Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsIf a background task matters after the HTTP request ends—or after a Node.js process restarts—put it in a persistent task queue rather than relying on a detached promise, timer, or in-memory list. A queue stores job state in a backend and lets a worker claim the work independently. That makes recovery and retries possible, but it does not guarantee that a job or its external side effects can never be lost or duplicated.
Why background jobs disappear
A promise started without being awaited, a process-local timer, or an in-memory work list belongs to the lifetime of the process that created it. If that process exits during a deploy, crash, or restart, the unfinished work goes with it. This is an architectural consequence of process-local state, not a measured loss rate.
Tasks that often need a longer life than a request include sending email, rendering a PDF, calling a slow third-party API, and handling an order-related operation. The pg-boss introduction describes putting such work in a queue and dispatching it to a worker: pg-boss introduction.
What a persistent queue changes—and what it does not
The producer records a job in an external backend; a worker claims it and performs the task. Because job state is no longer only in the producer’s memory, a producer restart need not erase work that was successfully stored. A queue also gives the system a place to record attempts and recover work when a worker stops making progress.
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Persistence is not the same as exactly-once execution. pg-boss documents at-least-once delivery: a job can run again after a crash or expiration. If a handler charges a card, sends a message, or updates another system and then crashes before marking the job complete, a retry can repeat that side effect. Make handlers safe to repeat with an idempotency key, a unique database constraint, or an application state transition. See the pg-boss delivery and transaction documentation.
Whether a job survives also depends on the producer receiving the acknowledgement it expects, backend durability settings, worker shutdown and recovery, retry and retention configuration, and the external service’s own behavior. Decide what “success” means for the request: do not tell a user work is safely queued until insertion has met the persistence requirement the application chose.
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Where jobs can still fail
The enqueue boundary
If the application changes a database row and separately inserts a Redis-backed job, either operation can succeed while the other fails. The reviewed BullMQ documentation does not establish a transaction spanning Redis insertion and an application SQL write, so treat this as a dual-write failure window. If both changes must commit together, use a documented transactional enqueue design or an outbox whose relay and recovery behavior you have validated.
pg-boss documents adding a job in the same PostgreSQL transaction as the associated data change: the job exists if and only if that transaction commits. Its documentation also describes using SKIP LOCKED to claim jobs. See pg-boss introduction.
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Worker crashes and stalled jobs
BullMQ tracks active jobs with a renewable lock. If a worker cannot renew that lock, a job may be marked stalled and returned to waiting; repeated stalls can exhaust the configured threshold and cause the job to fail. A busy Node.js event loop can prevent renewal, so CPU-heavy synchronous work should be moved to a sandboxed processor or separate process, or divided into smaller work units. See BullMQ stalled jobs.
Deploys and forced termination
During a normal shutdown, BullMQ recommends closing workers on SIGINT and SIGTERM so they can stop cleanly. Forced termination can leave jobs marked stalled until a worker returns, and a job that outlasts the deployment’s shutdown grace period can still stall. Allow enough grace time for expected in-flight work and make sure a worker will resume processing afterward. See BullMQ production guidance.
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Retries and permanent errors
BullMQ retries are not automatic by default: configure attempts greater than one to enable them. Its retry guide documents fixed and exponential backoff, with optional jitter. Fixed delays are predictable; exponential backoff with jitter can spread retries rather than making many failing jobs retry at once. Set a bounded retry policy and distinguish transient failures from permanent ones instead of retrying invalid input indefinitely. See BullMQ retry guidance.
Retention and payload exposure
BullMQ’s production guide says completed and failed jobs are retained by default unless automatic removal is configured. Retention supports inspection but consumes storage as job history accumulates. The same guide says job data is stored in clear text, so keep payloads minimal and do not put secrets or sensitive data in them unless you encrypt them. See BullMQ production guidance.
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Choosing a backend: Redis or PostgreSQL
BullMQ uses Redis by default and also offers an optional PostgreSQL backend. pg-boss uses PostgreSQL. For a team already running PostgreSQL, the choice is not simply whether a queue is persistent: weigh operational familiarity, transactional enqueue needs, throughput requirements, connection capacity, and whether operating another datastore is acceptable.
| Decision | BullMQ with Redis | PostgreSQL-backed option |
|---|---|---|
| Operational footprint | Uses Redis as a separate service; Redis is BullMQ’s default backend. | pg-boss uses PostgreSQL. BullMQ’s optional PostgreSQL backend is positioned for teams that prefer not to operate separate Redis or want jobs alongside relational data. BullMQ PostgreSQL backend; pg-boss introduction. |
| Transactional enqueue | The reviewed documentation does not establish a transaction spanning Redis queue insertion and application SQL writes; account for the dual-write failure window. | pg-boss documents inserting a job in the same transaction as the associated database change. pg-boss introduction. |
| Delivery and recovery | BullMQ documents retries and stalled-job recovery; configure attempts and backoff and understand its worker lock model. Retries; Stalled jobs. | pg-boss documents at-least-once delivery and SKIP LOCKED job claims, so handlers must be safe to repeat. pg-boss introduction. |
| Documented benchmark figures | BullMQ’s own same-machine benchmark reports about 7,500 sequential adds per second, 38,000 concurrent individual adds per second, 52,000 batched concurrent adds per second, and 6,000 processing jobs per second at concurrency 1. | BullMQ’s own same-machine benchmark reports about 7,000 sequential adds per second, 15,000 concurrent individual adds per second, 45,000 batched concurrent adds per second, and 2,300 processing jobs per second at concurrency 1. The documentation does not state a publication year or enough representative deployment details to generalize these figures. BullMQ PostgreSQL backend. |
| Requirements and capacity | Redis configuration and connectivity matter; plan for production error handling and graceful shutdown. BullMQ production guidance. | BullMQ’s PostgreSQL backend requires PostgreSQL 13 minimum and recommends 14 or later. Pool size and server max_connections must account for queues, workers, and event connections. BullMQ PostgreSQL backend. |
| Durability tuning | BullMQ says Redis persistence must be configured manually. BullMQ production guidance. | BullMQ warns that synchronous_commit = off or local can lose recent commits after a crash; use those settings only if that durability tradeoff is acceptable. BullMQ PostgreSQL backend. |
The benchmark numbers are BullMQ documentation figures, not independent measurements. They are contextual comparisons, not a throughput promise for a different machine, workload, or deployment.
Putting a queue into a Node.js SaaS
- Separate producer and worker responsibilities. The request path validates the request and enqueues the job; workers handle slow or retryable work outside the request lifecycle.
- Choose the enqueue guarantee. If application data and job creation must succeed or fail together, use a documented transactional approach such as pg-boss with PostgreSQL, or implement and validate an outbox relay. Otherwise, handle the separate-write failure window explicitly.
- Configure bounded retries. Set BullMQ’s
attemptsabove one only where retries are appropriate, choose fixed or exponential backoff, and decide how permanent errors are handled. - Make the handler repeat-safe. Use idempotency keys, unique constraints, or state transitions around side effects so a retry does not create a second charge, email, or order action.
- Shut down deliberately. Close workers on
SIGINTandSIGTERM, and set the deployment’s termination grace period with in-flight job duration in mind. - Surface infrastructure errors. Attach error handlers and logs to queue and worker connections, as BullMQ’s production guidance recommends.
- Monitor the states that reveal a stuck queue. Track waiting, active, and failed counts; the age of the oldest waiting job; stalled events; retry volume; worker availability or heartbeat; backend errors; and queue storage growth. Instrument the signals exposed by the chosen library and backend.
- Keep payloads small and appropriate. Store only the data needed to process the job, and account for the clear-text storage behavior documented by BullMQ.
When does your SaaS need a persistent queue?
Use one when unfinished work must outlive the request or process, when retries and recovery matter, or when a slow dependency should not hold up a user’s request. A queue is not automatically necessary for every short, disposable task; the deciding question is whether losing or repeating the work would matter to users or the business.
If the application already depends on PostgreSQL and job creation must be atomic with a database change, pg-boss is a documented fit. BullMQ’s PostgreSQL backend is another option for teams avoiding a separate Redis service, subject to its PostgreSQL requirements and capacity planning. Choose BullMQ with Redis when Redis is acceptable operationally and its default backend and documented behavior match the workload. In every case, the queue is one part of reliability: acknowledgements, backend durability, worker lifecycle, retry policy, retention, and idempotent side effects complete the design.
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