Write-through and write-back caching in HLD - Scalability & System Analysis

Scalability Analysis - Write-through and write-back caching

Growth Table: Write-through vs Write-back Caching

Users / Requests	Write-through Caching	Write-back Caching
100 users	Cache updates on every write; low latency; DB load manageable	Cache holds writes; DB updated less frequently; risk of data loss low
10,000 users	DB write load increases linearly; cache write latency impacts user experience	Cache write buffer grows; risk of stale data if cache fails; better DB load
1 million users	DB becomes bottleneck due to many writes; write latency affects throughput	Cache memory pressure high; complex eviction; risk of data loss higher; DB load manageable
100 million users	DB write capacity exceeded; system slows down; scaling DB costly	Cache cluster needed; data consistency challenges; complex recovery; better DB scaling

First Bottleneck

For write-through caching, the database write throughput is the first bottleneck because every write must be immediately persisted to the database, increasing DB load linearly with traffic.

For write-back caching, the cache memory and eviction policy become bottlenecks as writes accumulate in cache before flushing, risking data loss and stale data if cache fails.

Scaling Solutions

• Write-through: Use database write replicas to distribute read load; implement write batching if possible; add write caches with fast persistence layers (e.g., SSDs); horizontally scale DB with sharding.
• Write-back: Increase cache cluster size; use distributed cache with persistence (e.g., Redis with AOF); implement reliable write-back flush policies; use write-ahead logs to prevent data loss.
• For both: Use CDN for static content to reduce load; apply backpressure and rate limiting to control write bursts.

Back-of-Envelope Cost Analysis

Assuming 10,000 writes per second at 10KB each:

• Write-through: 10,000 DB writes/sec -> DB must handle 10K QPS write load (near upper limit for single instance)
• Write-back: Cache handles 10K writes/sec in memory; DB writes batched to 1,000/sec -> DB load reduced by 10x
• Storage: 10KB * 10,000 writes/sec = ~100MB/sec write data; 8.6TB/day
• Network: 1 Gbps link ~125MB/sec; write-through may saturate network; write-back reduces DB network usage

Interview Tip

Start by explaining the difference between write-through and write-back caching simply. Then discuss how each affects database load and data consistency. Identify the bottleneck for each approach and propose scaling solutions accordingly. Use real numbers to show understanding of system limits.

Self Check

Your database handles 1000 QPS. Traffic grows 10x to 10,000 QPS writes. What do you do first?

Answer: Implement write-back caching to buffer writes in cache and batch them to the database, reducing DB write load. Also consider sharding or adding write replicas to scale DB horizontally.

Key Result

Write-through caching hits database write throughput limits first, while write-back caching shifts bottleneck to cache memory and data consistency. Scaling requires balancing DB load and cache reliability.

Practice

1. Which caching method writes data to both the cache and main storage immediately to ensure data safety?

easy

A. Read-through caching

B. Write-back caching

C. Write-through caching

D. Lazy caching

Solution

1. Step 1: Understand write-through caching behavior

   Write-through caching updates both cache and main storage at the same time to keep data consistent.

2. Step 2: Compare with write-back caching

   Write-back caching delays updating main storage, only writing to cache first for speed.

3. Final Answer:

   Write-through caching -> Option C

4. Quick Check:

   Immediate write to both cache and storage = Write-through caching [OK]

Hint: Immediate write to storage means write-through caching [OK]

Common Mistakes:

• Confusing write-back with write-through
• Thinking write-back writes immediately to storage
• Mixing read-through with write caching

2. Which of the following correctly describes write-back caching?

easy

A. Data is written only to cache first, then to main storage later.

B. Data is written to cache and main storage simultaneously.

C. Data is never written to main storage.

D. Data is written only to main storage, skipping cache.

Solution

1. Step 1: Recall write-back caching process

   Write-back caching writes data to cache first, delaying main storage update for performance.

2. Step 2: Eliminate incorrect options

   The description of data written to cache and main storage simultaneously matches write-through. The claim that data is never written to main storage is false, as main storage is updated eventually. Writing only to main storage skips the cache, which is incorrect.

3. Final Answer:

   Data is written only to cache first, then to main storage later. -> Option A

4. Quick Check:

   Write-back delays main storage write = Data is written only to cache first, then to main storage later. [OK]

Hint: Write-back caches first, writes storage later [OK]

Common Mistakes:

• Choosing simultaneous write as write-back
• Assuming main storage is never updated
• Confusing cache skipping with write-back

3. Consider a system using write-back caching. If a power failure occurs before the cache is flushed to main storage, what is the likely outcome?

medium

A. Some recent data in cache may be lost.

B. All data is safely stored in main storage.

C. Cache and main storage remain fully synchronized.

D. The system automatically switches to write-through caching.

Solution

1. Step 1: Understand write-back caching risk

   Write-back caches data first and delays writing to main storage, so recent changes may only be in cache.

2. Step 2: Analyze power failure impact

   If power fails before cache flush, data in cache not yet saved to main storage is lost.

3. Final Answer:

   Some recent data in cache may be lost. -> Option A

4. Quick Check:

   Power failure before flush = possible data loss in cache [OK]

Hint: Power loss before flush loses cached data [OK]

Common Mistakes:

• Assuming all data is safe despite power failure
• Thinking cache and storage always sync instantly
• Believing system auto-switches caching modes

4. A developer implemented write-back caching but notices data inconsistencies after crashes. Which fix best addresses this issue?

medium

A. Flush cache to storage less frequently.

B. Switch to write-through caching to update storage immediately.

C. Disable caching entirely.

D. Increase cache size to hold more data.

Solution

1. Step 1: Identify cause of inconsistencies

   Write-back caching delays storage updates, causing data loss on crashes.

2. Step 2: Choose solution to ensure data safety

   Switching to write-through caching writes data immediately to storage, preventing inconsistencies.

3. Final Answer:

   Switch to write-through caching to update storage immediately. -> Option B

4. Quick Check:

   Immediate storage update fixes inconsistency = Switch to write-through caching to update storage immediately. [OK]

Hint: Immediate writes fix inconsistency in caching [OK]

Common Mistakes:

• Thinking bigger cache solves data loss
• Disabling cache reduces speed unnecessarily
• Flushing less often worsens data loss

5. You are designing a high-performance system that must balance speed and data reliability. Which caching strategy should you choose and why?
Options:
A) Write-through caching for data safety but slower writes.
B) Hybrid approach: write-back with periodic flush and battery-backed cache.
C) Write-back caching for faster writes but risk of data loss.
D) No caching to avoid complexity.

hard

A. Write-through caching for data safety but slower writes.

B. No caching to avoid complexity.

C. Write-back caching for faster writes but risk of data loss.

D. Hybrid approach: write-back with periodic flush and battery-backed cache.

Solution

1. Step 1: Analyze trade-offs of write-through and write-back

   Write-through is safe but slower; write-back is fast but risks data loss on failure.

2. Step 2: Consider hybrid approach benefits

   Hybrid uses write-back speed plus periodic flush and battery backup to reduce data loss risk.

3. Step 3: Evaluate no caching option

   No caching avoids complexity but sacrifices performance, not ideal for high-performance needs.

4. Final Answer:

   Hybrid approach: write-back with periodic flush and battery-backed cache. -> Option D

5. Quick Check:

   Balance speed and safety with hybrid caching = Hybrid approach: write-back with periodic flush and battery-backed cache. [OK]

Hint: Hybrid caching balances speed and safety best [OK]

Common Mistakes:

• Choosing only write-back ignoring data loss
• Picking no caching sacrificing performance
• Ignoring battery-backed cache benefits