What is the optimal query interval to avoid overloading Nordstrom servers while scraping?

When scraping websites like Nordstrom, it's important to be ethical and responsible to avoid overloading the servers. While there's no universal "optimal" query interval, a conservative approach is generally recommended.

Here are a few guidelines to determine a safe scraping interval:

  1. Respect robots.txt: Begin by checking Nordstrom's robots.txt file (accessible at https://www.nordstrom.com/robots.txt). This file may contain directives that include the allowed crawl rate, which you should follow.

  2. Throttling Requests: As a rule of thumb, making one request per second is often cited as a conservative rate. However, this can be adjusted based on the response from the server and the time sensitivity of the data you need.

  3. Adaptive Scraping: Start with a slower rate and monitor the server's response time and error rate. If responses are quick and error-free, you might cautiously increase the frequency. Conversely, if you receive any errors indicating you are sending too many requests, you should slow down.

  4. Server Load Times: Consider the server's peak hours, and try to schedule your scraping activities during off-peak times when the server is less busy.

  5. Handling Retries: Implement exponential backoff in your retry logic to handle rate-limiting or temporary blocks gracefully. This means if a request fails, you wait a little longer each time before trying again.

  6. Distributed Scraping: If you are scraping at a larger scale, distribute your requests over multiple IP addresses to avoid sending too many requests from a single IP, which could be mistaken for a DDoS attack.

  7. Legal Considerations: Always ensure that your scraping activities comply with the website's terms of service and any relevant laws or regulations.

Here is an example of how you might implement a conservative scraping interval in Python using the time module to add a delay between requests:

import requests
import time

base_url = 'https://www.nordstrom.com/some-endpoint'
headers = {
    'User-Agent': 'Your User Agent String'

def scrape(url):
        response = requests.get(url, headers=headers)
        # Handle response and parse data
        # ...
        print(f"Data retrieved from {url}")
    except requests.exceptions.RequestException as e:
    time.sleep(1)  # Sleep for 1 second between requests

for page in range(1, 10):  # Example: Scraping first 10 pages

For JavaScript (using Node.js with the axios library and async/await), you can use setTimeout to create a delay:

const axios = require('axios');

const base_url = 'https://www.nordstrom.com/some-endpoint';

async function scrape(url) {
    try {
        const response = await axios.get(url, {
            headers: { 'User-Agent': 'Your User Agent String' }
        // Handle response and parse data
        // ...
        console.log(`Data retrieved from ${url}`);
    } catch (error) {

async function scrapeWithInterval() {
    for (let page = 1; page <= 10; page++) {  // Example: Scraping first 10 pages
        await scrape(`${base_url}?page=${page}`);
        await new Promise(resolve => setTimeout(resolve, 1000)); // Wait for 1 second


In both examples, the delay is set to 1 second between requests, which is on the conservative side. Adjust this based on your findings and the factors mentioned above. Remember to use these scripts responsibly and ethically.

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