miss-rate traffic.
Cache the fact that something does NOT exist (e.g., 404 responses).
Prevents repeated expensive DB lookups for missing items.
How to learn it: Implement negative caching with a short TTL for “not found” entries. Compare DB query count before and after under high miss-rate traffic.
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13) Cache Poisoning & Security Pitfalls
Malicious or buggy data gets cached and served to everyone (e.g., private data in public cache).
Critical security concern in shared caches.
How to learn it: Intentionally poison a cache with privileged data in a test environment, then fix it with cache keys that include user context or authorization checks.
Malicious or buggy data gets cached and served to everyone (e.g., private data in public cache).
Critical security concern in shared caches.
How to learn it: Intentionally poison a cache with privileged data in a test environment, then fix it with cache keys that include user context or authorization checks.
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14) Hot Keys & Partitioning Issues
One key gets 90% of traffic → single node overload even in distributed cache.
Common in leaderboards, counters, or trending content.
How to learn it: Identify hot keys using Redis MONITOR or slow logs, then apply sharding or local caching. Measure load distribution improvement.
One key gets 90% of traffic → single node overload even in distributed cache.
Common in leaderboards, counters, or trending content.
How to learn it: Identify hot keys using Redis MONITOR or slow logs, then apply sharding or local caching. Measure load distribution improvement.
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15) Cache Monitoring & Observability
Track hit/miss ratio, eviction rate, latency, memory usage, and hot keys in real time.
Without metrics you’re flying blind.
How to learn it: Integrate Prometheus + Grafana with Redis exporter. Set alerts for hit rate < 80% or eviction spikes and run chaos tests to see your dashboards react.
Track hit/miss ratio, eviction rate, latency, memory usage, and hot keys in real time.
Without metrics you’re flying blind.
How to learn it: Integrate Prometheus + Grafana with Redis exporter. Set alerts for hit rate < 80% or eviction spikes and run chaos tests to see your dashboards react.
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Caching mastery isn’t about memorizing patterns — it’s about knowing exactly when to use (and not use) each one and how to debug the inevitable production fires.
These 15 strategies & pitfalls cover 95% of what you’ll face in system design interviews and real-world scaling.
Master them and you’ll design systems that stay fast even when traffic explodes.
If you want the full 90+ System Design Fundamentals (with diagrams, code examples, and interview answers), I’ve built exactly that for Senior → Principal engineers.
Check it out here: puneetpatwari.in/checkout?produ…
These 15 strategies & pitfalls cover 95% of what you’ll face in system design interviews and real-world scaling.
Master them and you’ll design systems that stay fast even when traffic explodes.
If you want the full 90+ System Design Fundamentals (with diagrams, code examples, and interview answers), I’ve built exactly that for Senior → Principal engineers.
Check it out here: puneetpatwari.in/checkout?produ…
