spidur 0.3.1

🕷️ A lightweight, generic parallel runner for custom scrapers

spidur 🕷️

spidur is a lightweight, hackable framework for running multiple custom scrapers in parallel — even on the same domain.
It helps you coordinate different scrapers, ensure valid URLs (no wasted work), and collect all results at once.

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Core ideas

• Multiple scrapers per domain — handle different content types (articles, images, comments, etc.) simultaneously.
• Parallel execution — utilizes all CPU cores.
• Async + multiprocessing safe — works across async methods and process pools.
• No opinions — you control discovery, validation, and scraping logic.
• Results collected automatically — each scraper contributes to a single aggregated result set.

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Install

pip install spidur

or with Poetry:

poetry add spidur

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Example

from typing import Any

from spidur import Runner, Scraper, ScraperFactory, ScrapeResult, Target


# --- define your scrapers ---
# Implement three small hooks. The discover -> validate -> scrape loop is
# provided for you by Scraper.fetch_round(), so you never write it yourself.

class ArticleScraper(Scraper):
    def is_valid_url(self, url: str) -> bool:
        return url.startswith("https://example.com/articles/")

    async def discover_urls(self, page: Any, known: set[str]) -> list[str]:
        return [
            "https://example.com/articles/1",
            "https://example.com/articles/2",
        ]

    async def scrape_page(self, page: Any, url: str) -> ScrapeResult | None:
        return {"type": "article", "url": url, "data": f"Content of {url}"}


class CommentScraper(Scraper):
    def is_valid_url(self, url: str) -> bool:
        return url.startswith("https://example.com/comments/")

    async def discover_urls(self, page: Any, known: set[str]) -> list[str]:
        return [
            "https://example.com/comments/1",
            "https://example.com/comments/2",
        ]

    async def scrape_page(self, page: Any, url: str) -> ScrapeResult | None:
        return {"type": "comment", "url": url, "data": f"Comments from {url}"}


# --- register both scrapers ---

ScraperFactory.register("articles", ArticleScraper)
ScraperFactory.register("comments", CommentScraper)


# --- define your scrape targets ---

targets = [
    Target(name="articles", start_url="https://example.com/articles"),
    Target(name="comments", start_url="https://example.com/comments"),
]


# --- run them all in parallel ---
# Put the code above in a module (e.g. `my_scrapers.py`) and pass its name as
# `bootstrap` so each worker process re-registers the scrapers. This is
# required on macOS/Windows, where multiprocessing uses the `spawn` start
# method and workers do not inherit the parent's registrations.

results = Runner.run(targets, bootstrap=["my_scrapers"])

for name, items in results.items():
    print(f"Results from {name}:")
    for item in items:
        print("  →", item)

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Command line

spidur ships a small CLI. Point it at the module(s) that register your scrapers and pass one or more name=start_url targets:

spidur --module my_scrapers articles=https://example.com/articles

Results are written to stdout as JSON. Use -v for info-level logging.

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How it works

1. Each Scraper subclass defines three single-purpose hooks:

   • is_valid_url(url) — a pure predicate; keeps invalid URLs out of scope.
   • discover_urls(page, known) — finds new pages to scrape.
   • scrape_page(page, url) — extracts structured data.

   The discover → validate → scrape orchestration is provided once by the base class as fetch_round(known), so subclasses never re-implement it.

2. You register scrapers in ScraperFactory.

3. The Runner:

   • Spawns multiple processes.
   • Executes all scrapers concurrently.
   • Aggregates their results into a single dictionary keyed by scraper name.

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Development

Install the project with its dev tooling and run the full quality gate:

poetry install
poetry run ruff check .     # lint
poetry run black --check .  # formatting
poetry run mypy             # static types (strict)
poetry run pytest           # tests

spidur is fully type-annotated and ships a py.typed marker, so downstream projects get type checking for free.

Why “spidur”?

Because it crawls the web — but cleanly, predictably, and in parallel.