CDN Caching Strategies: A Detailed Analysis of Techniques to Optimize Content Delivery

Introduction

Content Delivery Networks (CDNs) are distributed systems designed to optimize the delivery of static and dynamic content by caching data at edge locations closer to users, reducing latency, improving throughput, and offloading origin servers. CDNs are critical for applications requiring low-latency content delivery, such as streaming (Netflix), e-commerce (Amazon), and social media (Twitter). This comprehensive analysis explores the caching strategies employed by CDNs, including Cache-Aside, Read-Through, Write-Through, Write-Back, Time-To-Live (TTL)-Based Caching, Cache Invalidation, and Tiered Caching, with a focus on their implementation, performance impact, and integration with systems like Redis. It builds on prior discussions of Redis use cases (e.g., caching, session storage), caching strategies (e.g., Cache-Aside, Write-Back), eviction policies (e.g., LRU, LFU), probabilistic data structures (e.g., Bloom Filters), and latency reduction techniques (e.g., in-memory storage, pipelining). The analysis provides technical depth, real-world examples, trade-offs, and implementation considerations for system design professionals to optimize content delivery in high-performance applications.

Understanding CDN Caching

Definition

A CDN is a network of geographically distributed edge servers that cache content (e.g., HTML, images, videos, APIs) to serve users from the closest location, minimizing latency (e.g., < 10ms vs. 100ms from origin) and reducing origin server load (e.g., 80–90

Key Metrics

  • Latency: Time to deliver content (e.g., < 10ms for edge cache hits, 100ms for origin fetches).
  • Cache Hit Rate: Percentage of requests served from cache (e.g., 90–95
  • Throughput: Requests per second (req/s) handled by edge servers (e.g., 1M req/s).
  • Origin Offload: Percentage of traffic served by CDN vs. origin (e.g., 80–90
  • P99 Latency: 99th percentile latency for user experience (e.g., < 20ms).

CDN Architecture

  • Edge Servers: Cache content at Points of Presence (PoPs) near users (e.g., Cloudflare’s 300+ PoPs).
  • Origin Servers: Host original content (e.g., AWS S3, web servers).
  • Middle Tier: Optional caching layer between edge and origin for scalability (e.g., AWS CloudFront regional caches).
  • Protocols: HTTP/2 or HTTP/3 for low-latency delivery, TLS 1.3 for security (1ms handshake with session resumption).
  • Storage: In-memory (e.g., Redis, Memcached) or disk-based (e.g., SSD) caching at edge servers.

CDN Caching Strategies

1. Cache-Aside (Lazy Loading)

Context

Cache-Aside, also known as lazy loading, involves the application explicitly managing cache population. The CDN checks the cache for content; if absent (miss), it fetches from the origin and caches the response.

Implementation

  • Mechanism:
    • Edge server checks cache (e.g., Redis GET /images/logo.png) for content.
    • On miss, fetches from origin (e.g., AWS S3), caches result (SET /images/logo.png, TTL 3600s).
    • Uses Bloom Filters (BF.EXISTS cache_filter /images/logo.png) to reduce unnecessary origin fetches (< 0.5ms).
  • Configuration:
    • Redis Cluster on edge servers (16GB RAM, 16,384 slots, 3 replicas).
    • TTL: 300–3600s for static assets (images, CSS), 60s for dynamic content (APIs).
    • Eviction Policy: allkeys-lru for recency-based eviction.
    • Persistence: RDB snapshots for non-critical assets.
  • Integration:
    • Redis: In-memory cache for < 0.5ms latency.
    • AWS S3: Origin storage for static assets, handling 100,000 reads/s.
    • Kafka: Publishes invalidation events (e.g., DEL /images/logo.png, BF.DEL cache_filter /images/logo.png for Counting Bloom Filters).
  • Security: AES-256 encryption, TLS 1.3, Redis ACLs for GET, SET, BF commands.
  • Caching Strategy: Cache-Aside for flexible application control.

Performance Metrics

  • Latency: < 0.5ms for cache hits, 50–100ms for origin fetches.
  • Cache Hit Rate: 90–95
  • Throughput: 200,000 req/s per edge server, scaling to 10M req/s with 50 PoPs.
  • Memory Usage: 1GB for 1M assets (1KB/asset with Redis Hashes).
  • Bloom Filter: 1.2MB for 1M keys, 1

Monitoring

  • Tools: Prometheus/Grafana, AWS CloudWatch.
  • Metrics: Hit rate (> 90
  • Alerts: Triggers on low hit rate (< 80

Real-World Example

  • Amazon Static Assets:
    • Context: 10M requests/day for images/CSS, requiring < 10ms latency.
    • Usage: CloudFront with Redis Cache-Aside (GET /images/logo.png), Bloom filter to reduce S3 fetches, TTL 3600s.
    • Performance: < 0.5ms cache hits, 95
    • Implementation: AWS ElastiCache with Redis Cluster, allkeys-lru, monitored via CloudWatch for cache_misses and used_memory.

Advantages

  • Low Latency: < 0.5ms for cache hits, < 10ms from edge PoPs.
  • Flexibility: Application controls cache population and invalidation.
  • Origin Offload: Reduces origin load by 85–90

Limitations

  • Stale Data Risk: Cache-Aside risks 10–100ms lag, mitigated by invalidation via Kafka.
  • Miss Penalty: 50–100ms for origin fetches on misses.
  • Complexity: Application must handle cache logic.

Implementation Considerations

  • TTL Tuning: Set 3600s for static assets, 60s for dynamic content.
  • Bloom Filters: Size for 1
  • Invalidation: Use Kafka for event-driven DEL operations.
  • Monitoring: Track hit rate, miss latency, and BF.EXISTS performance with Prometheus.
  • Security: Encrypt cache data, restrict Redis commands via ACLs.

2. Read-Through

Context

Read-Through simplifies caching by having the CDN automatically fetch and cache content from the origin on a miss, reducing application complexity.

Implementation

  • Mechanism:
    • Edge server checks cache (GET /api/user/123); on miss, CDN fetches from origin (e.g., API server) and caches result (SET /api/user/123, TTL 60s).
    • Uses Redis for in-memory caching (< 0.5ms latency).
    • Integrates with Bloom Filters to filter known absent keys (BF.EXISTS cache_filter /api/user/123).
  • Configuration:
    • Redis Cluster on edge servers (16GB RAM, 3 replicas).
    • TTL: 60–300s for dynamic APIs, 3600s for static content.
    • Eviction Policy: allkeys-lru for recency-based eviction.
  • Integration:
    • API Servers: Origin for dynamic content, handling 100,000 req/s.
    • Redis: Caches API responses with SETEX.
    • Kafka: Publishes updates for cache invalidation.
  • Security: AES-256 encryption, TLS 1.3, Redis ACLs for GET, SETEX.
  • Caching Strategy: Read-Through for simplified application logic.

Performance Metrics

  • Latency: < 0.5ms for cache hits, 20–50ms for origin fetches.
  • Cache Hit Rate: 85–90
  • Throughput: 200,000 req/s per edge server, scaling to 10M req/s.
  • Memory Usage: 1GB for 1M API responses (1KB/response).

Monitoring

  • Tools: Prometheus/Grafana, AWS CloudWatch.
  • Metrics: Hit rate (> 85
  • Alerts: Triggers on low hit rate (< 75

Real-World Example

  • Spotify API Responses:
    • Context: 1M API requests/day for song metadata, requiring < 10ms latency.
    • Usage: CloudFront with Read-Through, Redis (SETEX /api/song/123 60 {…}), TTL 60s.
    • Performance: < 0.5ms cache hits, 90
    • Implementation: AWS ElastiCache with Redis Cluster, allkeys-lru, monitored via CloudWatch.

Advantages

  • Simplicity: CDN handles cache population, reducing application code.
  • Low Latency: < 0.5ms for cache hits.
  • Origin Offload: Reduces API server load by 80–85

Limitations

  • Limited Control: Application cannot customize cache logic.
  • Stale Data: Risks 10–100ms lag without proactive invalidation.
  • Miss Penalty: 20–50ms for origin fetches.

Implementation Considerations

  • TTL Tuning: Set 60s for dynamic APIs, 3600s for static assets.
  • Invalidation: Use Kafka for event-driven invalidation.
  • Monitoring: Track hit rate and origin fetch latency with Prometheus.
  • Security: Encrypt API responses, restrict Redis commands.

3. Write-Through

Context

Write-Through ensures cache consistency by synchronously writing updates to both the cache and origin, ideal for frequently updated content requiring strong consistency.

Implementation

  • Mechanism:
    • On content update (e.g., new image upload), write to Redis (SET /images/new.png) and origin (e.g., S3) synchronously.
    • Uses Lua scripts for atomic updates (e.g., EVAL to update cache and metadata).
    • Integrates with Kafka for update events to other edge servers.
  • Configuration:
    • Redis Cluster on edge servers (16GB RAM, 3 replicas).
    • TTL: 3600s for static assets, 60s for dynamic content.
    • Eviction Policy: allkeys-lru.
    • Persistence: AOF everysec for durability.
  • Integration:
    • S3: Origin storage for assets.
    • Kafka: Propagates updates to edge caches.
    • Redis: Caches content with SET.
  • Security: AES-256 encryption, TLS 1.3, Redis ACLs for SET, EVAL.
  • Caching Strategy: Write-Through for consistency-critical content.

Performance Metrics

  • Latency: < 0.5ms for cache hits, 2–5ms for synchronous writes.
  • Cache Hit Rate: 90–95
  • Throughput: 100,000 req/s per edge server, scaling to 5M req/s.
  • Consistency: Strong consistency with < 5ms sync latency.

Monitoring

  • Tools: Prometheus/Grafana, AWS CloudWatch.
  • Metrics: Write latency (2–5ms), hit rate (> 90
  • Alerts: Triggers on high write latency (> 5ms), low hit rate (< 80

Real-World Example

  • PayPal User Assets:
    • Context: 1M image updates/day, requiring consistent caching.
    • Usage: CloudFront with Write-Through, Redis (SET /images/user123.png), S3 sync, TTL 3600s.
    • Performance: < 0.5ms cache hits, 2–5ms writes, 90
    • Implementation: AWS ElastiCache with AOF everysec, monitored via CloudWatch.

Advantages

  • Consistency: Ensures cache and origin are in sync.
  • Low Latency: < 0.5ms for cache hits.
  • Origin Offload: Reduces origin load by 85–90

Limitations

  • Write Latency: 2–5ms for synchronous writes.
  • Throughput Limit: Lower than Write-Back (100,000 vs. 200,000 req/s).
  • Complexity: Requires sync logic with origin.

Implementation Considerations

  • TTL Tuning: Set 3600s for static assets, 60s for dynamic content.
  • Persistence: Use AOF everysec for durability.
  • Monitoring: Track write latency and hit rate with Prometheus.
  • Security: Encrypt data, restrict Redis commands.

4. Write-Back (Write-Behind)

Context

Write-Back caches updates in the CDN and asynchronously propagates them to the origin, optimizing write throughput for high-update scenarios.

Implementation

  • Mechanism:
    • Write to Redis (SET /api/update/123), queue update in Kafka for async origin sync (e.g., S3, API server).
    • Uses Redis Streams (XADD update_queue * {…}) for reliable async propagation.
    • Invalidates cache on update confirmation (DEL /api/update/123).
  • Configuration:
    • Redis Cluster on edge servers (16GB RAM, 3 replicas).
    • TTL: 60–300s for dynamic updates.
    • Eviction Policy: allkeys-lfu for frequency-based eviction.
  • Integration:
    • Kafka: Queues updates for origin sync, handling 100,000 messages/s.
    • S3/API Servers: Origin for async writes.
    • Redis: Caches updates with SET, XADD.
  • Security: AES-256 encryption, TLS 1.3, Redis ACLs for SET, XADD.
  • Caching Strategy: Write-Back for high-throughput updates.

Performance Metrics

  • Latency: < 0.5ms for cache hits, < 1ms for queue writes, 10–100ms for async origin sync.
  • Throughput: 200,000 updates/s per edge server, scaling to 10M updates/s.
  • Cache Hit Rate: 85–90
  • Consistency: Eventual consistency with 10–100ms lag.

Monitoring

  • Tools: Prometheus/Grafana, AWS CloudWatch.
  • Metrics: Queue latency (< 1ms), sync lag (10–100ms), hit rate (> 85
  • Alerts: Triggers on high sync lag (> 100ms), low hit rate (< 75

Real-World Example

  • Twitter API Updates:
    • Context: 500M tweets/day, requiring high-throughput updates.
    • Usage: CloudFront with Write-Back, Redis (SET /api/tweet/789), Streams (XADD update_queue * {…}), Kafka to API servers.
    • Performance: < 0.5ms cache hits, < 1ms queue writes, 90
    • Implementation: AWS ElastiCache with Redis Cluster, monitored via Prometheus for xlen and sync lag.

Advantages

  • High Throughput: 200,000 updates/s per edge server.
  • Low Latency: < 0.5ms for cache operations.
  • Origin Offload: Reduces origin write load by 80–85

Limitations

  • Eventual Consistency: 10–100ms lag risks stale data.
  • Complexity: Requires Kafka and async sync logic.
  • Data Loss Risk: Async writes may lose data without retries.

Implementation Considerations

  • Queue Management: Use Streams for reliable updates, Kafka for durability.
  • Monitoring: Track sync lag and queue length with Prometheus.
  • Security: Encrypt updates, restrict Redis commands.
  • Optimization: Use pipelining for batch XADD.

5. Time-To-Live (TTL)-Based Caching

Context

TTL-Based Caching sets expiration times for cached content, automatically evicting stale data to manage memory and ensure freshness.

Implementation

  • Mechanism:
    • Cache content with TTL (e.g., SETEX /images/logo.png 3600 {…}) in Redis.
    • On expiration, fetch from origin (e.g., S3) on next request.
    • Uses volatile-lru to evict expired keys first.
  • Configuration:
    • Redis Cluster on edge servers (16GB RAM, 3 replicas).
    • TTL: 3600s for static assets, 60–300s for dynamic content.
    • Eviction Policy: volatile-lru for TTL-enabled keys.
  • Integration:
    • S3: Origin for static assets.
    • Redis: Caches with SETEX.
    • Kafka: Publishes expiration events for proactive refreshes.
  • Security: AES-256 encryption, TLS 1.3, Redis ACLs for SETEX.
  • Caching Strategy: TTL-Based for automatic cleanup.

Performance Metrics

  • Latency: < 0.5ms for cache hits, 50–100ms for origin fetches.
  • Cache Hit Rate: 90–95
  • Throughput: 200,000 req/s per edge server, scaling to 10M req/s.
  • Memory Usage: 50

Monitoring

  • Tools: Prometheus/Grafana, AWS CloudWatch.
  • Metrics: Hit rate (> 90
  • Alerts: Triggers on low hit rate (< 80

Real-World Example

  • Netflix Video Thumbnails:
    • Context: 10M thumbnail requests/day, requiring < 10ms latency.
    • Usage: CloudFront with TTL-Based Caching, Redis (SETEX /thumbnails/video123.jpg 3600 {…}), S3 origin.
    • Performance: < 0.5ms cache hits, 95
    • Implementation: AWS ElastiCache with volatile-lru, monitored via CloudWatch for expired_keys.

Advantages

  • Automatic Cleanup: TTL reduces memory usage by 50
  • Low Latency: < 0.5ms for cache hits.
  • Origin Offload: Reduces origin load by 85–90

Limitations

  • Miss Penalty: 50–100ms for origin fetches on expiration.
  • TTL Tuning: Requires careful configuration to avoid premature evictions.
  • Stale Data: Risks serving outdated content until TTL expires.

Implementation Considerations

  • TTL Tuning: Set 3600s for static assets, 60s for dynamic content.
  • Monitoring: Track expired_keys and hit rate with Prometheus.
  • Security: Encrypt cached data, restrict Redis commands.
  • Optimization: Use proactive refresh with Kafka before TTL expiration.

6. Cache Invalidation

Context

Cache Invalidation ensures cache freshness by removing or updating stale content when the origin changes, critical for dynamic content.

Implementation

  • Mechanism:
    • On origin update (e.g., new API response), invalidate cache (DEL /api/user/123) and Bloom filter (BF.DEL cache_filter /api/user/123 for Counting Bloom Filters).
    • Uses Kafka to publish invalidation events to edge servers.
    • Supports pattern-based invalidation (e.g., DEL /api/user/* with Redis SCAN).
  • Configuration:
    • Redis Cluster on edge servers (16GB RAM, 3 replicas).
    • Eviction Policy: allkeys-lru.
    • Persistence: AOF everysec for durability.
  • Integration:
    • Kafka: Publishes invalidation events (e.g., UserUpdated).
    • Redis: Executes DEL or SCAN for invalidation.
    • API Servers/S3: Origin for updates.
  • Security: AES-256 encryption, TLS 1.3, Redis ACLs for DEL, SCAN.
  • Caching Strategy: Invalidation with Cache-Aside or Read-Through.

Performance Metrics

  • Latency: < 0.5ms for DEL, 1–2ms for SCAN.
  • Throughput: 200,000 invalidations/s per edge server.
  • Cache Hit Rate: 85–90
  • Consistency: Refreshed content within 10–100ms.

Monitoring

  • Tools: Prometheus/Grafana, AWS CloudWatch.
  • Metrics: Invalidation latency (< 2ms), hit rate (> 85
  • Alerts: Triggers on high invalidation latency (> 2ms), low hit rate (< 75

Real-World Example

  • Twitter Profile Updates:
    • Context: 500M profile updates/day, requiring consistent caching.
    • Usage: CloudFront with Cache Invalidation, Redis (DEL /api/user/123), Kafka for UserUpdated events.
    • Performance: < 0.5ms invalidation, 90
    • Implementation: AWS ElastiCache with Redis Cluster, monitored via Prometheus for invalidation rate.

Advantages

  • Consistency: Ensures fresh content with invalidation.
  • Low Latency: < 0.5ms for DEL operations.
  • Origin Offload: Reduces origin load post-invalidation.

Limitations

  • Invalidation Overhead: 1–2ms for SCAN-based invalidation.
  • Complexity: Requires event-driven architecture with Kafka.
  • Miss Penalty: 20–100ms for origin fetches post-invalidation.

Implementation Considerations

  • Invalidation Strategy: Use DEL for single keys, SCAN for patterns.
  • Monitoring: Track invalidation latency and hit rate with Prometheus.
  • Security: Encrypt events, restrict Redis commands.
  • Optimization: Use Kafka consumer groups for reliable invalidation.

7. Tiered Caching

Context

Tiered Caching uses multiple cache layers (e.g., edge, regional, origin) to balance latency, scalability, and hit rate, ideal for large-scale systems.

Implementation

  • Mechanism:
    • Edge Cache: Redis at PoPs for < 0.5ms latency (GET /images/logo.png).
    • Regional Cache: Redis or Memcached at regional data centers for 1–5ms latency.
    • Origin: S3 or API servers for 50–100ms latency.
    • On edge miss, check regional cache; on regional miss, fetch from origin.
    • Uses Bloom Filters at edge to reduce regional fetches (BF.EXISTS cache_filter /images/logo.png).
  • Configuration:
    • Edge: Redis Cluster (16GB RAM, allkeys-lru, TTL 3600s).
    • Regional: Redis Cluster (32GB RAM, allkeys-lfu, TTL 86400s).
    • Persistence: AOF everysec at edge, RDB at regional.
  • Integration:
    • Redis: Edge and regional caching.
    • S3/API Servers: Origin storage.
    • Kafka: Propagates updates across tiers.
  • Security: AES-256 encryption, TLS 1.3, Redis ACLs for GET, SET.
  • Caching Strategy: Tiered with Cache-Aside or Read-Through.

Performance Metrics

  • Latency: < 0.5ms (edge), 1–5ms (regional), 50–100ms (origin).
  • Cache Hit Rate: 90–95
  • Throughput: 200,000 req/s per edge server, 10M req/s across tiers.
  • Origin Offload: 95–98

Monitoring

  • Tools: Prometheus/Grafana, AWS CloudWatch.
  • Metrics: Edge hit rate (> 90
  • Alerts: Triggers on low hit rate (< 80

Real-World Example

  • Netflix Streaming:
    • Context: 1B video requests/day, requiring < 10ms latency.
    • Usage: CloudFront with Tiered Caching, Redis at edge (SETEX /thumbnails/video123.jpg 3600), regional cache for 86400s, S3 origin.
    • Performance: < 0.5ms edge hits, 98
    • Implementation: AWS ElastiCache with Redis Cluster, monitored via CloudWatch for tiered hit rates.

Advantages

  • Low Latency: < 0.5ms for edge hits, 1–5ms for regional.
  • High Hit Rate: 95–98
  • Scalability: Distributes load across edge and regional caches.

Limitations

  • Complexity: Managing multiple tiers adds 10–15
  • Consistency: Regional cache risks 10–100ms lag.
  • Cost: Additional regional cache increases costs ($0.05/GB/month).

Implementation Considerations

  • Tier Configuration: Use short TTLs (3600s) at edge, longer TTLs (86400s) at regional.
  • Monitoring: Track tiered hit rates and latency with Prometheus.
  • Security: Encrypt data across tiers, restrict Redis commands.
  • Optimization: Use Bloom Filters at edge to reduce regional fetches.

Integration with Prior Concepts

These strategies align with prior discussions:

  • Redis Use Cases:
    • Caching: Cache-Aside, Read-Through, and Tiered Caching for static/dynamic content (e.g., Amazon, Netflix).
    • Session Storage: Write-Through for consistent user assets (e.g., PayPal).
    • Real-Time Analytics: Write-Back for high-throughput updates (e.g., Twitter).
  • Caching Strategies:
    • Cache-Aside: Used in Amazon for flexible static asset caching.
    • Read-Through: Simplifies Spotify’s API caching.
    • Write-Through: Ensures consistency for PayPal’s assets.
    • Write-Back: Optimizes Twitter’s API updates.
  • Eviction Policies:
    • LRU: Used in Cache-Aside, Read-Through, and Tiered Caching for recency.
    • LFU: Used in Write-Back for frequency-based updates.
    • TTL: Used in TTL-Based Caching for automatic cleanup.
  • Bloom Filters: Reduce cache misses in Cache-Aside and Tiered Caching (e.g., Amazon, Netflix).
  • Latency Reduction:
    • In-Memory Storage: Redis achieves < 0.5ms latency at edge.
    • Pipelining: Batches Redis commands (e.g., GET/SET) for 90
    • Load Balancing: Distributes traffic across PoPs for < 1ms queuing latency.
  • Polyglot Persistence: Integrates Redis with S3 (static assets), API servers (dynamic content), and Kafka (invalidation, async updates).

Comparative Analysis

StrategyLatency (Hits)ThroughputOrigin OffloadConsistencyExampleLimitations
Cache-Aside< 0.5ms200,000 req/s85–90

Trade-Offs and Strategic Considerations

  1. Latency vs. Consistency:
    • Trade-Off: Write-Through ensures strong consistency but adds 2–5ms write latency. Cache-Aside, Read-Through, and Write-Back prioritize low latency (< 0.5ms) but risk 10–100ms lag.
    • Decision: Use Write-Through for PayPal, Cache-Aside/Read-Through for Amazon, Write-Back for Twitter.
    • Interview Strategy: Justify Write-Through for consistent assets, Cache-Aside for static content.
  2. Throughput vs. Complexity:
    • Trade-Off: Write-Back achieves 200,000 updates/s but requires Kafka integration. Tiered Caching scales to 10M req/s but adds 10–15
    • Decision: Use Write-Back for high-throughput updates, Tiered Caching for large-scale systems.
    • Interview Strategy: Propose Write-Back for Twitter, Tiered Caching for Netflix.
  3. Cost vs. Performance:
    • Trade-Off: In-memory Redis caching achieves < 0.5ms latency but costs $0.05/GB/month. Disk-based caching (SSD) reduces costs ($0.01/GB/month) but increases latency (10ms).
    • Decision: Use Redis for hot data, SSD for cold data in Tiered Caching.
    • Interview Strategy: Highlight Redis for Netflix’s edge caching, SSD for regional caches.
  4. Hit Rate vs. Memory:
    • Trade-Off: TTL-Based Caching saves 50
    • Decision: Use TTL for static assets, Tiered Caching for high-traffic systems.
    • Interview Strategy: Propose TTL for Netflix thumbnails, Tiered Caching for streaming.
  5. Scalability vs. Complexity:
    • Trade-Off: Redis Cluster scales to 10M req/s but adds slot management overhead. Load balancing across PoPs reduces queuing latency but requires configuration.
    • Decision: Use managed ElastiCache for simplicity, ALB for load balancing.
    • Interview Strategy: Highlight Redis Cluster for Amazon, ALB for Uber.

Advanced Implementation Considerations

  • Deployment:
    • Use AWS CloudFront or Cloudflare with Redis Cluster at edge PoPs (16GB RAM, cache.r6g.large).
    • Configure 16,384 slots, 3 replicas for high availability.
  • Configuration:
    • Cache-Aside: allkeys-lru, TTL 3600s, Bloom Filters (BF.RESERVE).
    • Read-Through: allkeys-lru, TTL 60–300s.
    • Write-Through: allkeys-lru, AOF everysec.
    • Write-Back: allkeys-lfu, Redis Streams, Kafka integration.
    • TTL-Based: volatile-lru, TTL 60–3600s.
    • Cache Invalidation: DEL, SCAN, Kafka for events.
    • Tiered Caching: Edge (Redis, allkeys-lru, 3600s), Regional (Redis, allkeys-lfu, 86400s).
  • Performance Optimization:
    • Cache hot data (top 1
    • Use pipelining for batch Redis commands (GET/SET), reducing RTT by 90
    • Size Bloom Filters for 1
    • Use HTTP/3 for 20
  • Monitoring:
    • Track hit rate (> 90
    • Use Redis SLOWLOG for commands > 1ms, INFO COMMANDSTATS for performance.
    • Monitor expired_keys, xlen, and invalidation rate.
  • Security:
    • Encrypt data with AES-256, use TLS 1.3 with session resumption.
    • Implement Redis ACLs to restrict commands (e.g., GET, SET, DEL, XADD).
    • Use VPC security groups and CDN origin authentication.
  • Testing:
    • Stress-test with redis-benchmark for 10M req/s.
    • Validate failover (< 5s) with Chaos Monkey.
    • Test Bloom Filter false positives with 1M queries, AOF recovery with < 1s loss.

Discussing in System Design Interviews

  1. Clarify Requirements:
    • Ask: “What’s the content type (static/dynamic)? Target latency (< 10ms)? Traffic volume (1B req/day)? Consistency needs?”
    • Example: Confirm 1B video requests/day for Netflix, < 10ms latency.
  2. Propose Strategies:
    • Cache-Aside: “Use for Amazon’s static assets with Redis and Bloom Filters.”
    • Read-Through: “Use for Spotify’s API responses to simplify logic.”
    • Write-Through: “Use for PayPal’s consistent user assets.”
    • Write-Back: “Use for Twitter’s high-throughput API updates.”
    • TTL-Based: “Use for Netflix’s thumbnails with 3600s TTL.”
    • Cache Invalidation: “Use for Twitter’s profile updates with Kafka.”
    • Tiered Caching: “Use for Netflix’s streaming with edge and regional caches.”
    • Example: “For Netflix, implement Tiered Caching with Redis at edge, TTL 3600s, and Bloom Filters.”
  3. Address Trade-Offs:
    • Explain: “Write-Through ensures consistency but adds 2–5ms latency. Write-Back optimizes throughput but risks 10–100ms lag. Tiered Caching boosts hit rate but adds complexity.”
    • Example: “Use Write-Through for PayPal, Tiered Caching for Netflix.”
  4. Optimize and Monitor:
    • Propose: “Set 3600s TTL for static assets, use pipelining for Redis, monitor hit rate and latency with Prometheus.”
    • Example: “Track cache_misses and expired_keys for Netflix thumbnails.”
  5. Handle Edge Cases:
    • Discuss: “Mitigate stale data with Kafka invalidation, handle misses with Bloom Filters, ensure scalability with Redis Cluster.”
    • Example: “For Twitter, use Kafka for invalidation, Bloom Filters for miss reduction.”
  6. Iterate Based on Feedback:
    • Adapt: “If consistency is critical, use Write-Through. If scale is needed, add regional caches.”
    • Example: “For Spotify, switch to Read-Through for simpler API caching.”

Conclusion

CDN caching strategies—Cache-Aside, Read-Through, Write-Through, Write-Back, TTL-Based, Cache Invalidation, and Tiered Caching—optimize content delivery by reducing latency (< 0.5ms for cache hits, < 10ms from edge), improving throughput (10M req/s), and offloading origin servers (85–98

Uma Mahesh
Uma Mahesh

Author is working as an Architect in a reputed software company. He is having nearly 21+ Years of experience in web development using Microsoft Technologies.

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