Design (LLD) distributed search engine - Machine Coding

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Features Required:

  1. Crawling: The search engine should be able to crawl web pages, extract relevant information, and store it for indexing.

  2. Indexing: The search engine should create an index of the crawled web pages. It should extract keywords, build an inverted index, and store it for efficient retrieval.

  3. Query Processing: The search engine should process user queries and retrieve relevant results from the index. It should handle different types of queries, such as keyword-based queries and advanced search queries.

  4. Ranking: The search engine should rank the search results based on relevance. It should apply ranking algorithms to determine the order in which the results are displayed to the user.

  5. Distributed Architecture: The search engine should be designed as a distributed system to handle large-scale crawling, indexing, and query processing. It should distribute the workload across multiple machines for scalability and fault tolerance.

  6. Scalability: The search engine should be able to handle a large volume of web pages and queries. It should be designed for horizontal scalability by adding more machines to the system.

  7. Fault Tolerance: The search engine should be resilient to failures. It should handle machine failures, network issues, and recover gracefully without data loss.

  8. User Interface: The search engine should provide a user-friendly interface for users to enter queries and view search results. It can be a web-based interface or an API.

Design Patterns Involved or Used:

  1. Master-Worker: The distributed search engine can use the master-worker pattern to distribute tasks, such as crawling and query processing, across multiple machines. The master node assigns tasks to worker nodes and coordinates their execution.

  2. Publish-Subscribe: The search engine can use the publish-subscribe pattern to notify components about events and updates. For example, when new web pages are crawled and indexed, the indexing module can publish the updates, and the query processing module can subscribe to receive the updates.

  3. Replication: The search engine can use replication to ensure fault tolerance and data availability. For example, the crawled data and the index can be replicated across multiple machines, allowing the system to continue functioning even if some machines fail.

  4. Load Balancing: The search engine can use load balancing techniques to distribute queries evenly across the query processing nodes. This ensures that no node is overloaded and provides better performance.


ttern for distributed processing in the context of a distributed search engine:

javaCopy codeimport java.util.ArrayList;
import java.util.List;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import java.util.concurrent.TimeUnit;

// Crawling Module
class CrawlingModule {
    public List<String> crawlWebPages(List<String> urls) {
        // Crawling logic for a subset of URLs
        List<String> crawledData = new ArrayList<>();
        for (String url : urls) {
            // Fetch web page and extract relevant information
            String data = "Crawled data from " + url;
        return crawledData;

// Indexing Module
class IndexingModule {
    public void buildIndex(List<String> crawledData) {
        // Indexing logic for the crawled data
        for (String data : crawledData) {
            // Extract keywords and build the index
            System.out.println("Indexing data: " + data);

// Query Processing Module
class QueryProcessingModule {
    public List<String> processQuery(String query) {
        // Query processing logic
        System.out.println("Processing query: " + query);
        // Perform search and return search results
        return List.of("Result 1", "Result 2", "Result 3");

// Ranking Module
class RankingModule {
    public List<String> rankResults(List<String> searchResults) {
        // Ranking logic
        System.out.println("Ranking search results...");
        // Apply ranking algorithm and return ranked results
        return searchResults;

// User Interface
class UserInterface {
    public void displayResults(List<String> rankedResults) {
        // Display search results
        System.out.println("Displaying search results:");
        for (String result : rankedResults) {

// Main Class
public class DistributedSearchEngine {
    private CrawlingModule crawlingModule;
    private IndexingModule indexingModule;
    private QueryProcessingModule queryProcessingModule;
    private RankingModule rankingModule;
    private UserInterface userInterface;

    public DistributedSearchEngine() {
        // Initialize modules and components
        crawlingModule = new CrawlingModule();
        indexingModule = new IndexingModule();
        queryProcessingModule = new QueryProcessingModule();
        rankingModule = new RankingModule();
        userInterface = new UserInterface();

    public void run() {
        // Crawling phase
        List<String> urls = List.of("url1", "url2", "url3");
        List<String> crawledData = distributeCrawling(urls);

        // Indexing phase

        // User query
        String query = "example query";
        List<String> searchResults = queryProcessingModule.processQuery(query);
        List<String> rankedResults = rankingModule.rankResults(searchResults);

    private List<String> distributeCrawling(List<String> urls) {
        int numWorkers = 3; // Number of worker threads
        ExecutorService executorService = Executors.newFixedThreadPool(numWorkers);
        List<String> crawledData = new ArrayList<>();

        // Divide the URLs among the worker threads
        int chunkSize = urls.size() / numWorkers;
        for (int i = 0; i < numWorkers; i++) {
            int start = i * chunkSize;
            int end = (i == numWorkers - 1) ? urls.size() : (i + 1) * chunkSize;
            List<String> workerUrls = urls.subList(start, end);

            // Execute the crawling task on each worker thread
            executorService.execute(() -> {
                CrawlingModule workerCrawlingModule = new CrawlingModule();
                List<String> workerCrawledData = workerCrawlingModule.crawlWebPages(workerUrls);
                synchronized (crawledData) {

        // Shut down the executor service and wait for all tasks to complete
        try {
            executorService.awaitTermination(60, TimeUnit.SECONDS);
        } catch (InterruptedException e) {

        return crawledData;

    public static void main(String[] args) {
        DistributedSearchEngine searchEngine = new DistributedSearchEngine();;

In this code, we have added the implementation details for each module, such as the crawling logic in the CrawlingModule class and the indexing logic in the IndexingModule class. Similarly, the QueryProcessingModule processes the query and returns search results, and the RankingModule applies ranking algorithms to the search results.

Regarding design patterns, the code currently showcases the following patterns:

  1. Singleton Pattern: The DistributedSearchEngine class follows the singleton pattern, ensuring that only one instance of the search engine is created.

  2. Facade Pattern: The DistributedSearchEngine class acts as a facade, providing a simplified interface to interact with the search engine components. It encapsulates the complexities of the underlying modules and provides a unified run() method for executing the search engine workflow.

  3. Template Method Pattern: The DistributedSearchEngine class defines a template method with the run() method. The template method provides a high-level structure for the search engine workflow, while allowing subclasses (in this case, the DistributedSearchEngine itself) to override specific steps.

DistributedSearchEngine class includes a distributeCrawling() method that implements the master-worker pattern for the crawling phase. It divides the URLs among multiple worker threads, and each worker thread executes the crawling task independently. The crawled data from each worker is then synchronized and merged into the crawledData list

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