OnlineProxy

Zero rejections with zero input is just a dead scraper so alert on rejection counts alongside input volume. The 10% cap only bounds single steps, compounding still walks you to the floor, so use a windowed cumulative drift breaker instead of counting consecutive capped moves, which is gameable by alternating capped and accepted ones Real bug in the guard too, if the price already sits below the floor a capped up move at current_price * 1.1 can still ship below it, clamp to max(floor_price, upper_bound). Tie the floor to current landed cost and say whether 20% means margin on price or markup on cost, those give different numbers. Add a per unit multipack check with an override lane for clearance stuff, and make rule_set_version and segment_version real columns in pricing_decisions so old prices stay explainable

That 8 minute default feels more unsupported than outright broken, same controls with only staleness different and it still passed their own check. Whether surge windows need more slack is really a question for the volatility data. Capture skew still works when captures aren't simultaneous, but it misses staleness at the source, so caches need their own detection. The generated timestamp helps when exposed but caches can overwrite it and it's unclear if it means issued or rendered, so pair it with cart vs checkout divergence and observed volatility, and just persist eligibility and cache age at capture time since neither rebuilds after the fact. Also the session_state_unknown check is treated as fatal but the article never shows how that flag actually gets set, if it comes from real line items it's fine, if it's weaker a flipped session could slip through so that reads more like a gap to clarify than a confirmed bug

testing three unrelated networks before blaming the IP is the step most people skip, and identical 429s on all three points to the fingerprint being the real issue here. so "not about your IP" doesn't hold up everywhere, there are reports of clean human browsers still catching code 11 on hetzner and code 99 on VPN until the VPN dropped. so a good client helps but a flagged exit can still sink you. also the camoufox switch changed engine, protocol, TLS and fingerprint all at once so it proves the recipe works, not which part actually mattered. same with the turnstile details, that's only ONE page's behavior. and nobody circled back to the opening question about whose accounts these are and under what authority, that's the part I'd actually want answered.

default celery is at most once delivery, so a crashed worker just loses the task, which hurts with flaky selenium runs. acks_late plus redis visibility_timeout fixes that, but then your writes need to be idempotent, upsert on a URL hash, and cap each task with soft_time_limit and time_limit so a hung browser doesn't get redelivered while still running. For retries, narrow autoretry_for to real transient errors, add backoff with jitter, and use a redis token bucket per domain since rate_limit only applies per worker. And yeah headless chromium eats way more memory than plain HTTP, so I check for a json endpoint before firing up a browser

The 429 thing bugs me most, your text says it just means overuse but the code treats it exactly like a 5xx and gives it the same escalating cooldown as a dead proxy. Retry after headers are also messier than people expect, sometimes missing, sometimes a full date instead of seconds, and 503s throw them too plus the limit might be on your account not the proxy so a separate throttle counter would help. Also that 403 branch quietly calls mark_success and wipes the fail streak even though 403 could mean banned, bad auth or just a url everyone gets blocked on. While you're in there, lok fail_count if this runs across threads, and narrow that bare except requestexception down to connectionerror and Timeout or you'll end up blaming the proxy for stuff like invalidURL that's actually your own mistake

For a beginner guide the browser vs http split works as a starting point but in production most people just mix both. I run the browser once to grab tokens or the request shape, then replay that over cheap http at volume and only bring out the full browser when nothing else works. Also a lot of js heavy retail sites hydrate from embedded state, like the data blob next.js dumps into the page, or from internal json and graphQL endpoints, so you can often skip the browser. Client still has to pass TLS fingerprint checks just to pull the html tho, and digging those APIs out takes real work and breaks silently once a private endpoint changes. Playwright has basically taken over as default for new projects, selenium 4 is still fine for legacy code or grid. Log the context with each availability record, location, variant, fulfillment, account state, timestamp, since availability shifts around constantly

the raw_title matching company check is the best bit here, catches selector drift a normal schema check would just wave through. my issue is the pooled 90 % gate tho, batches are mixed by domain so any single domain under 10% of rows can fail completely and the batch still passes, same regional variant problem the article itself talks about, per domain thresholds would fix that also a None in those string fields throws attributeerror in strip and except valueerror won't catch it, row.get with a fallback empty string is a cheap fix. bigger issue is quarantine, invalid rows never reach public_market_observations so acceptance_rate there is always 1.0 and that 0.95 alert can never fire, and validation_status plus source_domain arent even columns the python writes

the guide kind of buries that rent searches need a lot_id fallback in your dedup keys, otherwise apartment buildings get counted twice wherever map_bounds tiles overlap. also that free cache on identical searches within an hour deserves way more attention than a pagination footnote, it changes how you schedule collection jobs since repeat hits cost nothing. watch out with time_on_zillow too, it caps listing age server side so anything older than your filter never reaches the client, we read days_on_zillow ourselves and widen the query when we need to see what got filtered out. and the response only gives current price fields, no real history, so we snapshot every run and diff against previous ones, that's what actually makes price drop alerts reliable since price_reduction just means there was some reduction, not that it was recent

The part worth stealing is flagging arithmetic wherever it happens, whether its a scraper doing its own times two or the fill function guessing a ratio, all the way to the rendered approx symbol. On tronify id assert fee per quoted energy stays equal across probe sizes instead of checking the gap stays constant, since a normal rate change would trip that on its own for no reason. Curious about ergon tho, it has that 100k minimum order floor but still shows pro rata prices in the sortable one day and three day columns. Do those cells carry the estimate flag anywhere or does the floor only live in the row note, feels like that matters for the purchasable versus normalized point. Median ratio fill only works this clean because all twenty sellers quote the same literal commodity, wouldnt assume it ports to a market with non identical units. Same with the referral claim, only really holds once someone checks the sort code itself ignores referral status, not just the public json

The analysis ready claim oversells it, stuff like the percentages and that weirdly named rating_title column are plain strings so you're doing type cleanup in Pandas first. Fotmob's terms basically ban scraping and bulk retrieval and both worldfootballR and soccerdata already dropped their free scrapers after takedown requests, so this risk is real not hypothetical. Under apify's standard actor contract, sections 2.1 and 2.2 put rights clearance on you, and Fotmob could just complain to apify or block their IP range, no code fix saves you from that. I'd archive data locally, keep a licensed API as backup and pull through apps script instead of dropping your token into an IMPORTDATA cell where anyone with view access can read it. Calling it maintenance free is marketing until they commit to an uptime and update policy, what happens when the signature scheme rotates