DialCache

Fine-grained TypeScript caching with explicit enabled contexts, stable key construction, local and Redis TTL caching, runtime rollout controls, request coalescing, Prometheus-ready observability, and Redis watermark-based targeted invalidation.

Install

pnpm add dialcache
# Choose a Redis client when using the remote layer:
pnpm add redis@~4.7.1
# or
pnpm add @valkey/valkey-glide@^2.4.2

DialCache requires Node.js 20 or Node.js 22 and newer.

Quick start

import { DialCache, DialCacheKeyConfig } from "dialcache";

const dialcache = new DialCache();

const getUser = dialcache.cached(
  (userId: string) => db.fetchUser(userId),
  {
    keyType: "user_id",
    useCase: "GetUser",
    cacheKey: (userId) => userId,
    defaultConfig: DialCacheKeyConfig.enabled(60),
  },
);

// Caching is OFF outside an enable() scope (see "Enabled context"), so this runs the fn uncached:
await getUser("123");

// Inside enable(), reads are cached. Enable once at your request boundary (not per call site):
const user = await dialcache.enable(() => getUser("123"));

Redis-backed TTL cache

Register DialCache's native node-redis scripts when creating the client, then pass that client to DialCache:

import { createClient } from "redis";
import { DialCache, DialCacheKeyConfig } from "dialcache";
import { createNodeRedisDialCacheClient, dialcacheRedisScripts } from "dialcache/node-redis";

const redisClient = createClient({
  url: process.env.REDIS_URL,
  scripts: dialcacheRedisScripts,
});
await redisClient.connect();

const dialcache = new DialCache({
  redis: {
    client: createNodeRedisDialCacheClient(redisClient),
    keyPrefix: "dialcache:",
  },
});

The redis.client and redis.createClient options accept the semantic DialCacheRedisClient interface. Node-redis users should register the supplied scripts and wrap their client with createNodeRedisDialCacheClient as shown above.

Valkey GLIDE users pass an already-created standalone or cluster client to the GLIDE adapter:

import { GlideClient } from "@valkey/valkey-glide";
import { DialCache } from "dialcache";
import { createValkeyGlideDialCacheClient } from "dialcache/valkey-glide";

const glideClient = await GlideClient.createClient({
  addresses: [{ host: "127.0.0.1", port: 6379 }],
});
const redisClient = createValkeyGlideDialCacheClient(glideClient);
const dialcache = new DialCache({
  redis: { client: redisClient, keyPrefix: "dialcache:" },
});

function shutdown(): void {
  // Release adapter-owned scripts before closing GLIDE.
  redisClient.dispose();
  glideClient.close();
}

DialCache does not create, connect, or close the underlying Redis client. After outstanding cache operations finish, the GLIDE adapter's dispose() method releases its five native Script handles; it is idempotent and does not close the wrapped GLIDE connection.

When caching is enabled, reads flow through:

local cache -> Redis cache -> fallback function

Local hits return immediately.
Local misses try Redis and populate local on a Redis hit.
Redis misses call the fallback and write both Redis and local.
Redis cache read/write failures are logged, counted in metrics, and fail open; fallback results still return when fallback succeeds. Explicit maintenance calls (invalidateRemote, flushAll) log/count Redis failures and rethrow them so callers do not assume mutation succeeded.
Missing per-layer config disables that layer, records a disabled reason, and falls through to the next layer/fallback.

The local layer uses one process-local LRU per DialCache instance. It keeps at most 10,000 entries by default across all use cases while retaining each entry's configured local TTL. Set localMaxSize to a nonnegative safe integer to change the global entry cap; 0 disables local storage:

const dialcache = new DialCache({ localMaxSize: 25_000 });

The limit counts entries rather than estimating JavaScript object memory. Recently read entries stay resident ahead of less recently used entries when the limit is reached.

Node-redis computes each script's SHA, uses EVALSHA, and retries with EVAL after NOSCRIPT. Its cluster client routes scripts by their first key and performs that fallback on the selected shard. The GLIDE adapter uses GLIDE's native Script lifecycle and byte decoder; GLIDE routes scripts from their declared keys and the adapter broadcasts flushAll() to all cluster primaries. Tracked reads are deliberately routed to primaries so a lagging replica cannot hide an invalidation watermark.

You can also provide a lazy factory that returns a script-enabled client:

const dialcache = new DialCache({
  redis: {
    createClient: async () => {
      const client = createClient({
        url: process.env.REDIS_URL,
        scripts: dialcacheRedisScripts,
      });
      await client.connect();
      return createNodeRedisDialCacheClient(client);
    },
  },
});

The core Redis boundary is the client-agnostic DialCacheRedisClient interface. It exchanges serialized values as string | Buffer and does not expose client commands or wire encodings. Distinct untracked/tracked read and write Lua sources, the invalidation source, and wire constants are available from dialcache/redis-protocol. Custom adapters can use the root-exported DialCacheRedisPayloadError and DialCacheRedisPayloadEncodingError classes to preserve the standard metrics labels.

Redis values use a compact binary frame:

byte 1      format version
bytes 2-9   Redis-created timestamp in milliseconds (uint64, big-endian)
byte 10     payload encoding (0 = UTF-8, 1 = raw binary)
bytes 11... serialized payload

Redis's Lua struct library packs and unpacks the timestamp. Redis TTL is authoritative, so expiry metadata is not duplicated in the frame. payload is produced by the cached function's serializer, or by JsonSerializer by default. Custom serializers can return either string or Buffer; strings are stored as UTF-8 and Buffers are stored byte-for-byte without base64 expansion. Adapters restore the same representation before calling serializer.load.

Targeted invalidation and watermarks

Mutable Redis-backed use cases can opt into targeted invalidation by setting trackForInvalidation: true in the options and calling dialcache.invalidateRemote(keyType, id) after writes:

import { CacheLayer, DialCache, DialCacheKeyConfig } from "dialcache";
import { createNodeRedisDialCacheClient } from "dialcache/node-redis";

const dialcache = new DialCache({ redis: { client: createNodeRedisDialCacheClient(redisClient) } });

const getUser = dialcache.cached(
  (userId: string) => db.fetchUser(userId),
  {
    keyType: "user_id",
    useCase: "GetMutableUser",
    cacheKey: (userId) => userId,
    trackForInvalidation: true,
    // Strongly invalidated mutable data should usually disable local cache.
    defaultConfig: new DialCacheKeyConfig({
      ttlSec: { [CacheLayer.REMOTE]: 300 },
      ramp: { [CacheLayer.REMOTE]: 100 },
    }),
  },
);

await updateUser("123", patch);
await dialcache.invalidateRemote("user_id", "123");

Invalidation writes a Redis watermark at {encodedUrnPrefix:encodedKeyType:encodedId}#watermark. Tracked Redis cache entries use the same Redis Cluster hash tag, for example {urn:user_id:123}?locale=en#GetMutableUser:dialcache-frame-v1, so the value key and watermark key live in the same slot. Key components are percent-encoded before joining so delimiters inside IDs or args cannot collide with delimiters in the key format. Components may not contain { or } because those characters would corrupt the hash tag.

The internal :dialcache-frame-v1 suffix identifies values written with DialCache's binary protocol. Watermarks are stored as decimal timestamps.

A cached Redis value whose Redis-created timestamp is older than or equal to the watermark is treated as stale and refreshed through fallback. invalidateRemote(keyType, id, futureBufferMs) sets the watermark to the greater of its existing value and Redis's current time plus the buffer. While that future window is active, fallback results are returned but not written to Redis or local cache.

Tracked writes create a baseline watermark and extend its TTL to at least the value TTL plus one minute. Invalidation preserves that lifetime and extends it to cover the future buffer. DEFAULT_WATERMARK_TTL_SEC (4 hours) remains a configurable floor rather than a maximum, and reads do not extend watermark lifetime.

futureBufferMs must be a nonnegative safe integer. Size it to cover the longest interval from invalidation until any fallback that could have read stale source data completes its Redis write. Include source-replication lag, remaining fallback work, serializer.dump, Redis client queue/network latency, script execution, and a safety margin. Invalidate after the source mutation commits. The buffer prevents stale fallback results from being cached under those assumptions; it does not itself force the current fallback to read from an authoritative source.

Local cache limitation: targeted invalidation is enforced by Redis watermarks. Existing local cache hits are not synchronously invalidated across processes, so strongly invalidated mutable data should disable the local layer (or use very short local TTLs only when stale reads are acceptable).

Runtime config and ramp controls

Every cached function can provide a per-use-case defaultConfig; a cacheConfigProvider can override it at runtime. If the provider returns null, DialCache falls back to the cached function's defaultConfig. If neither exists, or a layer's TTL/ramp is missing or disabled, only that layer is skipped.

cacheConfigProvider is called for every enabled cached-function invocation before DialCache checks local or Redis. Keep it cheap, cache any remote/config-store reads inside the provider, and avoid work that would erase the benefit of a cache hit.

import { CacheLayer, DialCache, DialCacheKeyConfig } from "dialcache";

const dialcache = new DialCache({
  cacheConfigProvider: async (key) => {
    if (key.useCase === "GetUser") {
      return new DialCacheKeyConfig({
        ttlSec: { [CacheLayer.LOCAL]: 30, [CacheLayer.REMOTE]: 300 },
        ramp: { [CacheLayer.LOCAL]: 100, [CacheLayer.REMOTE]: 25 },
      });
    }
    return null; // use the cached function's defaultConfig
  },
  rampSampler: ({ key, layer }) => deterministicPercentFor(`${key.urn}:${layer}`),
});

ramp values are percentages from 0 to 100. 0 disables the layer, 100 enables it, and intermediate values use rampSampler; the default sampler is deterministic by cache key and layer, so the same key is consistently sampled in or out of a partial rollout. Provider errors fail open and execute the fallback function.

Request coalescing

When caching is enabled and a call misses local cache, concurrent callers for the same cache key share the same in-flight cache work. With Redis configured, the leader runs the Redis read and, on miss, the fallback/cache write; followers await that result. Local-only misses share the leader's fallback/cache write. This protects Redis and the source of truth from a thundering herd on hot keys.

Coalescing only applies after a real cache layer is active. Calls outside enable() are true pass-through, and calls where every layer is disabled by missing config, invalid TTL, or ramp are true pass-through.

Because coalescing is keyed by cacheKey, concurrent calls with the same key share the leader's execution. Any argument ignored by cacheKey must be safe to share this way; include inputs such as locale, auth context, or cancellation behavior in the key when they can change the returned value or whether the underlying function should run separately.

const getUser = dialcache.cached(
  (userId: string) => db.fetchUser(userId),
  {
    keyType: "user_id",
    useCase: "GetUser",
    cacheKey: (userId) => userId,
    defaultConfig: DialCacheKeyConfig.enabled(60),
  },
);

Enabled context

Caching is off by default and only active inside a dialcache.enable(...) scope. This is deliberate: it lets you turn caching off in write paths so a stale read can't be cached around a write. DialCache uses Node AsyncLocalStorage to keep enabled state scoped to the current asynchronous call chain.

Enable once at your request boundary (e.g. a middleware that wraps read-request handling) so individual call sites don't each need it; wrap mutation handlers in disable():

await dialcache.enable(async () => {
  await getUser("123"); // cached

  await dialcache.disable(async () => {
    await updateUser("123", patch); // reads here are uncached
  });

  await getUser("123"); // cached again
});

Default is disabled — a cached function called outside any enable() scope simply runs uncached (no error), so wrap your read paths to actually cache.
Enabled state is async-scope-local, not process-global.
Nested enable / disable scopes restore the previous behavior when the callback completes.

Keys, ids, and extra dimensions

cached(fn, options) wraps a function; the wrapped callable has the same parameters and always returns a Promise. The cache key comes from a required cacheKey selector whose parameters are inferred from fn. Return a bare id, or return { id, args } to extract a field or add secondary dimensions:

The cacheKey selector is the value identity contract. It must include every input dimension that can affect the returned value; otherwise distinct calls can reuse the same cached value or share the same in-flight fallback through request coalescing.

const searchPosts = dialcache.cached(
  (userId: string, page: number, filter: string) => db.searchPosts(userId, page, filter),
  {
    keyType: "user_id",
    useCase: "SearchPosts",
    cacheKey: (userId, page, filter) => ({ id: userId, args: { page, filter } }),
    defaultConfig: DialCacheKeyConfig.enabled(60),
  },
);
await dialcache.enable(() => searchPosts("u1", 2, "active"));

keyType + id is the invalidation unit for tracked Redis entries. dialcache.invalidateRemote("user_id", "123") writes one watermark for that user; any trackForInvalidation Redis entry with the same keyType and id is refreshed across all args variants when Redis is read. Existing local hits follow the local-cache limitation above, and untracked Redis entries do not consult the watermark. useCase identifies the individual cache (it's the metrics label and part of the stored key).
args are part of the cache key — different args produce different entries — but invalidation is by id only.
Non-key inputs (for example a db handle) are simply parameters the cacheKey selector ignores. They still reach fn for non-coalesced executions, but concurrent same-key cache misses share the leader's execution, so do not ignore values like AbortSignal, auth context, locale, or other request-scoped inputs unless sharing one result is correct.
Methods: pass obj.method.bind(obj) (or (...a) => obj.method(...a)) — a bare obj.method reference loses this.

Metrics

DialCache registers Prometheus metrics by default via prom-client:

Metric	Type	Labels	Description
`dialcache_request_counter`	Counter	`use_case`, `key_type`, `layer`	Cache-layer requests that reached an enabled layer
`dialcache_miss_counter`	Counter	`use_case`, `key_type`, `layer`	Cache misses
`dialcache_disabled_counter`	Counter	`use_case`, `key_type`, `layer`, `reason`	Cache skips (`context`, `missing_config`, `invalid_ttl`, `ramped_down`, `config_error`)
`dialcache_error_counter`	Counter	`use_case`, `key_type`, `layer`, `error`, `in_fallback`	Cache/fallback errors, with `in_fallback` separating cache plumbing failures from application fallback failures
`dialcache_invalidation_counter`	Counter	`key_type`, `layer`	Invalidation calls for the layers touched today
`dialcache_coalesced_counter`	Counter	`use_case`, `key_type`	Requests that awaited active in-flight cache work
`dialcache_get_timer`	Histogram	`use_case`, `key_type`, `layer`	Cache get latency in seconds
`dialcache_fallback_timer`	Histogram	`use_case`, `key_type`, `layer`	Time spent in the underlying function
`dialcache_serialization_timer`	Histogram	`use_case`, `key_type`, `layer`, `operation`	Redis serializer dump/load latency
`dialcache_size_histogram`	Histogram	`use_case`, `key_type`, `layer`	Serialized Redis payload size in bytes

The layer label is usually local or remote. Disabled-context, key-construction, and config-provider failures use noop because no cache layer was reached.

Use a custom registry or prefix when embedding DialCache in an app with its own metrics endpoint:

import { Registry } from "prom-client";
import { DialCache } from "dialcache";

const registry = new Registry();
const dialcache = new DialCache({
  metricsRegistry: registry,
  metricsPrefix: "myapp_", // myapp_dialcache_request_counter, etc.
});

app.get("/metrics", async (_req, res) => {
  res.type(registry.contentType).send(await registry.metrics());
});

For non-Prometheus telemetry, inject a DialCacheMetricsAdapter through new DialCache({ metrics }). Pass metrics: false to disable metrics entirely. DialCache reuses existing collectors in a registry so repeated instances with the same prefix do not throw duplicate-registration errors.

Current scope

Included:

Local TTL/LRU cache with a global entry-count bound
Redis TTL cache
Local → Redis → fallback read-through chain
Lazy Redis client factory support
Lua-backed Redis reads and writes with Redis-generated timestamps
Versioned binary Redis frames for UTF-8 and Buffer serializer output
Native node-redis script registration with automatic NOSCRIPT recovery
Native Valkey GLIDE adapter with explicit script disposal and automatic script-cache recovery
Standalone Redis, Valkey, and Redis Cluster support
JSON and custom serializer support for Redis values
Duplicate and reserved use-case validation
invalidateRemote for Redis watermark invalidation and flushAll across configured layers
Fail-open behavior for key/config/cache read-write errors; maintenance mutations surface failures
Runtime config provider with fallback to cached-function defaultConfig
Per-layer TTL and ramp controls
Deterministic default ramp sampler with injectable override hooks
Missing config disables only the relevant layer and falls through
Prometheus metrics with duplicate-registration safety
Custom metrics adapter/registry/prefix hooks
Cache-vs-fallback error classification through the in_fallback label
Serialization latency and cached payload size metrics for Redis values
Logger injection for cache operational failures
trackForInvalidation on cached functions
invalidateRemote(keyType, id, futureBufferMs) Redis watermark API
Redis Cluster hash-tagged value/watermark keys for invalidation-tracked entries
Dynamically extended watermark TTL with a configurable DEFAULT_WATERMARK_TTL_SEC floor
Future-buffer behavior that avoids cache writes during active invalidation windows
Request coalescing for active cache work after local misses

Not included yet:

Framework middleware helpers/integrations
cachedObject
Expanded examples

Releasing

Publishing is driven manually from the Release workflow in GitHub Actions. Run it from main; the workflow rejects any other ref or a stale main commit. It calculates the next patch version from the highest stable vX.Y.Z tag, using the package.json version as the seed when no release tag exists. For example, the current 0.1.0 seed produces the first v0.1.1 release, followed by v0.1.2.

Every release is a patch bump. Semantic PR-title prefixes such as feat:, fix:, and docs: remain in the generated release notes but do not change the version magnitude. After validating, building, and package-testing the release artifact, the workflow creates a draft GitHub release, publishes the public npm package with provenance, and publishes the GitHub release only after npm succeeds.

The first publish requires a granular npm token in the repository's NPM_TOKEN secret because npm trusted publishing can only be configured after the package exists. After the bootstrap release, configure lan17/DialCache and release.yaml as the package's trusted GitHub publisher, then remove the long-lived token.

Name		Name	Last commit message	Last commit date
Latest commit History 201 Commits
.github		.github
scripts		scripts
src		src
test		test
.gitignore		.gitignore
AGENTS.md		AGENTS.md
LICENSE		LICENSE
README.md		README.md
package.json		package.json
pnpm-lock.yaml		pnpm-lock.yaml
tsconfig.json		tsconfig.json
vitest.config.ts		vitest.config.ts
vitest.integration.config.ts		vitest.integration.config.ts

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DialCache

Install

Quick start

Redis-backed TTL cache

Targeted invalidation and watermarks

Runtime config and ramp controls

Request coalescing

Enabled context

Keys, ids, and extra dimensions

Metrics

Current scope

Releasing

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DialCache

Install

Quick start

Redis-backed TTL cache

Targeted invalidation and watermarks

Runtime config and ramp controls

Request coalescing

Enabled context

Keys, ids, and extra dimensions

Metrics

Current scope

Releasing

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