Scaling scraping operations: dos and don'ts
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Scraping is a basis of ad verification, price tracking, competitor monitoring, and many other tasks that help businesses stay informed and afloat. However, even with numerous present tools and lifehacks, it is still challenging to get data - websites’ layouts change, new policies arise, latest anti-bot instruments are developed, etc. This is especially noticeable when you scale up scraping operations - requests fail, data is inconsistent or incomplete, and money is wasted on retries and fixes. In this article, we break down what to do and what to avoid in order to scale up scraping tasks without a hitch.
Why is scraping hard to scale?
This is a widespread misconception - if scraping is going ok now, it will be alright when scaled up. In reality, what works for 1,000 requests is not going to work for 100,000. Ethical and legal concerns, blocked IPs, mismatching data, and rising costs - instead of accurate data, problems pile up.
It proves that scraping is not solely a technical process - it is on the operational level, and it calls for a thorough approach.
What to do to scale scraping operations successfully?
1. Design for failure
At small scales, failures are rather occasional and feel random. At scale, they are inevitable. Do not expect that things will unfold smoothly on their own. Layout changes without warnings, rate limits, timeouts, CAPTCHAs - they are not edge cases, but the routine reality of scraping. The only way to deal with them is to assume that they will unavoidably happen and build scraping pipelines that can deal with them.
While building a scraper, remember to:
- Implement retry logic with backoff
- Develop fallback logic
- Monitor alerts
2. Separate extraction and data handling
Sites change their HTML. If your scraper is monolithic, every change would make you readjust the whole pipeline.
Instead, separate parsing and storing modules. Make it possible to update or add selectors without redeploying the whole scraper.
3. Quality first
Uptime is an important metric, but not the key one. A running scraper does not automatically mean delivered or accurate data. At scale, silent failures like empty fields or incomplete data are easy to miss. Setting up validation rules is a solution, such as expected field formats or a minimum data threshold. Monitor other metrics as well - sudden drops or spikes may be a signal that the scraper is not working properly.
4. Schedule
When you need a lot of data, the first urge is to make more requests. This may lead to blocks or rising costs and zero data. To avoid that, set up time intervals between requests and keep up with the speed of updates. Data does not always change daily, and there is no point in scraping more often than shifts happen.
5. Account for maintainability
The more you scrape, the more tools and people are involved. When building and developing a pipeline, keep that in mind and make a system and documentation that a new employee can easily understand and that can adapt to site changes.
What to avoid while scaling scraping operations?
1. Do not ignore policies and laws
Legal risks, fines, image damage - you do not need those. However tempting or innocent it may seem, avoid disrespecting robot.txt files, rate limits, and terms of service. You are building a system that should work stably, not try to get it over with.
2. Do not hardcode data
Selectors change, and new ones appear, URLs get modified, and credentials are updated. If you hardcode everything, you will have to change the whole code every time something gets altered. It is better to rely on config-driven scraping, with selector abstraction layers and parsing logic versioning.
3. Do not use a single point
When at scale, depending on a single script or server means that in the case of that point’s failure, everything will go in vain. Having a backup is a must.
4. Do not allow unreasonable spending
Traffic, infrastructure maintenance, new tools, data storage - nothing is free. It is better to optimize early. Prioritize pages you need to scrape, decide on what data to store and for how long, and choose flexible pay-as-you-go models over fixed subscriptions to avoid wasting costs on unnecessary things instead of investing in development.
5. Do not overlook proxies
Proxies allow for distributing requests across IPs and prevent triggering anti-bot measures. Businesses often treat them as a cure-all because of that; however, proxies still have to be implemented wisely. Inconsistent geo-targeting, aggressive rotation where session consistency is necessary, or sticky sessions where rotation is key, carelessly scaling concurrency threads - all of this can lead to getting irrelevant data or getting nothing at all. Besides, proxies are a must, but they are not interchangeable with header rotating tools or fingerprint spoofing solutions.
Another proxy-related myth is that they are illegal and costly. However, when used in alignment with privacy policies and data protection laws, proxies are legal and not necessarily expensive. DataImpulse is an ethical proxy vendor that offers first-party IPs. As the provider does not resell addresses, there is no additional markup and the price starts from $1 per GB - right what high-volume scraping calls for.
About Us
DataImpulse is a legal proxy vendor of residential, mobile, and datacenter proxies. There are also premium residential proxies with a higher success rate and lower latency for sensitive scraping cases. The provider offers 90M+ first-party IPs at a pay-per-GB model for $1/GB in 195 locations.
Author: Yevheniia Revenko, content manager at DataImpulse