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When a warehouse’s hourly sign-in average looks healthy but devices stall as a shift begins, the capacity plan may be measuring the wrong interval. In Serguey Shinder’s account of depot sign-ins, about 1,400 scanners and 200 shared terminals across 18 depots logged in over roughly six minutes. His central lesson: plan and test for the busiest shift’s first ten minutes, not for an hourly average.
Why an hourly average missed the bottleneck
Shinder describes morning shifts starting at six across multiple depots. Average hourly capacity looked comfortable, but workers arrived in a concentrated wave: scanners displayed a spinning indicator, supervisors restarted them, and the first trailers were delayed. The problem appeared at five depots and could initially have been mistaken for a network issue.
As Shinder puts it, “A capacity figure averaged over an hour had hidden the one minute in which every one of them needed us at once.” The distinction is between total demand over a broad period and concurrency: how many sign-ins arrive together, and how much work each sign-in triggers.
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In Shinder’s account, roughly 1,400 scanners and 200 shared terminals across 18 depots signed in over about six minutes. Each sign-in opened a warehouse-system session and requested an identity token. Those figures and the diagnosis below are the author’s report; the post provides no linked telemetry or independent incident review.
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First-time profiles added work
Shinder says a first sign-in created an agency-worker profile and took about four times as long as a returning worker’s sign-in. When many new or returning workers arrived in the same short window, the extra work contributed to the rush rather than being spread across the day.
Queues and retries compounded the delay
The author says the identity service queued requests while the session broker timed out and retried. Supervisors’ scanner restarts then added more requests to the queue. In this account, restarting a stalled device did not remove the original system pressure; it could increase the amount of work waiting to be processed.
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How the team changed its capacity plan
Shinder says the response was to measure shift changes by the minute and size for the first ten minutes of the busiest shift in the busiest week. The measures he describes address different parts of the surge:
- Validate the peak interval: Replay those first ten minutes in load testing instead of relying on an hourly average.
- Move profile work earlier: Create agency-worker profiles the evening before booked shifts, reducing first-time setup during sign-in.
- Avoid unnecessary reauthentication: Keep sessions alive through breaks and while scanners are cradled.
- Reduce simultaneous arrivals: Stagger start times across zones at the three largest depots.
These are the operational changes Shinder reports, not a controlled comparison showing how much each one contributed or whether the same mix suits every warehouse.
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What to take from the account
- Model the arrival pattern, not just the daily total. A system can handle a reasonable amount of work over an hour and still struggle when much of it arrives within minutes.
- Include the full sign-in path. The reported transaction involved devices, warehouse sessions, identity-token requests, and a session broker. Testing only one component may miss queueing elsewhere in the path.
- Test first-time and returning users separately. If first-time profile creation costs more work, a test dominated by returning users may understate peak demand.
- Account for failure behavior. Timeouts, retries, and manual restarts can add traffic precisely when the system is already under strain.
- Use demand-shaping measures as well as capacity tests. Advance setup and staggered zone starts change when work arrives; a peak replay checks whether the system can handle the planned load.
How strong is the reported result?
Shinder writes: “In our peak rehearsal in August, the slowest depot had its whole shift signed in within eleven seconds.” The post does not state the rehearsal year or provide test records, so this should be understood as the author’s reported rehearsal result—not an independently verified benchmark or a guarantee for other depots.
The source is Shinder’s DEV Community post, “Every Depot Signs In at Six and We Had Sized for the Hour.” Its displayed publication date says October 1 but does not establish a year. The account is useful as an operational example, while its incident details and outcome remain attributable to its author.
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