Write-through

📝 Write-Through Caching — Explained

Write-Through is a caching strategy where every write operation goes through the cache, and the cache immediately writes the data to the backing database as well.

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💡 Simple Definition

“Write to cache and database at the same time.”

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🔄 How Write-Through Works

Client → Writes data
        ↓
  Cache ← Updates the in-memory copy ✅
        ↓
  DB ← Immediately updated (synchronously) ✅

So the cache always stays in sync with the database.

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✅ Advantages

• Data consistency: Cache and DB are always in sync

• Read performance: Since data is guaranteed to be in cache, reads are fast

• Simplifies cache invalidation logic

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❌ Disadvantages

• Slower writes (since it also writes to the DB synchronously)

• More DB load compared to write-behind

• Less fault-tolerant: If DB is down, writes fail (unless you design fallbacks)

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🧠 Real-World Analogy

Imagine a cashier enters a sale into a register and also writes it to the official ledger at the same time. There’s no lag—everything is instantly synced.

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⚖️ Write-Through vs Write-Behind

Feature	Write-Through	Write-Behind
DB Write	Immediately	Deferred (async)
Latency	Higher (sync write to DB)	Lower (async write to DB)
Consistency	Strong	Eventual
Cache Warmth	Always hot	May be cold
Risk of Data Loss	Low	Medium/High (if crash)

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📦 When to Use Write-Through

• Read-heavy workloads

• Data must always be consistent between cache and DB

• Use cases where reads outnumber writes, like:

  • Product catalogs

  • User profiles

  • Configuration settings

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🛠️ Common Tech Stack

• Redis with custom logic to sync writes to DB

• Java cache libraries like Caffeine, EHCache, and Guava

• ORM-level caching (e.g., Hibernate L2 Cache in write-through mode)

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📌 Key Considerations

• Use retries for DB writes to avoid inconsistencies

• Monitor for latency bottlenecks

• Can combine with refresh-ahead for better read performance