Redis job queue with node-redis

Implement a Redis job queue in Node.js with node-redis

This guide shows you how to implement a Redis-backed job queue in Node.js with node-redis. It includes a small local web server built with Node's standard http module so you can enqueue jobs, watch a pool of workers drain them, and see the reclaimer recover jobs from a simulated worker crash.

Overview

A job queue lets your application offload background work — sending email, processing payments, image transcoding, ML inference, webhooks — from the request path. Producers enqueue jobs in milliseconds and return to the user; workers pull from the queue and process them on their own schedule.

That gives you:

  • Low-latency user-facing requests, even when downstream work is slow or bursty
  • Horizontal scale across many worker processes that share one Redis instance
  • At-least-once delivery so a worker crash doesn't lose work
  • Visibility-timeout reclaim that returns stuck jobs to the queue automatically
  • Job metadata, retry counts, and completion results in Redis hashes with TTL

In this example, each job is identified by a random hex ID and its payload, status, and result live in a Redis hash under queue:jobs:job:{id}. Pending IDs sit in a list, claimed IDs move atomically to a processing list, and completed or failed IDs land in short history lists.

How it works

The flow looks like this:

  1. The application calls queue.enqueue(payload)
  2. The helper writes the job metadata hash and LPUSHes the job ID onto the pending list
  3. A worker calls queue.claim(timeoutMs)
  4. The helper runs BLMOVE (the successor to BRPOPLPUSH) to atomically move the next pending ID into the processing list and writes a per-claim claim_token plus claimed_at_ms on the hash
  5. The worker runs the job and calls queue.complete(job, result) or queue.fail(job, error)
  6. complete removes the job from the processing list, writes the result, and LPUSHes the ID onto the completed history (with LTRIM and an EXPIRE on the hash for cleanup)
  7. fail either retries the job (back to pending) or moves it to the failed list once retries are exhausted

If a worker dies before completing a job, the job sits in the processing list with a claimed_at_ms older than the visibility timeout. A periodic call to queue.reclaimStuck() finds those jobs and moves them back to pending so another worker can pick them up.

Every state change holds the token: a worker that has been reclaimed cannot later complete or fail a job another worker has already claimed.

The job queue helper

The RedisJobQueue class wraps the queue operations (source):

const { createClient } = require("redis");
const { RedisJobQueue } = require("./job_queue");

async function main() {
  const client = createClient({ url: "redis://localhost:6379" });
  await client.connect();

  const queue = new RedisJobQueue({ redisClient: client, visibilityMs: 5000 });

  const jobId = await queue.enqueue({ kind: "email", recipient: "[email protected]" });

  // In a worker process:
  const job = await queue.claim(1000);
  if (job !== null) {
    try {
      // ... run the job ...
      await queue.complete(job, { sent_at: "2026-05-11T15:00:00Z" });
    } catch (err) {
      await queue.fail(job, String(err));
    }
  }

  // In a periodic sweeper:
  const reclaimed = await queue.reclaimStuck();

  await client.quit();
}

main().catch(console.error);

Data model

Each job's state lives in a Redis hash plus a position in one of four lists:

queue:jobs:pending          (list)   pending job IDs, oldest at the right
queue:jobs:processing       (list)   claimed but not yet completed
queue:jobs:completed        (list)   recent successes (LTRIM-capped history)
queue:jobs:failed           (list)   terminally failed jobs
queue:jobs:job:{id}         (hash)   per-job metadata
queue:jobs:events           (pubsub) completion notifications

A job's hash carries:

queue:jobs:job:9a4f...
  id              = 9a4f...
  payload         = {"kind":"email","recipient":"[email protected]"}
  status          = pending | processing | completed | failed
  attempts        = 1
  enqueued_at_ms  = 1715441000000
  claimed_at_ms   = 1715441000123
  claim_token     = b3c0d1e2...        (per-claim random token)
  completed_at_ms = 1715441000456
  result          = {"sent_at":"..."}
  last_error      = "smtp timeout"

The implementation uses:

  • LPUSH to add new job IDs to the pending list
  • BLMOVE to atomically claim a job into the processing list (the modern replacement for the deprecated BRPOPLPUSH)
  • LREM to remove a claimed job from the processing list on complete or fail
  • LTRIM to cap the completed and failed history lists
  • HSET / HGETALL for job metadata
  • EXPIRE on completed and failed hashes for automatic cleanup
  • PUBLISH on queue:jobs:events for completion signalling
  • Lua scripting (EVALSHA) for the complete, fail, and reclaim flows so each runs atomically against the processing list and metadata hash

Enqueueing jobs

enqueue() writes the metadata hash and pushes the job ID onto the pending list in one transaction (MULTI pipeline):

async enqueue(payload) {
  const jobId = crypto.randomBytes(8).toString("hex");
  const nowMs = Date.now();
  const meta = {
    id: jobId,
    payload: JSON.stringify(payload),
    status: "pending",
    attempts: "0",
    enqueued_at_ms: String(nowMs),
    claim_token: "",
  };
  const multi = this.redis.multi();
  multi.hSet(this._metaKey(jobId), meta);
  multi.lPush(this.pendingKey, jobId);
  await multi.exec();
  this._enqueued += 1;
  return jobId;
}

The payload is stored as JSON so the queue can carry arbitrary nested structures without forcing every field into a hash. Hash field values are all strings — node-redis won't convert numbers for you, so the helper coerces them explicitly.

Claiming jobs with BLMOVE

A worker blocks until a job is available, then atomically pops it from the pending list and pushes it onto the processing list. In Redis 6.2 and later this is BLMOVE; the older BRPOPLPUSH is deprecated and was removed from node-redis in v5:

async claim(timeoutMs = 1000) {
  const timeoutS = Math.max(timeoutMs / 1000, 0.1);
  const jobId = await this.redis.blMove(
    this.pendingKey,
    this.processingKey,
    "RIGHT",
    "LEFT",
    timeoutS,
  );
  if (jobId === null || jobId === undefined) {
    return null;
  }

  const token = crypto.randomBytes(8).toString("hex");
  const nowMs = Date.now();
  const metaKey = this._metaKey(jobId);
  const multi = this.redis.multi();
  multi.hSet(metaKey, {
    status: "processing",
    claimed_at_ms: String(nowMs),
    claim_token: token,
  });
  multi.hIncrBy(metaKey, "attempts", 1);
  multi.hGetAll(metaKey);
  const results = await multi.exec();
  const meta = results[2] || {};
  return new ClaimedJob(jobId, JSON.parse(meta.payload || "{}"), Number(meta.attempts), token);
}

BLMOVE pending processing RIGHT LEFT timeoutS is the byte-for-byte equivalent of the old BRPOPLPUSH pending processing timeoutS — pop from the right end of pending, push onto the left end of processing, atomically, blocking up to timeoutS seconds.

The claim_token is the worker's proof of ownership for this attempt. Every subsequent state change (complete, fail) checks it before touching the processing list, so a worker that hung past the visibility timeout cannot interfere with the new claimant.

Completing jobs

complete() runs a Lua script via EVALSHA so the processing-list removal, the metadata write, and the history push happen atomically:

async complete(job, result) {
  const ok = await this._evalScript(COMPLETE_SCRIPT, this._completeSha, {
    keys: [this.metaPrefix, this.processingKey, this.completedKey],
    arguments: [
      job.id,
      job.claimToken,
      "completed",
      String(Date.now()),
      JSON.stringify(result),
      String(this.completedTtl),
      String(this.completedHistory),
    ],
  });
  if (!ok || Number(ok) === 0) {
    return false;
  }
  await this.redis.publish(
    this.eventsChannel,
    JSON.stringify({ id: job.id, status: "completed" }),
  );
  this._completed += 1;
  return true;
}

The helper preloads each script with SCRIPT LOAD at first use and prefers EVALSHA to avoid resending the script body on every call. If Redis evicts the script cache and returns NOSCRIPT, the wrapper falls back to a full EVAL and re-caches the SHA.

The Lua script checks the token first and returns 0 if the worker no longer owns the job (because the reclaimer moved it back to pending). The metadata hash also gets an EXPIRE so completed jobs are cleaned up automatically.

Failing and retrying

fail() either retries the job (back to pending) or moves it to the failed list once retries are exhausted:

async fail(job, error) {
  const retry = job.attempts < this.maxAttempts;
  const result = await this._evalScript(FAIL_SCRIPT, this._failSha, {
    keys: [this.metaPrefix, this.processingKey, this.pendingKey, this.failedKey],
    arguments: [
      job.id,
      job.claimToken,
      error,
      String(Date.now()),
      String(this.completedTtl),
      String(this.completedHistory),
      retry ? "1" : "0",
    ],
  });
  return Boolean(result) && Number(result) !== 0;
}

The attempt counter is incremented on every claim(), so a job that fails three times is moved to the failed list with attempts = 3 and the final last_error preserved.

Reclaiming stuck jobs

If a worker dies mid-job — the process is killed, the host loses power, the network partitions — the job sits in the processing list with a claimed_at_ms that never advances. A periodic call to reclaimStuck() walks the processing list and moves any job past the visibility timeout back to pending:

async reclaimStuck() {
  const reclaimed = await this._evalScript(RECLAIM_SCRIPT, this._reclaimSha, {
    keys: [this.pendingKey, this.processingKey, this.metaPrefix],
    arguments: [String(Date.now()), String(this.visibilityMs)],
  });
  return Array.isArray(reclaimed) ? reclaimed : [];
}

The Lua script also handles a narrower race: a worker that crashed between BLMOVE and writing claimed_at_ms. Those jobs are reclaimed after 2 × visibility_ms using enqueued_at_ms as a fallback timer, so they aren't stranded forever.

Stats and history

stats() reports queue depth plus per-process counters:

async stats() {
  const multi = this.redis.multi();
  multi.lLen(this.pendingKey);
  multi.lLen(this.processingKey);
  multi.lLen(this.completedKey);
  multi.lLen(this.failedKey);
  const [pending, processing, completed, failed] = await multi.exec();
  return {
    enqueued_total: this._enqueued,
    completed_total: this._completed,
    failed_total: this._failed,
    reclaimed_total: this._reclaimed,
    pending_depth: Number(pending) || 0,
    processing_depth: Number(processing) || 0,
    completed_depth: Number(completed) || 0,
    failed_depth: Number(failed) || 0,
    visibility_ms: this.visibilityMs,
  };
}

The completed and failed lists are capped via LTRIM so they never grow unbounded; a real deployment would also write completion events to a longer-term audit log if needed.

Prerequisites

  • Redis 6.2 or later running locally on the default port (6379). Earlier versions still work for the rest of the pattern, but BLMOVE requires Redis 6.2+; on older servers swap it for BRPOPLPUSH.

  • Node.js 18 or later (the helper uses native async/await and the crypto module).

  • The node-redis client at version 5.x. Install it with:

    npm install redis

Running the demo

Get the source files

The demo consists of four files. Download them from the nodejs source folder on GitHub, or grab them with curl:

mkdir job-queue-demo && cd job-queue-demo
BASE=https://raw.githubusercontent.com/redis/docs/main/content/develop/use-cases/job-queue/nodejs
curl -O $BASE/job_queue.js
curl -O $BASE/worker.js
curl -O $BASE/demoServer.js
curl -O $BASE/package.json

Then install dependencies:

npm install

Start the demo server

From that directory:

node demoServer.js

You should see:

Redis job-queue demo server listening on http://127.0.0.1:8791
Using Redis at localhost:6379
Visibility timeout: 5000 ms

Open http://127.0.0.1:8791 in a browser. You can:

  • Enqueue jobs of different kinds (email, webhook, thumbnail, invoice) in batches.
  • Start a pool of workers with configurable size, work latency, and failure / hang rates. A non-zero hang rate simulates worker crashes.
  • Click Run reclaim sweep to move any timed-out processing jobs back to pending.
  • Watch pending / processing / completed / failed lists update every 800 ms.

If your Redis server is running elsewhere, start the demo with --redis-host and --redis-port. You can also tune the visibility timeout with --visibility-ms and the bind address with --host / --port.

The mock worker pool

The demo includes a small Worker and WorkerPool (source) that stands in for whatever real background work your application would run. Each worker:

  • Blocks on queue.claim() for new jobs.
  • Sleeps workLatencyMs to simulate doing the work.
  • Either completes successfully, fails (calling queue.fail()), or hangs — returning without completing or failing the job so the reclaimer has to recover it.

The failRate and hangRate knobs let you watch the at-least-once delivery and reclaim behaviours from the UI without writing test code.

Because Node.js is single-threaded, each "worker" is an async loop running on the event loop rather than an OS thread. The claim(500) call uses Redis's blocking BLMOVE so the loop spends almost all its time waiting on the server rather than spinning; multiple workers share one Redis client and Node's I/O scheduler interleaves their claims naturally.

Production usage

Choose a visibility timeout that matches your worst-case job latency

The visibility timeout has to exceed the longest real job time, with margin. If it's too short, a healthy worker that's running a slow job will get its work duplicated when the reclaimer fires. If it's too long, a real crash takes longer to detect. Most production deployments use a per-queue value tuned to the 99th-percentile job latency — for example, 2 minutes for email and 30 minutes for video transcoding.

Run the reclaimer on a schedule

The demo only reclaims when you click the button. In production, run reclaimStuck() from a periodic task (use setInterval for fast queues, or a separate scheduler process for slow ones), or from each worker before it blocks on claim(). Both patterns work as long as someone runs the sweep.

Use a separate Redis database or key prefix per queue

The helper takes a queueName argument so you can run multiple independent queues against one Redis instance — for example, one queue per priority level, or one per job kind. Keep queue keys under a clearly-namespaced prefix (here, queue:jobs:*) so they're easy to inspect and easy to clear without touching application data.

Cap the completed and failed history

The demo keeps the last 50 completed and 50 failed job IDs via LTRIM. If you need longer history for audit purposes, write completion events to a separate Redis Stream (or to an external store) and keep the in-queue history short. Stream consumer groups give you the same fan-out semantics with a much richer history.

Tune maxAttempts per job kind

A blanket maxAttempts = 3 is a reasonable default for transient failures (network timeouts, rate limits). Jobs that talk to non-idempotent external systems — for example, posting a Stripe charge — need either application-level idempotency keys or a much lower retry count. The helper exposes maxAttempts so each queue can pick its own policy.

Prefer BLMOVE over BRPOPLPUSH

node-redis v5 removed client.brPopLPush(); use client.bLMove(src, dst, "RIGHT", "LEFT", timeoutSec) instead. The two commands are functionally identical for this pattern: pop from the right of the source list and push onto the left of the destination list, atomically, blocking until a value appears. BLMOVE is more general (it accepts any combination of LEFT/RIGHT on either end) and is the recommended modern command.

Use one shared client, not a pool

node-redis pipelines commands automatically across a single TCP connection, so for most workloads you should create one createClient() instance per process and reuse it everywhere. The only reason to add a second connection is to dedicate one to a blocking call (such as BLMOVE with a long timeout, or SUBSCRIBE); this demo's claim() uses a short 500 ms timeout so a single client is fine.

Inspect queue state directly in Redis

Because the queue is just lists and hashes, you can inspect it with redis-cli:

# How many pending jobs?
redis-cli LLEN queue:jobs:pending

# Look at the next 5 jobs to be picked up.
redis-cli LRANGE queue:jobs:pending -5 -1

# Read a job's metadata.
redis-cli HGETALL queue:jobs:job:9a4f0d1c

# How many jobs are currently being processed?
redis-cli LLEN queue:jobs:processing

# Clear everything for this queue (be careful — this deletes work).
redis-cli --scan --pattern 'queue:jobs:*' | xargs redis-cli DEL

Learn more

This example uses the following Redis commands:

  • LPUSH to enqueue a job ID.
  • BLMOVE to atomically claim a job into the processing list (modern replacement for BRPOPLPUSH).
  • LREM to remove a job from the processing list on complete or fail.
  • LRANGE and LLEN to read queue depth and list contents.
  • LTRIM to cap the completed and failed history.
  • HSET and HGETALL for job metadata.
  • HINCRBY for the attempt counter.
  • EXPIRE for automatic cleanup of completed and failed jobs.
  • PUBLISH for job-completion notifications.
  • EVALSHA for atomic complete, fail, and reclaim flows.

See the node-redis documentation for full client reference.

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