Why caching reduces latency in HLD - Design It to Understand It

Design: Caching System to Reduce Latency

Focus on caching layer design and its impact on latency reduction. Out of scope: detailed cache eviction policies and cache consistency mechanisms.

Functional Requirements

FR1: Serve user requests with minimal delay

FR2: Reduce load on primary data storage

FR3: Provide quick access to frequently requested data

Non-Functional Requirements

NFR1: Handle up to 10,000 requests per second

NFR2: API response time p99 under 100ms

NFR3: System availability 99.9%

Think Before You Design

Questions to Ask

❓ Question 1

❓ Question 2

❓ Question 3

❓ Question 4

Key Components

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

Primary database or data source

Application server

Cache invalidation or expiration mechanism

Design Patterns

Cache-aside pattern

Write-through and write-back caching

Time-to-live (TTL) based expiration

Lazy loading and prefetching

Reference Architecture

Client
  |
  v
Application Server
  |
  v
+------------------+
|   Cache Layer    |
| (Redis/Memcached) |
+------------------+
  |
  v
Primary Database

Components

Client

Web or Mobile App

Sends requests to the application server

Application Server

Node.js / Java / Python

Handles client requests, checks cache before database

Cache Layer

Redis or Memcached

Stores frequently accessed data in memory for fast retrieval

Primary Database

PostgreSQL / MySQL / NoSQL

Stores the authoritative data

Request Flow

1. Client sends a request to the application server.

2. Application server checks the cache for requested data.

3. If data is found in cache (cache hit), return data immediately to client.

4. If data is not found (cache miss), query the primary database.

5. Store the retrieved data in cache for future requests.

6. Return data to client.

Database Schema

Not applicable as this design focuses on caching mechanism rather than database schema.

Scaling Discussion

Bottlenecks

Cache becoming a single point of failure or bottleneck under high load.

Cache misses causing increased load on primary database.

Stale data in cache leading to inconsistent responses.

Solutions

Use distributed cache clusters with replication and sharding to handle load and provide high availability.

Implement cache warming and prefetching to reduce cache misses.

Use TTL and cache invalidation strategies to keep data fresh.

Interview Tips

Time: Spend 10 minutes explaining caching basics and why it reduces latency, 15 minutes on architecture and data flow, 10 minutes on scaling and trade-offs, and 10 minutes for questions.

Explain latency as the delay in getting data from the source.

Describe how cache stores data closer to the application for faster access.

Discuss cache hit vs cache miss and their impact on latency.

Mention cache placement and eviction strategies briefly.

Highlight scaling challenges and solutions like distributed caching.

Practice

1. Why does caching reduce latency in a system?

easy

A. Because it stores frequently accessed data closer to the user, reducing access time.

B. Because it increases the number of servers in the system.

C. Because it compresses data to make it smaller.

D. Because it deletes old data from the database.

Solution

1. Step 1: Understand caching location

   Caching stores data near the user or application, such as in memory or edge servers.

2. Step 2: Effect on access time

   Accessing data from cache is faster than fetching from the main database or server, reducing waiting time.

3. Final Answer:

   Because it stores frequently accessed data closer to the user, reducing access time. -> Option A

4. Quick Check:

   Caching reduces latency by storing data close [OK]

Hint: Caching stores data near users to speed up access [OK]

Common Mistakes:

• Thinking caching increases servers
• Confusing caching with data compression
• Believing caching deletes data

2. Which of the following is the correct way to describe caching in system design?

easy

A. Caching permanently deletes old data to save space.

B. Caching is a process of storing data in a remote backup server.

C. Caching temporarily stores data in a fast-access location to reduce retrieval time.

D. Caching encrypts data to improve security.

Solution

1. Step 1: Define caching purpose

   Caching temporarily holds data in a fast-access place like memory or local storage.

2. Step 2: Identify correct description

   Only caching temporarily stores data in a fast-access location to reduce retrieval time correctly states caching reduces retrieval time by temporary storage.

3. Final Answer:

   Caching temporarily stores data in a fast-access location to reduce retrieval time. -> Option C

4. Quick Check:

   Caching = temporary fast storage [OK]

Hint: Caching is temporary fast storage, not permanent or remote [OK]

Common Mistakes:

• Confusing caching with backup storage
• Thinking caching deletes data
• Mixing caching with encryption

3. Consider a system where fetching data from the main database takes 100ms, and fetching from cache takes 10ms. If 80% of requests hit the cache, what is the average latency per request?

medium

A. 18 ms

B. 90 ms

C. 50 ms

D. 28 ms

Solution

1. Step 1: Calculate cache hit and miss times

   Cache hit latency = 10ms (80% requests), cache miss latency = 100ms (20% requests).

2. Step 2: Compute weighted average latency

   Average latency = (0.8 * 10) + (0.2 * 100) = 8 + 20 = 28 ms.

3. Final Answer:

   28 ms -> Option D

4. Quick Check:

   Average latency = hit%*hit_time + miss%*miss_time [OK]

Hint: Use weighted average: hit%*hit_time + miss%*miss_time [OK]

Common Mistakes:

• Ignoring cache miss latency
• Adding times instead of weighted average
• Using wrong percentages

4. A system uses caching but users still experience high latency. Which of the following is a likely cause?

medium

A. Cache encrypts data, increasing retrieval time.

B. Cache hit rate is very low, so most requests go to the slow database.

C. Cache size is too large, slowing down the system.

D. Cache stores data permanently, causing outdated results.

Solution

1. Step 1: Analyze cache hit rate impact

   If cache hit rate is low, most requests bypass cache and hit slow database, increasing latency.

2. Step 2: Evaluate other options

   Permanent storage or encryption are not typical causes of high latency; large cache size usually improves speed.

3. Final Answer:

   Cache hit rate is very low, so most requests go to the slow database. -> Option B

4. Quick Check:

   Low cache hit rate = high latency [OK]

Hint: Low cache hit rate means slow database access dominates [OK]

Common Mistakes:

• Assuming cache size slows system
• Confusing cache permanence with latency
• Thinking encryption is main latency cause

5. In a large-scale web application, which caching strategy best reduces latency while keeping data fresh?

hard

A. Use a cache with a short expiration time and update cache asynchronously on data changes.

B. Use a cache that never expires and manually clear it once a month.

C. Disable caching and always fetch fresh data from the database.

D. Cache only rarely accessed data to save memory.

Solution

1. Step 1: Understand freshness and latency tradeoff

   Short expiration keeps data fresh; asynchronous updates reduce latency by avoiding blocking calls.

2. Step 2: Evaluate other options

   Never expiring cache risks stale data; disabling cache increases latency; caching rarely accessed data wastes resources.

3. Final Answer:

   Use a cache with a short expiration time and update cache asynchronously on data changes. -> Option A

4. Quick Check:

   Short expiry + async update = fresh data + low latency [OK]

Hint: Short expiry plus async update balances freshness and speed [OK]

Common Mistakes:

• Choosing never expiring cache causing stale data
• Disabling cache increases latency
• Caching rarely accessed data wastes resources