Overview of web crawling technologies and implementation in Python/Clojure

Overview of web crawling technology

Web crawling is a technique for automatically collecting information on the web. web crawling requires knowledge of networks (see “Network Technologies“), knowledge of web technologies (see “About Web Technologies“), and knowledge of Python (described in “Python and Machine Learning“), Clojure (described in “Clojure and Functional Programming“), PHP (described in “PHP and Web Development Frameworks“), or Javascript in “Front-end Development with Javascript and React“. It is necessary to implement a program that retrieves HTML source code from a web page and extracts information from it using various languages.

Web crawling techniques can be broadly categorized as follows

• Static web crawling

Static web crawling will be a method of retrieving web pages and extracting the necessary information from the HTML source code. Static web crawling requires a library for sending HTTP requests and retrieving web pages. For example, libraries commonly used in Python include Requests and BeautifulSoup.

• Dynamic Web crawling

Dynamic web crawling will be a technique for handling dynamically generated web pages such as JavaScript. Dynamic web crawling can automate the browser to retrieve dynamically generated web pages. A typical library is Selenium.

• Web crawling using API

Web crawling using APIs is a method of collecting data using APIs provided by websites; by using APIs, data can be collected more efficiently without the need to obtain the HTML source code of the web page.

Application Examples

Web crawling technology is used in a variety of fields. Some representative examples are listed below.

• search engine

The most typical web crawling technology will be for search engines. Search engines use web crawling technology to collect information on the Web and return appropriate search results for a user’s search query.

• News article collection

News media use web crawling technology to gather information from news sites around the world and provide it together on their sites.

• Price Comparison Site

Price comparison sites use web crawling technology to collect product information from multiple e-commerce sites to compare prices and provide product information.

• SNS Analysis

Web crawling techniques may be used to collect information on social networking sites to analyze trends and user opinions.

• Recruitment

Companies may use web crawling technology to collect information from websites that post job openings for use in recruiting activities.

Application of AI Technology

The use of AI techniques in web crawling can improve the accuracy, speed, and efficiency of web crawling, and can also be used to automatically collect machine learning source data. These are described below.

• Automatic page analysis

Because analysis methods differ depending on the structure and language of web pages, conventional web crawling requires manual setting of rules to analyze web pages. However, by using AI technology, the structure and language of a web page can be analyzed automatically, making crawling more efficient.

• Automatic Information Collection

AI technology can be used to automate the collection of information from web pages. For example, it can extract text from images and convert audio into text. This will improve the accuracy and speed of information collection from web pages.

• Automatic information classification

AI technology can be used to automatically classify collected information. Examples include topic classification of sentences and product category classification. This allows for more efficient analysis of collected information.

• Automatic learning

AI technology can be used to learn the data collected by crawling. For example, machine learning can automatically analyze web pages according to their structure and language. In addition, natural language processing using neural networks can understand the meaning of sentences and collect information with greater precision.

Implementation

The specific implementation of web crawling involves the following steps

1. Creating a Crawler

First, a crawler is created to crawl web pages. The crawler has the ability to download and analyze web pages from a specified URL. Programming languages such as Python can be used for these implementations (implementations in Python and Clojure are described below).

2. Page Analysis

Page analysis is performed to extract the necessary information from the web pages downloaded by the crawler. For analysis, knowledge of HTML, CSS, XPath, etc., which is the structure of a web page, is required. Specifically, libraries such as Python’s BeautifulSoup and Scrapy can be used to perform page analysis.

3. Data Extraction

Extract the necessary data from the information obtained by page analysis. For example, the name, price, and description of a product may be extracted from a product page. Extracting data requires knowledge of regular expressions and XPath, as well as natural language processing techniques as described in “Natural Language Processing Techniques.

4. Data Storage

Extracted data is stored in databases, CSV files, or various other databases as described in “Database Technology“. Access to databases is described in “Database Access Implementation in Various Languages” and access to various data files is described in “Examples of Data File Input/Output Implementation in Various Languages.

5. Schedule automation

To automate the crawling process, the crawler’s execution schedule can be automated. For example, cron can be used to run the crawler on a regular basis.

Implementation in python

The specific implementation of web crawling using Python involves the following steps.

1. Library Installation

First, install the necessary libraries for web crawling in Python. Typical libraries include BeautifulSoup and Scrapy. Install them using the pip command as follows.

pip install beautifulsoup4
pip install scrapy

2. Creating a Crawler

To create a crawler, we will use Scrapy, a framework for web scraping and web crawling in Python.

Below is an example of a crawler that uses Scrapy to crawl Google search results.

import scrapy

class GoogleSpider(scrapy.Spider):
    name = "google"
    allowed_domains = ["google.com"]
    start_urls = [
        "https://www.google.com/search?q=python",
    ]

    def parse(self, response):
        for result in response.css('div.g'):
            yield {
                'title': result.css('h3.r a::text').extract_first(),
                'url': result.css('h3.r a::attr(href)').extract_first(),
                'description': result.css('span.st::text').extract_first(),
            }

3. Data Extraction and Storage

The data retrieved by the crawler is extracted and the necessary information is saved; in Scrapy, the parse method can be defined as described above to extract the necessary information from the HTML tags. The extracted data can be saved in formats such as CSV and JSON.

import scrapy
from scrapy.exporters import CsvItemExporter

class GoogleSpider(scrapy.Spider):
    name = "google"
    allowed_domains = ["google.com"]
    start_urls = [
        "https://www.google.com/search?q=python",
    ]

    def parse(self, response):
        exporter = CsvItemExporter(open('results.csv', 'wb'))
        for result in response.css('div.g'):
            data = {
                'title': result.css('h3.r a::text').extract_first(),
                'url': result.css('h3.r a::attr(href)').extract_first(),
                'description': result.css('span.st::text').extract_first(),
            }
            exporter.export_item(data)
            yield data

4. execution 

To run the crawler, use the scrapy command as follows

scrapy crawl google

These are the specific steps for implementing web crawling using Python. However, there are some legal restrictions and ethical issues that need to be taken into consideration for web crawling. In addition, overloading a Web site can affect the server, so considerations for these must also be made.

Implementation by Clojure

Since Clojure is a type of Lisp that runs on the Java Virtual Machine, Java libraries can be used to implement web crawling.

Specifically, the following libraries are useful when implementing web crawling in Clojure.

• jsoup: HTML parsing library, used to extract data from HTML.
• clj-http: HTTP client library, used to retrieve web pages.
• enlive: A library used to parse HTML templates, useful for scraping and parsing Web pages.
• clojure.data.json: A library for parsing JSON data, used to retrieve JSON data from APIs.

The following is a simple example of implementing web crawling in Clojure: use clj-http to retrieve a web page and jsoup to extract data from the HTML.

(ns myapp.crawler
  (:require [clj-http.client :as http]
            [org.jsoup.Jsoup :as jsoup]))

(defn get-page [url]
  (let [response (http/get url)]
    (if (= (:status response) 200)
      (:body response)
      (throw (ex-info "Failed to retrieve page" {:url url})))))


(defn extract-data [html]
  (let [doc (jsoup/parse html)]
    (map #(str (-> % .text) ", " (-> % .attr "href")) (.select doc "a"))))


(let [url "https://www.example.com"
      html (get-page url)
      data (extract-data html)]
  (println data))

In this example, the URL of the Web page to be crawled is specified, and the get-page function is used to obtain the HTML of the page. Then, the extract-data function is used to extract data from the HTML. Finally, the extracted data is output.