Redis job queue with Lettuce

Implement a Redis job queue in Java with Lettuce

This guide shows you how to implement a Redis-backed job queue in Java with Lettuce. It includes a small local web server built on Java's standard com.sun.net.httpserver.HttpServer 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 runs a single Lua script that 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 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):

import io.lettuce.core.RedisClient;
import io.lettuce.core.api.StatefulRedisConnection;
import java.util.Map;

RedisClient client = RedisClient.create("redis://localhost:6379");
StatefulRedisConnection<String, String> conn = client.connect();

RedisJobQueue queue = new RedisJobQueue(conn, "jobs", 5000, 300, 50, 3);

String jobId = queue.enqueue(Map.of(
        "kind", "email",
        "recipient", "[email protected]"));

// In a worker thread:
RedisJobQueue.ClaimedJob job = queue.claim(1000);
if (job != null) {
    try {
        // ... run the job ...
        queue.complete(job, Map.of("sent_at", "2026-05-11T15:00:00Z"));
    } catch (Exception ex) {
        queue.fail(job, ex.getMessage());
    }
}

// In a periodic sweeper:
List<String> reclaimed = queue.reclaimStuck();

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
  • 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 (EVAL) for the enqueue, complete, fail, and reclaim flows so each runs atomically against the processing list and metadata hash

Why we use Lua for enqueue too

The redis-py reference implementation enqueues a job with a two-command pipeline (HSET + LPUSH). On a single Lettuce StatefulRedisConnection shared across HTTP handler threads, command pipelines from different threads can interleave their responses on the wire. That isn't a correctness problem for two unrelated commands, but it does make it harder to reason about the visible queue state under concurrency.

This port wraps the enqueue's two commands in a single Lua script so the metadata hash write and the pending-list push are atomic on the server. That keeps the demo lock-free without needing to serialise enqueue calls behind a ReentrantLock, and it matches the pattern already used for complete, fail, and reclaim.

For production code, prefer a connection pool over a shared connection: see Production usage below.

Enqueueing jobs

enqueue() runs a Lua script that writes the metadata hash and pushes the job ID onto the pending list in one round trip:

public String enqueue(Map<String, Object> payload) {
    String jobId = randomHex(8);
    long now = System.currentTimeMillis();
    String payloadJson = JsonUtil.toJson(payload);

    String[] keys = { metaKey(jobId), pendingKey };
    String[] argv = { jobId, payloadJson, Long.toString(now) };

    conn.sync().eval(ENQUEUE_SCRIPT, ScriptOutputType.INTEGER, keys, argv);
    return jobId;
}

The payload is stored as JSON so the queue can carry arbitrary nested structures without forcing every field into a hash.

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. BLMOVE does both in a single Redis call. (BRPOPLPUSH is deprecated in Redis 6.2+; BLMOVE with rightLeft() is the modern replacement.)

public ClaimedJob claim(long timeoutMs) {
    double timeoutSeconds = Math.max(timeoutMs / 1000.0, 0.1);
    RedisCommands<String, String> sync = conn.sync();
    String jobId = sync.blmove(pendingKey, processingKey,
            LMoveArgs.Builder.rightLeft(), timeoutSeconds);
    if (jobId == null) {
        return null;
    }

    String token = randomHex(8);
    long now = System.currentTimeMillis();
    String meta = metaKey(jobId);

    Map<String, String> updates = new LinkedHashMap<>();
    updates.put("status", "processing");
    updates.put("claimed_at_ms", Long.toString(now));
    updates.put("claim_token", token);
    sync.hset(meta, updates);
    sync.hincrby(meta, "attempts", 1);
    Map<String, String> hash = sync.hgetall(meta);

    Map<String, Object> payload = JsonUtil.parseObject(hash.getOrDefault("payload", "{}"));
    int attempts = Integer.parseInt(hash.getOrDefault("attempts", "1"));
    return new ClaimedJob(jobId, payload, attempts, token);
}

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 so the processing-list removal, the metadata write, and the history push happen atomically:

public boolean complete(ClaimedJob job, Map<String, Object> result) {
    String[] keys = { metaPrefix, processingKey, completedKey };
    String[] argv = {
            job.id,
            job.claimToken,
            "completed",
            Long.toString(System.currentTimeMillis()),
            JsonUtil.toJson(result),
            Integer.toString(completedTtl),
            Integer.toString(completedHistory),
    };
    Long ok = conn.sync().eval(COMPLETE_SCRIPT, ScriptOutputType.INTEGER, keys, argv);
    if (ok == null || ok == 0L) {
        return false;
    }
    publishEvent(job.id, "completed");
    return true;
}

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:

public boolean fail(ClaimedJob job, String error) {
    boolean retry = job.attempts < maxAttempts;
    String[] keys = { metaPrefix, processingKey, pendingKey, failedKey };
    String[] argv = {
            job.id, job.claimToken, error,
            Long.toString(System.currentTimeMillis()),
            Integer.toString(completedTtl),
            Integer.toString(completedHistory),
            retry ? "1" : "0",
    };
    Long result = conn.sync().eval(FAIL_SCRIPT, ScriptOutputType.INTEGER, keys, argv);
    return result != null && result != 0L;
}

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:

public List<String> reclaimStuck() {
    String[] keys = { pendingKey, processingKey, metaPrefix };
    String[] argv = {
            Long.toString(System.currentTimeMillis()),
            Long.toString(visibilityMs),
    };
    List<Object> raw = conn.sync().eval(RECLAIM_SCRIPT, ScriptOutputType.MULTI, keys, argv);
    List<String> reclaimed = new ArrayList<>();
    if (raw != null) {
        for (Object item : raw) {
            if (item != null) reclaimed.add(item.toString());
        }
    }
    return 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:

public Map<String, Object> stats() {
    RedisCommands<String, String> sync = conn.sync();
    long pending = sync.llen(pendingKey);
    long processing = sync.llen(processingKey);
    long completed = sync.llen(completedKey);
    long failed = sync.llen(failedKey);
    Map<String, Object> out = new LinkedHashMap<>();
    out.put("enqueued_total", enqueuedTotal);
    out.put("completed_total", completedTotal);
    out.put("failed_total", failedTotal);
    out.put("reclaimed_total", reclaimedTotal);
    out.put("pending_depth", pending);
    out.put("processing_depth", processing);
    out.put("completed_depth", completed);
    out.put("failed_depth", failed);
    out.put("visibility_ms", visibilityMs);
    return out;
}

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). BLMOVE was added in 6.2; on earlier versions, swap it for BRPOPLPUSH in claim().
  • JDK 17 or later.
  • Lettuce 6.1+ and its runtime dependencies (netty-*, reactor-core, reactive-streams).

Add Lettuce to your project:

  • If you use Maven:

    <dependency>
    <groupId>io.lettuce</groupId>
    <artifactId>lettuce-core</artifactId>
    <version>6.7.1.RELEASE</version>
</dependency>

  • If you use Gradle:

    implementation 'io.lettuce:lettuce-core:6.7.1.RELEASE'

Running the demo

Get the source files

The demo consists of four Java source files. Download them from the java-lettuce 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/java-lettuce
curl -O $BASE/RedisJobQueue.java
curl -O $BASE/JobWorker.java
curl -O $BASE/WorkerPool.java
curl -O $BASE/DemoServer.java

Start the demo server

From that directory, compile the sources and run the server. With the Lettuce + netty + reactor jars staged in a local lib/ directory:

javac -cp "lib/*" -d build RedisJobQueue.java JobWorker.java WorkerPool.java DemoServer.java
java -cp "build:lib/*" DemoServer --port 8794 --visibility-ms 5000

You should see:

Redis job-queue demo server listening on http://127.0.0.1:8794
Using Redis at localhost:6379
Visibility timeout: 5000 ms

Open http://127.0.0.1:8794 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 pick an alternate Redis key prefix with --queue-name.

The mock worker pool

The demo includes a small JobWorker and WorkerPool (source, WorkerPool.java) 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.

Production usage

Use a connection pool, not a shared connection

The demo shares a single StatefulRedisConnection across HTTP handlers and worker threads to keep the code compact. That has two consequences worth knowing about:

  • The claim() call uses BLMOVE, which blocks the shared connection for up to the claim timeout. With many workers sharing one connection, claim throughput is serialised. The demo uses a 500ms timeout so the connection stays responsive to other commands, but a real deployment will want each worker to own a connection.
  • Lettuce transactions (MULTI/EXEC) are connection-scoped, so any code that uses them would also need to serialise behind a ReentrantLock. This port avoids MULTI/EXEC entirely — the multi-command operations (enqueue, complete, fail, reclaim) all run as Lua scripts.

In production, use ConnectionPoolSupport.createGenericObjectPool(redisClient::connect, poolConfig) and acquire a connection per worker (and per request handler if you want fully concurrent pipelines):

GenericObjectPoolConfig<StatefulRedisConnection<String, String>> config = new GenericObjectPoolConfig<>();
config.setMaxTotal(32);
GenericObjectPool<StatefulRedisConnection<String, String>> pool =
        ConnectionPoolSupport.createGenericObjectPool(client::connect, config);

try (StatefulRedisConnection<String, String> conn = pool.borrowObject()) {
    new RedisJobQueue(conn, "jobs", 5000, 300, 50, 3).enqueue(payload);
}

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 ScheduledExecutorService (every few seconds for fast queues, every minute 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.

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.
  • 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.
  • EVAL for atomic enqueue, complete, fail, and reclaim flows.

See the Lettuce documentation for full client reference.

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