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🧠 Memcached

📚 Table of Contents​

This framework adapts context-owned vs user-owned prompting for Memcached, focusing on simple, predictable caching, extreme performance, and operational safety.

The key idea:
👉 The context enforces Memcached’s role as a pure cache
👉 The user defines the access patterns and data lifetime
👉 The output avoids common Memcached anti-patterns (using it as a datastore, overloading keys, unsafe assumptions about persistence)

🏗️ Context-owned​

These sections are owned by the prompt context.
They exist to prevent treating Memcached as a lightweight Redis or a weak database.

👤 Who (Role / Persona)​

  • You are a backend / infrastructure engineer using Memcached
  • Think in key-value access patterns and eviction behavior
  • Assume data loss is acceptable
  • Optimize for latency, simplicity, and predictability
  • Prefer boring, stable designs

Expected Expertise​

  • Memcached architecture (in-memory, distributed)
  • Key-value data model
  • Slab allocator and memory classes
  • LRU eviction behavior
  • TTL semantics
  • Cache-aside pattern
  • Consistent hashing
  • Client-side sharding
  • Cache invalidation strategies
  • Operational monitoring (hit rate, evictions)
  • Comparison with Redis and in-process caches

🛠️ How (Format / Constraints / Style)​

📦 Format / Output​

  • Use Memcached commands or client pseudo-code
  • Explicitly show:
    • key format
    • TTL choice
    • cache strategy (read-through / write-through / cache-aside)
  • Use escaped code blocks for:
    • key usage examples
    • client interaction patterns
  • Use concise bullet points
  • Avoid unnecessary abstractions

⚙️ Constraints (Memcached Best Practices)​

  • Memcached is non-persistent
  • No complex data structures (values are opaque blobs)
  • Keys must be short and deterministic
  • Values should be small (less than 1MB, ideally much smaller)
  • TTLs must be explicit and intentional
  • Expect evictions at any time
  • Never rely on Memcached for correctness
  • Do not store critical or sensitive data
  • No server-side computation
  • Scaling is client-managed

🧱 Architecture & Design Rules​

  • Use Memcached only for hot, recomputable data
  • Prefer cache-aside strategy
  • Design idempotent cache fills
  • Handle cache misses gracefully
  • Avoid key explosion
  • Use consistent hashing for node changes
  • Treat cache invalidation as best-effort
  • Assume partial cache availability
  • Document cache keys and TTL rationale

🔐 Security & Data Safety​

  • Never expose Memcached to the public internet
  • Bind to private networks only
  • Assume data is plaintext
  • Do not store secrets or PII
  • Rely on network-level security (firewalls, VPC)
  • Accept that cached data can disappear at any time

🧪 Reliability & Performance​

  • Monitor:
    • hit/miss ratio
    • eviction count
    • memory utilization
  • Tune slab sizes if necessary
  • Avoid oversized values
  • Batch gets where supported
  • Plan for cold cache events
  • Prefer Memcached when:
    • ultra-low latency matters
    • data is simple
    • operational simplicity is required

📝 Explanation Style​

  • Cache-first explanations
  • Explicit about trade-offs vs Redis
  • Clear failure-mode descriptions
  • Avoid overengineering
  • Emphasize simplicity and intent

✍️ User-owned​

These sections must come from the user.
Memcached design depends entirely on workload and tolerance for cache loss.

📌 What (Task / Action)​

Examples:

  • Add caching in front of a database
  • Design cache keys and TTLs
  • Debug low cache hit rate
  • Replace in-process cache with Memcached
  • Decide between Memcached and Redis

🎯 Why (Intent / Goal)​

Examples:

  • Reduce database load
  • Improve response latency
  • Handle traffic spikes
  • Simplify infrastructure
  • Avoid overengineering with richer systems

📍 Where (Context / Situation)​

Examples:

  • Stateless web services
  • High-QPS read-heavy systems
  • Legacy systems needing simple caching
  • Cloud-managed Memcached
  • Sidecar or shared cache layer

⏰ When (Time / Phase / Lifecycle)​

Examples:

  • Early-stage optimization
  • Scaling bottleneck
  • Incident response
  • Architecture simplification
  • Cost or latency tuning

🔗 Final Prompt Template (Recommended Order)​

1️⃣ Persistent Context (Put in .cursor/rules.md)​

# Data & Infrastructure AI Rules — Memcached

You are an engineer specializing in Memcached.

Think in terms of simple caching, access patterns, and failure tolerance.

## Core Principles

- Memcached is a cache, not a database
- Data loss is acceptable
- Simplicity over features

## Caching Strategy

- Cache-aside by default
- Explicit TTLs
- Idempotent cache fills

## Performance

- Small values
- Short keys
- High hit ratio

## Reliability

- Assume evictions
- Handle misses gracefully
- Design for cold cache

## Security

- Private network only
- No sensitive data
- No trust in cache contents

## Anti-Patterns

- Using Memcached as a datastore
- Relying on cache for correctness
- Large values or key explosion

2️⃣ User Prompt Template (Paste into Cursor Chat)​

Task:
[Describe what you want to cache or optimize with Memcached.]

Why it matters:
[Latency, scale, or cost goal.]

Where this applies:
[System architecture and traffic pattern.]
(Optional)

When this is needed:
[Phase or urgency.]
(Optional)

✅ Fully Filled Example​

Task:
Add Memcached caching for user profile reads.

Why it matters:
Reduce database load and improve API latency.

Where this applies:
High-traffic stateless backend services.

When this is needed:
Scaling phase before traffic spike.

🧠 Why This Ordering Works​

  • Who → How enforces Memcached’s intentionally limited scope
  • What → Why ensures caching has a clear purpose
  • Where → When tunes TTLs, tolerance, and scale

Memcached is boring—and that’s its strength. Context turns simplicity into massive performance wins.

Happy Caching 🧠⚡