Why Yelp Scraper Returns Incomplete Data Without Error
As data engineers, we often encounter tools that promise straightforward data extraction. The Yelp Scraper (available on the Apify Store: https://apify.com/crawlerbros/yelp-scraper ) is one such tool, designed to pull business data, reviews, and ratings from Yelp. While its input and output schemas seem clear, the implicit failure modes, the ways it can return incomplete or misleading data…
The Yelp Scraper tool, accessible via the Apify platform, is designed to extract business information, reviews, and ratings from Yelp. Despite its straightforward interface, the scraper can yield incomplete or misleading data due to its implicit failure modes. This article delves into these subtleties and provides guidance on coding defensively to mitigate potential issues.
One such parameter is runTimeoutSecs, which governs the maximum duration for a run. Set at a default of 1800 seconds (30 minutes), this parameter acts as a wall-clock limit. Once triggered, the scraper halts further fetches, archives the collected data, and concludes execution. This behavior is not flagged as an error by the scraper itself.
It's particularly pertinent when utilizing the synchronous run API, which imposes a stricter 300-second limit. If the scraper's operation extends beyond this duration, the client making the request will encounter an HTTP 408 (Request Timeout) error, and the client won't receive any additional data post the timeout. However, the scraper will persist on the Apify platform until it exhausts its allotted runTimeoutSecs.
For instances requiring prolonged execution, the asynchronous run endpoint is recommended, coupled with status polling or webhook configuration. The example provided illustrates a scenario where a runTimeoutSecs value of 400, coupled with a synchronous API call, could result in an unnoticed timeout if not managed proactively. On the platform, the scraper would continue operating for its full 400 seconds, amassing data, yet the client would not receive a direct response containing the dataset ID until the run concludes.
Therefore, when orchestrating lengthy data pipelines, it's advisable to opt for asynchronous execution and implement comprehensive status monitoring. Moreover, the reviewLimit parameter dictates the maximum number of reviews to be gathered per business. However, reviews are only retrieved from the first page's visible reviews. Thus, even a high reviewLimit setting may result in fewer reviews being captured if they aren't present on the initial page or if reviewLimit is set to 0.
To account for this, developers can compare the length of the reviews array for each business against the specified reviewLimit. If the length is less than the limit—or if the limit is 0—an anomaly might be indicated, potentially reflecting either a natural scarcity of reviews for that business or the scraper's inability to render additional reviews beyond the first page.
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