Solution Explanation for Web Crawler Multithreaded

This problem requires building a multithreaded web crawler that efficiently explores URLs within the same hostname as a given starting URL. The solution involves several key steps:

1. Hostname Extraction:

The first step is to extract the hostname from the startUrl. This is crucial for filtering URLs and ensuring that only those within the same domain are visited. A regular expression or string manipulation can achieve this. For instance, in Python, urllib.parse.urlparse can be used to extract the netloc (network location) part of a URL, which contains the hostname.

2. Multithreaded Crawling:

To improve efficiency, the crawling process should be multithreaded. This allows concurrent fetching of URLs, significantly reducing the overall crawling time, especially with many URLs. A ThreadPoolExecutor (Python) or similar constructs in other languages are suitable for managing threads.

3. URL Filtering and Deduplication:

Each thread must check if a URL is already visited or if it belongs to the correct hostname. A set data structure is ideal for efficiently tracking visited URLs and eliminating duplicates. A separate set or similar can store the hostname for comparison.

4. Depth-First Search (DFS) or Breadth-First Search (BFS):

The crawler can utilize either DFS or BFS to explore the web. DFS uses recursion or a stack to traverse deeply into a branch of the website before exploring other branches. BFS uses a queue, traversing level by level, ensuring that all URLs at a given depth are processed before moving to the next depth. In this problem, using DFS or BFS does not drastically affect performance as we are only exploring within one hostname.

5. HtmlParser Interface Interaction:

The provided HtmlParser interface is used to fetch links from each URL. The code must make the necessary call to HtmlParser.getUrls(url) for each URL, then proceed to process the returned URLs in a thread-safe manner.

Code Example (Python):

import concurrent.futures
from urllib.parse import urlparse
 
class Solution:
    def crawl(self, startUrl: str, htmlParser: 'HtmlParser') -> List[str]:
        hostname = urlparse(startUrl).netloc
        visited = set()
        queue = [startUrl]
        results = []
 
        with concurrent.futures.ThreadPoolExecutor() as executor:
            while queue:
                url = queue.pop(0)
                if url in visited or urlparse(url).netloc != hostname:
                    continue
                visited.add(url)
                results.append(url)
 
                future = executor.submit(htmlParser.getUrls, url)
                new_urls = future.result()
                queue.extend(new_urls)
 
        return results
 

Time Complexity Analysis:

The time complexity depends largely on the number of URLs in the target hostname and the structure of the website. In the worst case, where every page links to many other pages within the same hostname, the complexity can approach O(V + E), where V is the number of vertices (URLs) and E is the number of edges (links) within the same hostname. The multithreading reduces the wall-clock time but doesn't change the asymptotic complexity.

Space Complexity Analysis:

The space complexity is also affected by the number of URLs, the maximum depth of the website, and the size of the visited set which will hold at most all unique URLs explored. In a worst-case scenario (a fully connected graph of URLs), this would be O(V), where V is the number of vertices (URLs).

Other Languages (Conceptual):

The same principles apply to Java, C++, or Go. The key differences would be the choice of concurrency mechanisms (e.g., ExecutorService in Java, goroutines in Go) and the data structures (e.g., HashSet in Java, unordered_set in C++, map in Go for deduplication). The overall algorithm and complexity analysis remain consistent.

Follow-up Questions:

The follow-up questions about distributed crawling, node failure handling, and termination detection involve more complex distributed systems design considerations, that are beyond the scope of this explanation of the single-machine solution.