Caching Strategies implementation checklist

Set the maxmemory-policy to 'allkeys-lru' or 'volatile-lru' in Redis to ensure the system gracefully handles memory saturation without crashing.

Verify that the application client uses a persistent connection pool to eliminate the latency overhead of TCP handshakes on every request.

Limit cache memory usage to 75% of available system RAM to provide overhead for fragmentation and replication buffers.

Replace JSON serialization with binary formats like MessagePack or Protobuf for large objects to reduce memory footprint and CPU overhead.

Turn off RDB and AOF persistence if the cache is purely transient to reduce disk I/O and improve write throughput.

Trim whitespace, convert to lowercase, and sort parameters in prompt strings before hashing to increase cache hit rates for identical queries.

Use vector embeddings and similarity thresholds to identify and serve cached responses for semantically similar LLM prompts.

Remove unique identifiers, timestamps, and usage tokens from LLM provider responses before storing them in the cache.

Assign 1-hour TTLs to creative tasks and 24-hour+ TTLs to factual extraction tasks to balance freshness and cost.

Store the token count of cached responses to accurately report cost savings and monitor API quota utilization.

Use a mutex or 'singleflight' pattern to ensure only one upstream request is made when a high-traffic key expires.

Group related keys using sets or cache tags to allow for bulk purging of specific data categories (e.g., all products in a category).

Set up the client to serve the expired cache entry while asynchronously fetching fresh data from the origin.

Use Lua scripts or multi-key transactions to ensure that related cache keys are updated or deleted simultaneously.

Enforce a global default TTL on every 'SET' operation to prevent 'zombie' data from consuming memory indefinitely.

Ensure 's-maxage' is configured for shared CDN caching and 'max-age' for private browser caching.

Limit the 'Vary' header to 'Accept-Encoding' to prevent the CDN from creating unique cache entries for every User-Agent string.

Enable a regional cache layer between the edge nodes and the origin to collapse redundant requests across different geographic regions.

Explicitly list session and authentication cookies that should trigger a cache bypass to prevent serving private data to other users.

Secure the edge cache purge endpoints with API keys or IP allowlists to prevent unauthorized cache clearing.

Set up real-time dashboards and alerts for when the CHR falls below a defined baseline (e.g., < 80% for static assets).

Monitor the 99th percentile latency of cache lookups to detect network congestion or expensive key serialization.

Run periodic scans for keys exceeding 512KB, as these can block the event loop in single-threaded caches like Redis.

Verify that the application gracefully degrades and fetches directly from the database if the cache cluster is unreachable.

Monitor the 'evicted_keys' metric; an increasing rate indicates the cache size is too small for the working set.

Prefix all cache keys with a unique tenant or user ID to prevent cross-account data leakage in multi-tenant systems.

Audit cache values to ensure no unencrypted Personally Identifiable Information (PII) is stored in the caching layer.

Configure the application to connect to the cache using TLS 1.2+ to protect data against packet sniffing in the internal network.

Implement a policy for rotating Redis or CDN API keys every 90 days without causing downtime.

Rename or disable dangerous commands like 'FLUSHALL' or 'CONFIG' in production cache environments.