Distributed Cache Systems

"Should we use Redis or Memcached?" is one of the most frequently asked questions in distributed systems design. Both are battle-tested, widely deployed, and capable of handling millions of operations per second. Yet they make fundamentally different trade-offs that make each optimal for different scenarios.

The naive answer—"Redis has more features, so just use Redis"—oversimplifies the decision. Memcached's simplicity isn't a limitation; it's a deliberate design choice that yields tangible benefits in specific contexts. Similarly, Redis's feature richness comes with complexity and operational considerations that may not suit every workload.

This page provides a rigorous, nuanced comparison that goes beyond surface-level feature checklists. By the end, you'll have the analytical framework to make confident technology selections based on your specific requirements, not generic recommendations.

Before comparing specific features, it's essential to understand the design philosophies that shaped each system. These foundational choices permeate every aspect of their behavior.

Redis: The Data Structure Server

Redis positions itself as an in-memory data structure server. Its philosophy:

• Provide rich, atomic operations on complex data types
• Enable patterns that would otherwise require complex application logic
• Support optional persistence for durability
• Grow horizontally via Cluster while maintaining complex type support

Redis aims to be the Swiss Army knife of distributed data—a single system that can serve as cache, database, message broker, and more.

Memcached: The Cache Accelerator

Memcached positions itself as a high-performance distributed memory cache. Its philosophy:

• Do one thing exceptionally well: store and retrieve key-value pairs
• Maximize throughput and minimize latency above all else
• No persistence—it's a cache, not a database
• Scale horizontally through client-side sharding

Memcached aims to be the fastest possible acceleration layer—a predictable, simple component you can reason about completely.

The most visible difference between Redis and Memcached is their data model. This affects what operations you can perform efficiently without application-side logic.

Practical Implications

Scenario: Leaderboard System

With Redis:

ZADD leaderboard 1500 "player:alice"
ZADD leaderboard 2200 "player:bob"
ZREVRANGE leaderboard 0 9 WITHSCORES    # Top 10
ZREVRANK leaderboard "player:alice"      # Alice's rank

With Memcached:

• Store entire leaderboard as serialized JSON
• Every update: GET → deserialize → modify → serialize → SET
• Or maintain in database, cache rendered results
• Can't atomically update rankings

Scenario: Simple Session Cache

With Redis:

SET session:xyz "{...}" EX 3600
GET session:xyz

With Memcached:

set session:xyz 0 3600 <length>
{...}
get session:xyz

For simple key-value caching, both are equally capable. The difference emerges with complex data requirements.

Performance is nuanced—both systems are extremely fast, but they optimize for different dimensions.

Threading Architecture

Redis (Single-Threaded Core):

• Command processing runs in one thread
• I/O can be parallelized (Redis 6+ with io-threads)
• Background threads for persistence and cleanup
• Advantage: No locking, predictable latency, true atomicity
• Limitation: Single CPU core bounds throughput

Memcached (Multi-Threaded):

• Multiple worker threads handle both I/O and commands
• Threads lock items during operations (per-key, not global)
• Advantage: Utilizes all CPU cores naturally
• Limitation: Lock contention possible under heavy write load

When Threading Model Matters

Memcached Wins:

• Pure caching workloads on multi-core machines
• When you need maximum throughput from a single node
• When you can't or won't deploy Redis Cluster

Redis Wins (Despite Single-Threading):

• When you need Redis's data structures (no alternative)
• When predictable latency matters more than peak throughput
• When using Redis Cluster (distributes across cores via multiple processes)
• When complex operations benefit from guaranteed atomicity

Real-World Perspective:

For most applications, neither Redis nor Memcached is the bottleneck. Network latency, database queries, and application logic dominate. The threading model difference only matters at extreme scale or with CPU-bound workloads.

Persistence is perhaps the starkest difference between Redis and Memcached—because Memcached has none.

Redis Persistence Options

Redis offers multiple persistence strategies:

1. RDB Snapshots: Point-in-time snapshots at intervals

   • Fast recovery
   • Possible data loss between snapshots

2. AOF (Append-Only File): Log every write operation

   • Minimal data loss (configurable: always, everysec, no)
   • Slower recovery (replay log)

3. Hybrid (RDB + AOF): RDB preamble + AOF tail

   • Fast recovery + minimal data loss
   • Recommended for production

Memcached: No Persistence

Memcached deliberately has no persistence:

• Restart = empty cache
• Server crash = data gone
• This is a feature, not a bug

Why No Persistence?

Memcached is a cache—ephemeral by definition. Data should always be reconstructable from the source (database, computed, external API). If you need durability, you need a database, not a cache.

The Cold Start Consideration

Both systems face the "cold start" problem—an empty cache causes a thundering herd to the database. Redis persistence mitigates this by pre-loading data on restart. Memcached requires:

1. Warming scripts to pre-populate cache
2. Gradual traffic shift to new instances
3. Database capacity to handle initial cache misses
4. Application-level fallbacks

For large-scale systems, Redis's ability to restart with data intact can significantly reduce operational risk during maintenance or failures.

Both systems scale horizontally, but through fundamentally different mechanisms.

Redis Scaling

Vertical Scaling:

• Single instance up to available RAM
• I/O threading (Redis 6+) helps with connections
• CPU remains single-threaded for commands

Horizontal Scaling:

• Redis Cluster: Built-in sharding across 16,384 hash slots
• Automatic failover when master fails
• Server-side routing with MOVED/ASK redirects
• Multi-key operations limited to same slot

High Availability:

• Redis Sentinel: Monitoring and automatic failover
• Redis Cluster: Built-in HA with replicas per shard
• Replicas provide read scaling and failover targets

Memcached Scaling

Vertical Scaling:

• Multi-threaded: utilizes all cores
• Single instance up to available RAM
• Generally simpler to add RAM/CPU than manage cluster

Horizontal Scaling:

• Client-side consistent hashing: All intelligence in client library
• Servers unaware of each other
• Adding/removing servers: ~1/N keys remap (with consistent hashing)

High Availability:

• No built-in replication or failover
• Client libraries remove unresponsive servers from ring
• Deploy multiple independent pools for redundancy
• Cache misses handled by falling back to database

When scaling with expensive RAM, memory efficiency directly impacts cost. Both systems optimize memory, but differ in approach.

Redis Memory Usage

Redis uses automatic encoding optimization:

• Small objects use compact encodings (ziplist, intset, listpack)
• Large objects upgrade to full data structures (hash table, skip list)
• Strings with the embstr encoding for small sizes

Memory Overhead:

• Per-key overhead: ~50-70 bytes (key metadata, pointers)
• Small strings: very efficient with embedded encoding
• Large objects: more overhead from data structure pointers
• Persistence: AOF and replica buffers consume additional memory

Memcached Memory Usage

Memcached's slab allocator trades flexibility for predictability:

• Fixed chunk sizes within slab classes
• Internal fragmentation: item uses chunk, unused bytes wasted
• No per-key metadata beyond 48-byte item header

Memory Overhead:

• Per-item overhead: ~48 bytes (item header)
• Slab fragmentation: up to (growth_factor - 1) waste per item
• No replication buffers
• No persistence overhead

Practical Memory Considerations

Redis Memory Advantages:

• Hash optimization: storing objects as Hashes vs serialized JSON can save 30-50% memory
• Data structure sharing: multiple keys can reference same internal structures
• OBJECT ENCODING command helps debug memory usage

Memcached Memory Advantages:

• Predictable: slab stats show exact utilization
• No surprise memory growth from persistence
• Simpler capacity planning

Memory Recommendation: For pure caching, both are comparably efficient. If using Redis's data structures (especially Hashes for objects), Redis may be more memory-efficient despite higher per-key overhead.

Production operations encompass deployment, monitoring, maintenance, and incident response. Simpler systems are easier to operate—but may require more application-side complexity.

What This Means in Practice

Redis Operational Overhead:

• Must understand persistence trade-offs and monitor disk
• Must configure and monitor replication lag
• Cluster requires understanding slot migration, failover, and split-brain scenarios
• More runbook entries for more failure modes
• More expertise needed on the team

Memcached Operational Simplicity:

• Node dies? Client removes from ring, traffic redistributes
• Cold start? Traffic goes to database temporarily
• Need more capacity? Add nodes, update client configuration
• Fewer things that can go wrong = fewer pages at 3 AM

The Trade-off:

Memcached's simplicity means the cache layer is easier to manage but provides fewer guarantees. Redis's complexity provides more capabilities but demands more operational sophistication.

Based on our detailed comparison, here's a practical decision framework for selecting between Redis and Memcached.

Both Redis and Memcached are excellent technologies—the "right" choice depends on your specific context. Let's consolidate the key decision factors:

What's Next:

With a solid understanding of individual cache technologies, the next page explores Cache Cluster Management—operational strategies for deploying, scaling, monitoring, and maintaining distributed cache clusters in production environments.