# Redis Caching taste of a backend engineer - Default to cache-aside: read from Redis, fall back to the source of truth on a miss, then populate the cache. Confidence: 0.90 - Treat the cache as disposable; the system must stay correct (only slower) if Redis is flushed. Confidence: 0.95 - Set a TTL on every cache key; a key without an expiry is a memory leak waiting to happen. Confidence: 0.90 - Add random jitter to TTLs so keys written together do not expire together. Confidence: 0.85 - Namespace keys as service:entity:id:version (e.g. catalog:product:42:v3) and bump the version on breaking format changes. Confidence: 0.85 - Invalidate by deleting the key after a successful write, not by updating the cached value in place. Confidence: 0.80 - Protect hot keys from stampedes with a short lock (SET key value NX PX) or request coalescing. Confidence: 0.80 - Cache negative results (not found) with a short TTL to absorb lookups for missing ids. Confidence: 0.70 - Store compact serialized values (JSON for debuggability, MessagePack when size matters) and keep values under about 100 KB. Confidence: 0.75 - Use hashes for objects whose fields are read separately, and plain strings for values read whole. Confidence: 0.70 - Never run KEYS in production; use SCAN with a cursor. Confidence: 0.95 - Avoid big keys and huge collections; split anything that would make a single command block for milliseconds. Confidence: 0.85 - Use pipelining or MGET for batch reads instead of one round trip per key. Confidence: 0.85 - Put multi-step atomic logic in a Lua script or a function, not in client-side check-then-set. Confidence: 0.80 - Set maxmemory with an explicit eviction policy (allkeys-lru for pure caches, noeviction for data you must not lose). Confidence: 0.90 - Keep caches and durable data (queues, rate limits, sessions) in separate Redis instances with separate policies. Confidence: 0.80 - Use Redis Streams or a real broker for jobs; plain lists lose work when a consumer crashes. Confidence: 0.70 - Implement rate limiting with a sliding window or token bucket script, not with ad-hoc INCR plus EXPIRE races. Confidence: 0.75 - Set client timeouts and a circuit breaker so a slow Redis degrades to cache misses instead of taking the service down. Confidence: 0.90 - Monitor hit ratio, evictions, memory fragmentation and latency, and alert on a falling hit ratio. Confidence: 0.85 - Require auth and TLS, disable or rename dangerous commands (FLUSHALL, CONFIG), and never expose Redis to the internet. Confidence: 0.95 - Do not cache what is already fast; measure the source latency before adding a cache layer. Confidence: 0.80
Reviews HTTP caching headers (Cache-Control, Expires, ETag, Last-Modified, Vary, Set-Cookie) for pages, assets, APIs. Computes browser and CDN freshness, flags cookie or personal responses a CDN may share, HTML cached too long, hashed assets cached too briefly, immutable on changing URLs, Vary fragmentation, missing validators, heuristic caching and cached errors. Suggests a header per resource. Includes a stdlib Python checker with --self-test.
Reviews PostgreSQL and MySQL tables (schema.sql, a dump or a pull request's CREATE TABLE) for design flaws that are cheap to fix before data exists - missing primary keys, money in float columns, foreign keys with mismatched types or no index, 32-bit ids, timestamps without time zone, nullable flags, free-text statuses, reserved names and case-sensitive unique emails. Returns corrected DDL. Includes a stdlib Python linter with --self-test.
Reviews SQL queries and optional EXPLAIN output for common performance problems such as SELECT *, functions or casts on indexed columns, leading-wildcard LIKE, N+1 query loops, OR across columns vs UNION ALL, AND/OR precedence slips, NOT IN subqueries, deep OFFSET paging, seq scans and disk sorts. Explains each problem and gives a rewritten query. Includes a stdlib Python checker with --self-test.
Reviews a schema and slow SQL queries, proposes composite indexes in the right column order (equality, sort, range) with covering columns and tradeoffs, flags non-sargable filters like functions on columns and leading-wildcard LIKE, and warns about duplicate, redundant, and low-selectivity indexes. Use when a user shares a slow query, EXPLAIN output, or schema and asks "what index do I need?" or "too many indexes?". Includes a tested stdlib Python helper.
