Performance test (simulated)

5.5 Example Project: Optimizing a Web Scraper

Web scrapers are deceptively simple: fetch pages, parse HTML, extract data. The performance pitfalls appear when you scale beyond a handful of URLs. Synchronous requests block on network latency, parsers are used inefficiently, and retry logic is missing. Claude Code helps you reason about these trade-offs and evolve a straightforward scraper into an efficient, resilient pipeline without sacrificing clarity. In this section, you will build a baseline scraper and then optimize it using concurrency, connection reuse, sane timeouts, backoff, and faster parsing—all in clean, runnable Python.

Concept Development

The biggest performance wins in scraping come from reducing wasted time per request and increasing safe parallelism. Requests should share connections where possible, fail fast with timeouts, and retry transient errors with backoff. Parsing should avoid repeated work and use efficient libraries. Concurrency must be bounded to respect remote servers and your own system limits. Claude’s role in this workflow is to highlight where the code blocks, which resources are contended, and which optimizations change asymptotic behavior versus microseconds that don’t matter.

The optimization path you’ll apply follows a simple arc: start with a correct, readable synchronous baseline; measure the bottlenecks conceptually (network waits dominate); and introduce targeted improvements that preserve clarity. Each change remains justifiable and testable on its own.

Hands-On Example

Below are two complete programs. The first is a small, correct baseline that fetches a few pages synchronously withrequests and parses with BeautifulSoup. The second is an optimized version usingaiohttp for concurrent I/O, connection pooling, exponential backoff, bounded concurrency, faster parsing, and structured error handling.

Baseline: Synchronous Scraper