Layercache

    Migration Guide

    This guide helps you migrate from popular Node.js caching libraries to Layercache. Each section includes before/after code examples, API mappings, and key differences.

    Upgrading to 3.0

    Layercache 3.0 is a major release because it changes operational defaults for Redis-backed deployments and HTTP middleware cache keys.

    RedisTagIndex known-key shards

    RedisTagIndex now defaults to 16 known-key shards. Existing deployments that used the previous single-set layout at <prefix>:keys are still read for compatibility, but you should migrate them before relying on the sharded layout in production:

    npx layercache migrate-tag-index \
  --redis rediss://redis.example.com:6379 \
  --tag-index-prefix myapp:tag-index \
  --known-key-shards 16

    Use knownKeysShards: 1 only when you intentionally need the legacy layout during a staged rollout.

    Production Redis URLs in the CLI

    CLI commands now reject plaintext redis:// URLs when NODE_ENV=production, unless --allow-plaintext is passed:

    NODE_ENV=production npx layercache stats --redis rediss://redis.example.com:6379
NODE_ENV=production npx layercache stats --redis redis://localhost:6379 --allow-plaintext

    Implicit HTTP cache keys

    Express and Hono middleware now remove common sensitive query parameters from implicit URL cache keys. This avoids storing secrets in cache keys, but entries written with older URL keys that included parameters such as api_key, private_key, or credentials will miss until refreshed.

    Generation persistence

    Persist generation rotations when several instances share the same cache:

    import { CacheStack, RedisGenerationStore } from 'layercache'

const generations = new RedisGenerationStore({ client: redis })
const generation = await generations.getOrInitialize(1)
const cache = new CacheStack(layers, { generation })

const nextGeneration = await generations.bump()
cache.bumpGeneration(nextGeneration)

    From node-cache-manager

    Basic Setup

    Before (node-cache-manager):

    import { caching, multiCaching } from 'cache-manager'
import { redisStore } from 'cache-manager-redis-yet'

const memoryCache = await caching('memory', { max: 100, ttl: 60 * 1000 })
const redisCache = await caching(redisStore, {
  url: 'redis://localhost:6379',
  ttl: 300 * 1000
})
const cache = multiCaching([memoryCache, redisCache])

    After (layercache):

    import { CacheStack, MemoryLayer, RedisLayer } from 'layercache'
import Redis from 'ioredis'

const cache = new CacheStack([
  new MemoryLayer({ ttl: 60_000, maxSize: 100 }),
  new RedisLayer({ client: new Redis(), ttl: 300_000 })
])

    Read-Through Fetch

    Before:

    const user = await cache.wrap('user:123', () => db.findUser(123))

    After:

    const user = await cache.get('user:123', () => db.findUser(123))

    Set with TTL

    Before:

    await cache.set('user:123', user, 60000)  // TTL in milliseconds

    After:

    await cache.set('user:123', user, { ttl: 60_000 })  // TTL in milliseconds

    Delete

    Before:

    await cache.del('user:123')

    After:

    await cache.delete('user:123')

    Clear All

    Before:

    await cache.reset()

    After:

    await cache.clear()

    API Mapping

    node-cache-managerlayercacheNotes
    cache.wrap(key, fn)cache.get(key, fn)Read-through fetch
    cache.set(key, val, ttl)cache.set(key, val, { ttl })TTL in milliseconds
    cache.get(key)cache.get(key)Same API
    cache.del(key)cache.delete(key)Renamed
    cache.reset()cache.clear()Renamed
    Per-store TTLttl: { memory: 60_000, redis: 300_000 }Per-layer TTL map
    -cache.invalidateByTag(tag)New: tag invalidation
    -cache.wrap(prefix, fn)New: transparent function caching

    Key Differences

    TTL is in Milliseconds

    node-cache-manager uses milliseconds:

    await cache.set('key', value, 60000)  // 60 seconds

    Layercache uses milliseconds:

    await cache.set('key', value, { ttl: 60_000 })  // 60 seconds

    Auto Backfill

    node-cache-manager requires manual warming:

    // After a cache miss in L1, L1 stays cold
const value = await cache.wrap('key', fetcher)
// Next request might still hit L2 instead of L1

    Layercache auto-backfills L1:

    // After a cache miss in L1, L1 is automatically backfilled
const value = await cache.get('key', fetcher)
// Next request hits L1 immediately

    Stampede Prevention

    node-cache-manager requires plugins:

    // No built-in stampede prevention
// Need external solutions

    Layercache has built-in stampede prevention:

    // Enabled by default
// Multiple concurrent requests for same key share single fetcher

    Tag Invalidation

    node-cache-manager:

    // Manual key tracking required
const userKeys = [`user:${id}`, `user:${id}:posts`, `user:${id}:profile`]
await Promise.all(userKeys.map(key => cache.del(key)))

    Layercache:

    await cache.set('user:123', user, { tags: ['user:123'] })
await cache.set('user:123:posts', posts, { tags: ['user:123'] })
await cache.invalidateByTag('user:123')  // Deletes both

    From keyv

    Basic Setup

    Before (keyv):

    import Keyv from 'keyv'
import KeyvRedis from '@keyv/redis'

const keyv = new Keyv({
  store: new KeyvRedis('redis://localhost:6379')
})

await keyv.set('user:123', user, 60000)
const user = await keyv.get('user:123')

    After (layercache):

    import { CacheStack, MemoryLayer, RedisLayer } from 'layercache'

const cache = new CacheStack([
  new MemoryLayer({ ttl: 60_000 }),
  new RedisLayer({ client: new Redis(), ttl: 300_000 })
])

await cache.set('user:123', user, { ttl: 60_000 })
const user = await cache.get('user:123')

    Read-Through Fetch

    Before:

    // No built-in read-through
let user = await keyv.get('user:123')
if (!user) {
  user = await fetchUser(123)
  await keyv.set('user:123', user, 60000)
}

    After:

    // Built-in read-through fetch
const user = await cache.get('user:123', () => fetchUser(123))

    API Mapping

    keyvlayercacheNotes
    keyv.set(key, val, ttl)cache.set(key, val, { ttl })TTL in milliseconds
    keyv.get(key)cache.get(key)Same
    keyv.delete(key)cache.delete(key)Same
    keyv.clear()cache.clear()Same
    Namespace via constructorcache.namespace(prefix)Scoped views
    -cache.get(key, fetcher)New: read-through fetch
    -cache.wrap(prefix, fn)New: function caching

    Key Differences

    Multi-Layer is Native

    keyv requires plugins:

    // Multi-layer is not native
// Requires custom adapters

    Layercache has native multi-layer:

    const cache = new CacheStack([
  new MemoryLayer({ ttl: 60_000 }),
  new RedisLayer({ client: redis, ttl: 300_000 })
])
// Reads cascade through layers with auto backfill

    Read-Through Fetch

    keyv requires manual checks:

    let value = await keyv.get('key')
if (value === undefined) {
  value = await fetcher()
  await keyv.set('key', value)
}

    Layercache has built-in read-through:

    const value = await cache.get('key', fetcher)

    Namespaces

    keyv:

    const userCache = new Keyv({ namespace: 'users' })
const postCache = new Keyv({ namespace: 'posts' })

    Layercache:

    const userCache = cache.namespace('users')
const postCache = cache.namespace('posts')

// Full CacheStack API on namespaces
await userCache.set('123', data)  // Stored as "users:123"
await userCache.clear()           // Only deletes "users:*"

    From cacheable

    Basic Setup

    Before (cacheable):

    import { Cacheable } from 'cacheable'

const cache = new Cacheable({ ttl: '1h' })
await cache.set('key', value)

    After (layercache):

    import { CacheStack, MemoryLayer } from 'layercache'

const cache = new CacheStack([
  new MemoryLayer({ ttl: 3_600_000 })
])
await cache.set('key', value)

    API Mapping

    cacheablelayercacheNotes
    new Cacheable({ ttl: '1h' })new CacheStack([{ ttl: 3_600_000 }])TTL in milliseconds
    cache.set(key, val)cache.set(key, val)Same
    cache.get(key)cache.get(key)Same
    cache.delete(key)cache.delete(key)Same
    cache.clear()cache.clear()Same

    Key Differences

    TTL Format

    cacheable uses string durations:

    const cache = new Cacheable({ ttl: '1h' })

    Layercache uses numeric milliseconds:

    const cache = new CacheStack([new MemoryLayer({ ttl: 3_600_000 })])

    Multi-Layer Orchestration

    cacheable:

    // Limited multi-layer support
// Manual coordination required

    Layercache:

    const cache = new CacheStack([
  new MemoryLayer({ ttl: 60_000 }),
  new RedisLayer({ client: redis, ttl: 300_000 }),
  new DiskLayer({ ttl: 3_600_000 })
])
// Automatic cascading reads and writes

    Distributed Consistency

    cacheable:

    // No built-in distributed features
// Manual implementation required

    Layercache:

    import { RedisInvalidationBus, RedisTagIndex } from 'layercache'

const bus = new RedisInvalidationBus({ publisher: redis })
const tagIndex = new RedisTagIndex({ client: redis })

const cache = new CacheStack([/* ... */], {
  invalidationBus: bus,
  broadcastL1Invalidation: true,
  tagIndex
})

    Stampede Prevention

    cacheable:

    // No built-in stampede prevention

    Layercache:

    const cache = new CacheStack([/* ... */], {
  stampedePrevention: true  // Enabled by default
})

    Operational Migration Tips

    Replace Ad-Hoc Redis Key Scans

    Before:

    # Manual Redis scans
redis-cli --scan --pattern "user:*" | xargs redis-cli del

    After:

    # Use Layercache CLI
npx layercache keys --redis redis://localhost:6379 --pattern "user:*"
npx layercache invalidate --redis redis://localhost:6379 --pattern "user:*"

    Replace Manual Prefill Scripts

    Before:

    // Custom warm-up script
for (const key of criticalKeys) {
  const val = await fetch(key)
  await redis.set(key, JSON.stringify(val), 'EX', 300)
}

    After:

    // Built-in warm() method
await cache.warm(
  criticalKeys.map(key => ({
    key,
    fetcher: () => fetchByKey(key),
    priority: 10
  })),
  { concurrency: 4, continueOnError: true }
)

    Replace Custom Stats Endpoints

    Before:

    // Custom stats endpoint
app.get('/stats', (req, res) => {
  res.json({
    hits: myCounter.hits,
    misses: myCounter.misses
  })
})

    After:

    import { createCacheStatsHandler } from 'layercache'

app.get('/cache/stats', createCacheStatsHandler(cache))

    Replace Manual Tag Tracking

    Before:

    // Manual tag-to-key mapping
const tagMap = new Map()

async function setWithTags(key, value, tags) {
  await redis.set(key, JSON.stringify(value))
  for (const tag of tags) {
    const keys = tagMap.get(tag) || []
    keys.push(key)
    tagMap.set(tag, keys)
  }
}

async function invalidateByTag(tag) {
  const keys = tagMap.get(tag) || []
  await Promise.all(keys.map(key => redis.del(key)))
  tagMap.delete(tag)
}

    After:

    // Built-in tag support
await cache.set('user:123', user, { tags: ['user:123'] })
await cache.invalidateByTag('user:123')

    Replace Custom Invalidation Logic

    Before:

    // Manual prefix-based invalidation
async function invalidatePrefix(prefix) {
  const keys = await redis.keys(`${prefix}*`)
  await Promise.all(keys.map(key => redis.del(key)))
}

    After:

    // Built-in prefix invalidation
await cache.invalidateByPrefix('user:123:')

    Replace Custom Locking for Stampede Prevention

    Before:

    // Manual distributed locking
async function getWithLock(key, fetcher) {
  const lockKey = `lock:${key}`
  const lock = await redis.set(lockKey, '1', 'PX', 5000, 'NX')

  if (lock === 'OK') {
    try {
      const value = await fetcher()
      await redis.set(key, JSON.stringify(value))
      return value
    } finally {
      await redis.del(lockKey)
    }
  } else {
    // Wait and retry
    await sleep(100)
    return getWithLock(key, fetcher)
  }
}

    After:

    // Built-in distributed single-flight
import { RedisSingleFlightCoordinator } from 'layercache'

const coordinator = new RedisSingleFlightCoordinator({ client: redis })

const cache = new CacheStack([/* ... */], {
  singleFlightCoordinator: coordinator,
  singleFlightLeaseMs: 30000
})

const value = await cache.get(key, fetcher)

    Migration Checklist

    Phase 1: Setup

    • Install Layercache: npm install layercache
    • Create CacheStack with equivalent layers
    • Keep TTL values in milliseconds
    • Configure distributed features (if needed)

    Phase 2: Code Changes

    • Replace cache.wrap() with cache.get(key, fetcher)
    • Replace cache.del() with cache.delete()
    • Replace cache.reset() with cache.clear()
    • Update set operations to use options object: { ttl: 60_000 }
    • Add tags for group invalidation

    Phase 3: Testing

    • Verify cache hits/misses work correctly
    • Test tag-based invalidation
    • Test multi-layer cascading
    • Test distributed coordination (if applicable)
    • Monitor metrics and hit rates

    Phase 4: Optimization

    • Enable stampede prevention (default)
    • Configure stale-while-revalidate
    • Set up Prometheus metrics
    • Configure health checks
    • Set up distributed single-flight (if needed)

    Common Migration Issues

    TTL Confusion

    Issue: Keeping old second-based TTL values after migrating.

    Solution:

    // Wrong
await cache.set('key', value, { ttl: 60 })  // 60 milliseconds

// Correct
await cache.set('key', value, { ttl: 60_000 })  // 60 seconds

    Missing Tags

    Issue: Unable to invalidate related keys.

    Solution:

    // Add tags when setting
await cache.set('user:123', user, { tags: ['user:123'] })
await cache.set('user:123:posts', posts, { tags: ['user:123'] })

// Invalidate by tag
await cache.invalidateByTag('user:123')

    Namespace Confusion

    Issue: Not using namespaces for scoped caches.

    Solution:

    // Use namespaces instead of separate caches
const userCache = cache.namespace('users')
const postCache = cache.namespace('posts')

await userCache.set('123', data)  // Stored as "users:123"

    Not Using Read-Through

    Issue: Still using manual miss handling.

    Solution:

    // Old way
let value = await cache.get('key')
if (!value) {
  value = await fetcher()
  await cache.set('key', value)
}

// New way
const value = await cache.get('key', fetcher)

    Need Help?

    If you run into issues during migration:

    1. Check the API Reference for detailed method documentation
    2. Review examples for common patterns
    3. Open an issue with your current setup

    We're happy to help you find the right approach for your use case!