This is a follow-up question to my earlier post. Although @alecxe had very nice remarks in hist last answer, I decided to get all the products a bit different.

Instead of searching for a space (i.e. ASCII url-encoded %20) (which wasn't the best way to go as there were products without a space in their name / description), I decided to take every category from the index page and recursively go through all of them until I reach the product page.

Moreover, regarding the requests.Session(), I decided not to use it because I've created a txt file containing some user-agents (in my head, it didn't make sense to have multiple user-agents within the same session - maybe I'm wrong).

Other changes that I made:

  • sorted the header fields so that the product_id goes first and the images at the end
  • improved some of the xpaths
  • added a retry() function which recursively tries to get a specific link


Unfortunately, I didn't have the necessary time to go through Scrapy and wrap my head around it, so I didn't use it (even tough it might improve it considerably). As it is, the below code runs very slowly (for example, since morning it only went through 30k products). How can I speed it up more?


Scraper for https://www.richelieu.com/

Python version: 3.6.X

from collections import ChainMap
from csv import DictWriter, QUOTE_MINIMAL
from lxml import html
from random import choice, randint
from string import ascii_uppercase
from time import sleep
import os

import requests

base_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
utils = os.path.join(base_dir, 'utils')
user_agents = os.path.join(base_dir, 'utils')
result = os.path.join(base_dir, 'result')

INDEX_PAGE = 'https://www.richelieu.com/us/en/index'

CATEGORY_LINKS_FILE = os.path.join(utils, 'big_categories.txt')
PRODUCT_LINKS_FILE = os.path.join(utils, 'products.txt')
RESULT_FILE = os.path.join(result, 'products2.csv')
USER_AGENTS_FILE = os.path.join(user_agents, 'user_agents.txt')

def random_user_agent():
    return choice(open(USER_AGENTS_FILE).readlines()).strip()

def check_if_more_products(tree):
    more_prods = [
        for all_prod in tree.xpath("//div[@id='pm2_prodTableForm']//tbody/tr/td[1]//a")
    if not more_prods:
        return False
    return more_prods

def get_product_number(tree):
        prod_number = tree.xpath("//h2[contains(., 'Product number')]/following-sibling::span")[0].text
    except IndexError:
        prod_number = ""
    return {"Product number": prod_number}

def get_product_name(tree):
        prod_name = tree.xpath("//section[@id='pm2_topInfo']//h1/span")[0].text
    except IndexError:
        prod_name = ""
    return {"Name": prod_name}

def get_product_category(tree):
        breadcrumb = tree.xpath("/html/body/div[2]//li//span")
        category = " / ".join([x.text for x in breadcrumb][1:-1])
    except IndexError:
        category = ""
    return {"Category": category}

def get_product_description(tree):
        description = tree.xpath("//*[@id='pm2_topInfo']//p")[0].text
    except IndexError:
        description = ''
    return {"Description": description}

def get_extra_data(tree):
    keys = [
        for a in tree.xpath("//table//tr/th[position()>1]/a")
    vals = [
        for td in tree.xpath("//table//tr[1]/td[position()>1]")

    return dict(zip(keys, vals))

def create_technical_tables(tree):
    keys = [
        for el in tree.xpath("//table[@class='table']//span")
    vals = [
        for el in tree.xpath("//table[@class='table']//td")

    info_table = get_extra_data(tree)

    data, extra_data = {}, {}

    table_1, table_2 = '', ''

    for key, val in zip(keys, vals):
        if key is not None:
            key = key.lower()

            if val.startswith("From") and key == "suggested price":
            if key in ["product number", "our divisions"]:
            if "material" in key:
                extra_data[key] = val
            if "color" in key:
                extra_data[key] = val
            if "finish" in key:
                extra_data[key] = val

            data[key] = val

    for key, val in info_table.items():
        if key is not None:
            key = key.lower()

            if 'material' in key:
                extra_data[key] = val
            if 'color' in key:
                extra_data[key] = val

    for key, val in data.items():
        table_1 += "<p><strong>{}: </strong>{}</p>".format(key, val)

    for key, val in extra_data.items():
        table_2 += "<p><strong>{}: </strong>{}</p>".format(key, val)

    return table_1, table_2

def first_table(tree):
        ts = create_technical_tables(tree)[0]
    except Exception:
        ts = ""
    return {"Technical specifications": ts}

def second_table(tree):
        info = create_technical_tables(tree)[1]
    except Exception:
        info = ""
    return {"Info": info}

def get_catalog_link(tree):
        catalog_link = tree.xpath(
            "//h2[contains(., 'RELATED DOCUMENTS')]/following-sibling::div[contains(@id, 'carouselSegment')]//li[1]//a"
        if not catalog_link.startswith('https://'):
            catalog_link = 'https://www.richelieu.com{}'.format(catalog_link)
    except IndexError:
        catalog_link = ''
    return {"Catalog link": catalog_link}

def get_line_art_link(tree):
        line_art = tree.xpath(
             "//h2[contains(., 'RELATED DOCUMENTS')]/following-sibling::"
             "div[contains(@id, 'carouselSegment')]//li[last()]//a"
        if not line_art.startswith('https://'):
            line_art = 'https://www.richelieu.com{}'.format(line_art)
    except IndexError:
        line_art = ''
    return {"Line art": line_art}

def get_right_part_info(tree):
    data = {}
    extras = [
        for a in tree.xpath("//div[@id='pm2_blocDroitFixe']/div[@class='feAncetres clearfix']/ul/li/a")
    for item in extras:
        name, value = item.split(': ')
        data[name] = value
    return data

def get_product_images(tree):
    data = {}
    links = [
        for a in tree.xpath("//div[@id='rcMediaPlayerCarousel']//li/a")
    for i, link in enumerate(links, start=1):
        key = 'image_{}'.format(i)
        data[key] = link
    return data

def prepare_product(tree):
    product_number = get_product_number(tree)
    product_name = get_product_name(tree)
    product_category = get_product_category(tree)
    product_description = get_product_description(tree)
    first_table_product = first_table(tree)
    second_table_product = second_table(tree)
    catalog_link = get_catalog_link(tree)
    line_art_link = get_line_art_link(tree)
    right_part_info = get_right_part_info(tree)
    product_images = get_product_images(tree)

    data = [

    return dict(ChainMap(*data))

def retry(link):
    wait = randint(0, 10)
        return requests.get(link, headers={"User-Agent": random_user_agent()}).text
    except Exception as e:
        print('Retrying product page in {} seconds because: {}'.format(wait, e))
        return retry(link)

def rearrange(header):
    return sorted(header, key=lambda x: (x.startswith('image_') + (x != 'Product number'), x))

def get_categories():
    categories = set()
    html_ = retry(INDEX_PAGE)
    page = html.fromstring(html_)

    for letter in ascii_uppercase:
        for link in page.xpath("//div[@id='index-{}']//li/a".format(letter)):

    for link in page.xpath("//div[@id='index-0-9']//li/a"):

    with open(CATEGORY_LINKS_FILE, 'w', encoding='utf-8') as f:
        for category in categories:
            f.write('{}?imgMode=m&sort=&nbPerPage=200'.format(category) + '\n')

def dig_up_products(url):
    html_ = retry(url)
    page = html.fromstring(html_)

    for link in page.xpath('//h2[contains(., "CATEGORIES")]/following-sibling::*[@id="carouselSegment2b"]//li//a'):
        yield from dig_up_products(link.attrib["href"])

    for link in page.xpath('//ul[@id="prodResult"]/li//div[@class="imgWrapper"]/a'):
        yield link.attrib["href"]

def main():
    print('[# INFO #] START getting all product categories...')
    print('[# INFO #] END getting all product categories...\n')

    print('[# INFO #] START getting all product links...')
    all_product_links = set()
    with open(CATEGORY_LINKS_FILE) as in_file:
        for start in in_file:
            start = start.strip()

            for link in dig_up_products(start):

    with open(PRODUCT_LINKS_FILE, 'a+') as out_file:
        for pl in all_product_links:
            if not any(line.strip() == pl for line in out_file):
                out_file.write(pl + '\n')
    print('[# INFO #] END getting all product links...\n')

    print('[# INFO #] START getting all products info...')
    fieldnames, data = set(), []

    with open(PRODUCT_LINKS_FILE) as links:
        i = 0
        for link in links:
            link = link.strip()
            product_page = retry(link)

            if product_page:
                product_tree = html.fromstring(product_page)
                more_products = check_if_more_products(product_tree)

                if not more_products:
                    i += 1
                    print('Appended one more (if not more products): {}'.format(i))
                    for product_link in more_products:
                        new_page = retry(product_link)

                        if new_page:
                            i += 1
                            new_product_tree = html.fromstring(new_page)
                            print('Appended one more (if new_page): {}'.format(i))

    for dict_ in data:
    print('[# INFO #] END getting all products info...\n')

    print('[# INFO #] START writing products info to file...')
    with open(RESULT_FILE, 'w', newline='', encoding='utf-8') as f:
        writer = DictWriter(f, fieldnames=rearrange(fieldnames), delimiter=';', quoting=QUOTE_MINIMAL)

        for dict_ in data:
    print('[# INFO #] END writing products info to file...\n\n')

if __name__ == '__main__':

PS: for those of you who are going test it, here is the list of user-agents.

  • 2
    \$\begingroup\$ have you considered using multithreading or async requests? The bottleneck might be in waiting for requests instead of the hmtl parsing \$\endgroup\$
    – juvian
    Dec 5, 2017 at 17:04
  • \$\begingroup\$ I have, yes, but I didn't know what parts should I explicitly implement multithreading on. I'll probably start a bounty when possible as that might be a bit much to ask for :) \$\endgroup\$
    – Cajuu'
    Dec 5, 2017 at 21:09
  • \$\begingroup\$ Should use it on your retry function to make multiple http request simultaneously. \$\endgroup\$
    – juvian
    Dec 6, 2017 at 16:06
  • \$\begingroup\$ First you should test how long is the html parsing taking and how long is the waiting for http result taking though \$\endgroup\$
    – juvian
    Dec 6, 2017 at 16:13
  • 1
    \$\begingroup\$ I suspect you may end up spending more time reinventing the wheel than taking the proper time to wrap your head around Scrapy. There are lots of corner cases that are already taken care in the framework and it is easier to use it than painfully learn about them by experience. \$\endgroup\$ Dec 6, 2017 at 20:16

1 Answer 1


The blocking nature of the script - is your #1 bottleneck

There are 30K pages that you need to download. And, you are currently doing it sequentially in blocking manner, URL after URL.

We can work on speeding up HTML parsing and actually have some quick wins, but that would not dramatically change the overall performance picture.

The next natural step just has to be Scrapy. And, by the way, there is even that scrapy-fake-useragent package (shameless self-promotion) that would allow you to rotate popular real-life user agents making it harder to detect you. Though, of course, make sure to be a good web-scraping citizen and respect the website's Terms of Use and robots.txt.

Here is an initial spider for you to start with. It extracts all the categories, sub-categories and then goes into product pages from which extracts just the page title and the current URL:

from string import ascii_uppercase

import scrapy

class RichelieuSpider(scrapy.Spider):
    name = 'richelieu'
    allowed_domains = ['www.richelieu.com']
    start_urls = ["https://www.richelieu.com/us/en/index"]

    def parse(self, response):
        # parse categories
        for letter in ascii_uppercase:
            for link in response.xpath("//div[@id='index-{}']//li/a/@href".format(letter)).extract():
                yield scrapy.Request('{}?imgMode=m&sort=&nbPerPage=200'.format(link), callback=self.parse_category)

        for link in response.xpath("//div[@id='index-0-9']//li/a/@href").extract():
            yield scrapy.Request('{}?imgMode=m&sort=&nbPerPage=200'.format(link), callback=self.parse_category)

    def parse_category(self, response):
        # parse sub-categories
        for link in response.xpath('//h2[contains(., "CATEGORIES")]/following-sibling::*[@id="carouselSegment2b"]//li//a/@href').extract():
            yield scrapy.Request('{}?imgMode=m&sort=&nbPerPage=200'.format(link), callback=self.parse_category)

        # parse products
        for product_link in response.xpath('//ul[@id="prodResult"]/li//div[@class="imgWrapper"]/a/@href').extract():
            yield scrapy.Request(product_link, callback=self.parse_product)

    def parse_product(self, response):
        # TODO: more product specific data extraction logic here
        yield {
            'title': response.css("title::text").extract_first(),
            'url': response.url

I am sure you are going to see a dramatic performance difference of this approach versus the current one. And, not only that, the code would get more modular with a much nicer sepration of concerns. Things like "retries", "session management" and few others are just gonna be solved automatically.

Some of the next steps for you:

  • make/create a proper Scrapy project
  • add the User-Agent rotation
  • adjust the settings like DOWNLOAD_DELAY and CONCURRENT_REQUESTS to avoid hitting the site too often
  • create a Product Item definition
  • create a "Product Item Loader" and define the logic of extracting certain product fields using input and output processors. The Item Loader when done would roughly resemble what you currently have in prepare_product() function
  • incorporate the item loader into the spider

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