Cache invalidation strategies in HLD - Scalability & System Analysis

Scalability Analysis - Cache invalidation strategies

Growth Table: Cache Invalidation Strategies

Users / Requests	Cache Size	Invalidation Frequency	Complexity	Latency Impact
100 users	Small (MBs)	Manual or simple TTL	Low	Minimal
10,000 users	Medium (GBs)	TTL + Event-based invalidation	Moderate	Low
1,000,000 users	Large (10s GBs)	Complex event-driven + selective invalidation	High	Moderate
100,000,000 users	Very Large (100s GBs+)	Distributed cache with coordinated invalidation	Very High	Critical to optimize

First Bottleneck

The first bottleneck is cache consistency and stale data. As user scale grows, invalidation delays cause outdated data to be served, harming user experience and correctness. Also, the overhead of invalidation messages or TTL expiration increases, leading to higher latency and resource use.

Scaling Solutions

• TTL (Time To Live): Simple expiration to limit stale data duration.
• Write-through / Write-back Caches: Update cache on data writes to keep consistency.
• Event-based Invalidation: Use messaging or pub/sub to notify cache nodes of changes.
• Selective Invalidation: Invalidate only affected keys, not entire cache.
• Distributed Cache Coordination: Use consensus or coordination protocols to sync invalidations across nodes.
• Cache Versioning: Use version tags to detect stale cache and refresh selectively.
• Hybrid Approaches: Combine TTL with event-based invalidation for balance.

Back-of-Envelope Cost Analysis

• Requests per second (RPS): 1000 users ~ 100-500 RPS; 1M users ~ 10K-50K RPS.
• Cache storage: depends on data size; e.g., 1M keys * 1KB = ~1GB memory.
• Invalidation messages: event-based invalidation adds network overhead proportional to writes.
• Bandwidth: Invalidation traffic can be 1-5% of total traffic depending on write frequency.
• CPU: Processing invalidation events and cache updates adds load on cache servers.

Interview Tip

Start by explaining why cache invalidation is hard and important. Discuss simple TTL first, then event-driven invalidation. Mention trade-offs between stale data and overhead. Show awareness of distributed system challenges like coordination and consistency. Use examples like social media feeds or product catalogs to illustrate.

Self Check Question

Your cache system handles 1000 invalidation events per second. Traffic grows 10x. What do you do first?

Answer: Implement selective invalidation and event batching to reduce invalidation overhead. Consider adding distributed coordination to scale invalidation across cache nodes efficiently.

Key Result

Cache invalidation becomes the first bottleneck as user scale grows due to stale data and invalidation overhead; solutions include TTL, event-driven selective invalidation, and distributed coordination to maintain cache consistency efficiently.

Practice

1. Which cache invalidation strategy automatically removes cached data after a set time period?

easy

A. Time-to-Live (TTL)

B. Write-through caching

C. Explicit invalidation

D. Cache-aside

Solution

1. Step 1: Understand TTL concept

   TTL means cached data expires automatically after a fixed time, ensuring freshness.

2. Step 2: Compare with other strategies

   Write-through updates cache on writes, explicit invalidation requires manual removal, cache-aside loads on demand.

3. Final Answer:

   Time-to-Live (TTL) -> Option A

4. Quick Check:

   Automatic expiry = TTL [OK]

Hint: TTL means cache expires after fixed time automatically [OK]

Common Mistakes:

• Confusing write-through with automatic expiry
• Thinking explicit invalidation is automatic
• Mixing cache-aside with TTL

2. Which of the following is the correct description of the write-through cache invalidation strategy?

easy

A. Cache is cleared manually by the application

B. Cache is updated only when data is read

C. Cache is updated immediately when data is written

D. Cache data expires after a fixed time

Solution

1. Step 1: Define write-through caching

   Write-through means every write to the database also updates the cache immediately.

2. Step 2: Eliminate other options

   Cache is updated only when data is read describes cache-aside, C is explicit invalidation, D is TTL.

3. Final Answer:

   Cache is updated immediately when data is written -> Option C

4. Quick Check:

   Write-through = update cache on write [OK]

Hint: Write-through updates cache on every write instantly [OK]

Common Mistakes:

• Confusing read update with write update
• Mixing explicit invalidation with write-through
• Assuming TTL applies to write-through

3. Consider a cache-aside strategy where the cache is empty initially. If a client requests data key K and the cache misses, what happens next?

medium

A. The system returns an error immediately

B. The system fetches data from the database and updates the cache

C. The system waits for explicit invalidation before fetching

D. The system uses stale data from cache

Solution

1. Step 1: Understand cache-aside behavior on miss

   Cache-aside loads data from the database on a cache miss and updates the cache.

2. Step 2: Check other options

   The system returns an error immediately is wrong because system does not error on miss, C is incorrect as explicit invalidation is unrelated, D is wrong because cache is empty.

3. Final Answer:

   The system fetches data from the database and updates the cache -> Option B

4. Quick Check:

   Cache miss triggers DB fetch and cache update [OK]

Hint: Cache-aside loads from DB on miss, then caches data [OK]

Common Mistakes:

• Assuming cache returns error on miss
• Confusing explicit invalidation with cache miss handling
• Thinking stale data is returned on miss

4. A system uses explicit invalidation but sometimes serves stale data. What is the most likely cause?

medium

A. Cache entries expire too quickly

B. Cache updates on every write immediately

C. Cache-aside strategy is used incorrectly

D. Invalidation messages are delayed or lost

Solution

1. Step 1: Analyze explicit invalidation issues

   Explicit invalidation depends on timely invalidation messages to remove stale cache entries.

2. Step 2: Identify cause of stale data

   If invalidation messages are delayed or lost, stale data remains in cache and is served.

3. Final Answer:

   Invalidation messages are delayed or lost -> Option D

4. Quick Check:

   Delayed invalidation causes stale cache [OK]

Hint: Delayed invalidation messages cause stale cache data [OK]

Common Mistakes:

• Thinking cache expiry too fast causes stale data
• Confusing write-through with explicit invalidation
• Assuming cache-aside errors cause stale data here

5. You design a high-traffic e-commerce system where product prices update rarely but must be accurate. Which cache invalidation strategy best balances freshness and performance?

hard

A. Use explicit invalidation triggered by price update events

B. Use write-through caching to update cache on every price change

C. Use cache-aside with manual cache clearing

D. Use a short TTL to expire cache frequently

Solution

1. Step 1: Analyze system requirements

   Prices update rarely but must be accurate, so cache should update only on changes, not frequently expire.

2. Step 2: Evaluate strategies

   Short TTL causes unnecessary reloads, write-through may be costly if writes are rare, cache-aside with manual clearing is error-prone.

3. Step 3: Choose explicit invalidation

   Explicit invalidation triggered by price update events ensures cache is fresh only when needed, balancing performance and accuracy.

4. Final Answer:

   Use explicit invalidation triggered by price update events -> Option A

5. Quick Check:

   Rare updates + accuracy = explicit invalidation [OK]

Hint: Rare updates + accuracy = explicit invalidation best [OK]

Common Mistakes:

• Choosing short TTL causing frequent reloads
• Assuming write-through is best for rare updates
• Ignoring event-driven invalidation benefits