Web Scraping API Tools to Track, Manage and Visualize Your Data Pipeline
Web Scraping ?essentially is the way of gathering information from the Internet. Through Web Scraping Tools, one can download structured information from the internet to be utilized for examination in an automated style.
This blog targets at providing you with in-depth information about what Web Scraping is and why it's fundamental, alongside an extensive list of the 8 Best Web Scraping Tools out there in the market, remembering the components offered by each of these, pricing, evaluating target crowd, and weaknesses. It will help you make an informed decision with respect to the Best Web Scraping Tool obliging your business.
Web Scraping
Web Scraping ?alludes to the extraction of content and information from a site. This data is then extracted in an arrangement that is more valuable to the client.
Web Scraping should be possible manually. However, this is such tedious work. To accelerate the process, you can utilize Web Scraping APIs that A Web Scraping API would automate, cost less, and work more quickly.
How does a Web Scraper work exactly?
Top 9 Web Scraping Tools
Picking the best Web Scraping Tool that consummately meets your business requirements can be a difficult task, particularly when there's a huge assortment of Web Scraping Tools accessible in the market. To simplify your search, here is a comprehensive list of the 9 Best Web Scraping Tools that you can look over:
1.??ProxyCrawl
2.??ParseHub
3.??OctoParse
4.??Scraper API
5.??Mozenda
6.??Webhose.io
7.??Content Grabber
8.??Common Crawl
1.?ProxyCrawl
Target Audience
Scraper API ?will give information scraping to your business if it requires information for utilizations of web scraping tools. Web-based business scrapers scrape knowledge for business, value examination, survey extraction, and some other requirement for your business.
Key Features
?a. Worldwide Data Centers:
ProxyCrawl handles scraping information from overall areas and a wide range of sites with the assistance of its above 17 data centers all around the globe. ProxyCrawl has probably the biggest organization of proxies that will take all the heap of your projects.
?b. Limitless transfer speed:
Try not to stress over scraping gigantic pages
?c. Quit fixing scrappers:
Our artificial reasoning fixes the scrubbers for you, so your business won't ever stop
?d. Simple to utilize Scraper API:
An API is produced using engineers. Rapidly start in under 5 minutes.
ProxyCrawl Pricing:
Pre-Requisites for Web Scraping by Scrapy ProxyCrawl API
Code to create Data Pipelines:
import scrapy
from proxycrawl.proxycrawl_api import ProxyCrawlAPI
from datetime import datetime ?
# used to convert review date string into datetime object. Useful if you plan to insert into an SQL db.
api = ProxyCrawlAPI({'token': 'NON-JS TOKEN'})
apijava = ProxyCrawlAPI({'token': 'JS TOKEN'})
def start_requests(self):
? ?headers = {
? ? ? ?'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/86.0.4240.193 Safari/537.36'
? ?}
? ?url = 'https://www.amazon.com/product-reviews/B07ZPC9QD4/ref=cm_cr_arp_d_paging_btm_next_2?ie=UTF8&reviewerType=all_reviews' ?# you don't need the product title in the url
? ?# build proxcrawl url
? ?pcurl = api.buildURL(url, {})
? ?yield scrapy.Request(pcurl, callback=self.parse, errback=self.errback_httpbin, headers=headers, meta={'asin': 'B07ZC90D4'})
def parse(self, response):
? ?headers = {
? ? ? ?'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/86.0.4240.193 Safari/537.36'
? ?}
? ?reviews_count = response.xpath('//*[@id="cm_cr-review_list"]/div[@data-hook="review"]').getall()
? ?asin = response.meta['asin']
? ?asin_title = response.xpath('//*[@id="cm_cr-product_info"]/div/div[2]/div/div/div[2]/div[1]/h1/a/text()').get()
? ?if reviews_count is not None: ?# review_count = number of reviews
? ? ? ?for review_index in range(len(reviews_count)):
? ? ? ? ? ?review_index += 1
? ? ? ? ? ?review_title = response.xpath('//*[@id="cm_cr-review_list"]/div[@data-hook="review"][' +
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?str(review_index) + ']/div/div/div[2]/a[2]/span/text()').get()
? ? ? ? ? ?review_rating_string = response.xpath('//*[@id="cm_cr-review_list"]/div[@data-hook="review"][' +
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?str(review_index) + ']/div/div/div[2]/a[1]/@title').get()
? ? ? ? ? ?review_date_string = response.xpath('//*[@id="cm_cr-review_list"]/div[@data-hook="review"][' +
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?str(
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?review_index) + ']/div/div/span[@data-hook="review-date"]/text()').get()
? ? ? ? ? ?review_body = response.xpath('//*[@id="cm_cr-review_list"]/div[@data-hook="review"][' +
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? str(review_index) + ']/div/div/div[4]/span/span/text()').get()
? ? ? ? ? ?review_rating = str(review_rating_string).split(' ', 1)[0]
? ? ? ? ? ?# get rid of the 00:00:00 time
? ? ? ? ? ?review_date = str(datetime.strptime(review_date_string, '%B %d, %Y')).split(' ', 1)[0]
? ? ? ? ? ?date_of_cur_review = datetime.strptime(review_date, '%Y-%m-%d')
? ? ? ? ? ?# DO SOMETHING HERE. INSERT INTO A DB?
? ? ? ? ? ?#####
? ? ? ? ? ?# go to next page if there is one
? ? ? ? ? ?if review_index == 10:
? ? ? ? ? ? ? ?next_page = response.xpath('//*[@class="a-last"]/a/@href').get()
? ? ? ? ? ? ? ?if next_page is not None:
? ? ? ? ? ? ? ? ? ?headers = {'User-Agent': headers}
? ? ? ? ? ? ? ? ? ?yield response.follow(api.buildURL('https://www.amazon.com' + next_page, {}), callback=self.parse, errback=self.errback_httpbin, headers=headers, meta={'asin': response.meta['asin']})
In the items.py file, we will be defining the storage containers for the data we plan to scrape.
? ?plan to scrape
import scrapy
class RedditItem(scrapy.Item):
? ?'''
? ?Defining the storage containers for the data we
? ?plan to scrape
? ?'''
? ?date = scrapy.Field()
? ?date_str = scrapy.Field()
? ?sub = scrapy.Field()
? ?title = scrapy.Field()
? ?url = scrapy.Field()
? ?score = scrapy.Field()
commentsUrl = scrapy.Field()
in the spiders/__init__.py file, we will set up scrapy to scrape the data.
from datetime import datetime as dt
import scrapy
from reddit.items import RedditItem
class PostSpider(scrapy.Spider):
? ?name = 'post'
? ?allowed_domains = ['reddit.com']
? ?reddit_urls = [
? ? ? ?('datascience', 'week'),
? ? ? ?('python', 'week'),
? ? ? ?('programming', 'week'),
? ? ? ?('machinelearning', 'week')
? ?]
? ?start_urls = ['https://www.reddit.com/r/' + sub + '/top/?sort=top&t=' + period \
? ? ? ?for sub, period in reddit_urls]
? ?def parse(self, response):
? ? ? ?# get the subreddit from the URL
? ? ? ?sub = response.url.split('/')[4]
? ? ? ?# parse thru each of the posts
? ? ? ?for post in response.css('div.thing'):
? ? ? ? ? ?item = RedditItem()
? ? ? ? ? ?item['date'] = dt.today()
? ? ? ? ? ?item['date_str'] = item['date'].strftime('%Y-%m-%d')
? ? ? ? ? ?item['sub'] = sub
? ? ? ? ? ?item['title'] = post.css('a.title::text').extract_first()
? ? ? ? ? ?item['url'] = post.css('a.title::attr(href)').extract_first()
? ? ? ? ? ?## if self-post, add reddit base url (as it's relative by default)
? ? ? ? ? ?if item['url'][:3] == '/r/':
? ? ? ? ? ? ? ?item['url'] = 'https://www.reddit.com' + item['url']
? ? ? ? ? ?item['score'] = int(post.css('div.unvoted::text').extract_first())
? ? ? ? ? ?item['commentsUrl'] = post.css('a.comments::attr(href)').extract_first()
? ? ? ? ? ?yield item
Pipeline into MongoDB:
In the pipelines.py file, we pull in information from the settings file, open database connection, close when the spider is done scraping, and then handle each post storing in the MongoDB database.
import logging
import pymongo
class MongoPipeline(object):
? ?collection_name = 'top_reddit_posts'
? ?def __init__(self, mongo_uri, mongo_db):
? ? ? ?self.mongo_uri = mongo_uri
? ? ? ?self.mongo_db = mongo_db
? ?@classmethod
? ?def from_crawler(cls, crawler):
? ? ? ?## pull in information from settings.py
? ? ? ?return cls(
? ? ? ? ? ?mongo_uri=crawler.settings.get('MONGO_URI'),
? ? ? ? ? ?mongo_db=crawler.settings.get('MONGO_DATABASE')
? ? ? ?)
? ?def open_spider(self, spider):
? ? ? ?## initializing spider
? ? ? ?## opening db connection
? ? ? ?self.client = pymongo.MongoClient(self.mongo_uri)
? ? ? ?self.db = self.client[self.mongo_db]
? ?def close_spider(self, spider):
? ? ? ?## clean up when spider is closed
? ? ? ?self.client.close()
? ?def process_item(self, item, spider):
? ? ? ?## how to handle each post
? ? ? ?self.db[self.collection_name].insert(dict(item))
? ? ? ?logging.debug("Post added to MongoDB")
? ? ? ?return item
Configure Settings:
In the settings.py file, we configure a delay for requests for the same website and the pipeline.
# ...
# Configure a delay for requests for the same website (default: 0)
# See https://scrapy.readthedocs.org/en/latest/topics/settings.html#download-delay
# See also autothrottle settings and docs
DOWNLOAD_DELAY = .25
RANDOMIZE_DOWNLOAD_DELAY = True
# ...
# Configure item pipelines
# See https://scrapy.readthedocs.org/en/latest/topics/item-pipeline.html
ITEM_PIPELINES = {
? ?'reddit.pipelines.MongoPipeline': 300,
}
MONGO_URI = 'mongodb://localhost:27017'
MONGO_DATABASE = 'sivji-sandbox'
# ...
2.?ParseHub
Target Audience
ParseHub is an extraordinary and rich tool that permits you to build?web scraping tools ?without having a single line of code. It is consequently just about as simple as essentially choosing the information you need. ParseHub is targeted at essentially anybody that desires to play with data. This could be anybody from analysts and data scientists to journalists.
Key Features of ParseHub
ParseHub Pricing
ParseHub’s pricing structure looks like this:
Shortcomings
3.?OctoParse
Target Audience
OctoParse has an intended target group like ParseHub, obliging individuals who want to scrape information without composing a single line of code while having authority over the full process with their profoundly natural User Interface.
领英推荐
Key Features of OctoParse
OctoParse Pricing
OctoParse likewise offers Crawler Service and Data Service beginning at $189 and $399, respectively.
Shortcomings
If you run the crawler with local extraction instead of running it from the cloud, it stops naturally after 4 hours, which makes the way toward recovering, saving, and beginning once again with the following arrangement of data exceptionally cumbersome.
4.?Scraper API
Target Audience:
Scraper API is designed for creators building web scrapers. It handles programs, proxies, and CAPTCHAs, which implies that You can acquire raw HTML from any site through a basic API call.
Key Features of Scraper API
Scraper API Pricing
Scraper API offers 5000 free API calls to begin. Scraper API from there on offers a few amazing price plans to pick from.
Shortcomings
Scraper API as a Web Scraping Tool isn't considered appropriate for browsing.
5.?Mozenda
Target Audience
Mozenda caters to enterprises searching for a cloud-based self-serve Web Scraping platform. Having scraped more than 7 billion pages, Mozenda flaunts enterprise clients everywhere.
Key Features of Mozenda
Mozenda Pricing
Mozenda's pricing plan utilizes something many refer to as Processing Credits that separates itself from other Web Scraping Tools. Handling Credits estimates the amount of Mozenda's computing assets are utilized in different client activities like page route, premium harvesting, picture or file downloads.
Shortcomings
Mozenda is a little expensive compared to the other Web Scraping Tools discussed so far with their lowest plan beginning from $250/month.
6.?Webhose.io
Target Audience
Webhose.io is best suggested for platforms or services that are on the lookout for a totally developed web scraper and information provider for content advertising, sharing, and so on. The expense offered by the platform turns out to be very affordable for developing organizations.
Key Features of Webhose.io
Webhose.io Pricing
The free version gives 1000 HTTP demands each month. Paid plans offer more features like more calls, control over the separated information, and more advantages like picture analytics, Geo-location, dim web monitoring, and as long as 10 years of documented verifiable historical information.
The various plans are:
Shortcomings
7.?Content Grabber
Target Audience
Content Grabber is a cloud-based Web Scraping Tool that assists organizations of all sizes with information extraction.
Key Features of Content Grabber
Content Grabber Pricing
Two pricing models are available for clients of Content Grabber:-
For each, you have three subcategories:
Shortcomings
8.?Common Crawl
Target Audience
Common Crawl was developed for anyone wishing to explore and analyze information and uncover significant experiences from it.
Key Features of Common Crawl
Common Crawl Pricing
Common Crawl permits any intrigued individual to utilize this web scraping API tool without agonizing overcharges or other complexities. It is a registered non-benefit platform that depends on gifts to keep its tasks flawlessly running.
Shortcomings
Conclusion
This blog initially gave a thought regarding Web Scraping overall. It then, at that point, enlisted the fundamental factors to remember when making an informed decision about making a Web Scraping Tool buy followed by a sneak look at 8 of the best Web Scraping Tools in the market thinking about a series of components.
Accordingly, the fundamental takeaway from this blog is that eventually, a client should pick the Web Scraping Tools that suit their requirements. Extracting complex information from a different set of information sources can be a difficult task, and this is the place where?ProxyCrawl ?makes all the difference.