Cache Selection Guide

ElastiCache Redis

Advanced Data Structures

Lists, sets, sorted sets, hashes

Persistence

Data durability with snapshots

Replication

Multi-AZ with automatic failover

Pub/Sub

Real-time messaging capabilities

ElastiCache Memcached

Simple Key-Value

Basic string-based caching

Multi-threaded

Better CPU utilization

Horizontal Scaling

Easy to add/remove nodes

No Persistence

Pure in-memory cache

Caching Patterns

Lazy Loading

Cache-aside pattern

• Load data on cache miss
• Simple to implement
• Stale data possible

Write-Through

Update cache on write

• Always fresh data
• Higher write latency
• Cache churn possible

Write-Behind

Asynchronous write to DB

• Fast write performance
• Risk of data loss
• Complex implementation

When to Use Redis

Complex Data Types

Need lists, sets, or sorted sets

High Availability

Multi-AZ with failover required

Persistence Needed

Data backup and recovery

Pub/Sub Messaging

Real-time notifications

When to Use Memcached

Simple Caching

Basic key-value storage

Horizontal Scaling

Easy to scale out

Multi-threaded

Better CPU utilization

Cost Effective

Lower overhead

Other AWS Caching Options

CloudFront

Global CDN caching

API Gateway

API response caching

DAX

DynamoDB accelerator

Application Load Balancer

Session stickiness

Performance Factors

Network Latency

Same AZ for lowest latency

Memory Size

Right-size for working set

Connection Pooling

Reuse connections efficiently

Security Best Practices

VPC Placement

Deploy in private subnets

Encryption

At-rest and in-transit encryption

Authentication

Redis AUTH and IAM

Cache Selection Decision Tree

Need Advanced Data Structures?

Yes: ElastiCache Redis

No: Consider Memcached or other options

Need High Availability?

Yes: Redis with Multi-AZ

No: Memcached for simplicity

Need Persistence?

Yes: Redis with backup

No: Memcached for pure caching

Cache Selection Tips

• Redis for complex data types, persistence, and high availability
• Memcached for simple key-value caching with horizontal scaling
• Consider lazy loading for read-heavy workloads
• Use write-through for data consistency requirements
• Monitor cache hit ratios and adjust TTL accordingly

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