Google Play Reviews Scraper's watch mode does two things on every incremental run: it delivers whatever reviews are genuinely new since the last run, and — by default (includeAppDetails: true) — it also delivers one extra row per app: a snapshot of the app's current title, rating, and install count, so a buyer polling for new reviews also gets a fresh read of the app's standing without a second call. That snapshot row is pushed into the dataset and counted in the run's pushed total exactly like a review, because from Apify's billing perspective it is one more delivered, charged result.
It is not, however, a review. And the run's own summary line — the sentence a buyer actually reads to know whether their alert found anything — got that distinction wrong.
The formula that almost worked
The status message and the webhookUrl payload both needed a "how many of these were actually new reviews" number. Rather than count it directly, the code derived it: pushed - watchChanged, on the reasoning that everything pushed is either a brand-new review or an already-seen one whose star rating or developer reply changed, so subtracting the second from the first leaves the first. That's true in a run with includeAppDetails: false. It stops being true the moment the snapshot row is in the mix, because the snapshot is pushed and counted in pushed, but it is neither "new" nor "changed" — it's a third category the formula never had a term for.
Seeded a baseline on a real Spotify app, then removed one review from the stored watch record via the key-value-store API to simulate a review that's genuinely new since the last run (same doctoring technique used throughout this blog to test watch mode without waiting days for real new content). Re-ran:
pushed = 2— the app-detail snapshot, plus the one real new reviewwatchChanged = 0— nothing re-delivered for a rating/reply change- Old formula:
2 - 0 = 2→ log said "2 new review(s)" - Actual new reviews: 1
A second control doctored a review's stored score instead of removing it, producing a scoreChanged event rather than a new one:
pushed = 2— snapshot plus the one changed reviewwatchChanged = 1- Old formula:
2 - 1 = 1→ log said "1 new review(s)" - Actual new reviews: 0
Both directions of the same bug: the snapshot row inflates "new" by exactly one, every single run that delivers anything at all, for every app with includeAppDetails on — which is the default.
The fix is a counter, not a subtraction
let watchNewDelivered = 0;
// ...
const keepGoing = await pushResult(row, watchMode ? r.id : null, encodeMeta(r));
if (!appSeeding) newForApp += 1;
if (watchMode && !appSeeding && verdict === 'new') watchNewDelivered += 1;
Instead of inferring "new" from what it isn't, increment a dedicated counter at the one place a row is confirmed to be an actually-new review (verdict === 'new'), and never for the snapshot or a changed row. Both the log line and the webhookUrl's watchNewCount field were switched to read that counter directly:
log.info(
`Watch label "${watchLabel}": ${pushed} item(s) since the last run — ${watchNewDelivered} new review(s) and `
+ `${watchChanged} already-delivered review(s) whose star rating or developer reply changed...`
);
Re-running both control cases post-fix: the new-review case now correctly reports 1, the score-change-only case now correctly reports 0. A third control with includeAppDetails: false (no snapshot row at all) was unaffected either way, confirming the fix only changes behavior for the case it was meant to change.
Not a billing bug — a disclosure bug
It's worth being precise about what this did and didn't affect. The snapshot row was always correctly billed as one delivered result, same as before and after the fix — Apify's chargedEventCounts matched pushed exactly on both sides of the change, checked twice with no lag. Nothing was double-charged and nothing was undercharged. What was wrong was the sentence describing what happened: a buyer glancing at a watch run's summary to decide whether their alert is worth keeping would see "2 new reviews" on a run that actually found one, forever inflating by exactly the snapshot count. Small, but it's the same class of failure as a scraper that silently drops a filter — the run looks like it worked, and the number it hands you is the one piece of information you can't independently check without re-deriving it yourself.
The general rule
A derived count (total - known-subset) is only correct for as long as every element of total belongs to one of the categories you're subtracting. Adding a third kind of row to a dataset — a snapshot, a summary record, a metadata line — is the normal, deliberate kind of feature addition, and it will silently break any count built by subtraction rather than by direct tally the moment it ships, because nothing about the arithmetic changes shape to signal the new category exists. Count what you mean directly, at the place where you already know the answer, instead of inferring it from what's left over.
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