Lattes Scraper On GitHub Actions: A Viable Strategy

by Admin 52 views
Lattes Scraper on GitHub Actions: Defining a Viable Strategy

Hey guys! Today, we're diving deep into a challenge we faced while trying to automate the Lattes data collection process using GitHub Actions (GHA). If you're scratching your head about how to make your web scraping scripts play nice with CI/CD pipelines, especially when pesky CAPTCHAs get in the way, then you're in the right place. Let's break down the problem, explore potential solutions, and figure out a rock-solid strategy to get this done.

The Lattes Scraper Challenge

So, here's the deal. We built a Python script called lattes_scraper.py to gather detailed information from the Lattes Curriculum platform. This includes everything from projects and publications to academic advising activities. The goal was to automate this data collection using GHA, which would allow us to keep our data fresh and up-to-date. Sounds straightforward, right? Wrong!

The main roadblock we hit was the dreaded reCAPTCHA, implemented by Google/Lattes. This security measure is designed to prevent bots from accessing the site, which is a good thing in general. However, GHA runners often get flagged because their IP addresses are associated with datacenters. This triggers extremely difficult CAPTCHA challenges, or even worse, outright blocks with messages like "Try again later." Our initial solution, leveraging the "Buster Captcha Solver" extension, only added fuel to the fire by requiring a headless=False browser execution. While we managed to get headless=False working in GHA using Xvfb (a virtual display server), it didn't address the core issue: the CAPTCHA blocking due to the perceived suspicious origin IP.

Since automating the data collection pipeline via GHA is crucial for our project, we urgently needed to devise an alternative strategy or a workaround. We needed a way to reliably grab Lattes data, respecting the reCAPTCHA limitations within the GHA environment. This meant some serious brainstorming and research to find a solution that wouldn't break the bank or take forever to implement. The current situation has become a high priority because it's blocking a central automation requirement, meaning the frontend data can't be updated properly. We need to figure this out, and fast!

Our Objective: Cracking the CAPTCHA Code (or Finding a Way Around It)

Our primary goal is to investigate and define a technical strategy that allows the reliable execution of lattes_scraper within GitHub Actions, effectively bypassing the reCAPTCHA blockade caused by datacenter IPs. This is the ideal scenario – a fully automated solution that keeps the data flowing smoothly. However, we're also realistic. We need a Plan B in case 100% automation within GHA proves unfeasible due to technical, financial, or reliability constraints. This backup plan involves proposing and documenting an alternative workflow that still leverages GHA for other project components (like the sigaa_scraper or frontend deployment) but handles Lattes data collection differently. Think along the lines of scheduled local executions or manual triggers followed by data commits.

The ultimate objective is to ensure that the final solution or workflow is not only technically sound but also clearly documented and communicated to the entire team. We want everyone on the same page, understanding the rationale behind the chosen approach and how it fits into the overall project workflow. This means clear justifications, detailed documentation, and open communication every step of the way. We want to make sure this issue doesn't become a bottleneck, and everyone understands how to contribute to keeping the data pipeline running smoothly.

Requirements: What We Need to Make This Work

First and foremost, our strategy must directly address the reCAPTCHA blocking issue for datacenter IPs within GHA. This is the core problem we're tackling, and any solution needs to have this firmly in its sights. Our analysis needs to be thorough, encompassing alternative approaches such as:

  • Paid CAPTCHA Resolution Services: We're talking about services like 2Captcha and Anti-Captcha. We need to evaluate their costs, the complexity of integrating them into our workflow, and their reported success rates. Are they a reliable way to bypass the CAPTCHA, and how much will it set us back?
  • Proxy Services with Residential IPs: These services offer a way to route our requests through IP addresses that appear to be from regular households, rather than datacenters. This could potentially fool the reCAPTCHA system. Again, we need to weigh the costs, the complexity of setting them up, and their actual effectiveness against Google's detection mechanisms.

The viability of any solution needs to consider the limitations imposed by GHA runners themselves. We're working with a 6-hour time limit and finite CPU/RAM resources. This means we need a solution that's not only effective but also efficient. We don't want to max out the GHA resources and crash the whole operation. The final proposal must be grounded in solid evidence. We'll need to analyze error logs from GHA runs and conduct a thorough cost/complexity analysis for each potential solution. The goal is to present a clear recommendation backed by data, so the team can make an informed decision. Finally, whether we end up with a fully automated solution or an alternative workflow, we need to document it meticulously. This documentation should live alongside the code, ensuring that anyone can understand and maintain the data collection process in the future.

Diving Deep: Task Checklist

To make sure we leave no stone unturned, we've created a detailed checklist of tasks. This will help us stay organized and ensure that we systematically explore all possible options:

  • Documenting the Errors: First up, we need to formally document the exact errors encountered when running lattes_scraper in GHA. This means grabbing logs, taking screenshots, and compiling solid evidence of the reCAPTCHA blocking in action. This will be crucial for troubleshooting and demonstrating the problem to others.
  • CAPTCHA Service Research: Next, we'll be diving into the world of CAPTCHA resolution services. We'll research and compare 2-3 different providers, paying close attention to their per-resolution costs, API capabilities, existing Python libraries for integration, and overall reported reliability. We want a service that's both effective and easy to work with.
  • Residential Proxy Exploration: We'll also be exploring the option of using residential proxy services. This involves researching and comparing 2-3 different providers, focusing on their costs (per traffic or per IP), the types of proxies they offer (rotating vs. static), and how easy it is to integrate them with Playwright within GHA. Can we seamlessly swap our datacenter IP for a residential one?
  • Complexity and Cost Assessment: Once we've identified potential solutions, we need to evaluate the complexity and estimated cost of implementing both the CAPTCHA service and proxy options. This will involve considering the development time required, ongoing service fees, and any other associated expenses. We need to see if these solutions are financially viable for our project.
  • Modified Scope Proposal: In parallel, we'll be crafting a proposal for a 'modified scope' approach. This is our Plan B: a hybrid workflow where GHA handles parts of the project, but Lattes data collection is done manually or locally. This is a more realistic fallback if the paid options prove too expensive or complex.
  • Presentation and Recommendation: Finally, we'll compile all our research, cost analysis, and complexity assessments into a clear presentation. This will include our final recommendation for the team and the professor, outlining the pros and cons of each approach. The goal is to present a well-reasoned argument for the best way forward.
  • Implementation or Documentation: Once a solution is approved, we'll either implement the chosen workaround or meticulously document the alternative (manual/local) process. This documentation will live in the docs/ folder, ensuring that everyone can understand and follow the procedure.

Priority Check: Why This Matters Now

We've marked this as a high priority issue, and for good reason. The reCAPTCHA blocking is preventing us from fully automating our data pipeline, which is a core requirement for the project. Without a solution, we can't keep the frontend data updated, which impacts the overall functionality and user experience. This isn't just a minor inconvenience; it's a roadblock that needs to be addressed ASAP.

Wrapping Up: Let's Get This Done!

So, there you have it – the Lattes scraper challenge in all its glory! We've broken down the problem, outlined our objectives, and created a detailed plan of attack. Now, it's time to roll up our sleeves and get to work. Stay tuned for updates as we delve deeper into potential solutions and strive to conquer the reCAPTCHA beast. We're confident that with a bit of research, experimentation, and collaborative problem-solving, we'll find a viable strategy to keep the Lattes data flowing smoothly! Let's do this!