Write-through and write-back caching in HLD - System Design Exercise

Design: Caching System with Write-through and Write-back Strategies

Design focuses on caching strategies and data flow between cache and database. Does not cover specific database or cache technology selection or network infrastructure.

Functional Requirements

FR1: Support caching of data to improve read and write performance

FR2: Implement write-through caching where writes update both cache and database synchronously

FR3: Implement write-back caching where writes update cache first and database asynchronously

FR4: Ensure data consistency between cache and database

FR5: Handle cache misses by fetching data from the database

FR6: Support configurable cache eviction policies

FR7: Provide metrics on cache hit rate and write latency

Non-Functional Requirements

NFR1: System should handle 10,000 concurrent read/write requests

NFR2: Read latency p99 should be under 50ms

NFR3: Write latency p99 should be under 100ms for write-through and under 20ms for write-back

NFR4: Availability target of 99.9% uptime

NFR5: Data loss risk must be minimized especially for write-back caching

Think Before You Design

Questions to Ask

❓ Question 1

❓ Question 2

❓ Question 3

❓ Question 4

❓ Question 5

Key Components

Cache layer (in-memory store like Redis or Memcached)

Primary database (relational or NoSQL)

Write queue or buffer for asynchronous writes in write-back

Cache eviction mechanism

Monitoring and metrics system

Design Patterns

Write-through caching pattern

Write-back (write-behind) caching pattern

Cache-aside pattern for cache misses

Asynchronous processing with queues

Data consistency and synchronization techniques

Reference Architecture

Client
  |
  v
Cache Layer (supports write-through and write-back)
  |        |
  |        v
  |    Write Queue (for write-back)
  |        |
  v        v
Database <---- Monitoring & Metrics

Components

Cache Layer

In-memory store (e.g., Redis, Memcached)

Serve fast read requests and handle writes according to caching strategy

Primary Database

Relational or NoSQL database

Persistent data storage

Write Queue

Message queue or buffer (e.g., Kafka, RabbitMQ)

Buffer writes asynchronously in write-back caching

Monitoring & Metrics

Prometheus, Grafana or similar

Track cache hit rates, write latencies, and system health

Request Flow

1. Client sends read or write request to Cache Layer.

2. For reads: Cache Layer checks cache; if hit, returns data immediately.

3. If cache miss, Cache Layer fetches data from Database, updates cache, then returns data.

4. For writes in write-through mode: Cache Layer writes data to cache and synchronously to Database before confirming success to client.

5. For writes in write-back mode: Cache Layer writes data to cache and enqueues write operation to Write Queue, then immediately confirms success to client.

6. Write Queue processes queued writes asynchronously and updates Database.

7. Monitoring system collects metrics on cache hits, misses, write latencies, and errors.

Database Schema

Entities: DataItem(id PK, value, last_updated) Relationships: None specific to caching; schema supports data storage and timestamp for synchronization.

Scaling Discussion

Bottlenecks

Cache capacity limits causing frequent evictions and cache misses

Write Queue becoming a bottleneck under heavy write-back load

Database write throughput limits especially for write-through caching

Data inconsistency risk in write-back due to delayed writes

Monitoring overhead impacting system performance

Solutions

Scale cache horizontally with sharding or clustering to increase capacity

Use multiple partitions or consumers for Write Queue to increase throughput

Optimize database writes with batching and indexing; consider database scaling

Implement retry and failure detection mechanisms for write-back queue to reduce data loss

Use lightweight monitoring agents and sample metrics to reduce overhead

Interview Tips

Time: Spend 10 minutes understanding requirements and clarifying consistency needs, 15 minutes designing architecture and data flow, 10 minutes discussing scaling and trade-offs, 10 minutes answering questions.

Explain difference between write-through and write-back caching clearly

Discuss trade-offs between data consistency, latency, and durability

Describe how cache misses and evictions are handled

Highlight asynchronous processing benefits and risks in write-back

Address scaling challenges and mitigation strategies

Mention monitoring importance for operational visibility

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