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362 lines
14 KiB
Markdown
362 lines
14 KiB
Markdown
# Advanced usages
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## Introduction
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!!! success "Prerequisites"
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1. You've read the [Getting started](getting-started.md) page and know how to create and run a basic spider.
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This page covers the spider system's advanced features: concurrency control, pause/resume, streaming, lifecycle hooks, statistics, and logging.
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## Concurrency Control
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The spider system uses three class attributes to control how aggressively it crawls:
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| Attribute | Default | Description |
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|----------------------------------|---------|------------------------------------------------------------------|
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| `concurrent_requests` | `4` | Maximum number of requests being processed at the same time |
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| `concurrent_requests_per_domain` | `0` | Maximum concurrent requests per domain (0 = no per-domain limit) |
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| `download_delay` | `0.0` | Seconds to wait before each request |
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| `robots_txt_obey` | `False` | Respect robots.txt rules (Disallow, Crawl-delay, Request-rate) |
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```python
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class PoliteSpider(Spider):
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name = "polite"
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start_urls = ["https://example.com"]
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# Be gentle with the server
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concurrent_requests = 4
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concurrent_requests_per_domain = 2
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download_delay = 1.0 # Wait 1 second between requests
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async def parse(self, response: Response):
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yield {"title": response.css("title::text").get("")}
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```
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When `concurrent_requests_per_domain` is set, each domain gets its own concurrency limiter in addition to the global limit. This is useful when crawling multiple domains simultaneously, as you can allow high global concurrency while being polite to each individual domain.
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!!! tip
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The `download_delay` parameter adds a fixed wait before every request, regardless of the domain. Use it for simple rate limiting.
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### Using uvloop
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The `start()` method accepts a `use_uvloop` parameter to use the faster [uvloop](https://github.com/MagicStack/uvloop)/[winloop](https://github.com/nicktimko/winloop) event loop implementation, if available:
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```python
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result = MySpider().start(use_uvloop=True)
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```
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This can improve throughput for I/O-heavy crawls. You'll need to install `uvloop` (Linux/macOS) or `winloop` (Windows) separately.
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## Pause & Resume
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The spider supports graceful pause-and-resume via checkpointing. To enable it, pass a `crawldir` directory to the spider constructor:
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```python
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spider = MySpider(crawldir="crawl_data/my_spider")
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result = spider.start()
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if result.paused:
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print("Crawl was paused. Run again to resume.")
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else:
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print("Crawl completed!")
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```
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### How It Works
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1. **Pausing**: Press `Ctrl+C` during a crawl. The spider waits for all in-flight requests to finish, saves a checkpoint (pending requests + a set of seen request fingerprints), and then exits.
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2. **Force stopping**: Press `Ctrl+C` a second time to stop immediately without waiting for active tasks.
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3. **Resuming**: Run the spider again with the same `crawldir`. It detects the checkpoint, restores the queue and seen set, and continues from where it left off, skipping `start_requests()`.
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4. **Cleanup**: When a crawl completes normally (not paused), the checkpoint files are deleted automatically.
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**Checkpoints are also saved periodically during the crawl (every 5 minutes by default).**
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You can change the interval as follows:
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```python
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# Save checkpoint every 2 minutes
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spider = MySpider(crawldir="crawl_data/my_spider", interval=120.0)
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```
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The writing to the disk is atomic, so it's totally safe.
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!!! tip
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Pressing `Ctrl+C` during a crawl always causes the spider to close gracefully, even if the checkpoint system is not enabled. Doing it again without waiting forces the spider to close immediately.
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### Knowing If You're Resuming
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The `on_start()` hook receives a `resuming` flag:
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```python
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async def on_start(self, resuming: bool = False):
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if resuming:
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self.logger.info("Resuming from checkpoint!")
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else:
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self.logger.info("Starting fresh crawl")
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```
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## Development Mode
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When you're iterating on a spider's `parse()` logic, re-hitting the target servers on every run is slow and noisy. Development mode caches every response to disk on the first run and replays them from disk on subsequent runs, so you can tweak your selectors and re-run the spider as many times as you want without making a single network request.
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Enable it by setting `development_mode = True` on your spider:
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```python
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class MySpider(Spider):
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name = "my_spider"
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start_urls = ["https://example.com"]
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development_mode = True
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async def parse(self, response: Response):
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yield {"title": response.css("title::text").get("")}
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```
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The first run fetches normally and stores each response on disk. Every subsequent run serves the same requests from the cache, skipping the network entirely.
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### Cache Location
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By default, responses are cached in `.scrapling_cache/{spider.name}/` relative to the current working directory (where you ran the spider from, **not** where the spider script lives). You can override the location with `development_cache_dir`:
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```python
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class MySpider(Spider):
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name = "my_spider"
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start_urls = ["https://example.com"]
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development_mode = True
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development_cache_dir = "/tmp/my_spider_cache"
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```
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### How It Works
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1. **Cache key**: Each response is keyed by the request's fingerprint, so any change to fingerprint-affecting attributes (`fp_include_kwargs`, `fp_include_headers`, `fp_keep_fragments`) will produce a fresh fetch.
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2. **Storage format**: One JSON file per response, named `{fingerprint_hex}.json`. The body is base64-encoded so binary content is preserved exactly. Writes are atomic (temp file + rename).
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3. **Replay**: On a cache hit, the engine skips the network entirely, including `download_delay`, rate limiting, and the `is_blocked()` retry path. The cached response goes straight to your callback.
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4. **Stats**: Cached requests still count toward `requests_count`, `response_bytes`, and the per-status counters, so your stat output looks the same as a normal crawl. Two extra counters, `cache_hits` and `cache_misses`, let you see how the cache performed.
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### Clearing the Cache
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There's no automatic expiration. To force a fresh crawl, delete the cache directory or call the manager's `clear()` method directly.
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!!! warning
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Development mode is meant for development, not production. Cached responses never expire, and replay bypasses rate limiting and blocked-request retries. Don't ship a spider with `development_mode = True`.
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## Streaming
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For long-running spiders or applications that need real-time access to scraped items, use the `stream()` method instead of `start()`:
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```python
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import anyio
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async def main():
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spider = MySpider()
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async for item in spider.stream():
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print(f"Got item: {item}")
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# Access real-time stats
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print(f"Items so far: {spider.stats.items_scraped}")
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print(f"Requests made: {spider.stats.requests_count}")
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anyio.run(main)
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```
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Key differences from `start()`:
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- `stream()` must be called from an async context
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- Items are yielded one by one as they're scraped, not collected into a list
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- You can access `spider.stats` during iteration for real-time statistics
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!!! abstract
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The full list of all stats that can be accessed by `spider.stats` is explained below [here](#results--statistics)
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You can use it with the checkpoint system too, so it's easy to build UI on top of spiders. UIs that have real-time data and can be paused/resumed.
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```python
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import anyio
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async def main():
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spider = MySpider(crawldir="crawl_data/my_spider")
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async for item in spider.stream():
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print(f"Got item: {item}")
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# Access real-time stats
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print(f"Items so far: {spider.stats.items_scraped}")
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print(f"Requests made: {spider.stats.requests_count}")
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anyio.run(main)
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```
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You can also use `spider.pause()` to shut down the spider in the code above. If you used it without enabling the checkpoint system, it will just close the crawl.
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## Lifecycle Hooks
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The spider provides several hooks you can override to add custom behavior at different stages of the crawl:
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### on_start
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Called before crawling begins. Use it for setup tasks like loading data or initializing resources:
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```python
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async def on_start(self, resuming: bool = False):
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self.logger.info("Spider starting up")
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# Load seed URLs from a database, initialize counters, etc.
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```
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### on_close
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Called after crawling finishes (whether completed or paused). Use it for cleanup:
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```python
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async def on_close(self):
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self.logger.info("Spider shutting down")
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# Close database connections, flush buffers, etc.
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```
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### on_error
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Called when a request fails with an exception. Use it for error tracking or custom recovery logic:
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```python
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async def on_error(self, request: Request, error: Exception):
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self.logger.error(f"Failed: {request.url} - {error}")
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# Log to error tracker, save failed URL for later, etc.
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```
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### on_scraped_item
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Called for every scraped item before it's added to the results. Return the item (modified or not) to keep it, or return `None` to drop it:
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```python
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async def on_scraped_item(self, item: dict) -> dict | None:
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# Drop items without a title
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if not item.get("title"):
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return None
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# Modify items (e.g., add timestamps)
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item["scraped_at"] = "2026-01-01"
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return item
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```
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!!! tip
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This hook can also be used to direct items through your own pipelines and drop them from the spider.
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### start_requests
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Override `start_requests()` for custom initial request generation instead of using `start_urls`:
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```python
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async def start_requests(self):
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# POST request to log in first
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yield Request(
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"https://example.com/login",
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method="POST",
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data={"user": "admin", "pass": "secret"},
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callback=self.after_login,
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)
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async def after_login(self, response: Response):
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# Now crawl the authenticated pages
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yield response.follow("/dashboard", callback=self.parse)
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```
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## Results & Statistics
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The `CrawlResult` returned by `start()` contains both the scraped items and detailed statistics:
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```python
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result = MySpider().start()
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# Items
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print(f"Total items: {len(result.items)}")
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result.items.to_json("output.json", indent=True)
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# Did the crawl complete?
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print(f"Completed: {result.completed}")
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print(f"Paused: {result.paused}")
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# Statistics
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stats = result.stats
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print(f"Requests: {stats.requests_count}")
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print(f"Failed: {stats.failed_requests_count}")
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print(f"Blocked: {stats.blocked_requests_count}")
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print(f"Offsite filtered: {stats.offsite_requests_count}")
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print(f"Robots.txt disallowed: {stats.robots_disallowed_count}")
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print(f"Cache hits: {stats.cache_hits}")
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print(f"Cache misses: {stats.cache_misses}")
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print(f"Items scraped: {stats.items_scraped}")
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print(f"Items dropped: {stats.items_dropped}")
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print(f"Response bytes: {stats.response_bytes}")
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print(f"Duration: {stats.elapsed_seconds:.1f}s")
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print(f"Speed: {stats.requests_per_second:.1f} req/s")
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```
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### Detailed Stats
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The `CrawlStats` object tracks granular information:
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```python
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stats = result.stats
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# Status code distribution
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print(stats.response_status_count)
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# {'status_200': 150, 'status_404': 3, 'status_403': 1}
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# Bytes downloaded per domain
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print(stats.domains_response_bytes)
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# {'example.com': 1234567, 'api.example.com': 45678}
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# Requests per session
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print(stats.sessions_requests_count)
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# {'http': 120, 'stealth': 34}
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# Proxies used during the crawl
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print(stats.proxies)
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# ['http://proxy1:8080', 'http://proxy2:8080']
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# Log level counts
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print(stats.log_levels_counter)
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# {'debug': 200, 'info': 50, 'warning': 3, 'error': 1, 'critical': 0}
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# Timing information
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print(stats.start_time) # Unix timestamp when crawl started
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print(stats.end_time) # Unix timestamp when crawl finished
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print(stats.download_delay) # The download delay used (seconds)
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# Concurrency settings used
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print(stats.concurrent_requests) # Global concurrency limit
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print(stats.concurrent_requests_per_domain) # Per-domain concurrency limit
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# Custom stats (set by your spider code)
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print(stats.custom_stats)
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# {'login_attempts': 3, 'pages_with_errors': 5}
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# Export everything as a dict
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print(stats.to_dict())
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```
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## Logging
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The spider has a built-in logger accessible via `self.logger`. It's pre-configured with the spider's name and supports several customization options:
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| Attribute | Default | Description |
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|-----------------------|--------------------------------------------------------------|----------------------------------------------------|
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| `logging_level` | `logging.DEBUG` | Minimum log level |
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| `logging_format` | `"[%(asctime)s]:({spider_name}) %(levelname)s: %(message)s"` | Log message format |
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| `logging_date_format` | `"%Y-%m-%d %H:%M:%S"` | Date format in log messages |
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| `log_file` | `None` | Path to a log file (in addition to console output) |
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```python
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import logging
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class MySpider(Spider):
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name = "my_spider"
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start_urls = ["https://example.com"]
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logging_level = logging.INFO
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log_file = "logs/my_spider.log"
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async def parse(self, response: Response):
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self.logger.info(f"Processing {response.url}")
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yield {"title": response.css("title::text").get("")}
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```
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The log file directory is created automatically if it doesn't exist. Both console and file output use the same format. |