How to Scrape SimilarWeb
SimilarWeb is a great place to find useful information about any website such asrank, category, rank_change, average_vist, pages_per_visit, and bounce_rate. Each of these metrics can provide critical data and insight into what users are doing when they access the site.
How to Scrape SimilarWeb With Requests and BeautifulSoup
In this tutorial, we're going to learn how to scrape SimilarWeb using Requests & BeautifulSoup.- TLDR: How to Scrape SimilarWeb with Python
- How To Architect Our Scraper
- Understanding How To Scrape SimilarWeb
- Setting Up Our SimilarWeb Scraper
- Build A SimilarWeb Search Crawler
- Build A SimilarWeb Scraper
- Legal and Ethical Considerations
- Conclusion
- More Cool Articles
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TLDR - How to Scrape SimilarWeb with Python
Scraping SimilarWeb can be a really difficult job. For starters, SimilarWeb blocks people after a certain amount of access so we absolutely need a proxy with rotating IP addresses.If you want to scrape it, use this scraper below.- Create a new project folder with a
config.jsonfile. - After creating the config file, add your ScrapeOps API key
{"api_key": "your-super-secret-api-key"}. - Then, copy/paste the code below into a new python file.
import osimport csvimport requestsimport jsonimport loggingfrom urllib.parse import urlencodefrom bs4 import BeautifulSoupimport concurrent.futuresfrom dataclasses import dataclass, field, fields, asdict API_KEY = "" with open("config.json", "r") as config_file: config = json.load(config_file) API_KEY = config["api_key"] def get_scrapeops_url(url): payload = { "api_key": API_KEY, "url": url, "wait": 3000 } proxy_url = "https://proxy.scrapeops.io/v1/?" + urlencode(payload) return proxy_url ## Logginglogging.basicConfig(level=logging.INFO)logger = logging.getLogger(__name__) @dataclassclass SearchData: name: str = "" url: str = "" rank: int = 0 rank_change: int = 0 average_visit: str = "" pages_per_visit: float = 0.0 bounce_rate: str = "" def __post_init__(self): self.check_string_fields() def check_string_fields(self): for field in fields(self): # Check string fields if isinstance(getattr(self, field.name), str): # If empty set default text if getattr(self, field.name) == "": setattr(self, field.name, f"No {field.name}") continue # Strip any trailing spaces, etc. value = getattr(self, field.name) setattr(self, field.name, value.strip()) @dataclassclass CompetitorData: name: str = "" url: str = "" affinity: str = "" monthly_visits: str = "" category: str = "" category_rank: int = None def __post_init__(self): self.check_string_fields() def check_string_fields(self): for field in fields(self): # Check string fields if isinstance(getattr(self, field.name), str): # If empty set default text if getattr(self, field.name) == "": setattr(self, field.name, f"No {field.name}") continue # Strip any trailing spaces, etc. value = getattr(self, field.name) setattr(self, field.name, value.strip()) class DataPipeline: def __init__(self, csv_filename="", storage_queue_limit=50): self.names_seen = [] self.storage_queue = [] self.storage_queue_limit = storage_queue_limit self.csv_filename = csv_filename self.csv_file_open = False def save_to_csv(self): self.csv_file_open = True data_to_save = [] data_to_save.extend(self.storage_queue) self.storage_queue.clear() if not data_to_save: return keys = [field.name for field in fields(data_to_save[0])] file_exists = os.path.isfile(self.csv_filename) and os.path.getsize(self.csv_filename) > 0 with open(self.csv_filename, mode="a", newline="", encoding="utf-8") as output_file: writer = csv.DictWriter(output_file, fieldnames=keys) if not file_exists: writer.writeheader() for item in data_to_save: writer.writerow(asdict(item)) self.csv_file_open = False def is_duplicate(self, input_data): if input_data.name in self.names_seen: logger.warning(f"Duplicate item found: {input_data.name}. Item dropped.") return True self.names_seen.append(input_data.name) return False def add_data(self, scraped_data): if self.is_duplicate(scraped_data) == False: self.storage_queue.append(scraped_data) if len(self.storage_queue) >= self.storage_queue_limit and self.csv_file_open == False: self.save_to_csv() def close_pipeline(self): if self.csv_file_open: time.sleep(3) if len(self.storage_queue) > 0: self.save_to_csv() def scrape_search_results(keyword, data_pipeline=None, retries=3): url = f"https://www.similarweb.com/top-websites/{keyword['category']}/{keyword['subcategory']}/" tries = 0 success = False while tries <= retries and not success: try: scrapeops_proxy_url = get_scrapeops_url(url) response = requests.get(scrapeops_proxy_url) logger.info(f"Recieved [{response.status_code}] from: {url}") if response.status_code != 200: raise Exception(f"Failed request, Status Code {response.status_code}") soup = BeautifulSoup(response.text, "html.parser") rows = soup.find_all("tr", class_="top-table__row") rank = 1 for row in rows: link_holder = row.find("a", class_="tw-table__compare") site_name = link_holder.text link = f"https://www.similarweb.com/website/{site_name}/" rank_change_holder = row.find("td", class_="top-table__column top-table__column--rank-change") rank_change = 0 up_or_down = rank_change_holder.find("span").get("class")[1] if "change--up" in up_or_down: rank_change += int(rank_change_holder.text) elif "change--down" in up_or_down: rank_change -= int(rank_change_holder.text) average_visit = row.find("span", class_="tw-table__avg-visit-duration").text pages_per_visit = float(row.find("span", class_="tw-table__pages-per-visit").text) bounce_rate = row.find("span", class_="tw-table__bounce-rate").text search_data = SearchData( name=site_name, url=link, rank=rank, rank_change=rank_change, average_visit=average_visit, pages_per_visit=pages_per_visit, bounce_rate=bounce_rate ) rank+=1 data_pipeline.add_data(search_data) logger.info(f"Successfully parsed data from: {url}") success = True except Exception as e: logger.error(f"An error occurred while processing page {url}: {e}, retries left {retries-tries}") tries+=1 if not success: raise Exception(f"Max Retries exceeded: {retries}") def start_scrape(keywords, data_pipeline=None, max_threads=5, retries=3): with concurrent.futures.ThreadPoolExecutor(max_workers=max_threads) as executor: executor.map( scrape_search_results, keywords, [data_pipeline] * len(keywords), [retries] * len(keywords) ) def process_website(row, retries=3): url = row["url"] tries = 0 success = False while tries <= retries and not success: response = requests.get(url) try: if response.status_code != 200: raise Exception(f"Failed request, Status Code {response.status_code}") else: logger.info(f"Status: {response.status_code}") soup = BeautifulSoup(response.text, "html.parser") blocked = soup.find("div", class_="wa-limit-modal") if blocked: raise Exception(f"Blocked") competitors = soup.find_all("div", class_="wa-competitors__list-item") competitor_pipeline = DataPipeline(csv_filename=f"{row['name'].replace(' ', '-')}.csv") for competitor in competitors: site_name = competitor.find("span", class_="wa-competitors__list-item-title").text link = f"https://www.similarweb.com/website/{site_name}/" affinity = competitor.find("span", class_="app-progress__value").text target_spans = competitor.find_all("span", "wa-competitors__list-column") monthly_visits = target_spans[2].text category = target_spans[3].text category_rank = int(target_spans[4].text.replace("#", "").replace(",", "").replace("--", "0")) competitor_data = CompetitorData( name=site_name, url=link, affinity=affinity, monthly_visits=monthly_visits, category=category, category_rank=category_rank ) competitor_pipeline.add_data(competitor_data) competitor_pipeline.close_pipeline() success = True except Exception as e: logger.error(f"Exception thrown: {e}") logger.warning(f"Failed to process page: {row['url']}, Retries left: {retries-tries}") tries += 1 if not success: raise Exception(f"Max Retries exceeded: {retries}") else: logger.info(f"Successfully parsed: {row['url']}") def process_results(csv_file, retries=3): logger.info(f"processing {csv_file}") with open(csv_file, newline="") as file: reader = list(csv.DictReader(file)) for row in reader: process_website(row, retries=retries) if __name__ == "__main__": MAX_RETRIES = 3 MAX_THREADS = 5 logger.info(f"Crawl starting...") ## INPUT ---> List of keywords to scrape keyword_list = [{"category": "arts-and-entertainment", "subcategory": "humor"}, {"category": "arts-and-entertainment", "subcategory": "animation-and-comics"}] aggregate_files = [] ## Job Processes filename = "arts-and-entertainment" crawl_pipeline = DataPipeline(csv_filename=f"{filename}.csv") start_scrape(keyword_list, data_pipeline=crawl_pipeline, max_threads=MAX_THREADS, retries=MAX_RETRIES) crawl_pipeline.close_pipeline() aggregate_files.append(f"{filename}.csv") logger.info(f"Crawl complete.") for file in aggregate_files: process_results(file, retries=MAX_RETRIES)
MAX_THREADS: Defines the number of concurrent threads used during the scraping and processing tasks.MAX_RETRIES: Determines the maximum number of retries the script will attempt if a request fails (e.g., due to a network issue or a non-200 status code).keyword_list: A list of dictionaries where each dictionary contains a "category" and "subcategory" that specify the type of websites to scrape from SimilarWeb.filename: The base name used to create the CSV file where the scraped data will be saved.
How To Architect Our SimilarWeb Scraper
Scraping SimilarWeb can be challenging. As soon as you really try to do anything with it, you're prompted to create an account in order to gain any real access to the site.
This not only makes the site difficult to scrape, but it makes it difficult to even access it from a traditional browser! Even though we're prompted to create an account, we still can perform some actions before we wind up getting blocked every time.With ScrapeOps Proxy Aggregator, we can utilize rotating proxies to get past this... we're getting blocked based on our IP address. It's much more difficult to block us if we're using a fresh IP with each ping.Our SimilarWeb scraper will follow a similar architecture to most of our other scraping projects from our "How to Scrape" series. We'll need to build both a crawler and a scraper.- Our crawler will lookup top sites on a particular category.
- Then, our scraper will go through and scrape the competitors and their respective info for each of these top ranked sites.
- Lookup a particular site and parse its data.
- Store the parsed data inside an easy to manage CSV file.
- Concurrently search multiple categories at the same time.
- Use the ScrapeOps Proxy Aggregator to get past these anti-bots and free trial prompts.
- Look up and parse the competitors from a row in the CSV file generated earlier.
- Save the competitors of each site to a new CSV report.
- Concurrently run steps 1 and 2 simultaneously.
- Once again, use the ScrapeOps Proxy Aggregator to get past any anti-bots and free trial prompts.
Understanding How To Scrape SimilarWeb
We need to understand SimilarWeb at a high level before we start writing any serious code.In these next few sections, let's take a look at exactly how we're going to access our information and how to pick it from the page.Step 1: How To Request SimilarWeb Pages
Like everything else on the web, we need to begin with a simple GET request. The front page of SimilarWeb isn't really very useful, so we'll query a specific endpoint.In this case, we'll be looking up the top 50 humor sites. Here is our URL:https://www.similarweb.com/top-websites/arts-and-entertainment/humor/
https://www.similarweb.com/top-websites/{CATEGORY}/{SUBCATEGORY}/
category and a subcategory. In this case, our category is "arts-and-entertainment" while our subcategory is "humor".You can view a shot of the page below.
When you view the page for a specific site, the URL is looks like this:https://www.similarweb.com/website/pikabu.ru/
https://www.similarweb.com/website/{NAME_OF_SITE}/
Step 2: How To Extract Data From SimilarWeb Results and Pages
Extracting the data can be a little tricky. However, if we have access to site, this is completely doable. For starters, some of our content is loaded dynamically.To load our dynamic content, we need to use thewait parameter when talking to ScrapeOps. After we have our loaded page, we just need to find the information using its CSS class.For the results pages, each row has a class of top-table__row. We can find all these rows and easily extract their data from there.
To extract our competitors, we extract div elements with the class of wa-competitors__list-item. Each of these div tags holds all the data for each competitor.
On top if these pages, we need to be aware of the modal that SimilarWeb uses to block us. If this modal is present, we need to retry our request. As you can see in the image below, it's a div with a class of wa-limit-modal.
Step 3: Geolocated Data
ScrapeOps gives the ability to control our geolocation via thecountry parameter. However, with SimilarWeb we don't want to control our geolocation.Instead of controlling our location, we want as many IP addresses as possible to reduce our likelihood of getting blocked and asked to sign in/ sign up like you saw in the previous section.By not controlling our location, this gives us a much larger pool of IP addresses to use.Setting Up Our SimilarWeb Scraper Project
Let's get started. You can run the following commands to get setup.Create a New Project Foldermkdir similarweb-scraper cd similarweb-scraper
python -m venv venvsource venv/bin/activatepip install requestspip install beautifulsoup4Build A SimilarWeb Search Crawler
Time to start building! In the next few sections, we'll go through and build our crawler piece by piece.We'll start with a parser. Next we'll add data storage. Then, we'll add in concurrency. Finally, we'll add proxy integration.Step 1: Create Simple Search Data Parser
Parsing is the first step of our scrape. In the code below, we create our basic script and add structure like error handling and retries. Most importantly, we implement our base parsing function.To see how our data gets extracted, pay close attention toscrape_search_results().import osimport csvimport requestsimport jsonimport loggingfrom urllib.parse import urlencodefrom bs4 import BeautifulSoupimport concurrent.futuresfrom dataclasses import dataclass, field, fields, asdict API_KEY = "" with open("config.json", "r") as config_file: config = json.load(config_file) API_KEY = config["api_key"] ## Logginglogging.basicConfig(level=logging.INFO)logger = logging.getLogger(__name__) def scrape_search_results(keyword, retries=3): url = f"https://www.similarweb.com/top-websites/{keyword['category']}/{keyword['subcategory']}/" tries = 0 success = False while tries <= retries and not success: try: response = requests.get(url) logger.info(f"Recieved [{response.status_code}] from: {url}") if response.status_code != 200: raise Exception(f"Failed request, Status Code {response.status_code}") soup = BeautifulSoup(response.text, "html.parser") rows = soup.find_all("tr", class_="top-table__row") rank = 1 for row in rows: link_holder = row.find("a", class_="tw-table__compare") site_name = link_holder.text link = f"https://www.similarweb.com/website/{site_name}/" rank_change_holder = row.find("td", class_="top-table__column top-table__column--rank-change") rank_change = 0 up_or_down = rank_change_holder.find("span").get("class")[1] if "change--up" in up_or_down: rank_change += int(rank_change_holder.text) elif "change--down" in up_or_down: rank_change -= int(rank_change_holder.text) average_visit = row.find("span", class_="tw-table__avg-visit-duration").text pages_per_visit = float(row.find("span", class_="tw-table__pages-per-visit").text) bounce_rate = row.find("span", class_="tw-table__bounce-rate").text search_data = { "name": site_name, "url": link, "rank": rank, "rank_change": rank_change, "average_visit": average_visit, "pages_per_visit": pages_per_visit, "bounce_rate": bounce_rate } rank+=1 print(search_data) logger.info(f"Successfully parsed data from: {url}") success = True except Exception as e: logger.error(f"An error occurred while processing page {url}: {e}, retries left {retries-tries}") tries+=1 if not success: raise Exception(f"Max Retries exceeded: {retries}") def start_scrape(keywords, retries=3): for keyword in keywords: scrape_search_results(keyword, retries=retries) if __name__ == "__main__": MAX_RETRIES = 3 MAX_THREADS = 5 logger.info(f"Crawl starting...") ## INPUT ---> List of keywords to scrape keyword_list = [{"category": "arts-and-entertainment", "subcategory": "humor"}, {"category": "arts-and-entertainment", "subcategory": "animation-and-comics"}] aggregate_files = [] ## Job Processes filename = "arts-and-entertainment" start_scrape(keyword_list, retries=MAX_RETRIES) logger.info(f"Crawl complete.")
- First, we find all our rows,
rows = soup.find_all("tr", class_="top-table__row"). - We find our
link_holderwithrow.find("a", class_="tw-table__compare"). - Using the
link_holderobject, we extract oursite_nameand construct ourlink. rank_change_holder.find("span").get("class")[1]is used to find whether the rank went up or down.- We then find the average visit with
row.find("span", class_="tw-table__avg-visit-duration").text. float(row.find("span", class_="tw-table__pages-per-visit").text)finds ourpages_per_visit.- Finally, we get our
bounce_ratewithrow.find("span", class_="tw-table__bounce-rate").text.
Step 2: Storing the Scraped Data
Once we've got our data, we need to store it. To store our data, we need to make a couple classes. We need adataclass, SearchData.SearchData will be used to represent individual objects from our search results. Once we have a SearchData object, we need to pass it into a DataPipeline.Our DataPipeline is used to open a pipe to a CSV file. The pipeline filters out duplicates by name and then saves all non-duplicate objects to a CSV file.Here is our SearchData class. We use this to represent individual ranking results.@dataclassclass SearchData: name: str = "" url: str = "" rank: int = 0 rank_change: int = 0 average_visit: str = "" pages_per_visit: float = 0.0 bounce_rate: str = "" def __post_init__(self): self.check_string_fields() def check_string_fields(self): for field in fields(self): # Check string fields if isinstance(getattr(self, field.name), str): # If empty set default text if getattr(self, field.name) == "": setattr(self, field.name, f"No {field.name}") continue # Strip any trailing spaces, etc. value = getattr(self, field.name) setattr(self, field.name, value.strip())
DataPipeline.class DataPipeline: def __init__(self, csv_filename="", storage_queue_limit=50): self.names_seen = [] self.storage_queue = [] self.storage_queue_limit = storage_queue_limit self.csv_filename = csv_filename self.csv_file_open = False def save_to_csv(self): self.csv_file_open = True data_to_save = [] data_to_save.extend(self.storage_queue) self.storage_queue.clear() if not data_to_save: return keys = [field.name for field in fields(data_to_save[0])] file_exists = os.path.isfile(self.csv_filename) and os.path.getsize(self.csv_filename) > 0 with open(self.csv_filename, mode="a", newline="", encoding="utf-8") as output_file: writer = csv.DictWriter(output_file, fieldnames=keys) if not file_exists: writer.writeheader() for item in data_to_save: writer.writerow(asdict(item)) self.csv_file_open = False def is_duplicate(self, input_data): if input_data.name in self.names_seen: logger.warning(f"Duplicate item found: {input_data.name}. Item dropped.") return True self.names_seen.append(input_data.name) return False def add_data(self, scraped_data): if self.is_duplicate(scraped_data) == False: self.storage_queue.append(scraped_data) if len(self.storage_queue) >= self.storage_queue_limit and self.csv_file_open == False: self.save_to_csv() def close_pipeline(self): if self.csv_file_open: time.sleep(3) if len(self.storage_queue) > 0: self.save_to_csv()
DataPipeline and pass it into start_scrape(). start_scrape() then passes the pipeline into our parsing function.Instead of printing our parsed data, we now pass that into the pipeline. Once we're finished parsing the results, we go ahead and close the DataPipeline.import osimport csvimport requestsimport jsonimport loggingfrom urllib.parse import urlencodefrom bs4 import BeautifulSoupimport concurrent.futuresfrom dataclasses import dataclass, field, fields, asdict API_KEY = "" with open("config.json", "r") as config_file: config = json.load(config_file) API_KEY = config["api_key"] ## Logginglogging.basicConfig(level=logging.INFO)logger = logging.getLogger(__name__) @dataclassclass SearchData: name: str = "" url: str = "" rank: int = 0 rank_change: int = 0 average_visit: str = "" pages_per_visit: float = 0.0 bounce_rate: str = "" def __post_init__(self): self.check_string_fields() def check_string_fields(self): for field in fields(self): # Check string fields if isinstance(getattr(self, field.name), str): # If empty set default text if getattr(self, field.name) == "": setattr(self, field.name, f"No {field.name}") continue # Strip any trailing spaces, etc. value = getattr(self, field.name) setattr(self, field.name, value.strip()) class DataPipeline: def __init__(self, csv_filename="", storage_queue_limit=50): self.names_seen = [] self.storage_queue = [] self.storage_queue_limit = storage_queue_limit self.csv_filename = csv_filename self.csv_file_open = False def save_to_csv(self): self.csv_file_open = True data_to_save = [] data_to_save.extend(self.storage_queue) self.storage_queue.clear() if not data_to_save: return keys = [field.name for field in fields(data_to_save[0])] file_exists = os.path.isfile(self.csv_filename) and os.path.getsize(self.csv_filename) > 0 with open(self.csv_filename, mode="a", newline="", encoding="utf-8") as output_file: writer = csv.DictWriter(output_file, fieldnames=keys) if not file_exists: writer.writeheader() for item in data_to_save: writer.writerow(asdict(item)) self.csv_file_open = False def is_duplicate(self, input_data): if input_data.name in self.names_seen: logger.warning(f"Duplicate item found: {input_data.name}. Item dropped.") return True self.names_seen.append(input_data.name) return False def add_data(self, scraped_data): if self.is_duplicate(scraped_data) == False: self.storage_queue.append(scraped_data) if len(self.storage_queue) >= self.storage_queue_limit and self.csv_file_open == False: self.save_to_csv() def close_pipeline(self): if self.csv_file_open: time.sleep(3) if len(self.storage_queue) > 0: self.save_to_csv() def scrape_search_results(keyword, data_pipeline=None, retries=3): url = f"https://www.similarweb.com/top-websites/{keyword['category']}/{keyword['subcategory']}/" tries = 0 success = False while tries <= retries and not success: try: response = requests.get(url) logger.info(f"Recieved [{response.status_code}] from: {url}") if response.status_code != 200: raise Exception(f"Failed request, Status Code {response.status_code}") soup = BeautifulSoup(response.text, "html.parser") rows = soup.find_all("tr", class_="top-table__row") rank = 1 for row in rows: link_holder = row.find("a", class_="tw-table__compare") site_name = link_holder.text link = f"https://www.similarweb.com/website/{site_name}/" rank_change_holder = row.find("td", class_="top-table__column top-table__column--rank-change") rank_change = 0 up_or_down = rank_change_holder.find("span").get("class")[1] if "change--up" in up_or_down: rank_change += int(rank_change_holder.text) elif "change--down" in up_or_down: rank_change -= int(rank_change_holder.text) average_visit = row.find("span", class_="tw-table__avg-visit-duration").text pages_per_visit = float(row.find("span", class_="tw-table__pages-per-visit").text) bounce_rate = row.find("span", class_="tw-table__bounce-rate").text search_data = SearchData( name=site_name, url=link, rank=rank, rank_change=rank_change, average_visit=average_visit, pages_per_visit=pages_per_visit, bounce_rate=bounce_rate ) rank+=1 data_pipeline.add_data(search_data) logger.info(f"Successfully parsed data from: {url}") success = True except Exception as e: logger.error(f"An error occurred while processing page {url}: {e}, retries left {retries-tries}") tries+=1 if not success: raise Exception(f"Max Retries exceeded: {retries}") def start_scrape(keywords, data_pipeline=None, retries=3): for keyword in keywords: scrape_search_results(keyword, data_pipeline=data_pipeline, retries=retries) if __name__ == "__main__": MAX_RETRIES = 3 MAX_THREADS = 5 logger.info(f"Crawl starting...") ## INPUT ---> List of keywords to scrape keyword_list = [{"category": "arts-and-entertainment", "subcategory": "humor"}, {"category": "arts-and-entertainment", "subcategory": "animation-and-comics"}] aggregate_files = [] ## Job Processes filename = "arts-and-entertainment" crawl_pipeline = DataPipeline(csv_filename=f"{filename}.csv") start_scrape(keyword_list, data_pipeline=crawl_pipeline, retries=MAX_RETRIES) crawl_pipeline.close_pipeline() aggregate_files.append(f"{filename}.csv") logger.info(f"Crawl complete.")
- Each item in our results is represented in our code as
SearchData. - These
SearchDataobjects then get passed into ourDataPipelineand saved to a CSV file.
Step 3: Adding Concurrency
Now, we need to add concurrency. We'll useThreadPoolExecutor to add support for multithreading. Once we can open multiple threads, we can use those threads to run our parsing function on multiple pages concurrently.Here is our start_scrape() function adjusted for concurrency.def start_scrape(keywords, data_pipeline=None, max_threads=5, retries=3): with concurrent.futures.ThreadPoolExecutor(max_workers=max_threads) as executor: executor.map( scrape_search_results, keywords, [data_pipeline] * len(keywords), [retries] * len(keywords) )
scrape_search_resultsis the function we'd like to call using multiple threads.keywordsis the array of things we'd like to search.- All other args to
scrape_search_resultsget passed in as arrays.
import osimport csvimport requestsimport jsonimport loggingfrom urllib.parse import urlencodefrom bs4 import BeautifulSoupimport concurrent.futuresfrom dataclasses import dataclass, field, fields, asdict API_KEY = "" with open("config.json", "r") as config_file: config = json.load(config_file) API_KEY = config["api_key"] ## Logginglogging.basicConfig(level=logging.INFO)logger = logging.getLogger(__name__) @dataclassclass SearchData: name: str = "" url: str = "" rank: int = 0 rank_change: int = 0 average_visit: str = "" pages_per_visit: float = 0.0 bounce_rate: str = "" def __post_init__(self): self.check_string_fields() def check_string_fields(self): for field in fields(self): # Check string fields if isinstance(getattr(self, field.name), str): # If empty set default text if getattr(self, field.name) == "": setattr(self, field.name, f"No {field.name}") continue # Strip any trailing spaces, etc. value = getattr(self, field.name) setattr(self, field.name, value.strip()) class DataPipeline: def __init__(self, csv_filename="", storage_queue_limit=50): self.names_seen = [] self.storage_queue = [] self.storage_queue_limit = storage_queue_limit self.csv_filename = csv_filename self.csv_file_open = False def save_to_csv(self): self.csv_file_open = True data_to_save = [] data_to_save.extend(self.storage_queue) self.storage_queue.clear() if not data_to_save: return keys = [field.name for field in fields(data_to_save[0])] file_exists = os.path.isfile(self.csv_filename) and os.path.getsize(self.csv_filename) > 0 with open(self.csv_filename, mode="a", newline="", encoding="utf-8") as output_file: writer = csv.DictWriter(output_file, fieldnames=keys) if not file_exists: writer.writeheader() for item in data_to_save: writer.writerow(asdict(item)) self.csv_file_open = False def is_duplicate(self, input_data): if input_data.name in self.names_seen: logger.warning(f"Duplicate item found: {input_data.name}. Item dropped.") return True self.names_seen.append(input_data.name) return False def add_data(self, scraped_data): if self.is_duplicate(scraped_data) == False: self.storage_queue.append(scraped_data) if len(self.storage_queue) >= self.storage_queue_limit and self.csv_file_open == False: self.save_to_csv() def close_pipeline(self): if self.csv_file_open: time.sleep(3) if len(self.storage_queue) > 0: self.save_to_csv() def scrape_search_results(keyword, data_pipeline=None, retries=3): url = f"https://www.similarweb.com/top-websites/{keyword['category']}/{keyword['subcategory']}/" tries = 0 success = False while tries <= retries and not success: try: response = requests.get(url) logger.info(f"Recieved [{response.status_code}] from: {url}") if response.status_code != 200: raise Exception(f"Failed request, Status Code {response.status_code}") soup = BeautifulSoup(response.text, "html.parser") rows = soup.find_all("tr", class_="top-table__row") rank = 1 for row in rows: link_holder = row.find("a", class_="tw-table__compare") site_name = link_holder.text link = f"https://www.similarweb.com/website/{site_name}/" rank_change_holder = row.find("td", class_="top-table__column top-table__column--rank-change") rank_change = 0 up_or_down = rank_change_holder.find("span").get("class")[1] if "change--up" in up_or_down: rank_change += int(rank_change_holder.text) elif "change--down" in up_or_down: rank_change -= int(rank_change_holder.text) average_visit = row.find("span", class_="tw-table__avg-visit-duration").text pages_per_visit = float(row.find("span", class_="tw-table__pages-per-visit").text) bounce_rate = row.find("span", class_="tw-table__bounce-rate").text search_data = SearchData( name=site_name, url=link, rank=rank, rank_change=rank_change, average_visit=average_visit, pages_per_visit=pages_per_visit, bounce_rate=bounce_rate ) rank+=1 data_pipeline.add_data(search_data) logger.info(f"Successfully parsed data from: {url}") success = True except Exception as e: logger.error(f"An error occurred while processing page {url}: {e}, retries left {retries-tries}") tries+=1 if not success: raise Exception(f"Max Retries exceeded: {retries}") def start_scrape(keywords, data_pipeline=None, max_threads=5, retries=3): with concurrent.futures.ThreadPoolExecutor(max_workers=max_threads) as executor: executor.map( scrape_search_results, keywords, [data_pipeline] * len(keywords), [retries] * len(keywords) ) if __name__ == "__main__": MAX_RETRIES = 3 MAX_THREADS = 5 logger.info(f"Crawl starting...") ## INPUT ---> List of keywords to scrape keyword_list = [{"category": "arts-and-entertainment", "subcategory": "humor"}, {"category": "arts-and-entertainment", "subcategory": "animation-and-comics"}] aggregate_files = [] ## Job Processes filename = "arts-and-entertainment" crawl_pipeline = DataPipeline(csv_filename=f"{filename}.csv") start_scrape(keyword_list, data_pipeline=crawl_pipeline, max_threads=MAX_THREADS, retries=MAX_RETRIES) crawl_pipeline.close_pipeline() aggregate_files.append(f"{filename}.csv") logger.info(f"Crawl complete.")
Step 4: Bypassing Anti-Bots
To properly scrape SimilarWeb, we need a ton of IP addresses.To get as many addresses as possible, we're going to use just three parameters,API_KEY, url and wait. This tells ScrapeOps that we want to wait 3 seconds for content to render and we don't care which country we're routed through.This gives us the largest pool of potential IP addresses because we can be routed through any server that ScrapeOps supports.def get_scrapeops_url(url): payload = { "api_key": API_KEY, "url": url, "wait": 3000 } proxy_url = "https://proxy.scrapeops.io/v1/?" + urlencode(payload) return proxy_url
import osimport csvimport requestsimport jsonimport loggingfrom urllib.parse import urlencodefrom bs4 import BeautifulSoupimport concurrent.futuresfrom dataclasses import dataclass, field, fields, asdict API_KEY = "" with open("config.json", "r") as config_file: config = json.load(config_file) API_KEY = config["api_key"] def get_scrapeops_url(url): payload = { "api_key": API_KEY, "url": url, "wait": 3000 } proxy_url = "https://proxy.scrapeops.io/v1/?" + urlencode(payload) return proxy_url ## Logginglogging.basicConfig(level=logging.INFO)logger = logging.getLogger(__name__) @dataclassclass SearchData: name: str = "" url: str = "" rank: int = 0 rank_change: int = 0 average_visit: str = "" pages_per_visit: float = 0.0 bounce_rate: str = "" def __post_init__(self): self.check_string_fields() def check_string_fields(self): for field in fields(self): # Check string fields if isinstance(getattr(self, field.name), str): # If empty set default text if getattr(self, field.name) == "": setattr(self, field.name, f"No {field.name}") continue # Strip any trailing spaces, etc. value = getattr(self, field.name) setattr(self, field.name, value.strip()) class DataPipeline: def __init__(self, csv_filename="", storage_queue_limit=50): self.names_seen = [] self.storage_queue = [] self.storage_queue_limit = storage_queue_limit self.csv_filename = csv_filename self.csv_file_open = False def save_to_csv(self): self.csv_file_open = True data_to_save = [] data_to_save.extend(self.storage_queue) self.storage_queue.clear() if not data_to_save: return keys = [field.name for field in fields(data_to_save[0])] file_exists = os.path.isfile(self.csv_filename) and os.path.getsize(self.csv_filename) > 0 with open(self.csv_filename, mode="a", newline="", encoding="utf-8") as output_file: writer = csv.DictWriter(output_file, fieldnames=keys) if not file_exists: writer.writeheader() for item in data_to_save: writer.writerow(asdict(item)) self.csv_file_open = False def is_duplicate(self, input_data): if input_data.name in self.names_seen: logger.warning(f"Duplicate item found: {input_data.name}. Item dropped.") return True self.names_seen.append(input_data.name) return False def add_data(self, scraped_data): if self.is_duplicate(scraped_data) == False: self.storage_queue.append(scraped_data) if len(self.storage_queue) >= self.storage_queue_limit and self.csv_file_open == False: self.save_to_csv() def close_pipeline(self): if self.csv_file_open: time.sleep(3) if len(self.storage_queue) > 0: self.save_to_csv() def scrape_search_results(keyword, data_pipeline=None, retries=3): url = f"https://www.similarweb.com/top-websites/{keyword['category']}/{keyword['subcategory']}/" tries = 0 success = False while tries <= retries and not success: try: scrapeops_proxy_url = get_scrapeops_url(url) response = requests.get(scrapeops_proxy_url) logger.info(f"Recieved [{response.status_code}] from: {url}") if response.status_code != 200: raise Exception(f"Failed request, Status Code {response.status_code}") soup = BeautifulSoup(response.text, "html.parser") rows = soup.find_all("tr", class_="top-table__row") rank = 1 for row in rows: link_holder = row.find("a", class_="tw-table__compare") site_name = link_holder.text link = f"https://www.similarweb.com/website/{site_name}/" rank_change_holder = row.find("td", class_="top-table__column top-table__column--rank-change") rank_change = 0 up_or_down = rank_change_holder.find("span").get("class")[1] if "change--up" in up_or_down: rank_change += int(rank_change_holder.text) elif "change--down" in up_or_down: rank_change -= int(rank_change_holder.text) average_visit = row.find("span", class_="tw-table__avg-visit-duration").text pages_per_visit = float(row.find("span", class_="tw-table__pages-per-visit").text) bounce_rate = row.find("span", class_="tw-table__bounce-rate").text search_data = SearchData( name=site_name, url=link, rank=rank, rank_change=rank_change, average_visit=average_visit, pages_per_visit=pages_per_visit, bounce_rate=bounce_rate ) rank+=1 data_pipeline.add_data(search_data) logger.info(f"Successfully parsed data from: {url}") success = True except Exception as e: logger.error(f"An error occurred while processing page {url}: {e}, retries left {retries-tries}") tries+=1 if not success: raise Exception(f"Max Retries exceeded: {retries}") def start_scrape(keywords, data_pipeline=None, max_threads=5, retries=3): with concurrent.futures.ThreadPoolExecutor(max_workers=max_threads) as executor: executor.map( scrape_search_results, keywords, [data_pipeline] * len(keywords), [retries] * len(keywords) ) if __name__ == "__main__": MAX_RETRIES = 3 MAX_THREADS = 5 logger.info(f"Crawl starting...") ## INPUT ---> List of keywords to scrape keyword_list = [{"category": "arts-and-entertainment", "subcategory": "humor"}, {"category": "arts-and-entertainment", "subcategory": "animation-and-comics"}] aggregate_files = [] ## Job Processes filename = "arts-and-entertainment" crawl_pipeline = DataPipeline(csv_filename=f"{filename}.csv") start_scrape(keyword_list, data_pipeline=crawl_pipeline, max_threads=MAX_THREADS, retries=MAX_RETRIES) crawl_pipeline.close_pipeline() aggregate_files.append(f"{filename}.csv") logger.info(f"Crawl complete.")
Step 6: Production Run
Alright! It's time to run this code in production. As you've noticed, we have ourMAX_THREADS set to 5. We're only searching 2 categories, so ThreadPoolExecutor will run this on 2 threads and finish it out.In the next half of our article, when we write the scraper, we'll take advantage of all 5 threads.Here is our main.if __name__ == "__main__": MAX_RETRIES = 3 MAX_THREADS = 5 logger.info(f"Crawl starting...") ## INPUT ---> List of keywords to scrape keyword_list = [{"category": "arts-and-entertainment", "subcategory": "humor"}, {"category": "arts-and-entertainment", "subcategory": "animation-and-comics"}] aggregate_files = [] ## Job Processes filename = "arts-and-entertainment" crawl_pipeline = DataPipeline(csv_filename=f"{filename}.csv") start_scrape(keyword_list, data_pipeline=crawl_pipeline, max_threads=MAX_THREADS, retries=MAX_RETRIES) crawl_pipeline.close_pipeline() aggregate_files.append(f"{filename}.csv") logger.info(f"Crawl complete.")
Build A SimilarWeb Scraper
Now that we're running a proper crawl and saving the results, we need to do something with those results. In this section, we'll go through and scrape the competitors to each site we extracted during the crawl.The scraper needs to do the following:- Read the CSV into an array.
- Parse the websites from the array.
- Store the competitor data from the parsing stage.
- Run steps 2 and 3 concurrently for faster results.
- Integrate with the ScrapeOps Proxy Aggregator to get past anti-bots and other roadblocks.
Step 1: Create Simple Website Data Parser
Just like before, we're going to start with a parsing function. This one will find all of the competitor objects on the page and extract their data.def process_website(row, retries=3): url = row["url"] tries = 0 success = False while tries <= retries and not success: response = requests.get(url) try: if response.status_code != 200: raise Exception(f"Failed request, Status Code {response.status_code}") else: logger.info(f"Status: {response.status_code}") soup = BeautifulSoup(response.text, "html.parser") blocked = soup.find("div", class_="wa-limit-modal") if blocked: raise Exception(f"Blocked") competitors = soup.find_all("div", class_="wa-competitors__list-item") for competitor in competitors: site_name = competitor.find("span", class_="wa-competitors__list-item-title").text link = f"https://www.similarweb.com/website/{site_name}/" affinity = competitor.find("span", class_="app-progress__value").text target_spans = competitor.find_all("span", "wa-competitors__list-column") monthly_visits = target_spans[2].text category = target_spans[3].text category_rank = int(target_spans[4].text.replace("#", "").replace(",", "").replace("--", "0")) competitor_data = { "name": site_name, "url": link, "affinity": affinity, "monthly_visits": monthly_visits, "category": category, "category_rank": category_rank } print(competitor_data) success = True except Exception as e: logger.error(f"Exception thrown: {e}") logger.warning(f"Failed to process page: {row['url']}, Retries left: {retries-tries}") tries += 1 if not success: raise Exception(f"Max Retries exceeded: {retries}") else: logger.info(f"Successfully parsed: {row['url']}")
- Find all of the competitor rows:
soup.find_all("div", class_="wa-competitors__list-item"). - Iterate through the competitor rows.
- For each competitor, we pull the following:
site_nameaffinitymonthly_visitscategorycategory_link- We construct the url by once again formatting the
site_name.
Step 2: Loading URLs To Scrape
We have our parsing function, but it needs a url to work. Here, we'll add another function that reads urls from the CSV and callsprocess_website() on each row from the file.Here is our process_results() function.def process_results(csv_file, retries=3): logger.info(f"processing {csv_file}") with open(csv_file, newline="") as file: reader = list(csv.DictReader(file)) for row in reader: process_website(row, retries=retries)
import osimport csvimport requestsimport jsonimport loggingfrom urllib.parse import urlencodefrom bs4 import BeautifulSoupimport concurrent.futuresfrom dataclasses import dataclass, field, fields, asdict API_KEY = "" with open("config.json", "r") as config_file: config = json.load(config_file) API_KEY = config["api_key"] def get_scrapeops_url(url): payload = { "api_key": API_KEY, "url": url, "wait": 3000 } proxy_url = "https://proxy.scrapeops.io/v1/?" + urlencode(payload) return proxy_url ## Logginglogging.basicConfig(level=logging.INFO)logger = logging.getLogger(__name__) @dataclassclass SearchData: name: str = "" url: str = "" rank: int = 0 rank_change: int = 0 average_visit: str = "" pages_per_visit: float = 0.0 bounce_rate: str = "" def __post_init__(self): self.check_string_fields() def check_string_fields(self): for field in fields(self): # Check string fields if isinstance(getattr(self, field.name), str): # If empty set default text if getattr(self, field.name) == "": setattr(self, field.name, f"No {field.name}") continue # Strip any trailing spaces, etc. value = getattr(self, field.name) setattr(self, field.name, value.strip()) class DataPipeline: def __init__(self, csv_filename="", storage_queue_limit=50): self.names_seen = [] self.storage_queue = [] self.storage_queue_limit = storage_queue_limit self.csv_filename = csv_filename self.csv_file_open = False def save_to_csv(self): self.csv_file_open = True data_to_save = [] data_to_save.extend(self.storage_queue) self.storage_queue.clear() if not data_to_save: return keys = [field.name for field in fields(data_to_save[0])] file_exists = os.path.isfile(self.csv_filename) and os.path.getsize(self.csv_filename) > 0 with open(self.csv_filename, mode="a", newline="", encoding="utf-8") as output_file: writer = csv.DictWriter(output_file, fieldnames=keys) if not file_exists: writer.writeheader() for item in data_to_save: writer.writerow(asdict(item)) self.csv_file_open = False def is_duplicate(self, input_data): if input_data.name in self.names_seen: logger.warning(f"Duplicate item found: {input_data.name}. Item dropped.") return True self.names_seen.append(input_data.name) return False def add_data(self, scraped_data): if self.is_duplicate(scraped_data) == False: self.storage_queue.append(scraped_data) if len(self.storage_queue) >= self.storage_queue_limit and self.csv_file_open == False: self.save_to_csv() def close_pipeline(self): if self.csv_file_open: time.sleep(3) if len(self.storage_queue) > 0: self.save_to_csv() def scrape_search_results(keyword, data_pipeline=None, retries=3): url = f"https://www.similarweb.com/top-websites/{keyword['category']}/{keyword['subcategory']}/" tries = 0 success = False while tries <= retries and not success: try: scrapeops_proxy_url = get_scrapeops_url(url) response = requests.get(scrapeops_proxy_url) logger.info(f"Recieved [{response.status_code}] from: {url}") if response.status_code != 200: raise Exception(f"Failed request, Status Code {response.status_code}") soup = BeautifulSoup(response.text, "html.parser") rows = soup.find_all("tr", class_="top-table__row") rank = 1 for row in rows: link_holder = row.find("a", class_="tw-table__compare") site_name = link_holder.text link = f"https://www.similarweb.com/website/{site_name}/" rank_change_holder = row.find("td", class_="top-table__column top-table__column--rank-change") rank_change = 0 up_or_down = rank_change_holder.find("span").get("class")[1] if "change--up" in up_or_down: rank_change += int(rank_change_holder.text) elif "change--down" in up_or_down: rank_change -= int(rank_change_holder.text) average_visit = row.find("span", class_="tw-table__avg-visit-duration").text pages_per_visit = float(row.find("span", class_="tw-table__pages-per-visit").text) bounce_rate = row.find("span", class_="tw-table__bounce-rate").text search_data = SearchData( name=site_name, url=link, rank=rank, rank_change=rank_change, average_visit=average_visit, pages_per_visit=pages_per_visit, bounce_rate=bounce_rate ) rank+=1 data_pipeline.add_data(search_data) logger.info(f"Successfully parsed data from: {url}") success = True except Exception as e: logger.error(f"An error occurred while processing page {url}: {e}, retries left {retries-tries}") tries+=1 if not success: raise Exception(f"Max Retries exceeded: {retries}") def start_scrape(keywords, data_pipeline=None, max_threads=5, retries=3): with concurrent.futures.ThreadPoolExecutor(max_workers=max_threads) as executor: executor.map( scrape_search_results, keywords, [data_pipeline] * len(keywords), [retries] * len(keywords) ) def process_website(row, retries=3): url = row["url"] tries = 0 success = False while tries <= retries and not success: response = requests.get(url) try: if response.status_code != 200: raise Exception(f"Failed request, Status Code {response.status_code}") else: logger.info(f"Status: {response.status_code}") soup = BeautifulSoup(response.text, "html.parser") blocked = soup.find("div", class_="wa-limit-modal") if blocked: raise Exception(f"Blocked") competitors = soup.find_all("div", class_="wa-competitors__list-item") for competitor in competitors: site_name = competitor.find("span", class_="wa-competitors__list-item-title").text link = f"https://www.similarweb.com/website/{site_name}/" affinity = competitor.find("span", class_="app-progress__value").text target_spans = competitor.find_all("span", "wa-competitors__list-column") monthly_visits = target_spans[2].text category = target_spans[3].text category_rank = int(target_spans[4].text.replace("#", "").replace(",", "").replace("--", "0")) competitor_data = { "name": site_name, "url": link, "affinity": affinity, "monthly_visits": monthly_visits, "category": category, "category_rank": category_rank } print(competitor_data) success = True except Exception as e: logger.error(f"Exception thrown: {e}") logger.warning(f"Failed to process page: {row['url']}, Retries left: {retries-tries}") tries += 1 if not success: raise Exception(f"Max Retries exceeded: {retries}") else: logger.info(f"Successfully parsed: {row['url']}") def process_results(csv_file, retries=3): logger.info(f"processing {csv_file}") with open(csv_file, newline="") as file: reader = list(csv.DictReader(file)) for row in reader: process_website(row, retries=retries) if __name__ == "__main__": MAX_RETRIES = 3 MAX_THREADS = 5 logger.info(f"Crawl starting...") ## INPUT ---> List of keywords to scrape keyword_list = [{"category": "arts-and-entertainment", "subcategory": "humor"}, {"category": "arts-and-entertainment", "subcategory": "animation-and-comics"}] aggregate_files = [] ## Job Processes filename = "arts-and-entertainment" crawl_pipeline = DataPipeline(csv_filename=f"{filename}.csv") start_scrape(keyword_list, data_pipeline=crawl_pipeline, max_threads=MAX_THREADS, retries=MAX_RETRIES) crawl_pipeline.close_pipeline() aggregate_files.append(f"{filename}.csv") logger.info(f"Crawl complete.") for file in aggregate_files: process_results(file, retries=MAX_RETRIES)
process_results()reads our CSV into an array.- For each row of the file, we run
process_website()on the row.
Step 3: Storing the Scraped Data
Without storage, there wouldn't be a point in scraping to begin with. We've already got theDataPipeline, we just need a dataclass to feed into it. We're going to create a new one called CompetitorData. It's very much like our SearchData.Here is our CompetitorData class.@dataclassclass CompetitorData: name: str = "" url: str = "" affinity: str = "" monthly_visits: str = "" category: str = "" category_rank: int = None def __post_init__(self): self.check_string_fields() def check_string_fields(self): for field in fields(self): # Check string fields if isinstance(getattr(self, field.name), str): # If empty set default text if getattr(self, field.name) == "": setattr(self, field.name, f"No {field.name}") continue # Strip any trailing spaces, etc. value = getattr(self, field.name) setattr(self, field.name, value.strip())
DataPipeline inside our parsing function and we pass CompetitorData into it.import osimport csvimport requestsimport jsonimport loggingfrom urllib.parse import urlencodefrom bs4 import BeautifulSoupimport concurrent.futuresfrom dataclasses import dataclass, field, fields, asdict API_KEY = "" with open("config.json", "r") as config_file: config = json.load(config_file) API_KEY = config["api_key"] def get_scrapeops_url(url): payload = { "api_key": API_KEY, "url": url, "wait": 3000 } proxy_url = "https://proxy.scrapeops.io/v1/?" + urlencode(payload) return proxy_url ## Logginglogging.basicConfig(level=logging.INFO)logger = logging.getLogger(__name__) @dataclassclass SearchData: name: str = "" url: str = "" rank: int = 0 rank_change: int = 0 average_visit: str = "" pages_per_visit: float = 0.0 bounce_rate: str = "" def __post_init__(self): self.check_string_fields() def check_string_fields(self): for field in fields(self): # Check string fields if isinstance(getattr(self, field.name), str): # If empty set default text if getattr(self, field.name) == "": setattr(self, field.name, f"No {field.name}") continue # Strip any trailing spaces, etc. value = getattr(self, field.name) setattr(self, field.name, value.strip()) @dataclassclass CompetitorData: name: str = "" url: str = "" affinity: str = "" monthly_visits: str = "" category: str = "" category_rank: int = None def __post_init__(self): self.check_string_fields() def check_string_fields(self): for field in fields(self): # Check string fields if isinstance(getattr(self, field.name), str): # If empty set default text if getattr(self, field.name) == "": setattr(self, field.name, f"No {field.name}") continue # Strip any trailing spaces, etc. value = getattr(self, field.name) setattr(self, field.name, value.strip()) class DataPipeline: def __init__(self, csv_filename="", storage_queue_limit=50): self.names_seen = [] self.storage_queue = [] self.storage_queue_limit = storage_queue_limit self.csv_filename = csv_filename self.csv_file_open = False def save_to_csv(self): self.csv_file_open = True data_to_save = [] data_to_save.extend(self.storage_queue) self.storage_queue.clear() if not data_to_save: return keys = [field.name for field in fields(data_to_save[0])] file_exists = os.path.isfile(self.csv_filename) and os.path.getsize(self.csv_filename) > 0 with open(self.csv_filename, mode="a", newline="", encoding="utf-8") as output_file: writer = csv.DictWriter(output_file, fieldnames=keys) if not file_exists: writer.writeheader() for item in data_to_save: writer.writerow(asdict(item)) self.csv_file_open = False def is_duplicate(self, input_data): if input_data.name in self.names_seen: logger.warning(f"Duplicate item found: {input_data.name}. Item dropped.") return True self.names_seen.append(input_data.name) return False def add_data(self, scraped_data): if self.is_duplicate(scraped_data) == False: self.storage_queue.append(scraped_data) if len(self.storage_queue) >= self.storage_queue_limit and self.csv_file_open == False: self.save_to_csv() def close_pipeline(self): if self.csv_file_open: time.sleep(3) if len(self.storage_queue) > 0: self.save_to_csv() def scrape_search_results(keyword, data_pipeline=None, retries=3): url = f"https://www.similarweb.com/top-websites/{keyword['category']}/{keyword['subcategory']}/" tries = 0 success = False while tries <= retries and not success: try: scrapeops_proxy_url = get_scrapeops_url(url) response = requests.get(scrapeops_proxy_url) logger.info(f"Recieved [{response.status_code}] from: {url}") if response.status_code != 200: raise Exception(f"Failed request, Status Code {response.status_code}") soup = BeautifulSoup(response.text, "html.parser") rows = soup.find_all("tr", class_="top-table__row") rank = 1 for row in rows: link_holder = row.find("a", class_="tw-table__compare") site_name = link_holder.text link = f"https://www.similarweb.com/website/{site_name}/" rank_change_holder = row.find("td", class_="top-table__column top-table__column--rank-change") rank_change = 0 up_or_down = rank_change_holder.find("span").get("class")[1] if "change--up" in up_or_down: rank_change += int(rank_change_holder.text) elif "change--down" in up_or_down: rank_change -= int(rank_change_holder.text) average_visit = row.find("span", class_="tw-table__avg-visit-duration").text pages_per_visit = float(row.find("span", class_="tw-table__pages-per-visit").text) bounce_rate = row.find("span", class_="tw-table__bounce-rate").text search_data = SearchData( name=site_name, url=link, rank=rank, rank_change=rank_change, average_visit=average_visit, pages_per_visit=pages_per_visit, bounce_rate=bounce_rate ) rank+=1 data_pipeline.add_data(search_data) logger.info(f"Successfully parsed data from: {url}") success = True except Exception as e: logger.error(f"An error occurred while processing page {url}: {e}, retries left {retries-tries}") tries+=1 if not success: raise Exception(f"Max Retries exceeded: {retries}") def start_scrape(keywords, data_pipeline=None, max_threads=5, retries=3): with concurrent.futures.ThreadPoolExecutor(max_workers=max_threads) as executor: executor.map( scrape_search_results, keywords, [data_pipeline] * len(keywords), [retries] * len(keywords) ) def process_website(row, retries=3): url = row["url"] tries = 0 success = False while tries <= retries and not success: response = requests.get(url) try: if response.status_code != 200: raise Exception(f"Failed request, Status Code {response.status_code}") else: logger.info(f"Status: {response.status_code}") soup = BeautifulSoup(response.text, "html.parser") blocked = soup.find("div", class_="wa-limit-modal") if blocked: raise Exception(f"Blocked") competitors = soup.find_all("div", class_="wa-competitors__list-item") competitor_pipeline = DataPipeline(csv_filename=f"{row['name'].replace(' ', '-')}.csv") for competitor in competitors: site_name = competitor.find("span", class_="wa-competitors__list-item-title").text link = f"https://www.similarweb.com/website/{site_name}/" affinity = competitor.find("span", class_="app-progress__value").text target_spans = competitor.find_all("span", "wa-competitors__list-column") monthly_visits = target_spans[2].text category = target_spans[3].text category_rank = int(target_spans[4].text.replace("#", "").replace(",", "").replace("--", "0")) competitor_data = CompetitorData( name=site_name, url=link, affinity=affinity, monthly_visits=monthly_visits, category=category, category_rank=category_rank ) competitor_pipeline.add_data(competitor_data) competitor_pipeline.close_pipeline() success = True except Exception as e: logger.error(f"Exception thrown: {e}") logger.warning(f"Failed to process page: {row['url']}, Retries left: {retries-tries}") tries += 1 if not success: raise Exception(f"Max Retries exceeded: {retries}") else: logger.info(f"Successfully parsed: {row['url']}") def process_results(csv_file, retries=3): logger.info(f"processing {csv_file}") with open(csv_file, newline="") as file: reader = list(csv.DictReader(file)) for row in reader: process_website(row, retries=retries) if __name__ == "__main__": MAX_RETRIES = 3 MAX_THREADS = 5 logger.info(f"Crawl starting...") ## INPUT ---> List of keywords to scrape keyword_list = [{"category": "arts-and-entertainment", "subcategory": "humor"}, {"category": "arts-and-entertainment", "subcategory": "animation-and-comics"}] aggregate_files = [] ## Job Processes filename = "arts-and-entertainment" crawl_pipeline = DataPipeline(csv_filename=f"{filename}.csv") start_scrape(keyword_list, data_pipeline=crawl_pipeline, max_threads=MAX_THREADS, retries=MAX_RETRIES) crawl_pipeline.close_pipeline() aggregate_files.append(f"{filename}.csv") logger.info(f"Crawl complete.") for file in aggregate_files: process_results(file, retries=MAX_RETRIES)
CompetitorDatais used to represent the competitors we extract from the page.- We open a new
DataPipelineinside of our parsing function and pass theseCompetitorDataobjects into the pipeline.
Step 4: Adding Concurrency
We now need to add concurrency. Instead of searching multiple categories this time, we'll need to run our parsing function on multiple rows simultaneously.To accomplish this, we're going to refactorprocess_results() to take advantage of multiple threads using ThreadPoolExecutor.Here is our multithreaded process_results().def process_results(csv_file, max_threads=5, retries=3): logger.info(f"processing {csv_file}") with open(csv_file, newline="") as file: reader = list(csv.DictReader(file)) with concurrent.futures.ThreadPoolExecutor(max_workers=max_threads) as executor: executor.map( process_website, reader, [retries] * len(reader) )
process_websiteis the function we want to call on multiple threads.readeris the array of objects that we want to process with multiple threads.retriesgets passed in as an array the length ofreaderas well.
process_website get passed into executor.map() as arrays. These then get passed into process_website.Here is our full code up to this point.import osimport csvimport requestsimport jsonimport loggingfrom urllib.parse import urlencodefrom bs4 import BeautifulSoupimport concurrent.futuresfrom dataclasses import dataclass, field, fields, asdict API_KEY = "" with open("config.json", "r") as config_file: config = json.load(config_file) API_KEY = config["api_key"] def get_scrapeops_url(url): payload = { "api_key": API_KEY, "url": url, "wait": 3000 } proxy_url = "https://proxy.scrapeops.io/v1/?" + urlencode(payload) return proxy_url ## Logginglogging.basicConfig(level=logging.INFO)logger = logging.getLogger(__name__) @dataclassclass SearchData: name: str = "" url: str = "" rank: int = 0 rank_change: int = 0 average_visit: str = "" pages_per_visit: float = 0.0 bounce_rate: str = "" def __post_init__(self): self.check_string_fields() def check_string_fields(self): for field in fields(self): # Check string fields if isinstance(getattr(self, field.name), str): # If empty set default text if getattr(self, field.name) == "": setattr(self, field.name, f"No {field.name}") continue # Strip any trailing spaces, etc. value = getattr(self, field.name) setattr(self, field.name, value.strip()) @dataclassclass CompetitorData: name: str = "" url: str = "" affinity: str = "" monthly_visits: str = "" category: str = "" category_rank: int = None def __post_init__(self): self.check_string_fields() def check_string_fields(self): for field in fields(self): # Check string fields if isinstance(getattr(self, field.name), str): # If empty set default text if getattr(self, field.name) == "": setattr(self, field.name, f"No {field.name}") continue # Strip any trailing spaces, etc. value = getattr(self, field.name) setattr(self, field.name, value.strip()) class DataPipeline: def __init__(self, csv_filename="", storage_queue_limit=50): self.names_seen = [] self.storage_queue = [] self.storage_queue_limit = storage_queue_limit self.csv_filename = csv_filename self.csv_file_open = False def save_to_csv(self): self.csv_file_open = True data_to_save = [] data_to_save.extend(self.storage_queue) self.storage_queue.clear() if not data_to_save: return keys = [field.name for field in fields(data_to_save[0])] file_exists = os.path.isfile(self.csv_filename) and os.path.getsize(self.csv_filename) > 0 with open(self.csv_filename, mode="a", newline="", encoding="utf-8") as output_file: writer = csv.DictWriter(output_file, fieldnames=keys) if not file_exists: writer.writeheader() for item in data_to_save: writer.writerow(asdict(item)) self.csv_file_open = False def is_duplicate(self, input_data): if input_data.name in self.names_seen: logger.warning(f"Duplicate item found: {input_data.name}. Item dropped.") return True self.names_seen.append(input_data.name) return False def add_data(self, scraped_data): if self.is_duplicate(scraped_data) == False: self.storage_queue.append(scraped_data) if len(self.storage_queue) >= self.storage_queue_limit and self.csv_file_open == False: self.save_to_csv() def close_pipeline(self): if self.csv_file_open: time.sleep(3) if len(self.storage_queue) > 0: self.save_to_csv() def scrape_search_results(keyword, data_pipeline=None, retries=3): url = f"https://www.similarweb.com/top-websites/{keyword['category']}/{keyword['subcategory']}/" tries = 0 success = False while tries <= retries and not success: try: scrapeops_proxy_url = get_scrapeops_url(url) response = requests.get(scrapeops_proxy_url) logger.info(f"Recieved [{response.status_code}] from: {url}") if response.status_code != 200: raise Exception(f"Failed request, Status Code {response.status_code}") soup = BeautifulSoup(response.text, "html.parser") rows = soup.find_all("tr", class_="top-table__row") rank = 1 for row in rows: link_holder = row.find("a", class_="tw-table__compare") site_name = link_holder.text link = f"https://www.similarweb.com/website/{site_name}/" rank_change_holder = row.find("td", class_="top-table__column top-table__column--rank-change") rank_change = 0 up_or_down = rank_change_holder.find("span").get("class")[1] if "change--up" in up_or_down: rank_change += int(rank_change_holder.text) elif "change--down" in up_or_down: rank_change -= int(rank_change_holder.text) average_visit = row.find("span", class_="tw-table__avg-visit-duration").text pages_per_visit = float(row.find("span", class_="tw-table__pages-per-visit").text) bounce_rate = row.find("span", class_="tw-table__bounce-rate").text search_data = SearchData( name=site_name, url=link, rank=rank, rank_change=rank_change, average_visit=average_visit, pages_per_visit=pages_per_visit, bounce_rate=bounce_rate ) rank+=1 data_pipeline.add_data(search_data) logger.info(f"Successfully parsed data from: {url}") success = True except Exception as e: logger.error(f"An error occurred while processing page {url}: {e}, retries left {retries-tries}") tries+=1 if not success: raise Exception(f"Max Retries exceeded: {retries}") def start_scrape(keywords, data_pipeline=None, max_threads=5, retries=3): with concurrent.futures.ThreadPoolExecutor(max_workers=max_threads) as executor: executor.map( scrape_search_results, keywords, [data_pipeline] * len(keywords), [retries] * len(keywords) ) def process_website(row, retries=3): url = row["url"] tries = 0 success = False while tries <= retries and not success: response = requests.get(url) try: if response.status_code != 200: raise Exception(f"Failed request, Status Code {response.status_code}") else: logger.info(f"Status: {response.status_code}") soup = BeautifulSoup(response.text, "html.parser") blocked = soup.find("div", class_="wa-limit-modal") if blocked: raise Exception(f"Blocked") competitors = soup.find_all("div", class_="wa-competitors__list-item") competitor_pipeline = DataPipeline(csv_filename=f"{row['name'].replace(' ', '-')}.csv") for competitor in competitors: site_name = competitor.find("span", class_="wa-competitors__list-item-title").text link = f"https://www.similarweb.com/website/{site_name}/" affinity = competitor.find("span", class_="app-progress__value").text target_spans = competitor.find_all("span", "wa-competitors__list-column") monthly_visits = target_spans[2].text category = target_spans[3].text category_rank = int(target_spans[4].text.replace("#", "").replace(",", "").replace("--", "0")) competitor_data = CompetitorData( name=site_name, url=link, affinity=affinity, monthly_visits=monthly_visits, category=category, category_rank=category_rank ) competitor_pipeline.add_data(competitor_data) competitor_pipeline.close_pipeline() success = True except Exception as e: logger.error(f"Exception thrown: {e}") logger.warning(f"Failed to process page: {row['url']}, Retries left: {retries-tries}") tries += 1 if not success: raise Exception(f"Max Retries exceeded: {retries}") else: logger.info(f"Successfully parsed: {row['url']}") def process_results(csv_file, max_threads=5, retries=3): logger.info(f"processing {csv_file}") with open(csv_file, newline="") as file: reader = list(csv.DictReader(file)) with concurrent.futures.ThreadPoolExecutor(max_workers=max_threads) as executor: executor.map( process_website, reader, [retries] * len(reader) ) if __name__ == "__main__": MAX_RETRIES = 3 MAX_THREADS = 5 logger.info(f"Crawl starting...") ## INPUT ---> List of keywords to scrape keyword_list = [{"category": "arts-and-entertainment", "subcategory": "humor"}, {"category": "arts-and-entertainment", "subcategory": "animation-and-comics"}] aggregate_files = [] ## Job Processes filename = "arts-and-entertainment" crawl_pipeline = DataPipeline(csv_filename=f"{filename}.csv") start_scrape(keyword_list, data_pipeline=crawl_pipeline, max_threads=MAX_THREADS, retries=MAX_RETRIES) crawl_pipeline.close_pipeline() aggregate_files.append(f"{filename}.csv") logger.info(f"Crawl complete.") for file in aggregate_files: process_results(file, max_threads=MAX_THREADS, retries=MAX_RETRIES)
Step 5: Bypassing Anti-Bots
Particularly when viewing reports for individual sites, we tend to get blocked. You can see what that looks like in the image below. To get around this, we're going to use the proxy function we wrote earlier.We only need to change one line of our parsing function to implement this.response = requests.get(get_scrapeops_url(url))import osimport csvimport requestsimport jsonimport loggingfrom urllib.parse import urlencodefrom bs4 import BeautifulSoupimport concurrent.futuresfrom dataclasses import dataclass, field, fields, asdict API_KEY = "" with open("config.json", "r") as config_file: config = json.load(config_file) API_KEY = config["api_key"]© def get_scrapeops_url(url): payload = { "api_key": API_KEY, "url": url, "wait": 3000 } proxy_url = "https://proxy.scrapeops.io/v1/?" + urlencode(payload) return proxy_url ## Logginglogging.basicConfig(level=logging.INFO)logger = logging.getLogger(__name__) @dataclassclass SearchData: name: str = "" url: str = "" rank: int = 0 rank_change: int = 0 average_visit: str = "" pages_per_visit: float = 0.0 bounce_rate: str = "" def __post_init__(self): self.check_string_fields() def check_string_fields(self): for field in fields(self): # Check string fields if isinstance(getattr(self, field.name), str): # If empty set default text if getattr(self, field.name) == "": setattr(self, field.name, f"No {field.name}") continue # Strip any trailing spaces, etc. value = getattr(self, field.name) setattr(self, field.name, value.strip()) @dataclassclass CompetitorData: name: str = "" url: str = "" affinity: str = "" monthly_visits: str = "" category: str = "" category_rank: int = None def __post_init__(self): self.check_string_fields() def check_string_fields(self): for field in fields(self): # Check string fields if isinstance(getattr(self, field.name), str): # If empty set default text if getattr(self, field.name) == "": setattr(self, field.name, f"No {field.name}") continue # Strip any trailing spaces, etc. value = getattr(self, field.name) setattr(self, field.name, value.strip()) class DataPipeline: def __init__(self, csv_filename="", storage_queue_limit=50): self.names_seen = [] self.storage_queue = [] self.storage_queue_limit = storage_queue_limit self.csv_filename = csv_filename self.csv_file_open = False def save_to_csv(self): self.csv_file_open = True data_to_save = [] data_to_save.extend(self.storage_queue) self.storage_queue.clear() if not data_to_save: return keys = [field.name for field in fields(data_to_save[0])] file_exists = os.path.isfile(self.csv_filename) and os.path.getsize(self.csv_filename) > 0 with open(self.csv_filename, mode="a", newline="", encoding="utf-8") as output_file: writer = csv.DictWriter(output_file, fieldnames=keys) if not file_exists: writer.writeheader() for item in data_to_save: writer.writerow(asdict(item)) self.csv_file_open = False def is_duplicate(self, input_data): if input_data.name in self.names_seen: logger.warning(f"Duplicate item found: {input_data.name}. Item dropped.") return True self.names_seen.append(input_data.name) return False def add_data(self, scraped_data): if self.is_duplicate(scraped_data) == False: self.storage_queue.append(scraped_data) if len(self.storage_queue) >= self.storage_queue_limit and self.csv_file_open == False: self.save_to_csv() def close_pipeline(self): if self.csv_file_open: time.sleep(3) if len(self.storage_queue) > 0: self.save_to_csv() def scrape_search_results(keyword, data_pipeline=None, retries=3): url = f"https://www.similarweb.com/top-websites/{keyword['category']}/{keyword['subcategory']}/" tries = 0 success = False while tries <= retries and not success: try: scrapeops_proxy_url = get_scrapeops_url(url) response = requests.get(scrapeops_proxy_url) logger.info(f"Recieved [{response.status_code}] from: {url}") if response.status_code != 200: raise Exception(f"Failed request, Status Code {response.status_code}") soup = BeautifulSoup(response.text, "html.parser") rows = soup.find_all("tr", class_="top-table__row") rank = 1 for row in rows: link_holder = row.find("a", class_="tw-table__compare") site_name = link_holder.text link = f"https://www.similarweb.com/website/{site_name}/" rank_change_holder = row.find("td", class_="top-table__column top-table__column--rank-change") rank_change = 0 up_or_down = rank_change_holder.find("span").get("class")[1] if "change--up" in up_or_down: rank_change += int(rank_change_holder.text) elif "change--down" in up_or_down: rank_change -= int(rank_change_holder.text) average_visit = row.find("span", class_="tw-table__avg-visit-duration").text pages_per_visit = float(row.find("span", class_="tw-table__pages-per-visit").text) bounce_rate = row.find("span", class_="tw-table__bounce-rate").text search_data = SearchData( name=site_name, url=link, rank=rank, rank_change=rank_change, average_visit=average_visit, pages_per_visit=pages_per_visit, bounce_rate=bounce_rate ) rank+=1 data_pipeline.add_data(search_data) logger.info(f"Successfully parsed data from: {url}") success = True except Exception as e: logger.error(f"An error occurred while processing page {url}: {e}, retries left {retries-tries}") tries+=1 if not success: raise Exception(f"Max Retries exceeded: {retries}") def start_scrape(keywords, data_pipeline=None, max_threads=5, retries=3): with concurrent.futures.ThreadPoolExecutor(max_workers=max_threads) as executor: executor.map( scrape_search_results, keywords, [data_pipeline] * len(keywords), [retries] * len(keywords) ) def process_website(row, retries=3): url = row["url"] tries = 0 success = False while tries <= retries and not success: response = requests.get(get_scrapeops_url(url)) try: if response.status_code != 200: raise Exception(f"Failed request, Status Code {response.status_code}") else: logger.info(f"Status: {response.status_code}") soup = BeautifulSoup(response.text, "html.parser") blocked = soup.find("div", class_="wa-limit-modal") if blocked: raise Exception(f"Blocked") competitors = soup.find_all("div", class_="wa-competitors__list-item") competitor_pipeline = DataPipeline(csv_filename=f"{row['name'].replace(' ', '-')}.csv") for competitor in competitors: site_name = competitor.find("span", class_="wa-competitors__list-item-title").text link = f"https://www.similarweb.com/website/{site_name}/" affinity = competitor.find("span", class_="app-progress__value").text target_spans = competitor.find_all("span", "wa-competitors__list-column") monthly_visits = target_spans[2].text category = target_spans[3].text category_rank = int(target_spans[4].text.replace("#", "").replace(",", "").replace("--", "0")) competitor_data = CompetitorData( name=site_name, url=link, affinity=affinity, monthly_visits=monthly_visits, category=category, category_rank=category_rank ) competitor_pipeline.add_data(competitor_data) competitor_pipeline.close_pipeline() success = True except Exception as e: logger.error(f"Exception thrown: {e}") logger.warning(f"Failed to process page: {row['url']}, Retries left: {retries-tries}") tries += 1 if not success: raise Exception(f"Max Retries exceeded: {retries}") else: logger.info(f"Successfully parsed: {row['url']}") def process_results(csv_file, max_threads=5, retries=3): logger.info(f"processing {csv_file}") with open(csv_file, newline="") as file: reader = list(csv.DictReader(file)) with concurrent.futures.ThreadPoolExecutor(max_workers=max_threads) as executor: executor.map( process_website, reader, [retries] * len(reader) ) if __name__ == "__main__": MAX_RETRIES = 3 MAX_THREADS = 5 logger.info(f"Crawl starting...") ## INPUT ---> List of keywords to scrape keyword_list = [{"category": "arts-and-entertainment", "subcategory": "humor"}, {"category": "arts-and-entertainment", "subcategory": "animation-and-comics"}] aggregate_files = [] ## Job Processes filename = "arts-and-entertainment" crawl_pipeline = DataPipeline(csv_filename=f"{filename}.csv") start_scrape(keyword_list, data_pipeline=crawl_pipeline, max_threads=MAX_THREADS, retries=MAX_RETRIES) crawl_pipeline.close_pipeline() aggregate_files.append(f"{filename}.csv") logger.info(f"Crawl complete.") for file in aggregate_files: process_results(file, max_threads=MAX_THREADS, retries=MAX_RETRIES)
Step 6: Production Run
Time to run this entire thing in production! We're going to use the same settings as before. Here is ourmain if you need a refresher.Since there was such a spread in our crawl times, we'll estimate the crawl at 30 seconds.if __name__ == "__main__": MAX_RETRIES = 3 MAX_THREADS = 5 logger.info(f"Crawl starting...") ## INPUT ---> List of keywords to scrape keyword_list = [{"category": "arts-and-entertainment", "subcategory": "humor"}, {"category": "arts-and-entertainment", "subcategory": "animation-and-comics"}] aggregate_files = [] ## Job Processes filename = "arts-and-entertainment" crawl_pipeline = DataPipeline(csv_filename=f"{filename}.csv") start_scrape(keyword_list, data_pipeline=crawl_pipeline, max_threads=MAX_THREADS, retries=MAX_RETRIES) crawl_pipeline.close_pipeline() aggregate_files.append(f"{filename}.csv") logger.info(f"Crawl complete.") for file in aggregate_files: process_results(file, max_threads=MAX_THREADS, retries=MAX_RETRIES)
The crawl generated a CSV file with 100 results and took 618.173 seconds. As mentioned earlier, we'll estimate our crawl at 30 seconds. 618.173 - 30 = 588.173 seconds scraping competitors. 588.173 seconds / 100 results = 5.88173 seconds per result. Especially considering the 3 second wait time for content to render, this is pretty good!.Legal and Ethical Considerations
When scraping the web, we need to be conscious of our actions. When you scrape public data (data that is not behind a login), that data is considered to be public information and therefore legal to scrape... Not unlike taking a picture of a billboard.However, private data is a completely different story. If you choose to scrape private data, you are subject to a whole different slew of intellectual property laws and privacy regulations.Even if you're scraping public data, you need to be mindful of your target site's Terms and Conditions and theirrobots.txt file as well. Violating these could lead to suspension of your account or even a permanent ban.You can view these for SimilarWeb by checking the links below.If you're unsure of your scraper, you should talk to an attorney.Conclusion
In conclusion, SimilarWeb can be a very difficult site to access without an account let alone scrape. The ScrapeOps Proxy Aggregator gives us the ability to constantly rotate to new IP addresses and as SimilarWeb blocks them. You got a crash course in iterative development and you should understand parsing, data storage, concurrency and proxy integration.If you'd like to learn more about the tech stack used in this site, take a look at the links below.How to Scrape SimilarWeb With Selenium
In this tutorial, we will explore how to scrape SimilarWeb with Selenium.- TLDR: How to Scrape SimilarWeb
- How To Architect Our Scraper
- Understanding How To Scrape SimilarWeb
- Setting Up Our SimilarWeb Scraper
- Build A SimilarWeb Search Crawler
- Build A SimilarWeb Scraper
- Legal and Ethical Considerations
- Conclusion
- More Web Scraping Guides
Need help scraping the web?
Then check out ScrapeOps, the complete toolkit for web scraping.
**TLDR - How to Scrape SimilarWeb with Selenium
Scraping SimilarWeb can be quite challenging. To begin with, SimilarWeb restricts access after a certain point, which makes it essential to have a proxy that rotates IP addresses.If your goal is to scrape SimilarWeb, the scraper provided below should be used.- First, create a new project folder and include a
config.jsonfile. - Once the config file is created, input your ScrapeOps API key
{"api_key": "your-super-secret-api-key"}. - Afterwards, copy and paste the code below into a new Python file.
import os import csv import json import time import logging from urllib.parse import urlencode from selenium import webdriver from selenium.webdriver.chrome.service import Service as ChromeService from selenium.webdriver.chrome.options import Options from selenium.webdriver.common.by import By from webdriver_manager.chrome import ChromeDriverManager import concurrent.futures from dataclasses import dataclass, fields, asdict API_KEY = "" with open("config.json", "r") as config_file: config = json.load(config_file) API_KEY = config["api_key"] def get_scrapeops_url(url): payload = { "api_key": API_KEY, "url": url, "wait": 3000 } proxy_url = "https://proxy.scrapeops.io/v1/?" + urlencode(payload) return proxy_url # Set up logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) # Setup Selenium WebDriver def setup_driver(): options = Options() options.add_argument("--headless") # Run in headless mode for efficiency options.add_argument("--no-sandbox") options.add_argument("--disable-dev-shm-usage") return webdriver.Chrome(service=ChromeService(ChromeDriverManager().install()), options=options) @dataclass class SearchData: name: str = "" url: str = "" rank: int = 0 rank_change: int = 0 average_visit: str = "" pages_per_visit: float = 0.0 bounce_rate: str = "" def __post_init__(self): self.check_string_fields() def check_string_fields(self): for field in fields(self): if isinstance(getattr(self, field.name), str): if getattr(self, field.name) == "": setattr(self, field.name, f"No {field.name}") else: value = getattr(self, field.name) setattr(self, field.name, value.strip()) @dataclass class CompetitorData: name: str = "" url: str = "" affinity: str = "" monthly_visits: str = "" category: str = "" category_rank: int = None def __post_init__(self): self.check_string_fields() def check_string_fields(self): for field in fields(self): if isinstance(getattr(self, field.name), str): if getattr(self, field.name) == "": setattr(self, field.name, f"No {field.name}") else: value = getattr(self, field.name) setattr(self, field.name, value.strip()) class DataPipeline: def __init__(self, csv_filename="", storage_queue_limit=50): self.names_seen = [] self.storage_queue = [] self.storage_queue_limit = storage_queue_limit self.csv_filename = csv_filename self.csv_file_open = False def save_to_csv(self): self.csv_file_open = True data_to_save = [] data_to_save.extend(self.storage_queue) self.storage_queue.clear() if not data_to_save: return keys = [field.name for field in fields(data_to_save[0])] file_exists = os.path.isfile(self.csv_filename) and os.path.getsize(self.csv_filename) > 0 with open(self.csv_filename, mode="a", newline="", encoding="utf-8") as output_file: writer = csv.DictWriter(output_file, fieldnames=keys) if not file_exists: writer.writeheader() for item in data_to_save: writer.writerow(asdict(item)) self.csv_file_open = False def is_duplicate(self, input_data): if input_data.name in self.names_seen: logger.warning(f"Duplicate item found: {input_data.name}. Item dropped.") return True self.names_seen.append(input_data.name) return False def add_data(self, scraped_data): if not self.is_duplicate(scraped_data): self.storage_queue.append(scraped_data) if len(self.storage_queue) >= self.storage_queue_limit and not self.csv_file_open: self.save_to_csv() def close_pipeline(self): if self.csv_file_open: time.sleep(3) if self.storage_queue: self.save_to_csv() # Function to scrape search results (fully Selenium-based) def scrape_search_results(keyword, data_pipeline=None, retries=3): url = f"https://www.similarweb.com/top-websites/{keyword['category']}/{keyword['subcategory']}/" tries = 0 success = False while tries <= retries and not success: try: # Initialize WebDriver and load page driver = setup_driver() scrapeops_proxy_url = get_scrapeops_url(url) driver.get(scrapeops_proxy_url) time.sleep(3) # Allow page to load logger.info(f"Opened URL: {url}") # Find all rows of the search results table rows = driver.find_elements(By.CSS_SELECTOR, "tr.top-table__row") rank = 1 for row in rows: site_name = row.find_element(By.CSS_SELECTOR, "a.tw-table__compare").text.strip() link = f"https://www.similarweb.com/website/{site_name}/" # Rank change processing rank_change_holder = row.find_element(By.CSS_SELECTOR, "td.top-table__column--rank-change") rank_change = 0 up_or_down = rank_change_holder.find_element(By.TAG_NAME, "span").get_attribute("class").split()[-1] if "change--up" in up_or_down: rank_change += int(rank_change_holder.text.strip()) elif "change--down" in up_or_down: rank_change -= int(rank_change_holder.text.strip()) average_visit = row.find_element(By.CSS_SELECTOR, "span.tw-table__avg-visit-duration").text.strip() pages_per_visit = float(row.find_element(By.CSS_SELECTOR, "span.tw-table__pages-per-visit").text.strip()) bounce_rate = row.find_element(By.CSS_SELECTOR, "span.tw-table__bounce-rate").text.strip() # Create data object search_data = SearchData( name=site_name, url=link, rank=rank, rank_change=rank_change, average_visit=average_visit, pages_per_visit=pages_per_visit, bounce_rate=bounce_rate ) rank += 1 data_pipeline.add_data(search_data) logger.info(f"Successfully parsed data from: {url}") success = True except Exception as e: logger.error(f"An error occurred while processing page {url}: {e}, retries left {retries-tries}") tries += 1 finally: driver.quit() if not success: raise Exception(f"Max retries exceeded: {retries}") # Function to process and scrape all search results concurrently def start_scrape(keywords, data_pipeline=None, max_threads=5, retries=3): with concurrent.futures.ThreadPoolExecutor(max_workers=max_threads) as executor: executor.map( scrape_search_results, keywords, [data_pipeline] * len(keywords), [retries] * len(keywords) ) # Function to process websites (Selenium-based) and extract competitor data def process_website(row, retries=3): url = row["url"] tries = 0 success = False while tries <= retries and not success: try: driver = setup_driver() scrapeops_proxy_url = get_scrapeops_url(url) driver.get(scrapeops_proxy_url) time.sleep(3) # Allow page to load # Check if blocked by a modal or warning try: blocked_modal = driver.find_element(By.CSS_SELECTOR, "div.wa-limit-modal") if blocked_modal: raise Exception("Blocked by modal") except: pass # No blocking modal # Extract competitor data competitors = driver.find_elements(By.CSS_SELECTOR, "div.wa-competitors__list-item") competitor_pipeline = DataPipeline(csv_filename=f"{row['name'].replace(' ', '-')}_competitors.csv") for competitor in competitors: site_name = competitor.find_element(By.CSS_SELECTOR, "span.wa-competitors__list-item-title").text.strip() link = f"https://www.similarweb.com/website/{site_name}/" affinity = competitor.find_element(By.CSS_SELECTOR, "span.app-progress__value").text.strip() target_spans = competitor.find_elements(By.CSS_SELECTOR, "span.wa-competitors__list-column") monthly_visits = target_spans[2].text.strip() category = target_spans[3].text.strip() category_rank = int(target_spans[4].text.replace("#", "").replace(",", "").replace("--", "0").strip()) competitor_data = CompetitorData( name=site_name, url=link, affinity=affinity, monthly_visits=monthly_visits, category=category, category_rank=category_rank ) competitor_pipeline.add_data(competitor_data) competitor_pipeline.close_pipeline() success = True except Exception as e: logger.error(f"Exception thrown: {e}") logger.warning(f"Failed to process page: {url}, Retries left: {retries-tries}") tries += 1 finally: driver.quit() if not success: raise Exception(f"Max Retries exceeded: {retries}") else: logger.info(f"Successfully parsed: {row['url']}") def process_results(csv_file, max_threads=5, retries=3): logger.info(f"processing {csv_file}") with open(csv_file, newline="") as file: reader = list(csv.DictReader(file)) with concurrent.futures.ThreadPoolExecutor(max_workers=max_threads) as executor: executor.map( process_website, reader, [retries] * len(reader) ) if __name__ == "__main__": MAX_RETRIES = 3 MAX_THREADS = 5 logger.info(f"Crawl starting...") # Example keywords to scrape keyword_list = [ {"category": "arts-and-entertainment", "subcategory": "humor"}, {"category": "arts-and-entertainment", "subcategory": "animation-and-comics"} ] aggregate_files = [] # Crawl and save results filename = "arts-and-entertainment" crawl_pipeline = DataPipeline(csv_filename=f"{filename}.csv") start_scrape(keyword_list, data_pipeline=crawl_pipeline, max_threads=MAX_THREADS, retries=MAX_RETRIES) crawl_pipeline.close_pipeline() aggregate_files.append(f"{filename}.csv") logger.info(f"Crawl complete.") # Process each CSV file for file in aggregate_files: process_results(file,max_threads=MAX_THREADS, retries=MAX_RETRIES)
MAX_THREADS: Defines how many concurrent threads are used for processing and scraping tasks.MAX_RETRIESDetermines the number of retries the script will make if a request fails, such as due to a non-200 status code or network issues.keyword_listA list of dictionaries, each containing a "category" and "subcategory," which specify the type of websites to be scraped from SimilarWeb.filenameThe base name that is used to generate the CSV file where the data obtained from scraping will be saved.
How To Architect Our SimilarWeb Scraper
Scraping SimilarWeb can be difficult. Once you attempt to do anything substantial with it, the site prompts you to create an account to access its full features.
The site not only becomes difficult to scrape, but accessing it through a traditional browser is also challenging! While we are prompted to sign up for an account, there are still some actions we can take before being blocked every time.By using ScrapeOps Proxy Aggregator, rotating proxies allow us to bypass this issue because we are being blocked based on our IP address. When each ping uses a new IP, it becomes much harder to block us.Our SimilarWeb scraper will follow a similar structure to other scraping projects in our "How to Scrape" series. Both a crawler and a scraper will need to be built.- The crawler will search for the top sites in a particular category, and
- The scraper will collect data from competitors and their relevant information for each of these top-ranked sites.
- Search for a particular site and extract its data.
- Store the extracted data in an easily manageable CSV file.
- Simultaneously search multiple categories.
- Use ScrapeOps Proxy Aggregator to bypass anti-bots and free trial limitations.
- Find and extract competitors from a row in the CSV file created earlier.
- Save competitors of each site into a new CSV report.
- Run steps 1 and 2 concurrently.
- Once again, use ScrapeOps Proxy Aggregator to bypass anti-bots and free trial prompts.
Understanding How To Scrape SimilarWeb
Before diving into writing any serious code, it’s important to gain a high-level understanding of SimilarWeb.Over the next few sections, we’ll explore how to retrieve the necessary information and how to extract it from the page.Step 1: How To Request SimilarWeb Pages
As with most web interactions, the process starts with a basic GET request. The SimilarWeb homepage isn't particularly useful for our purposes, so we’ll target a specific endpoint.For this example, we’ll be retrieving the top 50 humor websites.Here’s the URL:https://www.similarweb.com/top-websites/arts-and-entertainment/humor/
https://www.similarweb.com/top-websites/{CATEGORY}/{SUBCATEGORY}/
When you visit the page for a particular site, the URL appears like this:https://www.similarweb.com/website/pikabu.ru/
https://www.similarweb.com/website/{NAME_OF_SITE}/
Step 2: How To Extract Data From SimilarWeb Results and Pages
Extracting the data can be somewhat tricky. However, this is completely doable if we have access to the site. To begin with, some of our content loads dynamically.When talking to ScrapeOps, we need to use thewait parameter to load our dynamic content. Once the page is loaded, we can simply locate the information by using its CSS class.For the results pages, each row has a class of top-table__row. From there, we can locate all these rows and extract their data with ease.
To extract our competitors, we first target div elements that have the class wa-competitors__list-item. These div tags contain all the information for each individual competitor.
At the top of these pages, we need to stay mindful of the modal that SimilarWeb uses to block our access. If this modal appears, we need to attempt the request again. As shown in the image below, it's a div with the class wa-limit-modal.
Step 3: Geolocated Data
ScrapeOps allows us to manage our geolocation using the country parameter. On the other hand, with SimilarWeb, we prefer not to manage our geolocation.Rather than managing our location, we aim to have as many IP addresses as possible to decrease the chances of being blocked or prompted to sign in/sign up, as mentioned in the previous section.By not managing our location, we have access to a significantly larger pool of IP addresses.Setting Up Our SimilarWeb Scraper Project
You can begin by executing the commands below to set up.Create a New Project FolderFirst, create a new project folder using the command:mkdir similarweb-scrapercd similarweb-scraperpython -m venv venvsource venv/bin/activatepip install selenium pip install webdriver-manager
Build A SimilarWeb Search Crawler
It's time to begin building! In the following sections, we'll construct our crawler step by step.We'll start with a parser and then move on to adding data storage.After that, we'll implement concurrency and, lastly, integrate proxy support.Step 1: Create Simple Search Data Parser
The first step in our scraping process is parsing.In the code provided below, we set up our basic script and introduce features such as error handling and retries.The key part is the implementation of our base parsing function. To observe how the data is extracted, focus onscrape_search_results().import os import json import logging from selenium import webdriver from selenium.webdriver.chrome.service import Service as ChromeService from selenium.webdriver.chrome.options import Options from selenium.webdriver.common.by import By from webdriver_manager.chrome import ChromeDriverManager import time API_KEY = "" # Load the API key from the config file with open("config.json", "r") as config_file: config = json.load(config_file) API_KEY = config["api_key"] # Logging configuration logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) # Function to set up the Selenium WebDriver with necessary options def setup_driver(): options = Options() options.add_argument("--headless") # Run in headless mode options.add_argument("--no-sandbox") options.add_argument("--disable-dev-shm-usage") return webdriver.Chrome(service=ChromeService(ChromeDriverManager().install()), options=options) # Main scraping function using Selenium def scrape_search_results(keyword, retries=3): url = f"https://www.similarweb.com/top-websites/{keyword['category']}/{keyword['subcategory']}/" tries = 0 success = False while tries <= retries and not success: try: # Set up and start the WebDriver driver = setup_driver() driver.get(url) logger.info(f"Received page from: {url}") # Wait for the page to load fully time.sleep(3) # Find all rows for the top websites table rows = driver.find_elements(By.CSS_SELECTOR, "tr.top-table__row") rank = 1 for row in rows: link_holder = row.find_element(By.CSS_SELECTOR, "a.tw-table__compare") site_name = link_holder.text link = f"https://www.similarweb.com/website/{site_name}/" rank_change_holder = row.find_element(By.CSS_SELECTOR, "td.top-table__column.top-table__column--rank-change") rank_change = 0 up_or_down = rank_change_holder.find_element(By.CSS_SELECTOR, "span").get_attribute("class").split(" ")[1] if "change--up" in up_or_down: rank_change += int(rank_change_holder.text) elif "change--down" in up_or_down: rank_change -= int(rank_change_holder.text) average_visit = row.find_element(By.CSS_SELECTOR, "span.tw-table__avg-visit-duration").text pages_per_visit = float(row.find_element(By.CSS_SELECTOR, "span.tw-table__pages-per-visit").text) bounce_rate = row.find_element(By.CSS_SELECTOR, "span.tw-table__bounce-rate").text # Collecting scraped data search_data = { "name": site_name, "url": link, "rank": rank, "rank_change": rank_change, "average_visit": average_visit, "pages_per_visit": pages_per_visit, "bounce_rate": bounce_rate } rank += 1 print("search data: ",search_data) logger.info(f"Successfully parsed data from: {url}") success = True except Exception as e: logger.error(f"An error occurred while processing page {url}: {e}, retries left {retries-tries}") tries += 1 finally: # Close the WebDriver after each attempt driver.quit() if not success: raise Exception(f"Max retries exceeded for: {url}") # Function to start the scraping process for a list of keywords def start_scrape(keywords, retries=3): for keyword in keywords: scrape_search_results(keyword, retries=retries) if __name__ == "__main__": MAX_RETRIES = 3 MAX_THREADS = 5 logger.info(f"Crawl starting...") # Input list of keywords to scrape keyword_list = [ {"category": "arts-and-entertainment", "subcategory": "humor"}, {"category": "arts-and-entertainment", "subcategory": "animation-and-comics"} ] # Start scraping process start_scrape(keyword_list, retries=MAX_RETRIES) logger.info(f"Crawl complete.")
- First, we locate all rows using
rows = driver.find_elements(By.CSS_SELECTOR, "tr.top-table__row"). - Then, we retrieve the
link_holderwithlink_holder = row.find_element(By.CSS_SELECTOR, "a.tw-table__compare"). - From the
link_holder, we extract thesite_nameand construct our link. - We determine whether the rank has increased or decreased by using
rank_change_holder.find_element(By.CSS_SELECTOR, "span").get_attribute("class").split(" ")[1]. - The average visit duration is obtained through
row.find_element(By.CSS_SELECTOR, "span.tw-table__avg-visit-duration").text. - The number of
pages_per_visitis retrieved withfloat(row.find_element(By.CSS_SELECTOR, "span.tw-table__pages-per-visit").text). - Lastly, the bounce rate is collected using
row.find_element(By.CSS_SELECTOR, "span.tw-table__bounce-rate").text.
Step 2: Storing the Scraped Data
After we have our data, we need to store it. In order to store it, we will create a few classes. A dataclass calledSearchData is required. This class will represent individual objects from the search results.Once the SearchData object is created, it needs to be passed into a DataPipeline. The DataPipeline is responsible for opening a pipe to a CSV file. It removes duplicates by name and then saves all the non-duplicate objects to the CSV file.Below is our SearchData class, which we use to represent individual ranking results.@dataclass class SearchData: name: str = "" url: str = "" rank: int = 0 rank_change: int = 0 average_visit: str = "" pages_per_visit: float = 0.0 bounce_rate: str = "" def __post_init__(self): self.check_string_fields() def check_string_fields(self): for field in fields(self): if isinstance(getattr(self, field.name), str): if getattr(self, field.name) == "": setattr(self, field.name, f"No {field.name}") else: value = getattr(self, field.name).strip() setattr(self, field.name, value)
DataPipeline.class DataPipeline: def __init__(self, csv_filename="", storage_queue_limit=50): self.names_seen = [] self.storage_queue = [] self.storage_queue_limit = storage_queue_limit self.csv_filename = csv_filename self.csv_file_open = False def save_to_csv(self): self.csv_file_open = True data_to_save = [] data_to_save.extend(self.storage_queue) self.storage_queue.clear() if not data_to_save: return keys = [field.name for field in fields(data_to_save[0])] file_exists = os.path.isfile(self.csv_filename) and os.path.getsize(self.csv_filename) > 0 with open(self.csv_filename, mode="a", newline="", encoding="utf-8") as output_file: writer = csv.DictWriter(output_file, fieldnames=keys) if not file_exists: writer.writeheader() for item in data_to_save: writer.writerow(asdict(item)) self.csv_file_open = False def is_duplicate(self, input_data): if input_data.name in self.names_seen: logger.warning(f"Duplicate item found: {input_data.name}. Item dropped.") return True self.names_seen.append(input_data.name) return False def add_data(self, scraped_data): if not self.is_duplicate(scraped_data): self.storage_queue.append(scraped_data) if len(self.storage_queue) >= self.storage_queue_limit and not self.csv_file_open: self.save_to_csv() def close_pipeline(self): if self.csv_file_open: time.sleep(3) if self.storage_queue: self.save_to_csv()
- We need to open a new DataPipeline and pass it into
start_scrape()when we put everything together. start_scrape()then sends the pipeline to our parsing function.- Rather than printing the parsed data, we now send it into the pipeline.
- After parsing the results, we close the DataPipeline.
import os import csv import json import logging import time from dataclasses import dataclass, fields, asdict from selenium import webdriver from selenium.webdriver.chrome.service import Service as ChromeService from selenium.webdriver.chrome.options import Options from selenium.webdriver.common.by import By from webdriver_manager.chrome import ChromeDriverManager API_KEY = "" # Load API key from config with open("config.json", "r") as config_file: config = json.load(config_file) API_KEY = config["api_key"] # Logging configuration logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) # Dataclass representing individual search results @dataclass class SearchData: name: str = "" url: str = "" rank: int = 0 rank_change: int = 0 average_visit: str = "" pages_per_visit: float = 0.0 bounce_rate: str = "" def __post_init__(self): self.check_string_fields() def check_string_fields(self): for field in fields(self): if isinstance(getattr(self, field.name), str): if getattr(self, field.name) == "": setattr(self, field.name, f"No {field.name}") else: value = getattr(self, field.name).strip() setattr(self, field.name, value) # Class for handling data storage to CSV class DataPipeline: def __init__(self, csv_filename="", storage_queue_limit=50): self.names_seen = [] self.storage_queue = [] self.storage_queue_limit = storage_queue_limit self.csv_filename = csv_filename self.csv_file_open = False def save_to_csv(self): self.csv_file_open = True data_to_save = [] data_to_save.extend(self.storage_queue) self.storage_queue.clear() if not data_to_save: return keys = [field.name for field in fields(data_to_save[0])] file_exists = os.path.isfile(self.csv_filename) and os.path.getsize(self.csv_filename) > 0 with open(self.csv_filename, mode="a", newline="", encoding="utf-8") as output_file: writer = csv.DictWriter(output_file, fieldnames=keys) if not file_exists: writer.writeheader() for item in data_to_save: writer.writerow(asdict(item)) self.csv_file_open = False def is_duplicate(self, input_data): if input_data.name in self.names_seen: logger.warning(f"Duplicate item found: {input_data.name}. Item dropped.") return True self.names_seen.append(input_data.name) return False def add_data(self, scraped_data): if not self.is_duplicate(scraped_data): self.storage_queue.append(scraped_data) if len(self.storage_queue) >= self.storage_queue_limit and not self.csv_file_open: self.save_to_csv() def close_pipeline(self): if self.csv_file_open: time.sleep(3) if self.storage_queue: self.save_to_csv() # Function to set up Selenium WebDriver def setup_driver(): options = Options() options.add_argument("--headless") options.add_argument("--no-sandbox") options.add_argument("--disable-dev-shm-usage") return webdriver.Chrome(service=ChromeService(ChromeDriverManager().install()), options=options) # Function to scrape search results using Selenium def scrape_search_results(keyword, data_pipeline=None, retries=3): url = f"https://www.similarweb.com/top-websites/{keyword['category']}/{keyword['subcategory']}/" tries = 0 success = False while tries <= retries and not success: try: # Setup and start Selenium WebDriver driver = setup_driver() driver.get(url) logger.info(f"Received page from: {url}") time.sleep(3) # Wait for the page to load # Find rows in the search results rows = driver.find_elements(By.CSS_SELECTOR, "tr.top-table__row") rank = 1 for row in rows: link_holder = row.find_element(By.CSS_SELECTOR, "a.tw-table__compare") site_name = link_holder.text.strip() link = f"https://www.similarweb.com/website/{site_name}/" rank_change_holder = row.find_element(By.CSS_SELECTOR, "td.top-table__column.top-table__column--rank-change") rank_change = 0 up_or_down = rank_change_holder.find_element(By.CSS_SELECTOR, "span").get_attribute("class").split(" ")[1] if "change--up" in up_or_down: rank_change += int(rank_change_holder.text) elif "change--down" in up_or_down: rank_change -= int(rank_change_holder.text) average_visit = row.find_element(By.CSS_SELECTOR, "span.tw-table__avg-visit-duration").text.strip() pages_per_visit = float(row.find_element(By.CSS_SELECTOR, "span.tw-table__pages-per-visit").text) bounce_rate = row.find_element(By.CSS_SELECTOR, "span.tw-table__bounce-rate").text.strip() # Create a SearchData object search_data = SearchData( name=site_name, url=link, rank=rank, rank_change=rank_change, average_visit=average_visit, pages_per_visit=pages_per_visit, bounce_rate=bounce_rate ) # Add data to the pipeline data_pipeline.add_data(search_data) rank += 1 logger.info(f"Successfully parsed data from: {url}") success = True except Exception as e: logger.error(f"An error occurred while processing page {url}: {e}, retries left {retries - tries}") tries += 1 finally: driver.quit() if not success: raise Exception(f"Max retries exceeded for: {url}") # Function to start the scraping process for a list of keywords def start_scrape(keywords, data_pipeline=None, retries=3): for keyword in keywords: scrape_search_results(keyword, data_pipeline=data_pipeline, retries=retries) # Main execution if __name__ == "__main__": MAX_RETRIES = 3 MAX_THREADS = 5 logger.info(f"Crawl starting...") # Input list of keywords to scrape keyword_list = [ {"category": "arts-and-entertainment", "subcategory": "humor"}, {"category": "arts-and-entertainment", "subcategory": "animation-and-comics"} ] # Initialize DataPipeline filename = "arts-and-entertainment" crawl_pipeline = DataPipeline(csv_filename=f"{filename}.csv") # Start the scraping process start_scrape(keyword_list, data_pipeline=crawl_pipeline, retries=MAX_RETRIES) # Close the pipeline after scraping crawl_pipeline.close_pipeline() logger.info(f"Crawl complete.")
SearchData. These SearchData objects are then passed into our DataPipeline and stored in a CSV file.Step 3: Adding Concurrency
Now, we need to incorporate concurrency. To add multithreading support, we’ll utilizeThreadPoolExecutor.Once we have the ability to open several threads, we can employ those threads to run our parsing function on multiple pages simultaneously.Below is our start_scrape() function modified for concurrency.def start_scrape(keywords, data_pipeline=None, max_threads=5, retries=3): with concurrent.futures.ThreadPoolExecutor(max_workers=max_threads) as executor: executor.map( scrape_search_results, keywords, [data_pipeline] * len(keywords), [retries] * len(keywords) )
scrape_search_results by utilizing multiple threads. The array keywords contains the items we wish to search for. All additional arguments to scrape_search_results are passed in as arrays.import os import csv import json import logging import time from dataclasses import dataclass, fields, asdict from selenium import webdriver from selenium.webdriver.chrome.service import Service as ChromeService from selenium.webdriver.chrome.options import Options from selenium.webdriver.common.by import By from webdriver_manager.chrome import ChromeDriverManager import concurrent.futures API_KEY = "" # Load API key from config with open("config.json", "r") as config_file: config = json.load(config_file) API_KEY = config["api_key"] # Logging configuration logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) # Dataclass representing individual search results @dataclass class SearchData: name: str = "" url: str = "" rank: int = 0 rank_change: int = 0 average_visit: str = "" pages_per_visit: float = 0.0 bounce_rate: str = "" def __post_init__(self): self.check_string_fields() def check_string_fields(self): for field in fields(self): if isinstance(getattr(self, field.name), str): if getattr(self, field.name) == "": setattr(self, field.name, f"No {field.name}") else: value = getattr(self, field.name).strip() setattr(self, field.name, value) # Class for handling data storage to CSV class DataPipeline: def __init__(self, csv_filename="", storage_queue_limit=50): self.names_seen = [] self.storage_queue = [] self.storage_queue_limit = storage_queue_limit self.csv_filename = csv_filename self.csv_file_open = False def save_to_csv(self): self.csv_file_open = True data_to_save = [] data_to_save.extend(self.storage_queue) self.storage_queue.clear() if not data_to_save: return keys = [field.name for field in fields(data_to_save[0])] file_exists = os.path.isfile(self.csv_filename) and os.path.getsize(self.csv_filename) > 0 with open(self.csv_filename, mode="a", newline="", encoding="utf-8") as output_file: writer = csv.DictWriter(output_file, fieldnames=keys) if not file_exists: writer.writeheader() for item in data_to_save: writer.writerow(asdict(item)) self.csv_file_open = False def is_duplicate(self, input_data): if input_data.name in self.names_seen: logger.warning(f"Duplicate item found: {input_data.name}. Item dropped.") return True self.names_seen.append(input_data.name) return False def add_data(self, scraped_data): if not self.is_duplicate(scraped_data): self.storage_queue.append(scraped_data) if len(self.storage_queue) >= self.storage_queue_limit and not self.csv_file_open: self.save_to_csv() def close_pipeline(self): if self.csv_file_open: time.sleep(3) if self.storage_queue: self.save_to_csv() # Function to set up Selenium WebDriver def setup_driver(): options = Options() options.add_argument("--headless") options.add_argument("--no-sandbox") options.add_argument("--disable-dev-shm-usage") return webdriver.Chrome(service=ChromeService(ChromeDriverManager().install()), options=options) # Function to scrape search results using Selenium def scrape_search_results(keyword, data_pipeline=None, retries=3): url = f"https://www.similarweb.com/top-websites/{keyword['category']}/{keyword['subcategory']}/" tries = 0 success = False while tries <= retries and not success: try: # Setup and start Selenium WebDriver driver = setup_driver() driver.get(url) logger.info(f"Received page from: {url}") time.sleep(3) # Wait for the page to load # Find rows in the search results rows = driver.find_elements(By.CSS_SELECTOR, "tr.top-table__row") rank = 1 for row in rows: link_holder = row.find_element(By.CSS_SELECTOR, "a.tw-table__compare") site_name = link_holder.text.strip() link = f"https://www.similarweb.com/website/{site_name}/" rank_change_holder = row.find_element(By.CSS_SELECTOR, "td.top-table__column.top-table__column--rank-change") rank_change = 0 up_or_down = rank_change_holder.find_element(By.CSS_SELECTOR, "span").get_attribute("class").split(" ")[1] if "change--up" in up_or_down: rank_change += int(rank_change_holder.text) elif "change--down" in up_or_down: rank_change -= int(rank_change_holder.text) average_visit = row.find_element(By.CSS_SELECTOR, "span.tw-table__avg-visit-duration").text.strip() pages_per_visit = float(row.find_element(By.CSS_SELECTOR, "span.tw-table__pages-per-visit").text) bounce_rate = row.find_element(By.CSS_SELECTOR, "span.tw-table__bounce-rate").text.strip() # Create a SearchData object search_data = SearchData( name=site_name, url=link, rank=rank, rank_change=rank_change, average_visit=average_visit, pages_per_visit=pages_per_visit, bounce_rate=bounce_rate ) # Add data to the pipeline data_pipeline.add_data(search_data) rank += 1 logger.info(f"Successfully parsed data from: {url}") success = True except Exception as e: logger.error(f"An error occurred while processing page {url}: {e}, retries left {retries - tries}") tries += 1 finally: driver.quit() if not success: raise Exception(f"Max retries exceeded for: {url}") # Function to start the scraping process for a list of keywords def start_scrape(keywords, data_pipeline=None, max_threads=5, retries=3): with concurrent.futures.ThreadPoolExecutor(max_workers=max_threads) as executor: executor.map( scrape_search_results, keywords, [data_pipeline] * len(keywords), [retries] * len(keywords) ) # Main execution if __name__ == "__main__": MAX_RETRIES = 3 MAX_THREADS = 5 logger.info(f"Crawl starting...") # Input list of keywords to scrape keyword_list = [ {"category": "arts-and-entertainment", "subcategory": "humor"}, {"category": "arts-and-entertainment", "subcategory": "animation-and-comics"} ] # Initialize DataPipeline filename = "arts-and-entertainment" crawl_pipeline = DataPipeline(csv_filename=f"{filename}.csv") # Start the scraping process start_scrape(keyword_list, data_pipeline=crawl_pipeline,max_threads=MAX_THREADS, retries=MAX_RETRIES) # Close the pipeline after scraping crawl_pipeline.close_pipeline() logger.info(f"Crawl complete.")
Step 4: Bypassing Anti-Bots
To scrape SimilarWeb effectively, a large number of IP addresses are required. By using only three parameters —API_KEY, url, and wait— we can obtain as many addresses as possible.This tells ScrapeOps that we’re willing to wait 3 seconds for the content to load, without concern for the country through which we’re routed.This approach provides us with the largest possible pool of IP addresses since routing can happen through any server that ScrapeOps supports.def get_scrapeops_url(url): payload = { "api_key": API_KEY, "url": url, "wait": 3000, } proxy_url = "https://proxy.scrapeops.io/v1/?" + urlencode(payload) return proxy_url
import os import csv import json import logging import time from urllib.parse import urlencode from dataclasses import dataclass, fields, asdict from selenium import webdriver from selenium.webdriver.chrome.service import Service as ChromeService from selenium.webdriver.chrome.options import Options from selenium.webdriver.common.by import By from webdriver_manager.chrome import ChromeDriverManager import concurrent.futures API_KEY = "" # Load API key from config with open("config.json", "r") as config_file: config = json.load(config_file) API_KEY = config["api_key"] def get_scrapeops_url(url): payload = { "api_key": API_KEY, "url": url, "wait": 3000, } proxy_url = "https://proxy.scrapeops.io/v1/?" + urlencode(payload) return proxy_url # Logging configuration logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) # Dataclass representing individual search results @dataclass class SearchData: name: str = "" url: str = "" rank: int = 0 rank_change: int = 0 average_visit: str = "" pages_per_visit: float = 0.0 bounce_rate: str = "" def __post_init__(self): self.check_string_fields() def check_string_fields(self): for field in fields(self): if isinstance(getattr(self, field.name), str): if getattr(self, field.name) == "": setattr(self, field.name, f"No {field.name}") else: value = getattr(self, field.name).strip() setattr(self, field.name, value) # Class for handling data storage to CSV class DataPipeline: def __init__(self, csv_filename="", storage_queue_limit=50): self.names_seen = [] self.storage_queue = [] self.storage_queue_limit = storage_queue_limit self.csv_filename = csv_filename self.csv_file_open = False def save_to_csv(self): self.csv_file_open = True data_to_save = [] data_to_save.extend(self.storage_queue) self.storage_queue.clear() if not data_to_save: return keys = [field.name for field in fields(data_to_save[0])] file_exists = os.path.isfile(self.csv_filename) and os.path.getsize(self.csv_filename) > 0 with open(self.csv_filename, mode="a", newline="", encoding="utf-8") as output_file: writer = csv.DictWriter(output_file, fieldnames=keys) if not file_exists: writer.writeheader() for item in data_to_save: writer.writerow(asdict(item)) self.csv_file_open = False def is_duplicate(self, input_data): if input_data.name in self.names_seen: logger.warning(f"Duplicate item found: {input_data.name}. Item dropped.") return True self.names_seen.append(input_data.name) return False def add_data(self, scraped_data): if not self.is_duplicate(scraped_data): self.storage_queue.append(scraped_data) if len(self.storage_queue) >= self.storage_queue_limit and not self.csv_file_open: self.save_to_csv() def close_pipeline(self): if self.csv_file_open: time.sleep(3) if self.storage_queue: self.save_to_csv() # Function to set up Selenium WebDriver def setup_driver(): options = Options() options.add_argument("--headless") options.add_argument("--no-sandbox") options.add_argument("--disable-dev-shm-usage") return webdriver.Chrome(service=ChromeService(ChromeDriverManager().install()), options=options) # Function to scrape search results using Selenium def scrape_search_results(keyword, data_pipeline=None, retries=3): url = f"https://www.similarweb.com/top-websites/{keyword['category']}/{keyword['subcategory']}/" tries = 0 success = False while tries <= retries and not success: try: # Setup and start Selenium WebDriver driver = setup_driver() scrapeops_proxy_url = get_scrapeops_url(url) driver.get(scrapeops_proxy_url) logger.info(f"Received page from: {url}") time.sleep(3) # Wait for the page to load # Find rows in the search results rows = driver.find_elements(By.CSS_SELECTOR, "tr.top-table__row") rank = 1 for row in rows: link_holder = row.find_element(By.CSS_SELECTOR, "a.tw-table__compare") site_name = link_holder.text.strip() link = f"https://www.similarweb.com/website/{site_name}/" rank_change_holder = row.find_element(By.CSS_SELECTOR, "td.top-table__column.top-table__column--rank-change") rank_change = 0 up_or_down = rank_change_holder.find_element(By.CSS_SELECTOR, "span").get_attribute("class").split(" ")[1] if "change--up" in up_or_down: rank_change += int(rank_change_holder.text) elif "change--down" in up_or_down: rank_change -= int(rank_change_holder.text) average_visit = row.find_element(By.CSS_SELECTOR, "span.tw-table__avg-visit-duration").text.strip() pages_per_visit = float(row.find_element(By.CSS_SELECTOR, "span.tw-table__pages-per-visit").text) bounce_rate = row.find_element(By.CSS_SELECTOR, "span.tw-table__bounce-rate").text.strip() # Create a SearchData object search_data = SearchData( name=site_name, url=link, rank=rank, rank_change=rank_change, average_visit=average_visit, pages_per_visit=pages_per_visit, bounce_rate=bounce_rate ) # Add data to the pipeline data_pipeline.add_data(search_data) rank += 1 logger.info(f"Successfully parsed data from: {url}") success = True except Exception as e: logger.error(f"An error occurred while processing page {url}: {e}, retries left {retries - tries}") tries += 1 finally: driver.quit() if not success: raise Exception(f"Max retries exceeded for: {url}") # Function to start the scraping process for a list of keywords def start_scrape(keywords, data_pipeline=None, max_threads=5, retries=3): with concurrent.futures.ThreadPoolExecutor(max_workers=max_threads) as executor: executor.map( scrape_search_results, keywords, [data_pipeline] * len(keywords), [retries] * len(keywords) ) # Main execution if __name__ == "__main__": MAX_RETRIES = 3 MAX_THREADS = 5 logger.info(f"Crawl starting...") # Input list of keywords to scrape keyword_list = [ {"category": "arts-and-entertainment", "subcategory": "humor"}, {"category": "arts-and-entertainment", "subcategory": "animation-and-comics"} ] # Initialize DataPipeline filename = "arts-and-entertainment" crawl_pipeline = DataPipeline(csv_filename=f"{filename}.csv") # Start the scraping process start_scrape(keyword_list, data_pipeline=crawl_pipeline,max_threads=MAX_THREADS, retries=MAX_RETRIES) # Close the pipeline after scraping crawl_pipeline.close_pipeline() logger.info(f"Crawl complete.")
Step 5: Production Run
Alright! Time to run this code in production.As you've noticed, ourMAX_THREADS are set to 5. Since we're only searching 2 categories, ThreadPoolExecutor will use 2 threads to run this and finish it.In the second half of our article, we'll make use of all 5 threads when writing the scraper.Here is our main:# Main execution if __name__ == "__main__": MAX_RETRIES = 3 MAX_THREADS = 5 logger.info(f"Crawl starting...") # Input list of keywords to scrape keyword_list = [ {"category": "arts-and-entertainment", "subcategory": "humor"}, {"category": "arts-and-entertainment", "subcategory": "animation-and-comics"} ] # Initialize DataPipeline filename = "arts-and-entertainment" crawl_pipeline = DataPipeline(csv_filename=f"{filename}.csv") # Start the scraping process start_scrape(keyword_list, data_pipeline=crawl_pipeline,max_threads=MAX_THREADS, retries=MAX_RETRIES) # Close the pipeline after scraping crawl_pipeline.close_pipeline() logger.info(f"Crawl complete.")
Build A SimilarWeb Scraper
Now that the results are being saved after running a proper crawl, we need to utilize those results. In this section, we'll scrape the competitors for each site that was extracted during the crawl.The scraper should do the following:- Load the CSV into an array.
- Extract the websites from the array.
- Save the competitor data after extracting it.
- Perform steps 2 and 3 simultaneously for quicker results.
- Work with the ScrapeOps Proxy Aggregator to bypass anti-bots and other obstacles.
Step 1: Create Simple Website Data Parser
Just like before, we'll begin with a parsing function. This one will locate all of the competitor objects on the page and pull out their data.def process_website(row, retries=3): url = row["url"] tries = 0 success = False while tries <= retries and not success: try: driver = setup_driver() driver.get(url) time.sleep(3) # Allow page to load # Check if blocked by a modal or warning try: blocked_modal = driver.find_element(By.CSS_SELECTOR, "div.wa-limit-modal") if blocked_modal: raise Exception("Blocked by modal") except: pass # No blocking modal # Extract competitor data competitors = driver.find_elements(By.CSS_SELECTOR, "div.wa-competitors__list-item") for competitor in competitors: site_name = competitor.find_element(By.CSS_SELECTOR, "span.wa-competitors__list-item-title").text.strip() link = f"https://www.similarweb.com/website/{site_name}/" affinity = competitor.find_element(By.CSS_SELECTOR, "span.app-progress__value").text.strip() target_spans = competitor.find_elements(By.CSS_SELECTOR, "span.wa-competitors__list-column") monthly_visits = target_spans[2].text.strip() category = target_spans[3].text.strip() category_rank = int(target_spans[4].text.replace("#", "").replace(",", "").replace("--", "0").strip()) competitor_data = { "name": site_name, "url": link, "affinity": affinity, "monthly_visits": monthly_visits, "category": category, "category_rank": category_rank } print(competitor_data) # Replace with actual storage mechanism success = True except Exception as e: logger.error(f"Exception thrown: {e}") logger.warning(f"Failed to process page: {url}, Retries left: {retries-tries}") tries += 1 finally: driver.quit() if not success: raise Exception(f"Max Retries exceeded: {retries}") else: logger.info(f"Successfully parsed: {row['url']}")
- Find all of the competitor rows:
driver.find_elements(By.CSS_SELECTOR, "div.wa-competitors__list-item") - Iterate through the competitor rows.
- For each competitor, we pull the following:
site_nameaffinitymonthly_visitscategorycategory_link- We construct the url by once again formatting the
site_name.
Step 2: Loading URLs To Scrape
We have our parsing function, but it requires a URL to operate.In this case, we'll include another function that retrieves URLs from the CSV file and appliesprocess_website() to every row in the file.Below is our process_results() function.def process_results(csv_file, retries=3): logger.info(f"Processing {csv_file}") with open(csv_file, newline="") as file: reader = list(csv.DictReader(file)) for row in reader: process_website(row, retries=retries)
import os import csv import json import time import logging from urllib.parse import urlencode from selenium import webdriver from selenium.webdriver.chrome.service import Service as ChromeService from selenium.webdriver.chrome.options import Options from selenium.webdriver.common.by import By from webdriver_manager.chrome import ChromeDriverManager import concurrent.futures from dataclasses import dataclass, field, fields, asdict # ScrapeOps API Key (if you're using a proxy service like ScrapeOps) API_KEY = "" with open("config.json", "r") as config_file: config = json.load(config_file) API_KEY = config["api_key"] def get_scrapeops_url(url): payload = { "api_key": API_KEY, "url": url, "wait": 3000 } proxy_url = "https://proxy.scrapeops.io/v1/?" + urlencode(payload) return proxy_url # Set up logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) # Setup Selenium WebDriver def setup_driver(): options = Options() options.add_argument("--headless") # Run in headless mode for efficiency options.add_argument("--no-sandbox") options.add_argument("--disable-dev-shm-usage") return webdriver.Chrome(service=ChromeService(ChromeDriverManager().install()), options=options) @dataclass class SearchData: name: str = "" url: str = "" rank: int = 0 rank_change: int = 0 average_visit: str = "" pages_per_visit: float = 0.0 bounce_rate: str = "" def __post_init__(self): self.check_string_fields() def check_string_fields(self): for field in fields(self): if isinstance(getattr(self, field.name), str): if getattr(self, field.name) == "": setattr(self, field.name, f"No {field.name}") else: value = getattr(self, field.name) setattr(self, field.name, value.strip()) class DataPipeline: def __init__(self, csv_filename="", storage_queue_limit=50): self.names_seen = [] self.storage_queue = [] self.storage_queue_limit = storage_queue_limit self.csv_filename = csv_filename self.csv_file_open = False def save_to_csv(self): self.csv_file_open = True data_to_save = [] data_to_save.extend(self.storage_queue) self.storage_queue.clear() if not data_to_save: return keys = [field.name for field in fields(data_to_save[0])] file_exists = os.path.isfile(self.csv_filename) and os.path.getsize(self.csv_filename) > 0 with open(self.csv_filename, mode="a", newline="", encoding="utf-8") as output_file: writer = csv.DictWriter(output_file, fieldnames=keys) if not file_exists: writer.writeheader() for item in data_to_save: writer.writerow(asdict(item)) self.csv_file_open = False def is_duplicate(self, input_data): if input_data.name in self.names_seen: logger.warning(f"Duplicate item found: {input_data.name}. Item dropped.") return True self.names_seen.append(input_data.name) return False def add_data(self, scraped_data): if not self.is_duplicate(scraped_data): self.storage_queue.append(scraped_data) if len(self.storage_queue) >= self.storage_queue_limit and not self.csv_file_open: self.save_to_csv() def close_pipeline(self): if self.csv_file_open: time.sleep(3) if self.storage_queue: self.save_to_csv() # Function to scrape search results (fully Selenium-based) def scrape_search_results(keyword, data_pipeline=None, retries=3): url = f"https://www.similarweb.com/top-websites/{keyword['category']}/{keyword['subcategory']}/" tries = 0 success = False while tries <= retries and not success: try: # Initialize WebDriver and load page driver = setup_driver() scrapeops_proxy_url = get_scrapeops_url(url) driver.get(scrapeops_proxy_url) time.sleep(3) # Allow page to load logger.info(f"Opened URL: {url}") # Find all rows of the search results table rows = driver.find_elements(By.CSS_SELECTOR, "tr.top-table__row") rank = 1 for row in rows: site_name = row.find_element(By.CSS_SELECTOR, "a.tw-table__compare").text.strip() link = f"https://www.similarweb.com/website/{site_name}/" # Rank change processing rank_change_holder = row.find_element(By.CSS_SELECTOR, "td.top-table__column--rank-change") rank_change = 0 up_or_down = rank_change_holder.find_element(By.TAG_NAME, "span").get_attribute("class").split()[-1] if "change--up" in up_or_down: rank_change += int(rank_change_holder.text.strip()) elif "change--down" in up_or_down: rank_change -= int(rank_change_holder.text.strip()) average_visit = row.find_element(By.CSS_SELECTOR, "span.tw-table__avg-visit-duration").text.strip() pages_per_visit = float(row.find_element(By.CSS_SELECTOR, "span.tw-table__pages-per-visit").text.strip()) bounce_rate = row.find_element(By.CSS_SELECTOR, "span.tw-table__bounce-rate").text.strip() # Create data object search_data = SearchData( name=site_name, url=link, rank=rank, rank_change=rank_change, average_visit=average_visit, pages_per_visit=pages_per_visit, bounce_rate=bounce_rate ) rank += 1 data_pipeline.add_data(search_data) logger.info(f"Successfully parsed data from: {url}") success = True except Exception as e: logger.error(f"An error occurred while processing page {url}: {e}, retries left {retries-tries}") tries += 1 finally: driver.quit() if not success: raise Exception(f"Max retries exceeded: {retries}") # Function to process and scrape all search results concurrently def start_scrape(keywords, data_pipeline=None, max_threads=5, retries=3): with concurrent.futures.ThreadPoolExecutor(max_workers=max_threads) as executor: executor.map( scrape_search_results, keywords, [data_pipeline] * len(keywords), [retries] * len(keywords) ) # Function to process websites (Selenium-based) def process_website(row, retries=3): url = row["url"] tries = 0 success = False while tries <= retries and not success: try: driver = setup_driver() driver.get(url) time.sleep(3) # Allow page to load # Check if blocked by a modal or warning try: blocked_modal = driver.find_element(By.CSS_SELECTOR, "div.wa-limit-modal") if blocked_modal: raise Exception("Blocked by modal") except: pass # No blocking modal # Extract competitor data competitors = driver.find_elements(By.CSS_SELECTOR, "div.wa-competitors__list-item") for competitor in competitors: site_name = competitor.find_element(By.CSS_SELECTOR, "span.wa-competitors__list-item-title").text.strip() link = f"https://www.similarweb.com/website/{site_name}/" affinity = competitor.find_element(By.CSS_SELECTOR, "span.app-progress__value").text.strip() target_spans = competitor.find_elements(By.CSS_SELECTOR, "span.wa-competitors__list-column") monthly_visits = target_spans[2].text.strip() category = target_spans[3].text.strip() category_rank = int(target_spans[4].text.replace("#", "").replace(",", "").replace("--", "0").strip()) competitor_data = { "name": site_name, "url": link, "affinity": affinity, "monthly_visits": monthly_visits, "category": category, "category_rank": category_rank } print(competitor_data) # Replace with actual storage mechanism success = True except Exception as e: logger.error(f"Exception thrown: {e}") logger.warning(f"Failed to process page: {url}, Retries left: {retries-tries}") tries += 1 finally: driver.quit() if not success: raise Exception(f"Max Retries exceeded: {retries}") else: logger.info(f"Successfully parsed: {row['url']}") # Function to load and process CSV results def process_results(csv_file, retries=3): logger.info(f"Processing {csv_file}") with open(csv_file, newline="") as file: reader = list(csv.DictReader(file)) for row in reader: process_website(row, retries=retries) if __name__ == "__main__": MAX_RETRIES = 3 MAX_THREADS = 5 logger.info(f"Crawl starting...") # Example keywords to scrape keyword_list = [ {"category": "arts-and-entertainment", "subcategory": "humor"}, {"category": "arts-and-entertainment", "subcategory": "animation-and-comics"} ] aggregate_files = [] # Crawl and save results filename = "arts-and-entertainment" crawl_pipeline = DataPipeline(csv_filename=f"{filename}.csv") start_scrape(keyword_list, data_pipeline=crawl_pipeline, max_threads=MAX_THREADS, retries=MAX_RETRIES) crawl_pipeline.close_pipeline() aggregate_files.append(f"{filename}.csv") logger.info(f"Crawl complete.") # Process each CSV file for file in aggregate_files: process_results(file, retries=MAX_RETRIES)
process_results() loads our CSV into an array. We apply process_website() to each row of the file.Step 3: Storing the Scraped Data
DataScraping would be pointless if we didn't store the data. Since we already have the DataPipeline, we just need a dataclass to input into it.We'll create a new one calledCompetitorData, which is quite similar to our SearchData. Below is our CompetitorData class.@dataclass class CompetitorData: name: str = "" url: str = "" affinity: str = "" monthly_visits: str = "" category: str = "" category_rank: int = None def __post_init__(self): self.check_string_fields() def check_string_fields(self): for field in fields(self): if isinstance(getattr(self, field.name), str): if getattr(self, field.name) == "": setattr(self, field.name, f"No {field.name}") else: value = getattr(self, field.name) setattr(self, field.name, value.strip())
DataPipeline and pass CompetitorData into it.import os import csv import json import time import logging from urllib.parse import urlencode from selenium import webdriver from selenium.webdriver.chrome.service import Service as ChromeService from selenium.webdriver.chrome.options import Options from selenium.webdriver.common.by import By from webdriver_manager.chrome import ChromeDriverManager import concurrent.futures from dataclasses import dataclass, fields, asdict API_KEY = "" with open("config.json", "r") as config_file: config = json.load(config_file) API_KEY = config["api_key"] def get_scrapeops_url(url): payload = { "api_key": API_KEY, "url": url, "wait": 3000 } proxy_url = "https://proxy.scrapeops.io/v1/?" + urlencode(payload) return proxy_url # Set up logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) # Setup Selenium WebDriver def setup_driver(): options = Options() options.add_argument("--headless") # Run in headless mode for efficiency options.add_argument("--no-sandbox") options.add_argument("--disable-dev-shm-usage") return webdriver.Chrome(service=ChromeService(ChromeDriverManager().install()), options=options) @dataclass class SearchData: name: str = "" url: str = "" rank: int = 0 rank_change: int = 0 average_visit: str = "" pages_per_visit: float = 0.0 bounce_rate: str = "" def __post_init__(self): self.check_string_fields() def check_string_fields(self): for field in fields(self): if isinstance(getattr(self, field.name), str): if getattr(self, field.name) == "": setattr(self, field.name, f"No {field.name}") else: value = getattr(self, field.name) setattr(self, field.name, value.strip()) @dataclass class CompetitorData: name: str = "" url: str = "" affinity: str = "" monthly_visits: str = "" category: str = "" category_rank: int = None def __post_init__(self): self.check_string_fields() def check_string_fields(self): for field in fields(self): if isinstance(getattr(self, field.name), str): if getattr(self, field.name) == "": setattr(self, field.name, f"No {field.name}") else: value = getattr(self, field.name) setattr(self, field.name, value.strip()) class DataPipeline: def __init__(self, csv_filename="", storage_queue_limit=50): self.names_seen = [] self.storage_queue = [] self.storage_queue_limit = storage_queue_limit self.csv_filename = csv_filename self.csv_file_open = False def save_to_csv(self): self.csv_file_open = True data_to_save = [] data_to_save.extend(self.storage_queue) self.storage_queue.clear() if not data_to_save: return keys = [field.name for field in fields(data_to_save[0])] file_exists = os.path.isfile(self.csv_filename) and os.path.getsize(self.csv_filename) > 0 with open(self.csv_filename, mode="a", newline="", encoding="utf-8") as output_file: writer = csv.DictWriter(output_file, fieldnames=keys) if not file_exists: writer.writeheader() for item in data_to_save: writer.writerow(asdict(item)) self.csv_file_open = False def is_duplicate(self, input_data): if input_data.name in self.names_seen: logger.warning(f"Duplicate item found: {input_data.name}. Item dropped.") return True self.names_seen.append(input_data.name) return False def add_data(self, scraped_data): if not self.is_duplicate(scraped_data): self.storage_queue.append(scraped_data) if len(self.storage_queue) >= self.storage_queue_limit and not self.csv_file_open: self.save_to_csv() def close_pipeline(self): if self.csv_file_open: time.sleep(3) if self.storage_queue: self.save_to_csv() # Function to scrape search results (fully Selenium-based) def scrape_search_results(keyword, data_pipeline=None, retries=3): url = f"https://www.similarweb.com/top-websites/{keyword['category']}/{keyword['subcategory']}/" tries = 0 success = False while tries <= retries and not success: try: # Initialize WebDriver and load page driver = setup_driver() scrapeops_proxy_url = get_scrapeops_url(url) driver.get(scrapeops_proxy_url) time.sleep(3) # Allow page to load logger.info(f"Opened URL: {url}") # Find all rows of the search results table rows = driver.find_elements(By.CSS_SELECTOR, "tr.top-table__row") rank = 1 for row in rows: site_name = row.find_element(By.CSS_SELECTOR, "a.tw-table__compare").text.strip() link = f"https://www.similarweb.com/website/{site_name}/" # Rank change processing rank_change_holder = row.find_element(By.CSS_SELECTOR, "td.top-table__column--rank-change") rank_change = 0 up_or_down = rank_change_holder.find_element(By.TAG_NAME, "span").get_attribute("class").split()[-1] if "change--up" in up_or_down: rank_change += int(rank_change_holder.text.strip()) elif "change--down" in up_or_down: rank_change -= int(rank_change_holder.text.strip()) average_visit = row.find_element(By.CSS_SELECTOR, "span.tw-table__avg-visit-duration").text.strip() pages_per_visit = float(row.find_element(By.CSS_SELECTOR, "span.tw-table__pages-per-visit").text.strip()) bounce_rate = row.find_element(By.CSS_SELECTOR, "span.tw-table__bounce-rate").text.strip() # Create data object search_data = SearchData( name=site_name, url=link, rank=rank, rank_change=rank_change, average_visit=average_visit, pages_per_visit=pages_per_visit, bounce_rate=bounce_rate ) rank += 1 data_pipeline.add_data(search_data) logger.info(f"Successfully parsed data from: {url}") success = True except Exception as e: logger.error(f"An error occurred while processing page {url}: {e}, retries left {retries-tries}") tries += 1 finally: driver.quit() if not success: raise Exception(f"Max retries exceeded: {retries}") # Function to process and scrape all search results concurrently def start_scrape(keywords, data_pipeline=None, max_threads=5, retries=3): with concurrent.futures.ThreadPoolExecutor(max_workers=max_threads) as executor: executor.map( scrape_search_results, keywords, [data_pipeline] * len(keywords), [retries] * len(keywords) ) # Function to process websites (Selenium-based) and extract competitor data def process_website(row, retries=3): url = row["url"] tries = 0 success = False while tries <= retries and not success: try: driver = setup_driver() driver.get(url) time.sleep(3) # Allow page to load # Check if blocked by a modal or warning try: blocked_modal = driver.find_element(By.CSS_SELECTOR, "div.wa-limit-modal") if blocked_modal: raise Exception("Blocked by modal") except: pass # No blocking modal # Extract competitor data competitors = driver.find_elements(By.CSS_SELECTOR, "div.wa-competitors__list-item") competitor_pipeline = DataPipeline(csv_filename=f"{row['name'].replace(' ', '-')}_competitors.csv") for competitor in competitors: site_name = competitor.find_element(By.CSS_SELECTOR, "span.wa-competitors__list-item-title").text.strip() link = f"https://www.similarweb.com/website/{site_name}/" affinity = competitor.find_element(By.CSS_SELECTOR, "span.app-progress__value").text.strip() target_spans = competitor.find_elements(By.CSS_SELECTOR, "span.wa-competitors__list-column") monthly_visits = target_spans[2].text.strip() category = target_spans[3].text.strip() category_rank = int(target_spans[4].text.replace("#", "").replace(",", "").replace("--", "0").strip()) competitor_data = CompetitorData( name=site_name, url=link, affinity=affinity, monthly_visits=monthly_visits, category=category, category_rank=category_rank ) competitor_pipeline.add_data(competitor_data) competitor_pipeline.close_pipeline() success = True except Exception as e: logger.error(f"Exception thrown: {e}") logger.warning(f"Failed to process page: {url}, Retries left: {retries-tries}") tries += 1 finally: driver.quit() if not success: raise Exception(f"Max Retries exceeded: {retries}") else: logger.info(f"Successfully parsed: {row['url']}") # Function to load and process CSV results def process_results(csv_file, retries=3): logger.info(f"Processing {csv_file}") with open(csv_file, newline="") as file: reader = list(csv.DictReader(file)) for row in reader: process_website(row, retries=retries) if __name__ == "__main__": MAX_RETRIES = 3 MAX_THREADS = 5 logger.info(f"Crawl starting...") # Example keywords to scrape keyword_list = [ {"category": "arts-and-entertainment", "subcategory": "humor"}, {"category": "arts-and-entertainment", "subcategory": "animation-and-comics"} ] aggregate_files = [] # Crawl and save results filename = "arts-and-entertainment" crawl_pipeline = DataPipeline(csv_filename=f"{filename}.csv") start_scrape(keyword_list, data_pipeline=crawl_pipeline, max_threads=MAX_THREADS, retries=MAX_RETRIES) crawl_pipeline.close_pipeline() aggregate_files.append(f"{filename}.csv") logger.info(f"Crawl complete.") # Process each CSV file for file in aggregate_files: process_results(file, retries=MAX_RETRIES)
CompetitorData is used to represent the competitors we extract from the page.Inside of our parsing function, we open a new DataPipeline and pass these CompetitorData objects into it.Step 4: Adding Concurrency
We now need to add concurrency. This time, instead of searching multiple categories, we'll need to run our parsing function on multiple rows at the same time.To achieve this, we're going to refactorprocess_results() to take advantage of multiple threads, using ThreadPoolExecutor.Below is our multithreaded version of process_results().def process_results(csv_file, max_threads=5, retries=3): logger.info(f"processing {csv_file}") with open(csv_file, newline="") as file: reader = list(csv.DictReader(file)) with concurrent.futures.ThreadPoolExecutor(max_workers=max_threads) as executor: executor.map( process_website, reader, [retries] * len(reader) )
process_website function across multiple threads. The reader is an array of objects that we aim to process using several threads. The retries are also passed as an array, matching the length of the reader array.All the arguments passed to process_website are given into executor.map() in array form, which are then forwarded into process_website.Below is the full code we've written so far.import os import csv import json import time import logging from urllib.parse import urlencode from selenium import webdriver from selenium.webdriver.chrome.service import Service as ChromeService from selenium.webdriver.chrome.options import Options from selenium.webdriver.common.by import By from webdriver_manager.chrome import ChromeDriverManager import concurrent.futures from dataclasses import dataclass, fields, asdict API_KEY = "" with open("config.json", "r") as config_file: config = json.load(config_file) API_KEY = config["api_key"] def get_scrapeops_url(url): payload = { "api_key": API_KEY, "url": url, "wait": 3000 } proxy_url = "https://proxy.scrapeops.io/v1/?" + urlencode(payload) return proxy_url # Set up logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) # Setup Selenium WebDriver def setup_driver(): options = Options() options.add_argument("--headless") # Run in headless mode for efficiency options.add_argument("--no-sandbox") options.add_argument("--disable-dev-shm-usage") return webdriver.Chrome(service=ChromeService(ChromeDriverManager().install()), options=options) @dataclass class SearchData: name: str = "" url: str = "" rank: int = 0 rank_change: int = 0 average_visit: str = "" pages_per_visit: float = 0.0 bounce_rate: str = "" def __post_init__(self): self.check_string_fields() def check_string_fields(self): for field in fields(self): if isinstance(getattr(self, field.name), str): if getattr(self, field.name) == "": setattr(self, field.name, f"No {field.name}") else: value = getattr(self, field.name) setattr(self, field.name, value.strip()) @dataclass class CompetitorData: name: str = "" url: str = "" affinity: str = "" monthly_visits: str = "" category: str = "" category_rank: int = None def __post_init__(self): self.check_string_fields() def check_string_fields(self): for field in fields(self): if isinstance(getattr(self, field.name), str): if getattr(self, field.name) == "": setattr(self, field.name, f"No {field.name}") else: value = getattr(self, field.name) setattr(self, field.name, value.strip()) class DataPipeline: def __init__(self, csv_filename="", storage_queue_limit=50): self.names_seen = [] self.storage_queue = [] self.storage_queue_limit = storage_queue_limit self.csv_filename = csv_filename self.csv_file_open = False def save_to_csv(self): self.csv_file_open = True data_to_save = [] data_to_save.extend(self.storage_queue) self.storage_queue.clear() if not data_to_save: return keys = [field.name for field in fields(data_to_save[0])] file_exists = os.path.isfile(self.csv_filename) and os.path.getsize(self.csv_filename) > 0 with open(self.csv_filename, mode="a", newline="", encoding="utf-8") as output_file: writer = csv.DictWriter(output_file, fieldnames=keys) if not file_exists: writer.writeheader() for item in data_to_save: writer.writerow(asdict(item)) self.csv_file_open = False def is_duplicate(self, input_data): if input_data.name in self.names_seen: logger.warning(f"Duplicate item found: {input_data.name}. Item dropped.") return True self.names_seen.append(input_data.name) return False def add_data(self, scraped_data): if not self.is_duplicate(scraped_data): self.storage_queue.append(scraped_data) if len(self.storage_queue) >= self.storage_queue_limit and not self.csv_file_open: self.save_to_csv() def close_pipeline(self): if self.csv_file_open: time.sleep(3) if self.storage_queue: self.save_to_csv() # Function to scrape search results (fully Selenium-based) def scrape_search_results(keyword, data_pipeline=None, retries=3): url = f"https://www.similarweb.com/top-websites/{keyword['category']}/{keyword['subcategory']}/" tries = 0 success = False while tries <= retries and not success: try: # Initialize WebDriver and load page driver = setup_driver() scrapeops_proxy_url = get_scrapeops_url(url) driver.get(scrapeops_proxy_url) time.sleep(3) # Allow page to load logger.info(f"Opened URL: {url}") # Find all rows of the search results table rows = driver.find_elements(By.CSS_SELECTOR, "tr.top-table__row") rank = 1 for row in rows: site_name = row.find_element(By.CSS_SELECTOR, "a.tw-table__compare").text.strip() link = f"https://www.similarweb.com/website/{site_name}/" # Rank change processing rank_change_holder = row.find_element(By.CSS_SELECTOR, "td.top-table__column--rank-change") rank_change = 0 up_or_down = rank_change_holder.find_element(By.TAG_NAME, "span").get_attribute("class").split()[-1] if "change--up" in up_or_down: rank_change += int(rank_change_holder.text.strip()) elif "change--down" in up_or_down: rank_change -= int(rank_change_holder.text.strip()) average_visit = row.find_element(By.CSS_SELECTOR, "span.tw-table__avg-visit-duration").text.strip() pages_per_visit = float(row.find_element(By.CSS_SELECTOR, "span.tw-table__pages-per-visit").text.strip()) bounce_rate = row.find_element(By.CSS_SELECTOR, "span.tw-table__bounce-rate").text.strip() # Create data object search_data = SearchData( name=site_name, url=link, rank=rank, rank_change=rank_change, average_visit=average_visit, pages_per_visit=pages_per_visit, bounce_rate=bounce_rate ) rank += 1 data_pipeline.add_data(search_data) logger.info(f"Successfully parsed data from: {url}") success = True except Exception as e: logger.error(f"An error occurred while processing page {url}: {e}, retries left {retries-tries}") tries += 1 finally: driver.quit() if not success: raise Exception(f"Max retries exceeded: {retries}") # Function to process and scrape all search results concurrently def start_scrape(keywords, data_pipeline=None, max_threads=5, retries=3): with concurrent.futures.ThreadPoolExecutor(max_workers=max_threads) as executor: executor.map( scrape_search_results, keywords, [data_pipeline] * len(keywords), [retries] * len(keywords) ) # Function to process websites (Selenium-based) and extract competitor data def process_website(row, retries=3): url = row["url"] tries = 0 success = False while tries <= retries and not success: try: driver = setup_driver() driver.get(url) time.sleep(3) # Allow page to load # Check if blocked by a modal or warning try: blocked_modal = driver.find_element(By.CSS_SELECTOR, "div.wa-limit-modal") if blocked_modal: raise Exception("Blocked by modal") except: pass # No blocking modal # Extract competitor data competitors = driver.find_elements(By.CSS_SELECTOR, "div.wa-competitors__list-item") competitor_pipeline = DataPipeline(csv_filename=f"{row['name'].replace(' ', '-')}_competitors.csv") for competitor in competitors: site_name = competitor.find_element(By.CSS_SELECTOR, "span.wa-competitors__list-item-title").text.strip() link = f"https://www.similarweb.com/website/{site_name}/" affinity = competitor.find_element(By.CSS_SELECTOR, "span.app-progress__value").text.strip() target_spans = competitor.find_elements(By.CSS_SELECTOR, "span.wa-competitors__list-column") monthly_visits = target_spans[2].text.strip() category = target_spans[3].text.strip() category_rank = int(target_spans[4].text.replace("#", "").replace(",", "").replace("--", "0").strip()) competitor_data = CompetitorData( name=site_name, url=link, affinity=affinity, monthly_visits=monthly_visits, category=category, category_rank=category_rank ) competitor_pipeline.add_data(competitor_data) competitor_pipeline.close_pipeline() success = True except Exception as e: logger.error(f"Exception thrown: {e}") logger.warning(f"Failed to process page: {url}, Retries left: {retries-tries}") tries += 1 finally: driver.quit() if not success: raise Exception(f"Max Retries exceeded: {retries}") else: logger.info(f"Successfully parsed: {row['url']}") def process_results(csv_file, max_threads=5, retries=3): logger.info(f"processing {csv_file}") with open(csv_file, newline="") as file: reader = list(csv.DictReader(file)) with concurrent.futures.ThreadPoolExecutor(max_workers=max_threads) as executor: executor.map( process_website, reader, [retries] * len(reader) ) if __name__ == "__main__": MAX_RETRIES = 3 MAX_THREADS = 5 logger.info(f"Crawl starting...") # Example keywords to scrape keyword_list = [ {"category": "arts-and-entertainment", "subcategory": "humor"}, {"category": "arts-and-entertainment", "subcategory": "animation-and-comics"} ] aggregate_files = [] # Crawl and save results filename = "arts-and-entertainment" crawl_pipeline = DataPipeline(csv_filename=f"{filename}.csv") start_scrape(keyword_list, data_pipeline=crawl_pipeline, max_threads=MAX_THREADS, retries=MAX_RETRIES) crawl_pipeline.close_pipeline() aggregate_files.append(f"{filename}.csv") logger.info(f"Crawl complete.") # Process each CSV file for file in aggregate_files: process_results(file,max_threads=MAX_THREADS, retries=MAX_RETRIES)
Step 5: Bypassing Anti-Bots
When viewing reports for individual sites, we often get blocked. You can see an example of this in the image below. To bypass this, we will use the proxy function that was written earlier.We only need to change two lines of our parsing function just to use proxy URL to implement this.proxy_url = get_scrapeops_url(url) driver.get(proxy_url)
import os import csv import json import time import logging from urllib.parse import urlencode from selenium import webdriver from selenium.webdriver.chrome.service import Service as ChromeService from selenium.webdriver.chrome.options import Options from selenium.webdriver.common.by import By from webdriver_manager.chrome import ChromeDriverManager import concurrent.futures from dataclasses import dataclass, fields, asdict API_KEY = "" with open("config.json", "r") as config_file: config = json.load(config_file) API_KEY = config["api_key"] def get_scrapeops_url(url): payload = { "api_key": API_KEY, "url": url, "wait": 3000 } proxy_url = "https://proxy.scrapeops.io/v1/?" + urlencode(payload) return proxy_url # Set up logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) # Setup Selenium WebDriver def setup_driver(): options = Options() options.add_argument("--headless") # Run in headless mode for efficiency options.add_argument("--no-sandbox") options.add_argument("--disable-dev-shm-usage") return webdriver.Chrome(service=ChromeService(ChromeDriverManager().install()), options=options) @dataclass class SearchData: name: str = "" url: str = "" rank: int = 0 rank_change: int = 0 average_visit: str = "" pages_per_visit: float = 0.0 bounce_rate: str = "" def __post_init__(self): self.check_string_fields() def check_string_fields(self): for field in fields(self): if isinstance(getattr(self, field.name), str): if getattr(self, field.name) == "": setattr(self, field.name, f"No {field.name}") else: value = getattr(self, field.name) setattr(self, field.name, value.strip()) @dataclass class CompetitorData: name: str = "" url: str = "" affinity: str = "" monthly_visits: str = "" category: str = "" category_rank: int = None def __post_init__(self): self.check_string_fields() def check_string_fields(self): for field in fields(self): if isinstance(getattr(self, field.name), str): if getattr(self, field.name) == "": setattr(self, field.name, f"No {field.name}") else: value = getattr(self, field.name) setattr(self, field.name, value.strip()) class DataPipeline: def __init__(self, csv_filename="", storage_queue_limit=50): self.names_seen = [] self.storage_queue = [] self.storage_queue_limit = storage_queue_limit self.csv_filename = csv_filename self.csv_file_open = False def save_to_csv(self): self.csv_file_open = True data_to_save = [] data_to_save.extend(self.storage_queue) self.storage_queue.clear() if not data_to_save: return keys = [field.name for field in fields(data_to_save[0])] file_exists = os.path.isfile(self.csv_filename) and os.path.getsize(self.csv_filename) > 0 with open(self.csv_filename, mode="a", newline="", encoding="utf-8") as output_file: writer = csv.DictWriter(output_file, fieldnames=keys) if not file_exists: writer.writeheader() for item in data_to_save: writer.writerow(asdict(item)) self.csv_file_open = False def is_duplicate(self, input_data): if input_data.name in self.names_seen: logger.warning(f"Duplicate item found: {input_data.name}. Item dropped.") return True self.names_seen.append(input_data.name) return False def add_data(self, scraped_data): if not self.is_duplicate(scraped_data): self.storage_queue.append(scraped_data) if len(self.storage_queue) >= self.storage_queue_limit and not self.csv_file_open: self.save_to_csv() def close_pipeline(self): if self.csv_file_open: time.sleep(3) if self.storage_queue: self.save_to_csv() # Function to scrape search results (fully Selenium-based) def scrape_search_results(keyword, data_pipeline=None, retries=3): url = f"https://www.similarweb.com/top-websites/{keyword['category']}/{keyword['subcategory']}/" tries = 0 success = False while tries <= retries and not success: try: # Initialize WebDriver and load page driver = setup_driver() scrapeops_proxy_url = get_scrapeops_url(url) driver.get(scrapeops_proxy_url) time.sleep(3) # Allow page to load logger.info(f"Opened URL: {url}") # Find all rows of the search results table rows = driver.find_elements(By.CSS_SELECTOR, "tr.top-table__row") rank = 1 for row in rows: site_name = row.find_element(By.CSS_SELECTOR, "a.tw-table__compare").text.strip() link = f"https://www.similarweb.com/website/{site_name}/" # Rank change processing rank_change_holder = row.find_element(By.CSS_SELECTOR, "td.top-table__column--rank-change") rank_change = 0 up_or_down = rank_change_holder.find_element(By.TAG_NAME, "span").get_attribute("class").split()[-1] if "change--up" in up_or_down: rank_change += int(rank_change_holder.text.strip()) elif "change--down" in up_or_down: rank_change -= int(rank_change_holder.text.strip()) average_visit = row.find_element(By.CSS_SELECTOR, "span.tw-table__avg-visit-duration").text.strip() pages_per_visit = float(row.find_element(By.CSS_SELECTOR, "span.tw-table__pages-per-visit").text.strip()) bounce_rate = row.find_element(By.CSS_SELECTOR, "span.tw-table__bounce-rate").text.strip() # Create data object search_data = SearchData( name=site_name, url=link, rank=rank, rank_change=rank_change, average_visit=average_visit, pages_per_visit=pages_per_visit, bounce_rate=bounce_rate ) rank += 1 data_pipeline.add_data(search_data) logger.info(f"Successfully parsed data from: {url}") success = True except Exception as e: logger.error(f"An error occurred while processing page {url}: {e}, retries left {retries-tries}") tries += 1 finally: driver.quit() if not success: raise Exception(f"Max retries exceeded: {retries}") # Function to process and scrape all search results concurrently def start_scrape(keywords, data_pipeline=None, max_threads=5, retries=3): with concurrent.futures.ThreadPoolExecutor(max_workers=max_threads) as executor: executor.map( scrape_search_results, keywords, [data_pipeline] * len(keywords), [retries] * len(keywords) ) # Function to process websites (Selenium-based) and extract competitor data def process_website(row, retries=3): url = row["url"] tries = 0 success = False while tries <= retries and not success: try: driver = setup_driver() scrapeops_proxy_url = get_scrapeops_url(url) driver.get(scrapeops_proxy_url) time.sleep(3) # Allow page to load # Check if blocked by a modal or warning try: blocked_modal = driver.find_element(By.CSS_SELECTOR, "div.wa-limit-modal") if blocked_modal: raise Exception("Blocked by modal") except: pass # No blocking modal # Extract competitor data competitors = driver.find_elements(By.CSS_SELECTOR, "div.wa-competitors__list-item") competitor_pipeline = DataPipeline(csv_filename=f"{row['name'].replace(' ', '-')}_competitors.csv") for competitor in competitors: site_name = competitor.find_element(By.CSS_SELECTOR, "span.wa-competitors__list-item-title").text.strip() link = f"https://www.similarweb.com/website/{site_name}/" affinity = competitor.find_element(By.CSS_SELECTOR, "span.app-progress__value").text.strip() target_spans = competitor.find_elements(By.CSS_SELECTOR, "span.wa-competitors__list-column") monthly_visits = target_spans[2].text.strip() category = target_spans[3].text.strip() category_rank = int(target_spans[4].text.replace("#", "").replace(",", "").replace("--", "0").strip()) competitor_data = CompetitorData( name=site_name, url=link, affinity=affinity, monthly_visits=monthly_visits, category=category, category_rank=category_rank ) competitor_pipeline.add_data(competitor_data) competitor_pipeline.close_pipeline() success = True except Exception as e: logger.error(f"Exception thrown: {e}") logger.warning(f"Failed to process page: {url}, Retries left: {retries-tries}") tries += 1 finally: driver.quit() if not success: raise Exception(f"Max Retries exceeded: {retries}") else: logger.info(f"Successfully parsed: {row['url']}") def process_results(csv_file, max_threads=5, retries=3): logger.info(f"processing {csv_file}") with open(csv_file, newline="") as file: reader = list(csv.DictReader(file)) with concurrent.futures.ThreadPoolExecutor(max_workers=max_threads) as executor: executor.map( process_website, reader, [retries] * len(reader) ) if __name__ == "__main__": MAX_RETRIES = 3 MAX_THREADS = 5 logger.info(f"Crawl starting...") # Example keywords to scrape keyword_list = [ {"category": "arts-and-entertainment", "subcategory": "humor"}, {"category": "arts-and-entertainment", "subcategory": "animation-and-comics"} ] aggregate_files = [] # Crawl and save results filename = "arts-and-entertainment" crawl_pipeline = DataPipeline(csv_filename=f"{filename}.csv") start_scrape(keyword_list, data_pipeline=crawl_pipeline, max_threads=MAX_THREADS, retries=MAX_RETRIES) crawl_pipeline.close_pipeline() aggregate_files.append(f"{filename}.csv") logger.info(f"Crawl complete.") # Process each CSV file for file in aggregate_files: process_results(file,max_threads=MAX_THREADS, retries=MAX_RETRIES)
Step 6: Production Run​
Time to run this entire thing in production! We're going to use the same settings as before. Here is ourmain if you need a refresher.Since there was such a spread in our crawl times, we'll estimate the crawl at 30 seconds.
if __name__ == "__main__": MAX_RETRIES = 3 MAX_THREADS = 5 logger.info(f"Crawl starting...") # Example keywords to scrape keyword_list = [ {"category": "arts-and-entertainment", "subcategory": "humor"}, {"category": "arts-and-entertainment", "subcategory": "animation-and-comics"} ] aggregate_files = [] # Crawl and save results filename = "arts-and-entertainment" crawl_pipeline = DataPipeline(csv_filename=f"{filename}.csv") start_scrape(keyword_list, data_pipeline=crawl_pipeline, max_threads=MAX_THREADS, retries=MAX_RETRIES) crawl_pipeline.close_pipeline() aggregate_files.append(f"{filename}.csv") logger.info(f"Crawl complete.") # Process each CSV file for file in aggregate_files: process_results(file,max_threads=MAX_THREADS, retries=MAX_RETRIES)
Legal and Ethical Considerations
Web scraping comes with important legal and ethical responsibilities. Scraping public data—information that is not gated behind a login—is typically considered legal, much like taking a photograph of a billboard.However, scraping private data introduces entirely different challenges. Accessing data behind a login or other restrictions may violate intellectual property laws and privacy regulations.Even when dealing with public data, it's essential to respect the Terms and Conditions of the target website and adhere to itsrobots.txt file. Ignoring these rules could result in account suspension or permanent bans.You can view these for SimilarWeb by checking the links below.If you're unsure of your scraper, you should talk to an attorney.