Write Through Cache Pattern in System Design

Learn the Write Through Cache Pattern from a System Design perspective. This guide explains how Write Through caching works, write and read flows, consistency, Redis integration, Spring Boot architecture, cache synchronization, advantages, disadvantages, and real-world examples from Banking, Amazon, Netflix, and Uber.

Introduction

Imagine an e-commerce application where thousands of customers update their shopping carts every second.

Every update changes:

Product Quantity
Cart Items
Wishlist
User Preferences

If only the database is updated, Redis may still contain old data.

Result:

Users see stale information.
Inventory becomes inconsistent.
Orders fail due to outdated cache.

To solve this synchronization problem, many enterprise systems use the Write Through Cache Pattern.

With Write Through, every write operation updates both the cache and the database at the same time, ensuring they stay synchronized.

This strategy is commonly used in:

Banking Systems
Financial Applications
Inventory Systems
Customer Profile Services
Product Catalog Management

Learning Objectives

After completing this article, you will understand:

What is Write Through Cache?
Why Write Through?
Read Flow
Write Flow
Cache Synchronization
Consistency
Redis Integration
Spring Boot Architecture
Advantages
Disadvantages
Real-World Examples

What is Write Through Cache?

Write Through is a caching strategy where every write request updates:

Redis Cache
Database

before returning success.

Unlike Cache Aside,

the cache is always updated immediately after every write.

Write Through Overview

Client

↓

Spring Boot

↓

Redis

↓

Database

↓

Success

Both Redis and the database always contain the latest value.

Why Write Through?

Without Write Through

flowchart TD
    CLIENT["Client"]
    APP["Spring Boot"]
    DB["Update Database"]
    REDIS["Redis Still Has Old Data"]

    CLIENT --> APP
    APP --> DB
    APP -. "Cache not updated" .-> REDIS

Problem

Cache becomes stale.

With Write Through

flowchart TD
    CLIENT["Client"]
    APP["Spring Boot"]
    CACHE["Update Redis Cache"]
    DB["Update Database"]
    SUCCESS["Return Success"]

    CLIENT --> APP
    APP --> CACHE
    CACHE --> DB
    DB --> SUCCESS

Redis and Database remain synchronized.

Write Through Architecture

flowchart TD
    USERS["Users"]
    LB["AWS ALB"]

    APP1["Spring Boot 1"]
    APP2["Spring Boot 2"]

    REDIS["Redis Cluster"]
    DB["PostgreSQL"]

    USERS --> LB
    LB --> APP1
    LB --> APP2

    APP1 --> REDIS
    APP2 --> REDIS

    REDIS --> DB

Every write updates Redis first (or atomically alongside the database, depending on implementation), ensuring both layers reflect the latest value.

Write Flow

flowchart TD
    CLIENT["Client"]
    APP["Spring Boot API"]
    CACHE["Write to Redis Cache"]
    DB["Write to PostgreSQL"]
    RESP["Return Success"]

    CLIENT --> APP
    APP --> CACHE
    CACHE --> DB
    DB --> RESP

Step-by-Step Write Flow

Step 1

Client sends

PUT /products/1001

Step 2

Spring Boot validates request.

Step 3

Update Redis.

Step 4

Persist changes in PostgreSQL.

Step 5

Return response.

Read Flow

Reading becomes very fast.

flowchart LR
    CLIENT["Client"]
    APP["Spring Boot"]
    REDIS["Redis Cache"]
    RESPONSE["Response"]

    CLIENT --> APP
    APP --> REDIS
    REDIS --> RESPONSE

Most reads never reach the database.

Complete Request Lifecycle

sequenceDiagram

participant Client

participant SpringBoot

participant Redis

participant PostgreSQL

Client->>SpringBoot: Update Product

SpringBoot->>Redis: Update Cache

SpringBoot->>PostgreSQL: Save Product

PostgreSQL-->>SpringBoot: Success

SpringBoot-->>Client: Success

Consistency

One major advantage is strong consistency.

Database

↓

Always Matches

↓

Redis

Applications always read the latest value.

Product Update Example

Current

Laptop

↓

$1200

Customer updates price

Laptop

↓

$1100

Write Through updates

Redis
Database

at the same time.

Shopping Cart Example

flowchart TD
    CUSTOMER["Customer"]
    APP["Spring Boot"]
    CART["Shopping Cart"]
    REDIS["Redis Cache"]
    DB["Database"]

    CUSTOMER --> APP
    APP --> CART
    CART --> REDIS
    REDIS --> DB

The next read immediately returns the updated cart.

Banking Example

Updating customer profile.

Customer Address

↓

Redis

↓

Database

↓

Always Same Value

This avoids inconsistent customer information.

Inventory Example

flowchart TD
    ORDER["Order Placed"]
    INVENTORY["Inventory Service"]
    DB["Update Inventory"]
    REDIS["Update Redis Cache"]
    RESPONSE["Stock Updated"]

    ORDER --> INVENTORY
    INVENTORY --> DB
    DB --> REDIS
    REDIS --> RESPONSE

Both cache and database reflect the latest inventory count.

Amazon Example

Amazon uses synchronized caching for data such as:

Product Metadata
Seller Profiles
Customer Preferences

Inventory and orders may use additional consistency mechanisms depending on the service.

Netflix Example

Netflix synchronizes

User Preferences
Watch History
Profile Settings

so that users immediately see updated information across devices.

Uber Example

Uber updates

Driver Status
Driver Availability
User Preferences

and keeps frequently accessed state synchronized for fast reads.

Spring Boot Architecture

flowchart TD
    C[Client]
    G[API Gateway]
    A[Spring Boot]
    R[Redis]
    D[(PostgreSQL)]

    C --> G
    G --> A
    A --> R
    R --> D

Popular Libraries

Spring Cache
Spring Data Redis
Redisson
Lettuce

Write Through vs Cache Aside

Write Through	Cache Aside
Writes update Redis immediately	Cache updated only after a read miss
Strong consistency	Possible stale cache
Faster reads	First read can be slower
Higher write latency	Lower write latency

Write Through vs Write Behind

Write Through	Write Behind
Database updated immediately	Database updated asynchronously
Strong consistency	Eventual consistency
Slower writes	Faster writes
Lower risk	Higher risk if cache fails before persistence

Advantages

Strong consistency
Fast read performance
No stale cache after writes
Simple read logic
Excellent for frequently read data
Good user experience

Disadvantages

Every write updates two systems.
Higher write latency than Cache Aside.
Increased Redis usage.
More write operations.
Additional infrastructure cost.

Best Use Cases

Ideal for

User Profiles
Product Catalog
Configuration Data
Shopping Cart
Customer Preferences
Banking Customer Information

Avoid for

High-volume log ingestion
Analytics pipelines
Streaming events
Extremely write-heavy workloads

Monitoring

Monitor

Cache Hit Ratio
Cache Write Latency
Database Write Latency
Redis CPU
Redis Memory
Cache Synchronization Errors
Failed Writes
API Response Time

Tools

Redis Insight
Datadog
Grafana
Prometheus
CloudWatch

Common Mistakes

❌ Updating only the database

❌ Ignoring Redis write failures

❌ No retry mechanism

❌ Large cache objects

❌ No monitoring

❌ Treating Redis as the primary database

Best Practices

Update Redis and the database within the same business transaction where possible.
Use Redis for frequently accessed data.
Keep cached objects small.
Monitor write latency.
Use Redis Cluster for production.
Configure high availability with Redis Sentinel.
Implement retries and proper error handling.
Secure Redis using authentication and network isolation.

Common Interview Questions

What is the Write Through Cache Pattern?

Write Through is a caching strategy where every successful write operation updates both the cache and the underlying database before the operation completes.

Why is Write Through more consistent than Cache Aside?

Because the cache is updated immediately whenever data changes, reducing the chance of stale cache entries after write operations.

What is the main disadvantage of Write Through?

Write operations become slower because every update must be written to both Redis and the database.

When should Write Through be used?

It is best suited for systems that perform frequent reads and require strong consistency, such as customer profiles, product catalogs, and configuration services.

Can Write Through replace a database?

No. Redis is a cache, while the database remains the system of record. Redis should complement—not replace—the primary database.

Summary

The Write Through Cache Pattern ensures that Redis and the database remain synchronized by updating both whenever data changes. This approach provides strong consistency and excellent read performance, making it suitable for enterprise applications where stale data cannot be tolerated.

In this article, we covered:

Write Through fundamentals
Read and write flows
Cache synchronization
Consistency
Redis integration
Spring Boot architecture
Banking, Amazon, Netflix, and Uber examples
Monitoring
Best practices

Write Through is an excellent choice for read-heavy applications that require up-to-date data, although it introduces additional write latency because every update must be persisted to both the cache and the database.

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