Distributed task queue practice: Celery + Redis million URL management

When the crawler meets the proxy IP: how to play the million-dollar task without crashing?

Do data collection brothers should understand, hard work to write a crawler script, the results just ran up on the target site blocked IP, the feeling is like eating noodles found no seasoning packets. At this timeDistributed Task Queue + Proxy IP PoolThe combination of punches will come in handy, let's take Celery + Redis today this pair of golden partners to say.

Express Sorting Task Processing

Imagine you have a delivery station, and there are millions of parcels to be sorted every day.Celery is like that intelligent sorter, which automatically distributes deliveries from different regions to various conveyor belts (Worker nodes). But there is a pitfall to be aware of:Don't let all sorters (Workers) pick up deliveries from the same door (IP address)Otherwise the post owner (the target site) pulls the plug on you in minutes.

It's time to bring out ouripipgo dynamic proxy poolIt's like having different overalls (IP addresses) for each sorter. See this table for the exact configuration:

Celery's anti-wrapping trick

Bury a hook in the task decorator to automatically change the IP before each task execution. a chestnut:

@task(bind=True)
def crawl_url(self, url):.
    current_ip = ipipgo.get_proxy() Call the ipipgo API here.
    headers = {'X-Forwarded-For': current_ip}
     Remember to add an exception retry mechanism

Be careful to eat it like rotisserie sushiRandom interval requestDon't send requests as if you've been hungry for three days. It is recommended to add rate_limit in the Celery configuration, such as up to 60 times per minute.

Redis Storage Riot Operations

You can't just store millions of URLs in memory, here's how to do it.the Great Law of Splitting the Library::

• Bank 0: queue to be captured (using List structure)
• Bank 1: Ongoing tasks (Sorted Set timestamping)
• Bank 2: Failure retry queue (hash structure holds retry counts)

The key is to fingerprint each URL and use MD5 to generate a unique ID to prevent repeated collection. It's like a courier order number to avoid sorting the same package twice.

Diary of a real-world pit stop

I fell on my ass last year when I was helping an e-commerce company with competitive monitoring:

1. Directly run without IP warm-up, the result triggered the wind control
2. Retesting mechanism too aggressive leads to avalanche
3. Choosing the wrong type of proxy IP is a waste of money

replaced byipipgo's smart routing packageIt is only then that the problem is solved, and his family can automatically match the server room or residential IP according to the target website, which is much more hassle-free than tossing it by yourself.

question-and-answer session

Q: What should I do if my proxy IP fails frequently?
A: Election of supporton-demand billingThe service providers, such as ipipgo's traffic packet model, use as much as you can without wasting. At the same time, you should set up a mechanism to automatically weed out invalid IPs, like this:

def check_proxy(ip).
    try.
        requests.get('http://check.ipipgo.com', proxies={'http': ip}, timeout=5)
    except: ipipgo.report_failure
        ipipgo.report_failure(ip) flag the problem IP

Q: How do you control agency costs?
A: Three tricks: ① set a reasonable number of concurrency ② distinguish between static resources and dynamic interfaces ③ use theRegional Directed Proxy for ipipgoIt's like ordering a takeaway, there's no need to pay for nationwide delivery.

final words

Distributed crawler is like opening a chain of milk tea stores, Celery is the central kitchen, Redis is the distribution system, the proxy IP is the business license of each store. If you're too lazy to toss your own license (to maintain the proxy pool), just look for theipipgoSuch a professional agency, save time to develop a few more explosive milk tea (data products) does not smell?