Build a Distributed Scrapy Crawler in Minutes with RabbitMQ and RedisBloom

This guide walks you through installing Scrapy-Distributed, setting up RabbitMQ and RedisBloom containers, creating a sitemap spider, configuring the distributed scheduler and dupefilter, and running the spider, while explaining why this non‑intrusive solution improves over existing Scrapy‑Redis and scrapy‑rabbitmq approaches.

MaGe Linux Operations
MaGe Linux Operations
MaGe Linux Operations
Build a Distributed Scrapy Crawler in Minutes with RabbitMQ and RedisBloom

Learn how to quickly develop a distributed Scrapy crawler using the Scrapy-Distributed extension, RabbitMQ, and RedisBloom.

Quick Start

Step 0: Install Scrapy-Distributed pip install scrapy-distributed If you lack the required services, start RabbitMQ and RedisBloom containers for testing:

# pull and run a RabbitMQ container.
docker run -d --name rabbitmq -p 0.0.0.0:15672:15672 -p 0.0.0.0:5672:5672 rabbitmq:3
# pull and run a RedisBloom container.
docker run -d --name redis-redisbloom -p 0.0.0.0:6379:6379 redislabs/rebloom:latest

Step 1 (optional): Create a spider project scrapy startproject simple_example Modify the spider file, for example:

from scrapy_distributed.spiders.sitemap import SitemapSpider
from scrapy_distributed.queues.amqp import QueueConfig
from scrapy_distributed.dupefilters.redis_bloom import RedisBloomConfig

class MySpider(SitemapSpider):
    name = "example"
    sitemap_urls = ["http://www.people.com.cn/robots.txt"]
    queue_conf: QueueConfig = QueueConfig(
        name="example", durable=True,
        arguments={"x-queue-mode": "lazy", "x-max-priority": 255}
    )
    redis_bloom_conf: RedisBloomConfig = RedisBloomConfig(key="example:dupefilter")

    def parse(self, response):
        self.logger.info(f"parse response, url: {response.url}")

Step 2: Edit settings.py to use the distributed scheduler and dupefilter

SCHEDULER = "scrapy_distributed.schedulers.DistributedScheduler"
SCHEDULER_QUEUE_CLASS = "scrapy_distributed.queues.amqp.RabbitQueue"
RABBITMQ_CONNECTION_PARAMETERS = "amqp://guest:guest@localhost:5672/example/?heartbeat=0"
DUPEFILTER_CLASS = "scrapy_distributed.dupefilters.redis_bloom.RedisBloomDupeFilter"
BLOOM_DUPEFILTER_REDIS_URL = "redis://:@localhost:6379/0"
BLOOM_DUPEFILTER_REDIS_HOST = "localhost"
BLOOM_DUPEFILTER_REDIS_PORT = 6379
REDIS_BLOOM_PARAMS = {"redis_cls": "redisbloom.client.Client"}
BLOOM_DUPEFILTER_ERROR_RATE = 0.001
BLOOM_DUPEFILTER_CAPACITY = 100_0000

You can also set queue and bloom‑filter parameters directly in the spider class:

class MySpider(SitemapSpider):
    ...
    queue_conf: QueueConfig = QueueConfig(
        name="example", durable=True,
        arguments={"x-queue-mode": "lazy", "x-max-priority": 255}
    )
    redis_bloom_conf: RedisBloomConfig = RedisBloomConfig(
        key="example:dupefilter", error_rate=0.001, capacity=100_0000
    )

Step 3: Run the spider scrapy crawl example Check that the RabbitMQ queue and RedisBloom filter are operating correctly.

Why Scrapy-Distributed?

Existing solutions such as Scrapy-Redis and scrapy‑rabbitmq have several drawbacks: Redis set‑based deduplication consumes large memory, Redis list queues can cause backlog, RabbitMQ integrations lack full parameter control, distributed dupefilter support is missing, and they require invasive code changes.

Scrapy-Distributed solves these problems by using RedisBloom for low‑memory deduplication, providing full RabbitMQ queue configuration (including lazy‑mode), offering a flexible scheduler that can combine components, and adopting a non‑intrusive design that only needs configuration changes to make any spider distributed.

The framework is open‑source on GitHub.

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PythonRabbitMQWeb ScrapingScrapyRedisBloomdistributed crawling
MaGe Linux Operations
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MaGe Linux Operations

Founded in 2009, MaGe Education is a top Chinese high‑end IT training brand. Its graduates earn 12K+ RMB salaries, and the school has trained tens of thousands of students. It offers high‑pay courses in Linux cloud operations, Python full‑stack, automation, data analysis, AI, and Go high‑concurrency architecture. Thanks to quality courses and a solid reputation, it has talent partnerships with numerous internet firms.

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