Choosing the Right Cache Strategy: Writing, Eviction, and Invalidation Based on Your Requirements

Caching is one of the most powerful techniques for improving application performance and scalability. But with great power comes great responsibility: the wrong caching strategy can introduce stale data, inconsistencies, or even performance bottlenecks.

In this post, we’ll break down:

The different caching strategies (writing, eviction, invalidation)
When to use each based on your requirements (fast reads, fast writes, high consistency, or combinations)
Practical examples to guide your decision

1. Cache Writing Strategies

Cache writing determines how and when data gets written into the cache relative to the source of truth (usually a database).

1.1 Write-Through Cache

How it works: Every write goes to the cache and the database simultaneously.
Pros:
- Data in cache is always consistent with DB
- Simple to reason about
Cons:
- Slower writes (since both cache and DB are updated)
- High write throughput may overload the cache unnecessarily

Best for:

Read-heavy applications where consistency is critical (e.g., financial systems, inventory management).

1.2 Write-Back (Write-Behind) Cache

How it works: Writes go to the cache first; DB is updated asynchronously.
Pros:
- Faster writes
- Reduces DB load during spikes
Cons:
- Risk of data loss if cache node crashes before syncing
- Higher operational complexity

Best for:

Applications where write performance matters more than absolute consistency (e.g., analytics, log ingestion).

1.3 Write-Around Cache

How it works: Writes go directly to the DB; cache is updated only on read (if data is requested later).
Pros:
- Prevents cache pollution with rarely-read data
Cons:
- First read after write will be slower (cache miss)

Best for:

Applications with write-heavy, read-sparse workloads (e.g., audit logs, archival data).

2. Cache Eviction Policies

Eviction determines which data is removed when cache is full.

2.1 LRU (Least Recently Used)

Removes data that hasn’t been accessed recently.
Best for: General-purpose caching where recency is a good predictor of future access (e.g., user sessions, product catalogs).

2.2 LFU (Least Frequently Used)

Removes data that is accessed least often.
Best for: Workloads with stable hot keys (e.g., recommendation systems, popular news feeds).

2.3 FIFO (First In, First Out)

Removes the oldest inserted item.
Best for: Streaming-like workloads where older data loses relevance (e.g., IoT sensor data, logs).

2.4 TTL (Time-to-Live) Based

Data expires after a fixed duration.
Best for: Scenarios where data freshness matters more than reuse (e.g., stock prices, cache of API responses).

3. Cache Invalidation Strategies

Invalidation determines how stale data is removed/updated in cache.

3.1 Manual Invalidation

Explicitly remove/update cache when DB changes.
Best for: Use cases with controlled data changes (e.g., admin updates product info).

3.2 TTL-Based Invalidation

Data expires automatically after a set period.
Best for: Systems where eventual consistency is acceptable (e.g., news articles cache, product listings).

3.3 Event/Write-Based Invalidation

Cache updated or cleared on every DB write.
Best for: High consistency scenarios (e.g., payment records, banking apps).

4. Matching Requirements to Cache Strategy

Now let’s put it all together.

Requirement	Cache Write	Eviction Policy	Invalidation Strategy
High Consistency	Write-Through	LRU/TTL	Write-Based/Event
Fast Reads	Write-Through or Write-Back	LRU or LFU	TTL or Event
Fast Writes	Write-Back	LFU/FIFO	Lazy or TTL
Read-Heavy, Consistency Needed	Write-Through	LRU	Manual/Event
Write-Heavy, Less Reads	Write-Around	FIFO	TTL
Mixed (Balanced)	Write-Through + TTL	LRU	Event + TTL

5. Practical Examples

E-commerce product catalog:
- Write-Through + LRU + TTL
- Ensures product prices/stock are up-to-date, while old items auto-expire.
Analytics dashboards:
- Write-Back + LFU + TTL
- Prioritize fast writes, cache hot metrics, let stale data expire naturally.
Banking transactions:
- Write-Through + LRU + Event-driven invalidation
- Ensures absolute consistency.
Social media feed:
- Write-Around + FIFO + TTL
- Prioritize fast writes and keep feed fresh with TTL.

Conclusion

Choosing the right caching strategy is not one-size-fits-all. It depends on whether your system prioritizes consistency, read performance, write performance, or a balance of all three.

When designing, always ask: