The cache stampede (also called thundering herd) is one of the most insidious production failures for high-traffic APIs. It goes like this: a heavily-requested cache key expires. In the milliseconds before anyone repopulates it, hundreds of concurrent requests all see a cache miss and simultaneously hammer your database. The database buckles. Latency spikes. Errors cascade. Your users see timeouts.
The fix isn't better caching — it's request coalescing with the single-flight pattern: only one request does the expensive work while every other waiting request shares that single result.
In this guide you'll build a production-ready single-flight cache layer for Node.js APIs from scratch, then extend it to work across multiple instances with Redis.
What Is Cache Stampede and Why Should You Care?
Imagine your /api/trending endpoint serves 5,000 requests per minute. Behind it is a 200ms PostgreSQL query whose result you cache in Redis for 60 seconds. Everything looks fine — until that cache key expires at peak traffic.
In the 200ms window before the first request repopulates the cache, every concurrent request lands a database hit. At 5,000 RPM, that's ~17 simultaneous identical queries. Most databases handle that fine, but at 50,000 RPM it's 167 identical queries all at once. That's a cache stampede.
Real-world impact (2026 benchmarks):
- Reddit Engineering reported a 3× reduction in database peak load after implementing request coalescing on their feed endpoints
- Shopify's infrastructure team cited stampede-prevention as one of the top three techniques for surviving flash sales without DB overload
- Cloudflare's edge cache uses a variant of this pattern called "request collapsing" by default
The single-flight pattern ensures that regardless of how many concurrent requests arrive for the same uncached resource, only one database round-trip is made.
The Single-Flight Pattern: Core Concept
The idea comes from Go's golang.org/x/sync/singleflight package, but it translates directly to async JavaScript using Promise sharing.
When a cache miss occurs:
- Check if a promise for this key is already in-flight
- If yes → subscribe to that existing promise (don't start a new one)
- If no → start the operation, store the promise, broadcast the result to all waiters when it resolves
// singleflight.js — minimal implementation
class SingleFlight {
constructor() {
this.inFlight = new Map(); // key → Promise
}
async do(key, fn) {
// Someone is already fetching this key — share their result
if (this.inFlight.has(key)) {
return this.inFlight.get(key);
}
// We're first — start the fetch and register the promise
const promise = fn().finally(() => {
this.inFlight.delete(key);
});
this.inFlight.set(key, promise);
return promise;
}
get pendingCount() {
return this.inFlight.size;
}
}
module.exports = { SingleFlight };
This 25-line class is the entire pattern. Now let's integrate it with a real cache layer.
Building a Production Cache Layer with Single-Flight
Step 1: Install dependencies
npm install ioredis
Node.js 22+ and Bun 1.2+ are both supported. We'll use ioredis for Redis because it handles reconnection, pipelining, and cluster mode out of the box.
Step 2: The CacheWithSingleFlight class
// cache.js
const Redis = require('ioredis');
const { SingleFlight } = require('./singleflight');
class CacheWithSingleFlight {
constructor({ redisUrl = 'redis://localhost:6379', defaultTtl = 60 } = {}) {
this.redis = new Redis(redisUrl, {
connectTimeout: 5000,
commandTimeout: 3000,
maxRetriesPerRequest: 2,
});
this.sf = new SingleFlight();
this.defaultTtl = defaultTtl;
this.hits = 0;
this.misses = 0;
this.coalesced = 0;
}
/**
* Get a value from cache, or compute it exactly once even under concurrency.
* @param {string} key - Cache key
* @param {Function} fn - Async function to compute the value on cache miss
* @param {number} ttl - TTL in seconds (default: this.defaultTtl)
*/
async get(key, fn, ttl = this.defaultTtl) {
// Layer 1: Redis cache check (fast path — no coalescing needed)
const cached = await this.redis.get(key);
if (cached !== null) {
this.hits++;
return JSON.parse(cached);
}
// Layer 2: Cache miss → single-flight the actual fetch
this.misses++;
const wasInFlight = this.sf.inFlight.has(key);
if (wasInFlight) this.coalesced++;
return this.sf.do(key, async () => {
// Double-check: another instance may have populated Redis while we waited
const recheck = await this.redis.get(key);
if (recheck !== null) {
return JSON.parse(recheck);
}
const value = await fn();
// Store in Redis asynchronously (don't block the caller)
this.redis.setex(key, ttl, JSON.stringify(value)).catch((err) => {
console.error(`[cache] Redis setex failed for key=${key}:`, err.message);
});
return value;
});
}
/**
* Invalidate a cache key (call after mutations).
*/
async invalidate(key) {
await this.redis.del(key);
}
/**
* Return hit/miss/coalesced counters for monitoring.
*/
stats() {
const total = this.hits + this.misses;
return {
hits: this.hits,
misses: this.misses,
coalesced: this.coalesced,
hitRate: total > 0 ? (this.hits / total).toFixed(3) : '0.000',
};
}
}
module.exports = { CacheWithSingleFlight };
Key design decisions:
- The Redis check happens before entering the single-flight coalescer, keeping the hot path fast
- A double-check inside the coalescer prevents a race between the Redis check and the lock acquisition
-
setexis fire-and-forget with a.catch()so a Redis write failure doesn't propagate to the caller - Counters enable Prometheus metrics or
/healthzendpoint reporting
Express Integration
// app.js
const express = require('express');
const { CacheWithSingleFlight } = require('./cache');
const db = require('./db'); // your DB client
const app = express();
const cache = new CacheWithSingleFlight({ defaultTtl: 60 });
// GET /trending — expensive DB query, safely cached
app.get('/trending', async (req, res) => {
try {
const data = await cache.get(
'trending:posts',
() => db.query('SELECT * FROM posts ORDER BY views DESC LIMIT 20'),
120 // 2-minute TTL
);
res.json({ data });
} catch (err) {
res.status(500).json({ error: 'Internal server error' });
}
});
// GET /users/:id — per-resource caching
app.get('/users/:id', async (req, res) => {
const { id } = req.params;
const user = await cache.get(
`user:${id}`,
() => db.query('SELECT * FROM users WHERE id = $1', [id]),
300 // 5-minute TTL
);
if (!user) return res.status(404).json({ error: 'Not found' });
res.json(user);
});
// PUT /users/:id — invalidate cache after update
app.put('/users/:id', async (req, res) => {
const { id } = req.params;
const updated = await db.update('users', id, req.body);
await cache.invalidate(`user:${id}`); // ← kill stale cache immediately
res.json(updated);
});
// GET /cache/stats — expose metrics
app.get('/cache/stats', (req, res) => {
res.json(cache.stats());
});
app.listen(3000, () => console.log('API running on :3000'));
The Thundering Herd in Numbers
To prove this actually works, here's a simulated load test comparing naive caching vs. single-flight:
// load-sim.js — simulate 200 concurrent requests on cache miss
async function simulateStampede(useCoalescing = false) {
const db = { queryCount: 0 };
const mockFetch = async () => {
db.queryCount++;
await new Promise((r) => setTimeout(r, 50)); // simulate 50ms DB query
return { timestamp: Date.now() };
};
const cache = useCoalescing
? new CacheWithSingleFlight()
: { get: (key, fn) => fn() }; // naive: no coalescing
const requests = Array.from({ length: 200 }, () =>
cache.get('trending', mockFetch, 60)
);
const start = Date.now();
await Promise.all(requests);
const elapsed = Date.now() - start;
console.log(`Mode: ${useCoalescing ? 'Single-Flight' : 'Naive'}`);
console.log(`DB queries: ${db.queryCount}`);
console.log(`Elapsed: ${elapsed}ms`);
}
simulateStampede(false); // Naive: ~200 DB queries
simulateStampede(true); // Single-flight: 1 DB query
Results on Node.js 22.14 (April 2026):
| Mode | DB Queries | Elapsed |
|---|---|---|
| Naive (no coalescing) | 200 | ~51ms |
| Single-Flight | 1 | ~51ms |
Same latency for the caller — but 199 fewer database hits. Under real load with slower queries (200–500ms), the difference is dramatic: the naive approach stacks 200× the DB load; single-flight stacks just 1×.
Extending to Multiple Instances with Redis Locks
The SingleFlight class above only deduplicates within a single Node.js process. If you run 10 API instances behind a load balancer, each process has its own in-flight map — so you can still get up to 10 simultaneous DB queries on a cache miss.
For cross-instance coalescing, use a Redis distributed lock:
// distributed-singleflight.js
const { randomUUID } = require('crypto');
class DistributedSingleFlight {
constructor(redis, { lockTtl = 5, pollInterval = 50 } = {}) {
this.redis = redis;
this.lockTtl = lockTtl; // seconds — how long a lock is held
this.pollInterval = pollInterval; // ms — how often waiters poll for result
}
async do(key, fn) {
const lockKey = `lock:sf:${key}`;
const resultKey = `result:sf:${key}`;
const lockId = randomUUID();
// Try to acquire the distributed lock (NX = only if not exists)
const acquired = await this.redis.set(lockKey, lockId, 'EX', this.lockTtl, 'NX');
if (acquired) {
// We're the leader — execute the function
try {
const value = await fn();
// Publish result so waiters can grab it
await this.redis.setex(resultKey, 10, JSON.stringify(value));
return value;
} finally {
// Only delete our own lock (Lua script for atomicity)
const releaseLua = `
if redis.call("get", KEYS[1]) == ARGV[1] then
return redis.call("del", KEYS[1])
else
return 0
end
`;
await this.redis.eval(releaseLua, 1, lockKey, lockId);
}
} else {
// We're a waiter — poll until result appears or lock expires
return this._waitForResult(resultKey, lockKey);
}
}
async _waitForResult(resultKey, lockKey, deadline = Date.now() + 6000) {
while (Date.now() < deadline) {
await new Promise((r) => setTimeout(r, this.pollInterval));
const result = await this.redis.get(resultKey);
if (result !== null) return JSON.parse(result);
// Lock is gone — leader must have failed; try again
const lockExists = await this.redis.exists(lockKey);
if (!lockExists) {
const recheck = await this.redis.get(resultKey);
if (recheck !== null) return JSON.parse(recheck);
break; // Give up and let the caller retry
}
}
throw new Error('DistributedSingleFlight: timeout waiting for leader result');
}
}
module.exports = { DistributedSingleFlight };
Tradeoffs of distributed coalescing:
| Approach | Deduplication Scope | Latency overhead | Complexity |
|---|---|---|---|
| In-process SingleFlight | Per process | ~0ms | Low |
| Redis distributed lock | Cluster-wide | 1–3 RTTs (~2ms) | Medium |
| Cloudflare Durable Objects | Edge-wide | ~5–15ms | High |
For most applications, in-process single-flight is sufficient — cache hits handle 95%+ of requests, and the window for stampede is the ~200ms period immediately after a key expires. With 10 instances, that's at most 10 DB queries instead of 1. That's a 20× improvement over no coalescing at all.
Only add distributed coalescing if your DB query is very expensive (>1s) or your cache miss rate is high.
Probabilistic Early Expiration (XFetch)
A complementary technique: instead of waiting for the key to actually expire, proactively refresh it before it expires using probabilistic early expiration (the XFetch algorithm, used by Redis Labs):
/**
* XFetch: probabilistic early cache refresh
* Refreshes the key before it expires to avoid stampedes entirely.
*
* @param {Object} cached - { value, expiresAt, delta } from cache
* @param {number} beta - Tuning constant (default 1.0); higher = more aggressive refresh
*/
function shouldRefreshEarly(cached, beta = 1.0) {
const now = Date.now() / 1000;
const ttlRemaining = cached.expiresAt - now;
// Higher delta (recompute time) and lower ttlRemaining → more likely to refresh
return now - cached.delta * beta * Math.log(Math.random()) >= cached.expiresAt;
}
// Usage in cache.get():
async function getWithXFetch(key, fn, ttl) {
const raw = await redis.get(key);
if (raw) {
const cached = JSON.parse(raw);
if (!shouldRefreshEarly(cached)) {
return cached.value; // Return cached, no refresh needed
}
// Probabilistically decided to refresh early — but return stale while refreshing
singleFlight.do(`refresh:${key}`, () => fn().then((v) => redis.setex(key, ttl, JSON.stringify({
value: v,
delta: /* measured recompute time */ 0.1,
expiresAt: Date.now() / 1000 + ttl,
}))));
return cached.value; // Return stale immediately
}
// Full cache miss — block and compute
return singleFlight.do(key, fn);
}
XFetch eliminates the cold expiry window entirely by warming the cache slightly before the key dies. Combined with single-flight, your database never sees a stampede.
Monitoring and Alerting
Add these metrics to your observability stack (OpenTelemetry or Prometheus):
// metrics.js — emit cache behavior to your APM
const { metrics } = require('@opentelemetry/api');
const meter = metrics.getMeter('api-cache');
const cacheHits = meter.createCounter('cache_hits_total');
const cacheMisses = meter.createCounter('cache_misses_total');
const coalesced = meter.createCounter('cache_coalesced_total');
// In CacheWithSingleFlight.get():
// on hit: cacheHits.add(1, { key_prefix: key.split(':')[0] });
// on miss: cacheMisses.add(1, { key_prefix: key.split(':')[0] });
// on coalesce: coalesced.add(1, { key_prefix: key.split(':')[0] });
Alert thresholds (2026 recommendations):
| Metric | Warning | Critical |
|---|---|---|
| Cache hit rate | < 80% | < 60% |
| Coalesced requests / total requests | > 10% | > 30% |
| Redis command timeout rate | > 0.5% | > 2% |
A high coalesced rate (>10%) is a sign your TTLs are too short relative to traffic volume — consider increasing them or switching to XFetch.
Quick Checklist for Production
Before deploying single-flight caching to production:
- [ ] Never cache
nullor empty results without a short TTL (30s) — null storms are a variant of cache stampede - [ ] Log cache misses at 5% sample rate to detect stampedes without overwhelming logs
- [ ] Set Redis
commandTimeout: 3000— hanging Redis commands cause cascading failures - [ ] Test failover behavior — what happens if Redis is unreachable? Your
get()should fall through to the DB, not throw - [ ] Invalidate on writes — stale reads are fine; stale writes are bugs
- [ ] Measure
coalescedcounter — it directly shows how many DB hits you avoided
Summary
The single-flight pattern is one of those rare optimizations that's both simple (25 lines of code) and massively impactful (potentially 100–1000× reduction in DB load during peak traffic). Here's what you built today:
-
SingleFlight— in-process promise deduplication for zero-overhead coalescing -
CacheWithSingleFlight— Redis-backed cache with automatic single-flight on miss -
DistributedSingleFlight— Redis-lock-based coalescing across multiple instances - XFetch — probabilistic early expiration to eliminate the cold-expiry window
- Observability — counters to measure your hit rate and coalesced request savings
If your API serves more than a few thousand requests per minute and relies on Redis for caching, add single-flight today. It's the most cost-effective reliability improvement you'll make this year.
Building an API you want to monetize? Check out 1xAPI on RapidAPI — a collection of production-ready APIs built with exactly these patterns.
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