What happens to @Scheduled jobs when a Spring Boot service has multiple replicas?

Designs scheduled work for overlap, multiple replicas, restart, failure, and durable ownership instead of assuming one execution.

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An @Scheduled method runs in every application replica that enables scheduling. If three instances are running, the same schedule can execute three times. I make the work idempotent and decide explicitly whether parallel executions are acceptable. If the business requires one logical execution, I use a durable scheduler, queue, database claim, or proven distributed lock rather than relying on one application instance.

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@Scheduled semantics, scheduler capacity, overlap, replicas, idempotency, locking, durable scheduling, and observability.

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Each running application instance owns its own scheduler, so horizontal scaling can multiply a supposedly singular job.
An @Scheduled method runs in every application replica that enables scheduling. If three instances are running, the same schedule can execute three times. I make the work idempotent and decide explicitly whether parallel executions are acceptable. If the business requires one logical execution, I use a durable scheduler, queue, database claim, or proven distributed lock rather than relying on one application instance.
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Fixed rate can try to maintain a cadence, while fixed delay waits after completion; neither guarantees exactly once. A long run, scheduler pool size, or multiple declarations can create overlap. I persist job identity and progress when work must survive restart, use a clock and time zone deliberately, and record start, finish, outcome, duration, and skipped or duplicate claims. The job's downstream calls still need timeouts and retry-safe behavior.

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Assuming @Scheduled runs once for the whole cluster.
Calling it exactly-once execution.
Keeping no record of missed or failed runs.

What happens to @Scheduled jobs when a Spring Boot service has multiple replicas?

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