For long-running work, return control to the caller quickly, give each job a durable identity, and run it outside the request. Make repeated delivery safe, persist progress when restarting from scratch would be costly, and expose status that both users and operators can act on. A queue and worker often fit a discrete task; a durable workflow is worth evaluating when the work involves coordinated steps, long waits, or state that must survive failures.
How should an async job work?
Separate submission from execution. The caller submits work; your service records or starts it and returns a job identifier or status resource rather than keeping an HTTP request open until the task finishes. The worker then performs the work independently. This lets the caller check progress or retrieve a result later. Microsoft describes status polling, reply queues, events, webhooks, and shared storage as ways to communicate job progress and completion in its background-job guidance.
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Define a stable job contract
Give each job a durable ID and define a small set of externally visible states. For example: queued, running, succeeded, failed, and cancelled. Decide what each state means before exposing it: a job marked succeeded should have a defined result location or completion outcome, while failed should communicate whether the failure is final or eligible for another attempt.
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Return the ID and a way to check status. Depending on the application, progress can be delivered through polling, an event or callback, a reply queue, or shared result storage. Choose one that fits how callers operate; do not make a caller infer completion from a connection that may time out.
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How can you resume a job after a worker crashes?
A worker can stop at any point: before doing work, midway through it, or after changing an external system but before acknowledging the message. The last case can lead to redelivery. For jobs where restarting from the beginning is expensive, save checkpoints or incremental progress so a replacement worker can continue from a known point. Microsoft’s background-job guidance, AWS’s durable-execution patterns, and Google Cloud’s Cloud Run job retry and checkpoint guidance describe approaches to durable state and recovery.
Make checkpoints useful and safe
- Record which durable unit of work has completed, not merely that a worker was active.
- Make checkpoint updates safe to repeat. A retry that writes the same completed position should not corrupt progress or skip unfinished work.
- Keep large inputs and outputs in suitable storage and persist references in the job state when that is more practical than copying the payload into every queue message.
- Define what happens if a checkpoint and an external side effect do not complete together. Recovery must be safe whether the worker resumes before or after that boundary.
Checkpointing is not a substitute for safe side effects: a restart can still repeat work performed after the last saved checkpoint.
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How do you retry without duplicating side effects?
Assume a logical operation may be delivered or started more than once. Queue redelivery, worker restarts, overlapping schedules, and replay can all repeat work. Make side effects idempotent where possible: repeating the same operation should leave the system in the intended state rather than create another charge, record, or notification. A stable idempotency key or an upsert-style write can help, provided the key identifies the same logical operation on every attempt. AWS’s idempotency and retry guidance explains why retry behavior depends on idempotent work; Microsoft covers duplicate processing in its background-job guidance.
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Classify failures before scheduling another attempt
Retry transient failures, such as a timeout or temporary throttling, using a bounded policy with backoff appropriate to the dependency. A malformed request or a business-rule rejection is generally not made valid by repeating it. Route permanent or repeatedly unsuccessful work to dead-letter handling or another operator-visible failure path instead of consuming attempts indefinitely. Microsoft’s guidance for background jobs discusses failure handling and dead-lettering.
How should callers and operators observe progress?
Caller-facing status and operational monitoring serve different needs. A user may need to know whether a report is queued, processing, or ready; an operator needs to know whether jobs are stuck, repeatedly failing, or piling up. Design both views around the job ID and its state rather than treating a successful enqueue as proof that the work completed.
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Expose meaningful progress
- Show a state and, when the work supports it, a useful progress measure or last-updated time.
- Make the completion path clear: return or link to a result, or provide an explicit failure outcome.
- Choose polling, event notification, callback, reply queue, or shared storage based on the caller’s needs and system design; these are alternative reporting mechanisms, not guarantees that the work itself is durable.
Monitor the job system
Track attempts, job age, queue depth, progress, failure class, and dead-letter volume. Those signals help distinguish a slow task from a stuck worker, a dependency outage, or a growing backlog. Set concurrency limits and partitioning with downstream rate limits and shared-resource contention in mind; there is no universal threshold that fits every workload, so validate those settings against the actual system.
Should you use a queue and worker or a durable workflow?
Choose based on the shape of the work and the recovery behavior you need, not on a blanket claim that one architecture is always better. A queue can buffer bursts and hand off discrete units of work. A durable workflow is worth evaluating when the job coordinates multiple steps, waits for long periods, must preserve state through restarts, or needs progress queries. This is a design distinction, not a neutral benchmark or a universal selection rule.
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| Approach | Often fits | Evaluate carefully |
|---|---|---|
| Queue and worker | A discrete task that can be executed independently, with the queue separating submission from processing. Microsoft’s background-job guidance covers background processing patterns. | Redelivery, safe retries, checkpointing needs, and how callers learn the outcome. The application still needs a defined status and recovery strategy. |
| Durable workflow orchestration | Work with multiple coordinated steps, long waits, recoverable state, or a need to query progress. Temporal documents background-job patterns, activities, retries, IDs, cancellation, queries, and heartbeats in its background jobs and long-running work guidance; Google Cloud documents its Workflows service. | Whether the platform’s execution model, deployment, service limits, pricing, and operational ownership fit the application. Verify current details for the chosen service and region. |
Vendor documentation describes each product’s capabilities; it does not establish a cross-platform performance or cost winner. Compare the options against your task’s state requirements, retry and idempotency design, caller-facing status needs, and operational constraints.
Quick Recap
What should you decide before shipping?
- Define the job contract. Assign a durable job ID, specify the externally visible states, and decide how callers retrieve status and results.
- Choose the completion channel. Select polling, notification, callback, reply queue, or shared storage based on how the caller needs to receive progress and completion.
- Protect side effects. Identify operations that would be harmful if repeated, then give them a stable idempotency or deduplication strategy.
- Set retry and failure paths. Bound retries for transient faults, use backoff, distinguish permanent failures, and define dead-letter or operator handling.
- Plan recovery. Add checkpoints where restart cost warrants them, make checkpoint updates repeatable, and account for a worker stopping between a side effect and message acknowledgment.
- Set operating limits. Tune concurrency and partitioning against real downstream limits and contention, then monitor job age, queue depth, attempts, progress, failure class, and dead-letter volume.
- Match the execution model to the workflow. Use a queue-and-worker approach for discrete work when its state and recovery model is sufficient; evaluate durable orchestration for coordinated, long-waiting work that needs durable workflow state.
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