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For work that may take minutes or hours, accept the request, start or enqueue a background job, and return a durable job ID instead of keeping the initiating request open. Then make the job observable and safe to resume: define its states, persist useful progress, make side effects idempotent, and retry only failures that may clear on another attempt.
How should a long-running job work?
Separate the caller’s request from the work itself. The request handler validates and records the submission, arranges for execution, and returns a job identifier or status resource. A worker performs the task independently. The caller can then check status or receive completion through an event, webhook, reply queue, or shared result storage. Microsoft describes these as options for reporting background-job progress and completion in its background-job guidance, last updated March 31, 2026.
Keep the job record separate from the worker’s in-memory state. A useful record has a durable ID, current state, creation and update times, attempt information, progress where meaningful, and either the result or a reference to it. For large inputs or outputs, store the payload separately and persist a reference rather than making every queue message carry the full data.
Define states callers and operators can understand
Choose a small, explicit state model, for example: queued, running, succeeded, failed, and cancelled. Define which transitions are valid and what each state means. In particular, distinguish a terminal failure from a job waiting for a retry; otherwise clients may interpret a temporary delay as a final outcome.
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Expose only progress the system can report reliably. A percentage is useful when the amount of work is known; for indeterminate work, a stage name or completed-item count may be more honest. Specify whether progress is approximate and how often it is updated. Do not make clients infer completion merely because a worker has stopped reporting.
How do you resume work after a worker stops?
Persist checkpoints when restarting from the beginning would be costly or would repeat consequential work. A checkpoint should identify the last safely completed unit and contain enough information for another worker to continue. For a batch task, that might mean a durable cursor or the IDs of completed items; for a multistage task, it might mean the current stage and its saved output. Microsoft’s background-job guidance covers checkpointing, and Google Cloud documents retry and checkpoint practices for Cloud Run jobs.
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Save the checkpoint in a way that is safe to repeat. A worker can stop after writing a result but before saving progress, or after saving progress but before acknowledging delivery. Design the boundary so recovery can determine what is complete without silently losing work or applying a harmful effect twice. The appropriate transaction or recovery technique depends on the storage and external systems involved; the sources do not establish one universal implementation.
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- Make checkpoint updates repeatable or verifiable.
- Store large payloads separately and checkpoint durable references where that is more suitable.
- Plan for graceful shutdown, while treating abrupt termination and redelivery as normal recovery cases.
How can retries avoid duplicate side effects?
Assume the same logical operation can be delivered or executed more than once. Redelivery can follow a restart, a lost acknowledgement, an overlapping schedule, or an explicit replay. Make the operation idempotent at the point where it changes state: repeating the same request should not charge twice, create duplicate records, or send repeated effects that matter.
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Use a stable idempotency key tied to the logical operation, not a fresh key for each attempt. Where suitable, use an upsert or a uniqueness constraint so that repeating a write converges on the same result. AWS’s idempotency and retries guidance explains why at-least-once retry behavior is safe only when the operation is idempotent. An at-most-once-per-retry setting does not, by itself, make an entire multi-step workflow exactly once.
Classify the failure before scheduling another attempt
- Likely transient: a timeout, temporary service interruption, or throttling response may clear. Retry with bounded attempts and backoff appropriate to the dependency.
- Likely permanent: malformed input, a violated business rule, or another condition that will not change on its own should fail promptly or go to dead-letter handling rather than consume repeated attempts.
- Unclear: record the error and its class, then choose a policy that limits repeated work while preserving enough information for diagnosis.
Retries need a stopping condition and an operational destination for jobs that exhaust attempts. A dead-letter path is useful only if someone can inspect, correct, replay, or intentionally discard those jobs. Preserve the original job identity and attempt history so a replay remains traceable and can still use the same idempotency boundary.
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How should callers and operators observe progress?
Choose the completion channel to fit the caller. Polling a status resource is straightforward when callers can check periodically; events, callbacks, or a reply queue can reduce polling when the system already supports those patterns. Shared result storage can keep large outputs out of messages. Microsoft documents these alternatives for background-job status and completion in its architecture guidance.
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Set expectations for status freshness. If progress is persisted only at stage boundaries, callers should not expect a live per-item update. If a job is delayed by retries or queue capacity, its age and current state should make that visible instead of leaving it apparently active without explanation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Should you use a queue and worker or a workflow engine?
A queue and worker are often a natural fit for independent, discrete tasks, especially when the queue buffers bursts and the application owns execution and recovery. Evaluate a durable workflow platform when the job coordinates multiple steps, waits for external events or long periods, needs recoverable state, or must support durable progress queries. This is a design choice based on the shape and operational needs of the work, not a universal benchmark.
| Decision factor | Queue and worker | Durable workflow |
|---|---|---|
| Work shape | Discrete tasks that can be handled independently. | Multiple coordinated steps or long-running processes with waits. |
| State and recovery | The application typically defines and persists job state and checkpoints. | Evaluate when durable execution state, pause/resume, or progress queries are central requirements. |
| Delivery and retries | Design for repeat delivery and make effects idempotent. | Still define safe side effects and understand the platform’s retry and replay semantics. |
| Progress and results | Expose status using polling, events, callbacks, reply queues, or shared storage as appropriate. | Assess built-in query, cancellation, and long-running execution features against caller needs. |
| Operational fit | Your team owns worker deployment, queue behavior, recovery, and monitoring. | Assess platform limits, deployment constraints, pricing, and operational ownership for the selected service. |
Temporal documents standalone activities, retries, IDs, cancellation and querying, and heartbeats for long-running work in its background-jobs and long-running-work guidance. Google Cloud describes its managed Workflows service. These are vendor descriptions of product capabilities, not independent comparative performance tests. Verify the chosen platform’s current limits, pricing, and deployment requirements for your region and workload; the available documentation does not establish a like-for-like cost or quota comparison.
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Implementation checklist
- Accept and identify: validate the submission, assign a durable job ID, and return a status resource or another agreed status mechanism without waiting for the long-running work to finish.
- Specify the contract: document visible states, valid transitions, progress semantics, completion delivery, and where results can be retrieved.
- Protect side effects: select a stable idempotency key and enforce duplicate safety at each consequential write or external operation.
- Define recovery: decide what is checkpointed, how a restarted worker resumes, how shutdown and redelivery behave, and when a job becomes terminal.
- Bound retries: distinguish transient from permanent failures, set a retry limit and backoff for transient cases, and route exhausted or poison jobs for inspection.
- Instrument the system: observe queue depth and age, attempts, progress, failure classes, and dead-letter volume using the job ID for correlation.
- Validate capacity: measure concurrency and partitioning against actual downstream limits and contention rather than assuming a generic safe threshold.
What to verify for the selected platform
Retry limits, checkpoint behavior, service quotas, pricing, regional availability, and SDK semantics vary by product and may change. Confirm them in the documentation for the exact service, region, and SDK version you plan to deploy. AWS’s idempotency guidance is specifically for its Durable Execution SDK, while Google’s retry and checkpoint reference concerns Cloud Run jobs; neither should be treated as a universal queue contract.
Quick Recap
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