Which platform's API/scraping restrictions have burned you the most?
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Building an API that pulls data across 13 social platforms, and the thing that's eaten the most engineering time isn't the scraping itself, it's how differently every platform breaks. Rate limits that change without notice, auth flows that expire silently, structured data one week and a wall of JS the next.
Curious what's bitten other builders here. Which platform has been the worst to depend on, and what did you end up doing about it?
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Feels like maintaining integrations never really ends...
@lukem121 Did any platform make you rethink the whole product?
@lukem121 The wall of JS line made me laugh because it's way too real.
For us, it's usually not one platform but the constant undocumented changes that cause the biggest headaches. Something works perfectly for weeks, then a small frontend update or stricter rate limits suddenly break the workflow. We've found that building good monitoring and fallback logic saves more time than trying to make a scraper "perfect."
Curious....out of the 13 platforms you're supporting, which one has been the most unpredictable lately?
I've probably been burned the most by Reddit's API changes. Just when I thought I had a stable workflow, the rules changed and I had to rebuild everything from scratch.
Different domain — I do this on the e-commerce side, not social — but the failure taxonomy is identical, and the one that's burned me most isn't rate limits or a clean 403. It's the silent shadow-block: the endpoint returns 200 with subtly stale or degraded data instead of erroring. A 403 you can detect and escalate; a 200 with poisoned data quietly corrupts your dataset for days before anyone notices.
+1 to Laiba on monitoring — but the check that actually catches this is a schema/shape contract on the response body, not HTTP status. Status lies; the payload doesn't. If a field that's populated 99% of the time suddenly goes null across a whole batch, that's your real alarm.
On the "structured one week, wall of JS the next" whiplash: I go straight for whatever JSON the page already ships (JSON-LD, the internal API the frontend calls) rather than parsing rendered HTML — markup churns, the data contract behind it is stickier. And once you hit a real JS sensor (Akamai _abck, Kasada), stop — no server-side trick beats an L3 block.
I spent more time fighting X than building features. the undocumented limits kept changing and responses become inconsistent. Have you considered a fallback queue that retries through multiple collection methods when one path starts failing?
Social Fetch
@advin_jadis Yes, definitely. If an endpoint even remotely starts becoming flaky, we will immediately spin up two separate instances that both have fallbacks and retries. Because we have found that when one request fails, often the other will succeed.
i had biggest issue with Facebook because authentication suddenly failed even when nothing changed in my code. Keeping detailed health checks exposed problems earlier. Would separate monitoring for every platform save more time than shared alerts?