From Scrapy to Custom: Engineering Trade-offs in a Solo Long-Term Crawler Project

This article is neither a technical comparison of framework merits nor an implementation guide for building a custom crawler. It documents how I reassessed engineering risks in a solo, long-term maintenance project and gradually realized that, compared to feature completeness, what I truly needed was a more controllable system. The shift from Scrapy to a custom solution wasn't a technical upgrade—it was a pivot in engineering judgment.

Why Scrapy Was Chosen Initially

At the project's inception, I needed a crawler framework that could be deployed quickly for MVP validation. Given the project's nature as a long-term, non-profit solo endeavor, I had to strictly control development costs and time investment.

Scrapy Cloud (Zyte) generously offered a permanently free unit to users who had ever been GitHub Education members. Ultimately, I turned to Scrapy, which directly met my core needs for cost and speed of launch, allowing me to rapidly validate the MVP without incurring additional expenses.

How the Problem Emerged: Not a Single Failure, but Persistent Friction

During the MVP phase, Scrapy helped me rapidly implement the desired functionality. However, as the project scaled, Scrapy’s modules gradually became more heavyweight. Minor changes began affecting multiple modules, and the number of external dependencies requiring resolution steadily increased. Returning to the project after months-long breaks meant I had to revisit historical implementations more frequently when modifying or adding features.

What truly alarmed me wasn't any specific error, but the growing uncertainty about my ability to maintain consistent, predictable control over the system.

Three Engineering Risks I Couldn't Afford

Uncontrollable Dependencies: The Deadliest Risk for Solo Projects

During Scrapy's compilation and deployment, non-business-related third-party dependencies must be integrated. Version conflicts between some of these dependencies frequently caused deployment failures. As the business evolves, such conflicts occur more frequently, and resolving them often depends on the maintenance status of external ecosystems.

As a solo developer, I cannot predict or control the update pace of these external dependencies, making project progress unstable. For long-term solo projects, issues requiring "waiting for external fixes" represent unacceptable systemic risks.

Maintenance Model Mismatch: A Solo Developer Can't Be in Two Places at Once

In actual project operation, issues like crawler errors, data anomalies, or missing data typically require me to proactively check logs to detect and fix them. Under long-term unattended conditions, the project cannot proactively expose problems. Instead, it implicitly assumes a dedicated maintainer who monitors it frequently and continuously—an assumption that doesn't hold for solo projects.

What I actually need is a "system that exposes problems even when unattended," not a "fully functional system requiring my constant attention." Reliance on manual monitoring is a maintenance model unsustainable for a solo project over the long term.

Increasing Structural Rigidity: Rigid Conventions Eroding Boundaries

In Scrapy, data flows from the spider to the item pipeline for processing after crawling completes. As data processing complexity increased, pipelines gradually became "universal processing zones," with a single pipeline shouldering excessive responsibilities.

Simultaneously, the sequential execution model prolonged modification paths. Over time, boundaries between processing units blurred, triggering increased coupling and conflicts.

When the system's primary data flow is locked into a rigidly defined channel, structural evolution becomes impossible, and systemic risks gradually accumulate.

Not Pursuing Greater Complexity, But Greater Control

Faced with these risks, I decided to develop a more controllable crawler system from scratch. This wasn't about chasing more complex features—distributed architectures and high concurrency sound appealing, but they don't impose real constraints on my current project.

My true goal is a system with manageable dependencies, a maintenance model aligned with practical needs, and a clear architecture. This isn't a technical upgrade but a redistribution of engineering risk within existing resource constraints.

Three Design Principles Shaped the Framework's Form

• Features may be limited, but dependencies must remain controllable

I can accept the absence of advanced, complex features, but I refuse to tolerate long-term uncertainty caused by uncontrollable external dependencies.

• Prioritize maintenance investment early to reduce cognitive load later

I'm willing to frontload operational management by dedicating more effort upfront to monitoring and alerting design, thereby lowering future cognitive burden and maintenance costs. Errors must be systematically organized, not discovered manually.

• Boundaries must be clear, even if development efficiency is constrained

I require a system architecture with well-defined boundaries and single-responsibility units. I reject temporarily stuffing logic into "catch-all zones," even if it means sacrificing some development efficiency in the short term.

A Crawler Framework Designed Exclusively for My Own Use

This framework does not aspire to be a more general-purpose crawler framework. It simply aims to reduce the number of engineering decisions I must make daily during long-term maintenance in a solo, long-term project.

References

• Item Pipeline