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A capped deduplication list can quietly forget rows that are still present in a source. When that happens, a later scrape may treat the rows as new, deliver them again and potentially bill for them again. FetchSmith says it found this failure in its watch-mode pipeline and made the data loss visible—but its reported fix does not prevent repeat delivery.
How a storage cap turned into a billing risk
In FetchSmith’s account, watch-mode Actors poll a source and deliver rows considered new since an earlier run. Each Actor stores IDs for rows it has already delivered in a key-value record. To limit storage growth, the record has a configured maximum—examples in the post include 5,000, 20,000 and 60,000 IDs.
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The failure was in what happened at the limit: the Actor removed the oldest IDs without warning. If a run produced more IDs than the baseline could retain, some previously delivered IDs disappeared from the record. Those rows could still be visible upstream. On a later poll, the Actor no longer recognized them and could deliver them as new, creating a risk of charging for the same rows again.
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What FetchSmith reported in its reproduction
FetchSmith described a google-play-reviews-scraper example configured with WATCH_KEEP = 20000. In its reported seed run, 1,000 IDs were recorded and 997 were dropped by the cap. In the next incremental run, the Actor delivered 40 rows and skipped zero as already seen. The author identified that zero-skipped result as the signature of a broken watch in this setup, and said the 40 rows had already been paid for in the seed run.
These figures are the publisher’s reported reproduction, not an independent audit. FetchSmith says the same failure pattern was reproduced on 22 Actors, spanning sources such as Google Play reviews, court records, clinical trials, public tenders, FDA recalls, grants and job listings—where a source could plausibly exceed the retention cap during a poll window. Its original account was published September 23, 2026: FetchSmith’s incident report.
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Why a successful run did not prove billing correctness
The scrape could complete with a SUCCEEDED status and a plausible row count. From the Actor’s narrow perspective, the rows it returned had IDs missing from its stored baseline, so there was no necessary run error to report. The consequence showed up later, when those rows could be billed again.
A useful incident clue in FetchSmith’s example is a high delivered-row count paired with zero rows skipped as previously seen during an incremental poll that should substantially overlap with the earlier result. It is a clue, not a universal test: a source may genuinely have many new rows, or its behavior may produce little overlap. Operators should interpret the signal against the source’s normal cadence and retention behavior rather than alerting on zero skips alone.
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Checking only for failed or incomplete runs will miss this class of problem. Billing correctness also depends on whether deduplication state was truncated, and whether that truncation is visible to the people and systems responsible for downstream charges.
What the fix exposed—and what it did not prevent
FetchSmith says it added signals around baseline truncation: a warning when saving after eviction, a note in run status, last-run and cumulative truncation counts in the watch record, and baselineTruncated plus baselineTruncatedTotal in RUN_SUMMARY and any configured webhook payload. It also documented the cap in the README.
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Those changes make the loss of deduplication history observable; they do not stop the Actor from forgetting IDs or delivering those rows again. FetchSmith describes larger design directions such as unbounded retention or setting capacity with actual poll volume in mind, but its post does not benchmark them or establish a best option. A related UK tender Actor listing describes a 60,000-ID cap, warns that an ID that falls off can be returned and charged again, and documents warnings, status messages, counters and run-summary/webhook fields. The listing also recommends narrowing watches when truncation occurs; its operational details can change: Apify UK tender Actor listing.
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For a pipeline that charges per delivered row, the practical design question is whether the baseline can cover the volume and time horizon in which old rows may reappear. A capacity that handles ordinary polls can still be too small for a spike. If the system reports truncation, route that signal to both an operator and downstream billing or delivery integrations; an internal counter that nobody acts on is not a safeguard.
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Row deduplication is not the same as retry idempotency
Retry idempotency can prevent a client from performing the same API operation twice when it retries a request. Stripe’s documentation states, “The API supports idempotency for safely retrying requests without accidentally performing the same operation twice.” It describes keys for eligible repeated requests, with key retention and matching request parameters affecting behavior: Stripe’s idempotent requests documentation.
That is related but distinct from FetchSmith’s reported failure. An API idempotency key identifies a repeated request; the watch baseline identifies rows already delivered. If an old row ID has been evicted, retry protection at a payment or delivery API does not by itself restore the missing row history. Stripe is not reported as involved in this incident.
What is known about the financial impact
FetchSmith’s post describes a reproduction in which rows from a seed run were delivered again in an incremental run, and says the issue was closed across 22 Actors. It does not quantify actual duplicate charges, the number of buyers affected or refunds. The reproduction establishes a billing risk; it does not establish a total of completed double charges.
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