docs/spiders/architecture.md

Spiders architecture

!!! success "Prerequisites"

1. You've completed or read the [Fetchers basics](../fetching/choosing.md) page to understand the different fetcher types and when to use each one.
2. You've completed or read the [Main classes](../parsing/main_classes.md) page to understand the [Selector](../parsing/main_classes.md#selector) and [Response](../fetching/choosing.md#response-object) classes.

Scrapling's spider system is a Scrapy-inspired async crawling framework designed for concurrent, multi-session crawls with built-in pause/resume support. It brings together Scrapling's parsing engine and fetchers into a unified crawling API while adding scheduling, concurrency control, and checkpointing.

If you're familiar with Scrapy, you'll feel right at home. If not, don't worry — the system is designed to be straightforward.

Data Flow

The diagram below shows how data flows through the spider system when a crawl is running:

Here's what happens step by step when you run a spider without many details:

1. The Spider produces the first batch of Request objects. By default, it creates one request for each URL in start_urls, but you can override start_requests() for custom logic.
2. The Scheduler receives requests and places them in a priority queue, and creates fingerprints for them. Higher-priority requests are dequeued first.
3. The Crawler Engine asks the Scheduler to dequeue the next request, respecting concurrency limits (global and per-domain) and download delays. Once the Crawler Engine receives the request, it passes it to the Session Manager, which routes it to the correct session based on the request's sid (session ID).
4. The session fetches the page and returns a Response object to the Crawler Engine. The engine records statistics and checks for blocked responses. If the response is blocked, the engine retries the request up to max_blocked_retries times. Of course, the blocking detection and the retry logic for blocked requests can be customized.
5. The Crawler Engine passes the Response to the request's callback. The callback either yields a dictionary, which gets treated as a scraped item, or a follow-up request, which gets sent to the scheduler for queuing.
6. The cycle repeats from step 2 until the scheduler is empty and no tasks are active, or the spider is paused.
7. If crawldir is set while starting the spider, the Crawler Engine periodically saves a checkpoint (pending requests + seen URLs set) to disk. On graceful shutdown (Ctrl+C), a final checkpoint is saved. The next time the spider runs with the same crawldir, it resumes from where it left off — skipping start_requests() and restoring the scheduler state.

Components

Spider

The central class you interact with. You subclass Spider, define your start_urls and parse() method, and optionally configure sessions and override lifecycle hooks.

from scrapling.spiders import Spider, Response, Request

class MySpider(Spider):
    name = "my_spider"
    start_urls = ["https://example.com"]

    async def parse(self, response: Response):
        for link in response.css("a::attr(href)").getall():
            yield response.follow(link, callback=self.parse_page)

    async def parse_page(self, response: Response):
        yield {"title": response.css("h1::text").get("")}

Crawler Engine

The engine orchestrates the entire crawl. It manages the main loop, enforces concurrency limits, dispatches requests through the Session Manager, and processes results from callbacks. You don't interact with it directly — the Spider.start() and Spider.stream() methods handle it for you.

Scheduler

A priority queue with built-in URL deduplication. Requests are fingerprinted based on their URL, HTTP method, body, and session ID. The scheduler supports snapshot() and restore() for the checkpoint system, allowing the crawl state to be saved and resumed.

Session Manager

Manages one or more named session instances. Each session is one of:

• FetcherSession
• AsyncDynamicSession
• AsyncStealthySession

When a request comes in, the Session Manager routes it to the correct session based on the request's sid field. Sessions can be started with the spider start (default) or lazily (started on the first use).

Checkpoint System

An optional system that, if enabled, saves the crawler's state (pending requests + seen URL fingerprints) to a pickle file on disk. Writes are atomic (temp file + rename) to prevent corruption. Checkpoints are saved periodically at a configurable interval and on graceful shutdown. Upon successful completion (not paused), checkpoint files are automatically cleaned up.

Output

Scraped items are collected in an ItemList (a list subclass with to_json() and to_jsonl() export methods). Crawl statistics are tracked in a CrawlStats dataclass which contains a lot of useful info.

Comparison with Scrapy

If you're coming from Scrapy, here's how Scrapling's spider system maps:

Concept	Scrapy	Scrapling
Spider definition	scrapy.Spider subclass	scrapling.spiders.Spider subclass
Initial requests	start_requests()	async start_requests()
Callbacks	def parse(self, response)	async def parse(self, response)
Following links	response.follow(url)	response.follow(url)
Item output	yield dict or yield Item	yield dict
Request scheduling	Scheduler + Dupefilter	Scheduler with built-in deduplication
Downloading	Downloader + Middlewares	Session Manager with multi-session support
Item processing	Item Pipelines	on_scraped_item() hook
Blocked detection	Through custom middlewares	Built-in is_blocked() + retry_blocked_request() hooks
Concurrency	CONCURRENT_REQUESTS setting	concurrent_requests class attribute
Domain filtering	allowed_domains	allowed_domains
Pause/Resume	JOBDIR setting	crawldir constructor argument
Export	Feed exports	result.items.to_json() / to_jsonl() or custom through hooks
Running	scrapy crawl spider_name	MySpider().start()
Streaming	N/A	async for item in spider.stream()
Multi-session	N/A	Multiple sessions with different types per spider