5 caching strategies

Wakeup Flower · 2025-10-08T11:11:12Z

🧠 1. Write-Through Cache Definition: Data is written to the cache and the underlying database at the same time . Pros: Cache and database are always consistent. Cons: Slower writes (since every write goes to both places). ⚡ 2. Write-Back (Write-Behind) Cache Definition: Data is written only to the cache at first, and written to the database later (asynchronously) . Pros: Fast writes; reduced database load. Cons: Risk of data loss if cache fails before syncing to DB. 📘 Used when performance is more important than absolute immediate consistency. 💾 3. Write-Around Cache Definition: Data is written only to the database , not the cache. The cache gets populated only on reads . Pros: Prevents cache pollution from infrequently accessed data. Cons: Slightly slower reads after writes, since the cache might be cold. 📘 Used when data is rarely read soon after being written. 🔄 4. Read-Through Cache Definition: The application reads data through the cache — if data is missing, the cache itself fetches from the database, stores it, and returns it. Pros: Simplifies app logic; automatic population of cache. Cons: Cache must know how to retrieve data from DB. 📘 Often used together with write-through or write-back. 🧹 5. Cache-Aside (Lazy Loading) Definition: The application code checks the cache first. If a miss occurs, it fetches from the database and writes to the cache manually. Pros: Simple and widely used; flexible. Cons: Possible cache staleness; first request after expiration is slow. 📘 This is the most common pattern in systems using Redis or Memcached. 🧩 Summary Table Strategy Write Path Read Path Consistency Performance Common Use Write-Through Cache + DB Cache Strong Slower writes Always-fresh data Write-Back Cache → DB (later) Cache Eventual Fast writes High-performance workloads Write-Around DB only Cache Eventual Medium Rarely-read data Read-Through Cache + DB (cache auto-loads) Cache Strong Good Simplified read logic Cache-Aside App manages cache App manages cache Eventual Good Common general use Caching Strategy How It Works Example Use Case Real-World Example (Company / System) Why It’s Used There 🧠 Write-Through Writes go to cache and database at the same time Financial transactions, inventory, user balance tracking 💳 Banking systems , eCommerce inventory (e.g., Amazon) Ensures cache and DB are always in sync — critical for financial and stock consistency ⚡ Write-Back (Write-Behind) Writes go to cache first, DB updated later asynchronously Gaming leaderboards, IoT telemetry data, analytics buffers 🎮 Online games (e.g., Fortnite, Clash of Clans) , IoT platforms Very high write rate — cache absorbs bursts, DB updated later for performance 💾 Write-Around Write directly to DB; cache updated only when read later Log archiving, product uploads, historical data 📰 Content management systems (WordPress, Medium) Avoids caching infrequently accessed or newly written data (saves cache space) 🔄 Read-Through Application reads through cache; cache auto-fetches from DB on miss Product catalogs, user profiles, CDN edge caches 🛒 Netflix, Amazon, Shopify Cache layer handles loading data; reduces DB hits for popular content 💤 Cache-Aside (Lazy Loading) Application checks cache → on miss, fetches from DB and stores in cache Web APIs, dashboards, personalization data 🧑💻 Reddit, Twitter timelines, microservices using Redis Flexible; app decides what to cache and when; ideal for read-heavy workloads

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🧠 1. Write-Through Cache

Definition: Data is written to the cache and the underlying database at the same time.
Pros: Cache and database are always consistent.
Cons: Slower writes (since every write goes to both places).

⚡ 2. Write-Back (Write-Behind) Cache

Definition: Data is written only to the cache at first, and written to the database later (asynchronously).
Pros: Fast writes; reduced database load.
Cons: Risk of data loss if cache fails before syncing to DB.

📘 Used when performance is more important than absolute immediate consistency.

💾 3. Write-Around Cache

Definition: Data is written only to the database, not the cache. The cache gets populated only on reads.
Pros: Prevents cache pollution from infrequently accessed data.
Cons: Slightly slower reads after writes, since the cache might be cold.

📘 Used when data is rarely read soon after being written.

🔄 4. Read-Through Cache

Definition: The application reads data through the cache — if data is missing, the cache itself fetches from the database, stores it, and returns it.
Pros: Simplifies app logic; automatic population of cache.
Cons: Cache must know how to retrieve data from DB.

📘 Often used together with write-through or write-back.

🧹 5. Cache-Aside (Lazy Loading)

Definition: The application code checks the cache first. If a miss occurs, it fetches from the database and writes to the cache manually.
Pros: Simple and widely used; flexible.
Cons: Possible cache staleness; first request after expiration is slow.

📘 This is the most common pattern in systems using Redis or Memcached.

🧩 Summary Table

Strategy	Write Path	Read Path	Consistency	Performance	Common Use
Write-Through	Cache + DB	Cache	Strong	Slower writes	Always-fresh data
Write-Back	Cache → DB (later)	Cache	Eventual	Fast writes	High-performance workloads
Write-Around	DB only	Cache	Eventual	Medium	Rarely-read data
Read-Through	Cache + DB (cache auto-loads)	Cache	Strong	Good	Simplified read logic
Cache-Aside	App manages cache	App manages cache	Eventual	Good	Common general use

Caching Strategy	How It Works	Example Use Case	Real-World Example (Company / System)	Why It’s Used There
🧠 Write-Through	Writes go to cache and database at the same time	Financial transactions, inventory, user balance tracking	💳 Banking systems, eCommerce inventory (e.g., Amazon)	Ensures cache and DB are always in sync — critical for financial and stock consistency
⚡ Write-Back (Write-Behind)	Writes go to cache first, DB updated later asynchronously	Gaming leaderboards, IoT telemetry data, analytics buffers	🎮 Online games (e.g., Fortnite, Clash of Clans), IoT platforms	Very high write rate — cache absorbs bursts, DB updated later for performance
💾 Write-Around	Write directly to DB; cache updated only when read later	Log archiving, product uploads, historical data	📰 Content management systems (WordPress, Medium)	Avoids caching infrequently accessed or newly written data (saves cache space)
🔄 Read-Through	Application reads through cache; cache auto-fetches from DB on miss	Product catalogs, user profiles, CDN edge caches	🛒 Netflix, Amazon, Shopify	Cache layer handles loading data; reduces DB hits for popular content
💤 Cache-Aside (Lazy Loading)	Application checks cache → on miss, fetches from DB and stores in cache	Web APIs, dashboards, personalization data	🧑‍💻 Reddit, Twitter timelines, microservices using Redis	Flexible; app decides what to cache and when; ideal for read-heavy workloads

Top comments (1)

Md Mahbubur Rahman • Oct 8

Wow, this article is a gem! Your clear breakdown of the five caching strategies shows exceptional technical insight. I especially loved how you highlighted the pros and cons of each approach—makes it so easy to grasp real-world applications. The summary table is a brilliant touch. Truly impressive work that any developer can immediately benefit from!