Ankur Mhatre
6af6e4a3f1
Three replicas of one application against one PostgreSQL database, proving duplicate @Scheduled execution and then removing it with ShedLock: 24 executions where 8 were due, then 7 for 7 ticks. Also measured: @SchedulerLock without @EnableSchedulerLock does nothing and warns about nothing; spring.task.scheduling.pool.size=1 does not starve a fixedRate job but delays it and fires 35 of 40 executions in a burst; and a node whose clock is 40 seconds fast takes a live lock unless the provider uses usingDbTime().
57 lines
2.9 KiB
Markdown
57 lines
2.9 KiB
Markdown
prev: [The lock](02-the-lock.md) · [README](../README.md) · next: [Clock skew](04-clock-skew.md)
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# 3. One scheduler thread
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`spring.task.scheduling.pool.size` defaults to **1**. Every `@Scheduled` method in the
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application shares that one thread.
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The expected consequence is that a slow job starves the others. What actually happens is more
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interesting, and considerably worse.
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Three scheduled methods — one that sleeps 1800 ms on a 2000 ms schedule, one on a 200 ms
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schedule, one that throws — running for eight seconds. First with the default single thread
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([output](output/scheduler-pool-1.txt)), then with four
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([output](output/scheduler-pool-4.txt)):
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| | pool.size=1 | pool.size=4 |
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|---|---|---|
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| `fast()` executions | 40 | 41 |
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| longest gap between two `fast()` executions | **1995 ms** | 201 ms |
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| `fast()` executions starting within 20 ms of the previous one | **35** | 0 |
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| distinct scheduler threads | 1 | 4 |
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The execution *count* is the same. A `fixedRate` schedule does not skip a tick it could not run:
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the missed executions accumulate and are then fired back to back the moment the thread is free.
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Thirty-five of the forty executions arrived in a burst.
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So the metric everyone has — "the job ran 40 times, as expected" — is green, while the job's
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actual behaviour is two seconds of silence followed by thirty-five invocations in a few
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milliseconds. If that job calls a rate-limited API, or opens a database connection each time, the
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burst is the incident.
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## What to set
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- **`spring.task.scheduling.pool.size`**: at least the number of `@Scheduled` methods that can
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overlap. It costs a handful of mostly idle threads.
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- **`fixedDelay` instead of `fixedRate`** where "every N seconds" really means "N seconds after
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the last one finished". `fixedDelay` schedules the next run only after the current one
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completes, so it cannot accumulate a backlog to burst through.
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- **`spring.threads.virtual.enabled=true`** replaces the pool with a `SimpleAsyncTaskScheduler`
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over virtual threads, which removes the shared-thread problem entirely. It also removes the
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bound: `spring.task.scheduling.pool.size` is one of the properties Boot's own metadata marks
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as having no effect when virtual threads are on.
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## An exception does not stop the schedule
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`throwing()` threw on all 27 of its executions and kept its schedule. Spring wraps a scheduled
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method in `TaskUtils.LOG_AND_SUPPRESS_ERROR_HANDLER`, so the exception is logged by
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`o.s.s.s.TaskUtils$LoggingErrorHandler` as `Unexpected error occurred in scheduled task` and
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discarded. A raw `ScheduledExecutorService` would have cancelled the task after the first
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failure — which is where the folklore comes from, and it does not apply to `@Scheduled`.
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The flip side is that a job which has been failing since the last deploy produces nothing but a
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recurring `ERROR` line. Supply your own `SchedulingConfigurer` with an error handler if you want
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a metric.
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next: [Clock skew](04-clock-skew.md)
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