Table of contents
1. Use multi-threaded or asynchronous programming:
2. Set an appropriate request frequency:
4. Handle exceptions and errors:
5. Monitor and manage task queues:
6. Data storage and processing:
7. Randomize request parameters and headers:
8. Scheduled tasks and continuous monitoring:
Collecting big data with batch crawlers is a complex and challenging task, and various tricks and strategies need to be considered to ensure efficient and reliable data collection. Here are some common tips and strategies to help you batch crawl big data.
1. Use multi-threaded or asynchronous programming:
Using multi-thread or asynchronous programming technology can process multiple requests or tasks at the same time to improve the efficiency of data collection. This reduces latency and allows multiple requests to be made at the same time, resulting in faster data fetches.
import requests
import concurrent.futures
def fetch_data(url):
response = requests.get(url)
return response.json()
urls = [
"http://api.example.com/data1",
"http://api.example.com/data2",
"http://api.example.com/data3"
]
# 使用多线程或异步编程进行并发请求
with concurrent.futures.ThreadPoolExecutor() as executor:
results = executor.map(fetch_data, urls)
for result in results:
print(result)2. Set an appropriate request frequency:
When performing batch crawler collection, it is necessary to abide by the visit frequency limit of the target website. Set an appropriate request frequency or add a delay to avoid excessive load on the target website to prevent being banned or triggering the anti-crawler mechanism.
import time
import requests
base_url = "http://api.example.com/data"
for i in range(10):
url = base_url + str(i)
response = requests.get(url)
data = response.json()
# 处理数据
time.sleep(1) # 添加延迟，控制请求频率3. Use a proxy server:
Using a proxy server hides your real IP address and distributes requests, reducing the risk of being detected and banned. Choose a high-quality proxy server, change and check the proxy regularly to ensure reliability and stability.
import requests
proxies = {
'http': 'http://proxy.example.com:8080',
'https': 'https://proxy.example.com:8080'
}
url = "http://api.example.com/data"
response = requests.get(url, proxies=proxies)
data = response.json()
# 处理数据
4. Handle exceptions and errors:
Write robust crawler code, including proper error handling and exception handling. In the event of network failures, timeouts, or other errors, be able to handle these situations gracefully, with error retries or logging.
import requests
url = "http://api.example.com/data"
try:
response = requests.get(url)
response.raise_for_status() # 检查是否有请求错误
data = response.json()
# 处理数据
except requests.exceptions.RequestException as e:
print("请求错误:", e)
except requests.exceptions.HTTPError as e:
print("HTTP错误:", e)
except requests.exceptions.ConnectionError as e:
print("连接错误:", e)
# 其他异常处理5. Monitor and manage task queues:
Establish a task queue system to manage and schedule crawler tasks to ensure orderly execution of tasks and monitor progress. This helps you track task status, monitor exceptions and errors, and automatically handle retries or rollbacks.
import requests
from queue import Queue
from threading import Thread
queue = Queue()
# 添加任务到队列
def enqueue_task(url):
queue.put(url)
# 处理任务的函数
def process_task():
while True:
url = queue.get()
response = requests.get(url)
# 处理数据
queue.task_done()
# 添加任务到队列
enqueue_task("http://api.example.com/data1")
enqueue_task("http://api.example.com/data2")
enqueue_task("http://api.example.com/data3")
# 启动多个线程来处理任务
for _ in range(4):
t = Thread(target=process_task)
t.start()
# 等待所有任务完成
queue.join()6. Data storage and processing:
Choose a suitable data storage method, such as a database or file system, to store the crawled data. A well-designed data processing process, including data cleaning, deduplication, formatting and analysis, makes the data available for subsequent applications or analysis.
import csv
data = [
{"name": "Alice", "age": 25, "city": "New York"},
{"name": "Bob", "age": 30, "city": "London"},
{"name": "Charlie", "age": 35, "city": "Paris"}
]
# CSV文件写入数据
with open('data.csv', 'w', newline='') as csvfile:
fieldnames = ['name', 'age', 'city']
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
writer.writeheader()
writer.writerows(data)
# CSV文件读取数据
with open('data.csv', 'r') as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
print(row)
7. Randomize request parameters and headers:
By randomizing request parameters, adding random User-Agent header information, etc., different requests and user behaviors are simulated to reduce the probability of being identified as a crawler.
import random
import requests
user_agents = [
"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.116 Safari/537.36",
"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.116 Safari/537.36",
"Mozilla/5.0 (X11; Ubuntu; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.116 Safari/537.36"
]
url = "http://api.example.com/data"
headers = {
"User-Agent": random.choice(user_agents),
"Accept-Language": "en-US,en;q=0.9"
}
response = requests.get(url, headers=headers)
data = response.json()
# 处理数据8. Scheduled tasks and continuous monitoring:
Regularly run crawler collection tasks, and monitor the running status, errors, data updates, etc. of the tasks. Tools or frameworks can be used to set up cron jobs and monitor alerts to keep data up to date and up and running.
import schedule
import time
def task():
# 执行定时任务
print("执行任务")
# 每隔一段时间执行一次任务
schedule.every(10).minutes.do(task)
# 每天的固定时间执行任务
schedule.every().day.at("08:00").do(task)
# 每周一的固定时间执行任务
schedule.every().monday.at("13:00").do(task)
while True:
schedule.run_pending()
time.sleep(1)It should be noted that before large-scale data collection, please ensure that you abide by relevant laws and regulations and the terms of use of the target website, and respect the privacy policy of the website. Also, try to avoid overburdening the target website and maintain a friendly and legitimate web crawler behavior.