Caching Strategies: Redis, Memcached, and CDN for Performance

As systems scale, effective caching strategies become essential to reduce latency, lower infrastructure costs, and improve user experience. This article, by Arvucore, explains how Redis, Memcached, and CDNs can be combined to optimize application performance, practical trade-offs, and implementation patterns. Readers will gain strategic recommendations, measurement techniques, and real-world considerations for deploying multi-tier caches reliably. For performance optimization strategies, see our web performance guide.

Caching strategies and business impact

Modern businesses choose caching not just for speed, but for clear commercial outcomes: faster velocity, higher conversion rates, and infrastructure spend. Technical drivers include reducing end-to-end latency, increasing request throughput, and smoothing load spikes. A well-placed cache drops median response times from hundreds to tens of milliseconds, improves requests-per-second capacity by multiples, and converts lower tail latencies into user satisfaction gains.

European enterprises often track metrics such as p50/p95/p99 latency, cache hit ratio, RPS, cost per million requests, and availability/SLA percentages. An e‑commerce checkout cache that raises hit ratio from 60% to 90% can lift conversions by reducing friction; a marketing carousel cached at the CDN edge lowers origin egress bills and frees backend capacity for business-critical flows.

Trade-offs are real. Stale reads, coherence complexity, and cache-warmth create correctness and UX risks. Consistency strategies — TTLs, cache invalidation, write-through vs write-back — influence freshness and write amplification. Operational choices affect observability: choose metrics and alerts for cache misses, eviction rates, and population latencies.

Decisions should map to KPIs: if latency directly impacts revenue, favor aggressive edge caching and short TTLs with background refresh; if data correctness is paramount, prefer conservative caching with synchronous invalidation.

In-memory caching with Redis and Memcached

Redis and Memcached diverge where operations matter most: architecture, data model, and failure characteristics. Redis is an in-memory, single-threaded (with optional I/O threads), rich-data-structure store with persistence (RDB/AOF), replication, and an official Cluster mode. Memcached is a lightweight, multithreaded slab-allocator key-value cache designed for raw speed and minimal feature surface.

Operationally, choose based on workload. For simple, high-concurrency small-value caching prefer Memcached: consistent hashing, low overhead, predictable eviction behavior. For stateful caches, counters, sorted sets, atomic operations, or pub/sub, pick Redis. Think "redis memcached" when you need a side-by-side decision: use Memcached for blazingly fast ephemeral caches; use Redis when functionality and durability matter.

Practical config and runbook tips:

• Tune maxmemory and eviction policy (Redis: volatile/allkeys + LRU/TTL variants; Memcached: LRU with slab sizing).
• For Redis enable replication + Sentinel/Cluster for HA; watch AOF rewrite IO and RDB snapshot intervals.
• For Memcached use client-side consistent hashing or a proxy like twemproxy; monitor slab fragmentation and evictions.
• Track hit rate, eviction/sec, memory fragmentation, and replication lag.

Common failure modes: Redis risks persistence stalls and split-brain without proper failover; Memcached risks silent key loss via evictions and slab hot-spots. Align choice to data semantics, operational maturity, and expected failure tolerances.

CDN caching for edge performance

CDNs sit at the top of a multi-tier cache, bringing content physically closer to users and offloading requests from origin and in-memory caches. Edge caches excel for static assets and cacheable API responses; they also accelerate dynamic content through techniques like origin shielding, request coalescing, and edge logic (Edge Workers, ESI) that transforms or assemble responses without a round trip. Use Cache-Control and surrogate headers deliberately: public, s-maxage for CDNs, max-age for browsers, and consider stale-while-revalidate / stale-if-error to trade freshness for availability. TTL strategy should be tier-aware — short origin TTLs for rapidly changing data, longer s-maxage at the edge when eventual consistency is acceptable.

Invalidation must be explicit and fast: prefer surrogate keys or tags so you can soft-purge (mark stale) and background-refresh, rather than brute-force purges that are costly and slow. Design invalidation flows so that when application writes update a backend cache (Redis/Memcached), it also emits a purge request for the associated surrogate key.

Select vendors by PoP coverage, invalidation APIs, pricing model (egress, requests, invalidations), edge compute features, and observability. Cost calculus balances CDN egress versus origin compute and database load. Example integration: cache public API responses at the edge; origin retrieves compiled payload from Redis, returns it with a Surrogate-Key header; on updates, app writes Redis, then triggers CDN purge by surrogate key — fast, predictable, and scalable.

Designing multi-tier cache architectures

Designing resilient multi‑tier caches starts with selecting the right pattern. Use cache‑aside for simplicity: app reads check cache, then DB, populate cache on miss. Read‑through and write‑through suit scenarios where cache is authoritative; write‑behind improves write throughput but risks data loss on failure.

Address consistency by classifying data: strong consistency (synchronous write‑through or short TTL with versioning) versus eventual (write‑behind, relaxed TTL). Coherence tactics include versioned keys, pub/sub invalidation, and request coalescing to prevent thundering herds.

Prepopulate hot keys during deploys with warming jobs and coordinate invalidations with tombstones or version bumps. TTL and eviction tuning: set TTLs per object class, prefer LRU with sized namespaces, reserve memory pools for critical data, monitor evictions and tail latency.

When caches fail, detect eviction surge, rising DB latency or a spike in miss rate. Mitigate by enabling rate limits, opening a circuit to the DB, and diverting to read replicas. Recover by flushing and warming selected keys and replaying write‑behind logs when safe.

Canary cache policies by service and percent while observing hit ratio and P95. Trade‑offs: shorter TTL lowers staleness but increases DB load; write‑behind boosts throughput but complicates recovery.

App -> L1 -> L2 -> DB

Measuring impact and optimizing for cost

Start by instrumenting everything that matters: record cache hit ratio, request latency (P50/P95/P99), throughput (requests/sec), cache footprint (memory used, active keys), eviction and miss rates, and cost per request. Translate those signals into SLIs and SLOs—example: 99th‑percentile latency ≤ 50 ms for cached reads, and a cache hit SLO of 90% for high‑value objects. Concrete SLOs let you prioritize engineering work and correlate cost with user experience.

Run controlled experiments. Use A/B testing or canary rollouts where variant B uses a tuned cache policy. Compare cohorts on latency percentiles, error rates, and cost per 1M requests. Ensure statistical power: calculate sample sizes and run long enough to capture tail behavior. Complement A/B with synthetic benchmarking: replay production traffic, test cold and steady‑state scenarios, and measure throughput limits of Redis/Memcached and CDN edge hit rates.

Build dashboards that combine operational and business views: time series of hit ratio, P50/P95/P99, eviction spikes, cost curves, and estimated dollars per cached request. Use tools like Prometheus/Grafana, Datadog, or cloud provider metrics; pull Redis INFO and CDN analytics. Set alerts (e.g., hit ratio drop >5% for 10 minutes, P99 > SLO) and automated playbooks.

Finally, iterate. Weekly reviews, hypothesis-driven tuning (TTL, fragmentation, instance sizing), and post‑mortems create a continuous optimization cycle that balances latency gains against incremental cost.

Conclusion

Effective caching strategies using Redis, Memcached, and CDNs are key levers for reducing latency and scaling infrastructure cost-effectively. By selecting appropriate cache tiers, eviction policies, and measurement practices, teams can significantly boost application performance while managing complexity. Follow pragmatic rollout, monitoring, and invalidation techniques today to sustain reliability and align caching decisions with business objectives.