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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsFor an image batch API, give each image its own queue job when callers need per-image progress, failure details, and retries. Store batch status in application data, use BullMQ job progress for work-in-progress updates, and use QueueEvents when another process needs live events. Retry only the failed images that are safe to run again.
The examples below use BullMQ. Check the documentation for your installed BullMQ major version before copying API calls: the Job API reference linked here is versioned v1, and the current API surface may differ.
Choose the failure boundary: one job per image or one job per batch
Decide what should fail and retry together before designing the API. BullMQ documents several ways to model batches, including independent jobs, flows, a single job that processes many items, and BullMQ Pro worker batches. These are not interchangeable designs; see BullMQ’s batch patterns.
| Design | Failure and retry scope | Progress granularity | Best fit |
|---|---|---|---|
| Independent job per image | One image can fail and be retried without retrying successful images. | Per-image job state; the API aggregates batch totals. | Separate reporting, retries, or completion outcomes for each image. |
| One job processes the batch | The batch shares a job’s retry, timeout, and completion outcome. | The job can publish aggregate progress, such as completed images out of total. | All images should be treated as one unit of work. |
| BullMQ Pro worker batch | Uses Pro-specific batch and wrapper-job semantics. | Depends on the Pro batch API and events. | Use only when you intend to adopt and handle those distinct semantics. |
For most APIs that must show which images succeeded and retry only the failures, independent jobs provide the clearest boundary. Flows can model dependencies between jobs, but do not choose them merely to represent a flat list of unrelated images.
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Submit a batch and return stable identifiers
A practical REST shape is POST /batches to accept image references and return a stable batch ID plus each image’s job ID. This is an application design, not a BullMQ-mandated contract. Use a durable batch record to connect those identifiers to caller-visible status.
// Illustrative Express/BullMQ shape; adapt to your installed BullMQ version.
app.post('/batches', async (req, res, next) => {
try {
const batchId = createStableBatchId();
const imageIds = validateImageIds(req.body.imageIds);
await batchStore.create({ batchId, imageIds, status: 'queued' });
const jobs = await imageQueue.addBulk(
imageIds.map((imageId) => ({
name: 'process-image',
data: { batchId, imageId },
opts: { attempts: 3, backoff: { type: 'exponential', delay: 1000 } },
})),
);
await batchStore.recordJobs(batchId, jobs.map((job, index) => ({
imageId: imageIds[index],
jobId: job.id,
status: 'queued',
})));
res.status(202).json({ batchId, jobs: jobs.map((job, index) => ({ imageId: imageIds[index], jobId: job.id })) });
} catch (error) {
next(error);
}
});
The attempt and backoff values above illustrate explicit configuration; choose them for your image processor and its dependencies rather than treating them as universal defaults. Keep the input references and output destinations stable enough that a retry can safely repeat the operation.
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Track progress for a BullMQ job
For a single job that processes many images, call job.updateProgress as each item finishes. BullMQ supports numeric progress and object progress; an object makes batch counters explicit. See the BullMQ job progress guide and the versioned v1 Job API reference.
const imageQueue = new Queue('images', { connection });
const worker = new Worker('images', async (job) => {
const { imageIds } = job.data;
for (let index = 0; index < imageIds.length; index += 1) {
await processImage(imageIds[index]);
await job.updateProgress({ completed: index + 1, total: imageIds.length });
}
return { processed: imageIds.length };
}, { connection });
Do not use one job’s progress object as if it were per-image retry state: it describes that job’s work. With one job per image, track each item’s state separately and calculate batch totals from the item records or job states.
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Expose progress through a batch-status endpoint
Provide a read endpoint such as GET /batches/{id} that reads durable status and returns an aggregate plus item-level details. The precise response format is yours to define; a useful shape is:
{
"batchId": "batch_123",
"status": "processing",
"counts": { "total": 3, "completed": 1, "failed": 1, "active": 1 },
"items": [
{ "imageId": "img_a", "jobId": "job_a", "status": "completed", "attemptsMade": 1 },
{ "imageId": "img_b", "jobId": "job_b", "status": "failed", "attemptsMade": 3, "error": { "code": "PROCESSING_FAILED", "message": "Image processing failed" } },
{ "imageId": "img_c", "jobId": "job_c", "status": "active", "progress": { "stage": "resize" }, "attemptsMade": 1 }
]
}
Sanitize failure information before returning it. Expose actionable codes and safe messages, not stack traces, credentials, internal paths, or raw downstream responses. Make the batch’s overall status rule explicit—for example, whether a batch is “completed” only when all items finish, or “partially_failed” when at least one item ultimately fails.
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Choose polling or live updates
Polling
For a simple API, clients can periodically call the batch-status endpoint. This keeps the status contract independent of a long-lived connection and lets clients recover after disconnecting. Use a sensible polling interval and avoid querying Redis or recalculating an entire batch more often than your service can support.
Server-Sent Events or WebSockets
For live dashboards, a service can listen for cross-worker job lifecycle and progress events with BullMQ QueueEvents, then relay updates over Server-Sent Events or WebSockets. QueueEvents is designed to observe events from workers across processes. It uses Redis Streams, which the guide describes as resilient to disconnections compared with ordinary pub/sub.
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const queueEvents = new QueueEvents('images', { connection });
queueEvents.on('progress', ({ jobId, data }) => {
// Resolve jobId to batchId, then publish a sanitized update to connected clients.
publishProgress(jobId, data);
});
queueEvents.on('completed', ({ jobId, returnvalue }) => {
publishCompleted(jobId, returnvalue);
});
queueEvents.on('failed', ({ jobId, failedReason }) => {
publishFailed(jobId, sanitizeFailure(failedReason));
});
// During application shutdown:
await queueEvents.close();
Close QueueEvents during shutdown to release its Redis connection. The events guide says the stream is automatically trimmed by default to approximately 10,000 events; configuration can change this. Treat that as bounded event history, not as a permanent audit log or the sole source for a later API read.
Retry failed image jobs deliberately
BullMQ automatic retries require attempts greater than one. If you configure attempts without a backoff option, failed jobs are retried immediately. A fixed backoff waits a set interval; exponential backoff increases the delay with each retry and supports jitter to vary it. You can also define a custom worker backoff strategy. Consult the BullMQ retry guide for the exact options supported by your installed version.
The guide’s example using three total attempts and a 1,000 ms exponential seed produces retry delays of one, two, and four seconds. That is an example, not a general policy: choose the delay and attempt count based on the failure class and the downstream service’s limits. Do not retry permanent failures such as invalid image input without first correcting the underlying data.
BullMQ’s retry guide states: “The exceptions thrown in a processor must be an Error object for BullMQ to work correctly.” Throw actual JavaScript Error instances so queue failure handling works as intended.
const worker = new Worker('images', async (job) => {
try {
return await processImage(job.data.imageId);
} catch (cause) {
throw new Error('Image processing failed', { cause });
}
}, {
connection,
settings: {
// Configure a custom backoff strategy here only if the built-in policies
// do not fit your retry rules; verify the installed version's API.
},
});
For manual recovery, first identify eligible failed jobs and then retry those jobs rather than replaying the whole batch. The versioned Job API reference includes a manual retry method, but verify its availability and behavior for your BullMQ version and job state.
Quick Recap
Keep status durable and retries safe
- Persist batch and per-image status somewhere the API can query reliably; queue events are useful for updates but their stream is trimmed.
- Record attempt counts, final state, output reference, and a sanitized failure code/message for each image.
- Make output writes and side effects safe to repeat. BullMQ does not prescribe a universal idempotency scheme, so use application-level protections such as deterministic output keys or duplicate detection appropriate to your system.
- Retry only items whose failure is transient or otherwise eligible under your policy; retain the original batch ID while recording that a new attempt was requested.
- Test interrupted workers, duplicate events, client reconnects, partial batch success, exhausted attempts, and retry requests against the BullMQ major version you deploy.
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