mirror of
https://github.com/kjanat/livedash-node.git
synced 2026-02-13 12:55:42 +01:00
Kaj Kowalski
5bfd762e55
- Update tsconfig to ES2024 target and bundler moduleResolution - Add dynamic imports for chart.js and recharts (bundle optimization) - Consolidate 17 useState into useReducer in sessions page - Fix 18 .js extension imports across lib files - Add type declarations for @rapideditor/country-coder - Fix platform user types (PlatformUserRole enum) - Fix Calendar component prop types - Centralize next-auth type augmentation - Add force-dynamic to all API routes (prevent build-time prerender) - Fix Prisma JSON null handling with Prisma.DbNull - Fix various type mismatches (SessionMessage, ImportRecord, etc.) - Export ButtonProps from button component - Update next-themes import path - Replace JSX.Element with React.ReactElement - Remove obsolete debug scripts and pnpm lockfile - Downgrade eslint to v8 for next compatibility
1.3 KiB
1.3 KiB
title, impact, impactDescription, tags
| title | impact | impactDescription | tags |
|---|---|---|---|
| Cross-Request LRU Caching | HIGH | caches across requests | server, cache, lru, cross-request |
Cross-Request LRU Caching
React.cache() only works within one request. For data shared across sequential requests (user clicks button A then button B), use an LRU cache.
Implementation:
import { LRUCache } from 'lru-cache'
const cache = new LRUCache<string, any>({
max: 1000,
ttl: 5 * 60 * 1000 // 5 minutes
})
export async function getUser(id: string) {
const cached = cache.get(id)
if (cached) return cached
const user = await db.user.findUnique({ where: { id } })
cache.set(id, user)
return user
}
// Request 1: DB query, result cached
// Request 2: cache hit, no DB query
Use when sequential user actions hit multiple endpoints needing the same data within seconds.
With Vercel's Fluid Compute: LRU caching is especially effective because multiple concurrent requests can share the same function instance and cache. This means the cache persists across requests without needing external storage like Redis.
In traditional serverless: Each invocation runs in isolation, so consider Redis for cross-process caching.
Reference: https://github.com/isaacs/node-lru-cache