answer:Sure, here is an example code using Puppeteer library in Node.js to create a web crawler that finds URLs and emails on pages: ```javascript const puppeteer = require('puppeteer'); const emailRegex = /b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+.[A-Z|a-z]{2,}b/g; // Function to scrape the page and extract all URLs and emails async function scrapePage(page) { // Extract all URLs const urls = await page.eval('a', links => links.map(link => link.href)); // Extract all email addresses const pageContent = await page.content(); const emails = pageContent.match(emailRegex); // Return all URLs and emails as an object return { urls, emails }; } // Launch Puppeteer and navigate to the target website (async () => { const browser = await puppeteer.launch(); const page = await browser.newPage(); await page.goto('https://example.com'); // Scrape the page for URLs and emails const results = await scrapePage(page); console.log('URLs:', results.urls); console.log('Emails:', results.emails); // Close the browser await browser.close(); })(); ``` In this code, we first define a regular expression to match email addresses. We then define a `scrapePage` function that extracts all URLs and emails from the given page by using Puppeteer's `eval` function to get all the hyperlinks on the page and then executing a regular expression match on the page source to extract the email addresses. Finally, we launch Puppeteer, navigate to the target website, and call the `scrapePage` function to extract the URLs and emails from the page. The results are then logged to the console. Once the operation is finished, we close the browser using `browser.close()`.

answer:To introduce multithreading in the web scraper, you can create multiple instances of Puppeteer, with each instance running on a separate thread, and each instance responsible for scraping a different URL. This can be achieved using the `worker_threads` module in Node.js. Below is an example code that demonstrates how to scrape multiple URLs using multithreading: ```javascript const { Worker, isMainThread, parentPort, workerData } = require('worker_threads'); const puppeteer = require('puppeteer'); // Function to scrape a URL using Puppeteer async function scrapeUrl(url) { const browser = await puppeteer.launch(); const page = await browser.newPage(); await page.goto(url); // Scrape the page for URLs and emails const results = await scrapePage(page); // Close the browser await browser.close(); return results; } // Function to scrape the page and extract all URLs and emails async function scrapePage(page) { // Extract all URLs const urls = await page.eval('a', links => links.map(link => link.href)); // Extract all email addresses const pageContent = await page.content(); const emails = pageContent.match(emailRegex); // Return all URLs and emails as an object return { urls, emails }; } // Check if the current script is running in the main thread or a worker thread if (isMainThread) { // Define the URLs to be scraped const urls = ['https://example.com', 'https://yahoo.com', 'https://google.com']; // Create a new worker thread for each URL to be scraped for (const url of urls) { const worker = new Worker(__filename, { workerData: url }); // Listen to messages sent by the worker thread worker.on('message', result => { console.log('Result:', result); }); } } else { // Scrape the URL passed to the worker thread const result = await scrapeUrl(workerData); // Send the result back to the main thread parentPort.postMessage(result); } ``` In this code, we first define the `scrapeUrl` and `scrapePage` functions as before. Then, we check if the current thread is the main thread using `isMainThread`. If so, we define the URLs to be scraped and create a worker thread for each URL using `new Worker`. For each worker thread, we pass the URL to be scraped as the `workerData` parameter. We then listen for messages sent by the worker thread using `worker.on('message', ...)` and log the result to the console. If the current thread is not the main thread, we scrape the URL passed to the worker thread using `scrapeUrl` and send the result back to the main thread using `parentPort.postMessage`.

answer:As an AI language model, I do not have the capability to run server code or host servers. However, this server code implements a simple HTTP server which listens on port 8000 and allows GET requests with a query parameter "word". The server then uses two Merriam-Webster APIs to get the meaning and synonyms of the word, and sends back a response to the client. The server allows requests from any origin.

answer:The document is called a gold certificate or a gold note.