{ "id": "java-lettuce", "title": "Redis job queue with Lettuce", "url": "https://redis.io/docs/latest/develop/use-cases/job-queue/java-lettuce/", "summary": "Implement a Redis job queue in Java with Lettuce", "tags": [ "docs", "develop", "stack", "oss", "rs", "rc" ], "last_updated": "2026-05-14T08:58:05-05:00", "children": [], "page_type": "content", "content_hash": "c28757d2f407795dec9143eb45ca5e7f3a5b35b83c03b198ccde94f461d9b794", "sections": [ { "id": "overview", "title": "Overview", "role": "overview", "text": "This guide shows you how to implement a Redis-backed job queue in Java with [`Lettuce`](https://redis.io/docs/latest/develop/clients/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." }, { "id": "overview", "title": "Overview", "role": "overview", "text": "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.\n\nThat gives you:\n\n* Low-latency user-facing requests, even when downstream work is slow or bursty\n* Horizontal scale across many worker processes that share one Redis instance\n* At-least-once delivery so a worker crash doesn't lose work\n* Visibility-timeout reclaim that returns stuck jobs to the queue automatically\n* Job metadata, retry counts, and completion results in Redis hashes with TTL\n\nIn 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." }, { "id": "how-it-works", "title": "How it works", "role": "content", "text": "The flow looks like this:\n\n1. The application calls `queue.enqueue(payload)`\n2. The helper runs a single Lua script that writes the job metadata hash and `LPUSH`es the job ID onto the pending list\n3. A worker calls `queue.claim(timeoutMs)`\n4. 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\n5. The worker runs the job and calls `queue.complete(job, result)` or `queue.fail(job, error)`\n6. `complete` removes the job from the processing list, writes the result, and `LPUSH`es the ID onto the completed history (with `LTRIM` and an `EXPIRE` on the hash for cleanup)\n7. `fail` either retries the job (back to pending) or moves it to the failed list once retries are exhausted\n\nIf 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.\n\nEvery state change holds the token: a worker that has been reclaimed cannot later complete or fail a job another worker has already claimed." }, { "id": "the-job-queue-helper", "title": "The job queue helper", "role": "content", "text": "The `RedisJobQueue` class wraps the queue operations\n([source](https://github.com/redis/docs/blob/main/content/develop/use-cases/job-queue/java-lettuce/RedisJobQueue.java)):\n\n[code example]" }, { "id": "data-model", "title": "Data model", "role": "content", "text": "Each job's state lives in a Redis hash plus a position in one of four lists:\n\n[code example]\n\nA job's hash carries:\n\n[code example]\n\nThe implementation uses:\n\n* [`LPUSH`](https://redis.io/docs/latest/commands/lpush) to add new job IDs to the pending list\n* [`BLMOVE`](https://redis.io/docs/latest/commands/blmove) to atomically claim a job into the processing list\n* [`LREM`](https://redis.io/docs/latest/commands/lrem) to remove a claimed job from the processing list on complete or fail\n* [`LTRIM`](https://redis.io/docs/latest/commands/ltrim) to cap the completed and failed history lists\n* [`HSET`](https://redis.io/docs/latest/commands/hset) / [`HGETALL`](https://redis.io/docs/latest/commands/hgetall) for job metadata\n* [`EXPIRE`](https://redis.io/docs/latest/commands/expire) on completed and failed hashes for automatic cleanup\n* [`PUBLISH`](https://redis.io/docs/latest/commands/publish) on `queue:jobs:events` for completion signalling\n* [Lua scripting](https://redis.io/docs/latest/develop/programmability/eval-intro) ([`EVAL`](https://redis.io/docs/latest/commands/eval)) for the enqueue, complete, fail, and reclaim flows so each runs atomically against the processing list and metadata hash" }, { "id": "why-we-use-lua-for-enqueue-too", "title": "Why we use Lua for enqueue too", "role": "content", "text": "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.\n\nThis 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`.\n\nFor production code, prefer a connection pool over a shared connection: see [Production usage](#production-usage) below." }, { "id": "enqueueing-jobs", "title": "Enqueueing jobs", "role": "content", "text": "`enqueue()` runs a Lua script that writes the metadata hash and pushes the job ID onto the pending list in one round trip:\n\n[code example]\n\nThe payload is stored as JSON so the queue can carry arbitrary nested structures without forcing every field into a hash." }, { "id": "claiming-jobs-with-blmove", "title": "Claiming jobs with BLMOVE", "role": "content", "text": "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.)\n\n[code example]\n\nThe `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." }, { "id": "completing-jobs", "title": "Completing jobs", "role": "content", "text": "`complete()` runs a Lua script so the processing-list removal, the metadata write, and the history push happen atomically:\n\n[code example]\n\nThe 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." }, { "id": "failing-and-retrying", "title": "Failing and retrying", "role": "content", "text": "`fail()` either retries the job (back to pending) or moves it to the failed list once retries are exhausted:\n\n[code example]\n\nThe 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." }, { "id": "reclaiming-stuck-jobs", "title": "Reclaiming stuck jobs", "role": "content", "text": "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:\n\n[code example]\n\nThe 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." }, { "id": "stats-and-history", "title": "Stats and history", "role": "content", "text": "`stats()` reports queue depth plus per-process counters:\n\n[code example]\n\nThe 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." }, { "id": "prerequisites", "title": "Prerequisites", "role": "content", "text": "* 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()`.\n* JDK 17 or later.\n* Lettuce 6.1+ and its runtime dependencies (`netty-*`, `reactor-core`, `reactive-streams`).\n\nAdd Lettuce to your project:\n\n* If you use **Maven**:\n\n [code example]\n\n* If you use **Gradle**:\n\n [code example]" }, { "id": "running-the-demo", "title": "Running the demo", "role": "content", "text": "" }, { "id": "get-the-source-files", "title": "Get the source files", "role": "content", "text": "The demo consists of four Java source files. Download them from the [`java-lettuce` source folder](https://github.com/redis/docs/tree/main/content/develop/use-cases/job-queue/java-lettuce) on GitHub, or grab them with `curl`:\n\n[code example]" }, { "id": "start-the-demo-server", "title": "Start the demo server", "role": "content", "text": "From that directory, compile the sources and run the server. With the Lettuce + netty + reactor jars staged in a local `lib/` directory:\n\n[code example]\n\nYou should see:\n\n[code example]\n\nOpen [http://127.0.0.1:8794](http://127.0.0.1:8794) in a browser. You can:\n\n* Enqueue jobs of different kinds (email, webhook, thumbnail, invoice) in batches.\n* Start a pool of workers with configurable size, work latency, and *failure* / *hang* rates. A non-zero hang rate simulates worker crashes.\n* Click **Run reclaim sweep** to move any timed-out processing jobs back to pending.\n* Watch pending / processing / completed / failed lists update every 800 ms.\n\nIf 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`." }, { "id": "the-mock-worker-pool", "title": "The mock worker pool", "role": "content", "text": "The demo includes a small `JobWorker` and `WorkerPool` ([source](https://github.com/redis/docs/blob/main/content/develop/use-cases/job-queue/java-lettuce/JobWorker.java), [WorkerPool.java](https://github.com/redis/docs/blob/main/content/develop/use-cases/job-queue/java-lettuce/WorkerPool.java)) that stands in for whatever real background work your application would run. Each worker:\n\n* Blocks on `queue.claim()` for new jobs.\n* Sleeps `workLatencyMs` to simulate doing the work.\n* Either completes successfully, fails (calling `queue.fail()`), or *hangs* — returning without completing or failing the job so the reclaimer has to recover it.\n\nThe `failRate` and `hangRate` knobs let you watch the at-least-once delivery and reclaim behaviours from the UI without writing test code." }, { "id": "production-usage", "title": "Production usage", "role": "content", "text": "" }, { "id": "use-a-connection-pool-not-a-shared-connection", "title": "Use a connection pool, not a shared connection", "role": "content", "text": "The demo shares a single `StatefulRedisConnection` across HTTP handlers and worker threads to keep the code compact. That has two consequences worth knowing about:\n\n* 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.\n* 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.\n\nIn production, use `ConnectionPoolSupport.createGenericObjectPool(redisClient::connect, poolConfig)` and acquire a connection per worker (and per request handler if you want fully concurrent pipelines):\n\n[code example]" }, { "id": "choose-a-visibility-timeout-that-matches-your-worst-case-job-latency", "title": "Choose a visibility timeout that matches your worst-case job latency", "role": "content", "text": "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." }, { "id": "run-the-reclaimer-on-a-schedule", "title": "Run the reclaimer on a schedule", "role": "content", "text": "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." }, { "id": "use-a-separate-redis-database-or-key-prefix-per-queue", "title": "Use a separate Redis database or key prefix per queue", "role": "content", "text": "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." }, { "id": "cap-the-completed-and-failed-history", "title": "Cap the completed and failed history", "role": "content", "text": "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." }, { "id": "tune-maxattempts-per-job-kind", "title": "Tune `maxAttempts` per job kind", "role": "content", "text": "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." }, { "id": "inspect-queue-state-directly-in-redis", "title": "Inspect queue state directly in Redis", "role": "content", "text": "Because the queue is just lists and hashes, you can inspect it with `redis-cli`:\n\n[code example]" }, { "id": "learn-more", "title": "Learn more", "role": "related", "text": "This example uses the following Redis commands:\n\n* [`LPUSH`](https://redis.io/docs/latest/commands/lpush) to enqueue a job ID.\n* [`BLMOVE`](https://redis.io/docs/latest/commands/blmove) to atomically claim a job into the processing list.\n* [`LREM`](https://redis.io/docs/latest/commands/lrem) to remove a job from the processing list on complete or fail.\n* [`LRANGE`](https://redis.io/docs/latest/commands/lrange) and [`LLEN`](https://redis.io/docs/latest/commands/llen) to read queue depth and list contents.\n* [`LTRIM`](https://redis.io/docs/latest/commands/ltrim) to cap the completed and failed history.\n* [`HSET`](https://redis.io/docs/latest/commands/hset) and [`HGETALL`](https://redis.io/docs/latest/commands/hgetall) for job metadata.\n* [`HINCRBY`](https://redis.io/docs/latest/commands/hincrby) for the attempt counter.\n* [`EXPIRE`](https://redis.io/docs/latest/commands/expire) for automatic cleanup of completed and failed jobs.\n* [`PUBLISH`](https://redis.io/docs/latest/commands/publish) for job-completion notifications.\n* [`EVAL`](https://redis.io/docs/latest/commands/eval) for atomic enqueue, complete, fail, and reclaim flows.\n\nSee the [Lettuce documentation](https://redis.io/docs/latest/develop/clients/lettuce) for full client reference." } ], "examples": [ { "id": "the-job-queue-helper-ex0", "language": "java", "code": "import io.lettuce.core.RedisClient;\nimport io.lettuce.core.api.StatefulRedisConnection;\nimport java.util.Map;\n\nRedisClient client = RedisClient.create(\"redis://localhost:6379\");\nStatefulRedisConnection conn = client.connect();\n\nRedisJobQueue queue = new RedisJobQueue(conn, \"jobs\", 5000, 300, 50, 3);\n\nString jobId = queue.enqueue(Map.of(\n \"kind\", \"email\",\n \"recipient\", \"alice@example.com\"));\n\n// In a worker thread:\nRedisJobQueue.ClaimedJob job = queue.claim(1000);\nif (job != null) {\n try {\n // ... run the job ...\n queue.complete(job, Map.of(\"sent_at\", \"2026-05-11T15:00:00Z\"));\n } catch (Exception ex) {\n queue.fail(job, ex.getMessage());\n }\n}\n\n// In a periodic sweeper:\nList reclaimed = queue.reclaimStuck();", "section_id": "the-job-queue-helper" }, { "id": "data-model-ex0", "language": "text", "code": "queue:jobs:pending (list) pending job IDs, oldest at the right\nqueue:jobs:processing (list) claimed but not yet completed\nqueue:jobs:completed (list) recent successes (LTRIM-capped history)\nqueue:jobs:failed (list) terminally failed jobs\nqueue:jobs:job:{id} (hash) per-job metadata\nqueue:jobs:events (pubsub) completion notifications", "section_id": "data-model" }, { "id": "data-model-ex1", "language": "text", "code": "queue:jobs:job:9a4f...\n id = 9a4f...\n payload = {\"kind\":\"email\",\"recipient\":\"alice@example.com\"}\n status = pending | processing | completed | failed\n attempts = 1\n enqueued_at_ms = 1715441000000\n claimed_at_ms = 1715441000123\n claim_token = b3c0d1e2... (per-claim random token)\n completed_at_ms = 1715441000456\n result = {\"sent_at\":\"...\"}\n last_error = \"smtp timeout\"", "section_id": "data-model" }, { "id": "enqueueing-jobs-ex0", "language": "java", "code": "public String enqueue(Map payload) {\n String jobId = randomHex(8);\n long now = System.currentTimeMillis();\n String payloadJson = JsonUtil.toJson(payload);\n\n String[] keys = { metaKey(jobId), pendingKey };\n String[] argv = { jobId, payloadJson, Long.toString(now) };\n\n conn.sync().eval(ENQUEUE_SCRIPT, ScriptOutputType.INTEGER, keys, argv);\n return jobId;\n}", "section_id": "enqueueing-jobs" }, { "id": "claiming-jobs-with-blmove-ex0", "language": "java", "code": "public ClaimedJob claim(long timeoutMs) {\n double timeoutSeconds = Math.max(timeoutMs / 1000.0, 0.1);\n RedisCommands sync = conn.sync();\n String jobId = sync.blmove(pendingKey, processingKey,\n LMoveArgs.Builder.rightLeft(), timeoutSeconds);\n if (jobId == null) {\n return null;\n }\n\n String token = randomHex(8);\n long now = System.currentTimeMillis();\n String meta = metaKey(jobId);\n\n Map updates = new LinkedHashMap<>();\n updates.put(\"status\", \"processing\");\n updates.put(\"claimed_at_ms\", Long.toString(now));\n updates.put(\"claim_token\", token);\n sync.hset(meta, updates);\n sync.hincrby(meta, \"attempts\", 1);\n Map hash = sync.hgetall(meta);\n\n Map payload = JsonUtil.parseObject(hash.getOrDefault(\"payload\", \"{}\"));\n int attempts = Integer.parseInt(hash.getOrDefault(\"attempts\", \"1\"));\n return new ClaimedJob(jobId, payload, attempts, token);\n}", "section_id": "claiming-jobs-with-blmove" }, { "id": "completing-jobs-ex0", "language": "java", "code": "public boolean complete(ClaimedJob job, Map result) {\n String[] keys = { metaPrefix, processingKey, completedKey };\n String[] argv = {\n job.id,\n job.claimToken,\n \"completed\",\n Long.toString(System.currentTimeMillis()),\n JsonUtil.toJson(result),\n Integer.toString(completedTtl),\n Integer.toString(completedHistory),\n };\n Long ok = conn.sync().eval(COMPLETE_SCRIPT, ScriptOutputType.INTEGER, keys, argv);\n if (ok == null || ok == 0L) {\n return false;\n }\n publishEvent(job.id, \"completed\");\n return true;\n}", "section_id": "completing-jobs" }, { "id": "failing-and-retrying-ex0", "language": "java", "code": "public boolean fail(ClaimedJob job, String error) {\n boolean retry = job.attempts < maxAttempts;\n String[] keys = { metaPrefix, processingKey, pendingKey, failedKey };\n String[] argv = {\n job.id, job.claimToken, error,\n Long.toString(System.currentTimeMillis()),\n Integer.toString(completedTtl),\n Integer.toString(completedHistory),\n retry ? \"1\" : \"0\",\n };\n Long result = conn.sync().eval(FAIL_SCRIPT, ScriptOutputType.INTEGER, keys, argv);\n return result != null && result != 0L;\n}", "section_id": "failing-and-retrying" }, { "id": "reclaiming-stuck-jobs-ex0", "language": "java", "code": "public List reclaimStuck() {\n String[] keys = { pendingKey, processingKey, metaPrefix };\n String[] argv = {\n Long.toString(System.currentTimeMillis()),\n Long.toString(visibilityMs),\n };\n List raw = conn.sync().eval(RECLAIM_SCRIPT, ScriptOutputType.MULTI, keys, argv);\n List reclaimed = new ArrayList<>();\n if (raw != null) {\n for (Object item : raw) {\n if (item != null) reclaimed.add(item.toString());\n }\n }\n return reclaimed;\n}", "section_id": "reclaiming-stuck-jobs" }, { "id": "stats-and-history-ex0", "language": "java", "code": "public Map stats() {\n RedisCommands sync = conn.sync();\n long pending = sync.llen(pendingKey);\n long processing = sync.llen(processingKey);\n long completed = sync.llen(completedKey);\n long failed = sync.llen(failedKey);\n Map out = new LinkedHashMap<>();\n out.put(\"enqueued_total\", enqueuedTotal);\n out.put(\"completed_total\", completedTotal);\n out.put(\"failed_total\", failedTotal);\n out.put(\"reclaimed_total\", reclaimedTotal);\n out.put(\"pending_depth\", pending);\n out.put(\"processing_depth\", processing);\n out.put(\"completed_depth\", completed);\n out.put(\"failed_depth\", failed);\n out.put(\"visibility_ms\", visibilityMs);\n return out;\n}", "section_id": "stats-and-history" }, { "id": "prerequisites-ex0", "language": "xml", "code": "\n io.lettuce\n lettuce-core\n 6.7.1.RELEASE\n ", "section_id": "prerequisites" }, { "id": "prerequisites-ex1", "language": "groovy", "code": "implementation 'io.lettuce:lettuce-core:6.7.1.RELEASE'", "section_id": "prerequisites" }, { "id": "get-the-source-files-ex0", "language": "bash", "code": "mkdir job-queue-demo && cd job-queue-demo\nBASE=https://raw.githubusercontent.com/redis/docs/main/content/develop/use-cases/job-queue/java-lettuce\ncurl -O $BASE/RedisJobQueue.java\ncurl -O $BASE/JobWorker.java\ncurl -O $BASE/WorkerPool.java\ncurl -O $BASE/DemoServer.java", "section_id": "get-the-source-files" }, { "id": "start-the-demo-server-ex0", "language": "bash", "code": "javac -cp \"lib/*\" -d build RedisJobQueue.java JobWorker.java WorkerPool.java DemoServer.java\njava -cp \"build:lib/*\" DemoServer --port 8794 --visibility-ms 5000", "section_id": "start-the-demo-server" }, { "id": "start-the-demo-server-ex1", "language": "text", "code": "Redis job-queue demo server listening on http://127.0.0.1:8794\nUsing Redis at localhost:6379\nVisibility timeout: 5000 ms", "section_id": "start-the-demo-server" }, { "id": "use-a-connection-pool-not-a-shared-connection-ex0", "language": "java", "code": "GenericObjectPoolConfig> config = new GenericObjectPoolConfig<>();\nconfig.setMaxTotal(32);\nGenericObjectPool> pool =\n ConnectionPoolSupport.createGenericObjectPool(client::connect, config);\n\ntry (StatefulRedisConnection conn = pool.borrowObject()) {\n new RedisJobQueue(conn, \"jobs\", 5000, 300, 50, 3).enqueue(payload);\n}", "section_id": "use-a-connection-pool-not-a-shared-connection" }, { "id": "inspect-queue-state-directly-in-redis-ex0", "language": "bash", "code": "# How many pending jobs?\nredis-cli LLEN queue:jobs:pending\n\n# Look at the next 5 jobs to be picked up.\nredis-cli LRANGE queue:jobs:pending -5 -1\n\n# Read a job's metadata.\nredis-cli HGETALL queue:jobs:job:9a4f0d1c\n\n# How many jobs are currently being processed?\nredis-cli LLEN queue:jobs:processing\n\n# Clear everything for this queue (be careful — this deletes work).\nredis-cli --scan --pattern 'queue:jobs:*' | xargs redis-cli DEL", "section_id": "inspect-queue-state-directly-in-redis" } ] }