My Solution for Design Google Search with Score: 8/10

by iridescent_luminous693

System requirements


Functional Requirements

  1. Web Crawling:
    • Crawl billions of web pages regularly to discover and update content.
  2. Indexing:
    • Store and organize crawled data into an index for efficient querying.
  3. Search Query Processing:
    • Accept user queries and interpret intent using natural language processing (NLP).
  4. Ranking and Relevance:
    • Rank results based on relevance, freshness, and quality using ranking algorithms.
  5. Personalization:
    • Tailor search results based on user preferences, location, and past behavior.
  6. Autocomplete and Suggestions:
    • Provide query suggestions and auto-completions to improve user experience.
  7. Real-Time Updates:
    • Update indices in near real-time to reflect changes in web content.

Non-Functional Requirements

  1. Scalability:
    • Handle millions of queries per second and billions of indexed pages.
  2. Performance:
    • Return search results within milliseconds.
  3. Fault Tolerance:
    • Ensure high availability even during component failures.
  4. Consistency:
    • Provide up-to-date results while maintaining a balance between freshness and index update frequency.
  5. Security:
    • Protect user data and ensure search privacy.
  6. Usability:
    • Provide intuitive interfaces with clear and relevant results.





Capacity estimation

Estimate the scale of the system you are going to design...


Assumptions:

  1. Index Size:
    • Average size of a web page: 500 KB.
    • Number of pages: 1 trillion.
    • Total data size: 500 KB×1 trillion=500 PB500 \, \text{KB} \times 1 \, \text{trillion} = 500 \, \text{PB}500KB×1trillion=500PB.
  2. Query Traffic:
    • Average queries per second (QPS): 100,000.
    • Peak QPS: 1 million.
    • Average query result size: 1 MB.
    • Daily query volume: 100,000 QPS×86,400 sec=8.64 billion queries100,000 \, \text{QPS} \times 86,400 \, \text{sec} = 8.64 \, \text{billion queries}100,000QPS×86,400sec=8.64billion queries.
  3. Crawling:
    • Daily crawled pages: 1 billion.
    • Daily crawled data: 1 billion×500 KB=500 TB1 \, \text{billion} \times 500 \, \text{KB} = 500 \, \text{TB}1billion×500KB=500TB.
  4. Indexing:
    • Daily index updates: 500 TB.
  5. Storage:
    • Total index size with replication (factor 3): 500 PB×3=1.5 EB500 \, \text{PB} \times 3 = 1.5 \, \text{EB}500PB×3=1.5EB.




API design

Define what APIs are expected from the system...


1. Crawling APIs

  • GET /api/crawl/start:
    • Input: { seed_url: string, depth: int }.
    • Output: { success: boolean }.
    • Starts crawling from the seed URL.
  • GET /api/crawl/status:
    • Input: { task_id: string }.
    • Output: { status: string, pages_crawled: int }.
    • Fetches the status of a crawling task.

2. Indexing APIs

  • POST /api/index/update:
    • Input: { url: string, content: string }.
    • Output: { success: boolean }.
    • Updates the index with new or modified content.
  • GET /api/index/search:
    • Input: { query: string, filters: object }.
    • Output: { results: [ { url, snippet, rank } ] }.
    • Executes a search query.

3. Query and Ranking APIs

  • POST /api/query/process:
    • Input: { query: string, user_id: string, location: string }.
    • Output: { results: [ { url, snippet, rank } ] }.
    • Processes a query and returns ranked results.
  • GET /api/query/suggest:
    • Input: { partial_query: string }.
    • Output: { suggestions: [string] }.
    • Provides autocomplete suggestions.




Database design

Defining the system data model early on will clarify how data will flow among different components of the system. Also you could draw an ER diagram using the diagramming tool to enhance your design...


1. Crawl Storage

  • Schema Details:
    • Table Name: CrawlQueue
      • url (Primary Key): URL to be crawled.
      • priority: Crawling priority.
      • last_crawled: Timestamp of the last crawl.
      • status: Crawling status (e.g., pending, completed).
  • Purpose:
    • Manage URLs to be crawled and track their status.
  • Tech Used:
    • Distributed NoSQL Database (e.g., DynamoDB, HBase).
  • Tradeoff:
    • Pros: High scalability and low-latency updates.
    • Cons: Limited support for complex relational queries.

2. Index Storage

  • Schema Details:
    • Table Name: InvertedIndex
      • term (Primary Key): Search term or keyword.
      • doc_list: List of document IDs containing the term.
      • tf_idf: Term frequency-inverse document frequency score.
  • Purpose:
    • Store the mapping of terms to documents for efficient search.
  • Tech Used:
    • Columnar Databases (e.g., Apache Druid, Elasticsearch).
  • Tradeoff:
    • Pros: Optimized for analytical and search queries.
    • Cons: Higher write latency compared to traditional databases.

3. User Data Storage

  • Schema Details:
    • Table Name: UserPreferences
      • user_id (Primary Key): Unique identifier for each user.
      • search_history: List of past queries.
      • location: Last known user location.
      • preferences: User-specific preferences for search personalization.
  • Purpose:
    • Store user-specific data for personalized search results.
  • Tech Used:
    • Relational Databases (e.g., PostgreSQL).
  • Tradeoff:
    • Pros: Strong consistency and transactional capabilities.
    • Cons: Requires sharding for scalability with billions of users.

4. Ranking Metadata Storage

  • Schema Details:
    • Table Name: RankingSignals
      • doc_id (Primary Key): Document identifier.
      • page_rank: PageRank score.
      • click_through_rate: Historical CTR data.
      • freshness_score: Recency of the content.
  • Purpose:
    • Store metadata for ranking algorithms to compute relevance.
  • Tech Used:
    • Key-Value Stores (e.g., Redis, RocksDB).
  • Tradeoff:
    • Pros: Low-latency access to ranking signals.
    • Cons: Limited query capabilities for complex analytics.




High-level design

You should identify enough components that are needed to solve the actual problem from end to end. Also remember to draw a block diagram using the diagramming tool to augment your design. If you are unfamiliar with the tool, you can simply describe your design to the chat bot and ask it to generate a starter diagram for you to modify...


1. Web Crawling Service

Overview:

Responsible for discovering and retrieving web content. It continuously crawls the web to find new or updated pages and adds them to the crawling queue for indexing.

Responsibilities:

  • Crawl billions of pages using web crawlers (bots).
  • Handle crawling priorities to refresh popular or frequently updated pages.
  • Respect robots.txt and adhere to site-specific crawling limits.

2. Indexing Service

Overview:

Processes crawled pages by extracting key information and organizing it into an inverted index, enabling efficient search queries.

Responsibilities:

  • Parse and tokenize web pages into terms and metadata.
  • Build and update the inverted index.
  • Store ranking signals like term frequency and PageRank.

3. Query Processing Service

Overview:

Interprets user search queries and matches them against the index to retrieve relevant documents.

Responsibilities:

  • Tokenize and analyze search queries for intent and context.
  • Identify synonyms and related terms using NLP techniques.
  • Generate a list of potential matches from the index.

4. Ranking and Personalization Service

Overview:

Ranks the retrieved documents based on relevance, user preferences, and contextual factors such as location and query intent.

Responsibilities:

  • Calculate a relevance score for each document using ranking algorithms.
  • Incorporate personalization based on user history and preferences.
  • Adjust rankings based on freshness, click-through rates (CTR), and other signals.

5. Autocomplete and Suggestion Service

Overview:

Enhances the user experience by providing real-time query suggestions and autocomplete results as users type.

Responsibilities:

  • Predict user intent based on partially entered queries.
  • Suggest related or trending search terms.
  • Provide results that improve query formulation and speed.

6. Real-Time Updates and Feedback Loop

Overview:

Ensures the index remains up-to-date with real-time changes, and integrates user behavior data to improve relevance and ranking.

Responsibilities:

  • Monitor changes in crawled content and update indices dynamically.
  • Capture user interactions (clicks, dwell time) to refine ranking algorithms.

7. Frontend Service

Overview:

Handles user interactions and displays search results in a user-friendly interface.

Responsibilities:

  • Render search results, snippets, and metadata (e.g., publication date).
  • Display filters for query refinement (e.g., images, news, videos).
  • Provide options for feedback or query refinement.




Request flows

Explain how the request flows from end to end in your high level design. Also you could draw a sequence diagram using the diagramming tool to enhance your explanation...



Search Query Request

Objective: Process a user’s search query and return ranked results.

Step 1: User Sends Query

  1. Frontend Service:
    • Receives the search query from the user through the search bar.
    • Forwards the query to the Query Processing Service.

Step 2: Query Processing

  1. Query Processing Service:
    • Tokenizes the query into terms and identifies synonyms or related terms.
    • Applies NLP techniques to interpret query intent (e.g., informational, navigational).
    • Sends the processed query to the Indexing Service.

Step 3: Retrieve Matching Documents

  1. Indexing Service:
    • Looks up the query terms in the inverted index.
    • Retrieves a list of documents containing the terms, along with ranking metadata (e.g., term frequency, PageRank).

Step 4: Rank Results

  1. Ranking and Personalization Service:
    • Scores the retrieved documents using ranking algorithms like:
      • Relevance Factors: Term frequency, proximity, and PageRank.
      • Contextual Factors: User preferences, location, and query intent.
    • Adjusts rankings based on freshness and CTR.
    • Sends the ranked results back to the Frontend Service.

Step 5: Display Results

  1. Frontend Service:
    • Formats the ranked results into a user-friendly layout.
    • Displays snippets, links, and metadata (e.g., publication dates, authors).
    • Includes features like related searches, filters, and query suggestions.

Web Crawling and Indexing Request

Objective: Discover new or updated web pages and update the search index.

Step 1: Initiate Crawling

  1. Web Crawling Service:
    • Starts crawling based on scheduled tasks or real-time triggers (e.g., sitemap updates).
    • Fetches web pages and extracts content.

Step 2: Parse and Analyze Content

  1. Indexing Service:
    • Parses the crawled pages to extract terms, metadata, and links.
    • Analyzes the content to calculate ranking signals like freshness and PageRank.

Step 3: Update Index

  1. Indexing Service:
    • Updates the inverted index with the new or modified content.
    • Stores metadata (e.g., term frequency, link structure) for ranking.

Step 4: Feedback Integration

  1. Real-Time Updates and Feedback Loop:
    • Integrates signals from user interactions (e.g., click-through rates).
    • Adjusts rankings and refreshes index data dynamically.

Autocomplete and Suggestions Request

Objective: Provide query suggestions and auto-completions.

Step 1: User Starts Typing

  1. Frontend Service:
    • Captures partial query input from the user.

Step 2: Generate Suggestions

  1. Autocomplete and Suggestion Service:
    • Analyzes trending searches, popular queries, and user history.
    • Predicts user intent and generates query suggestions.

Step 3: Display Suggestions

  1. Frontend Service:
    • Displays query suggestions and refinements in real-time.



Detailed component design

Dig deeper into 2-3 components and explain in detail how they work. For example, how well does each component scale? Any relevant algorithm or data structure you like to use for a component? Also you could draw a diagram using the diagramming tool to enhance your design...


1. Web Crawling Service

End-to-End Working:

The Web Crawling Service discovers and retrieves web pages from the internet. It starts with seed URLs, fetches the content, and identifies hyperlinks to other pages for recursive crawling. The service adheres to robots.txt rules to respect site-specific crawling policies. Crawled data is queued for further processing by the Indexing Service.

Communication:

  • Protocols Used:
    • HTTP/HTTPS: To fetch content from web servers.
    • Kafka: To enqueue crawled content for the Indexing Service.
  • Inter-Service Communication:
    • Publishes fetched URLs and content metadata to the Indexing Service.
    • Reports crawl status to the Monitoring Service via REST.

Data Structures and Algorithms:

  • Frontier Queue:
    • Maintains a priority queue for URLs to crawl, prioritizing freshness or popularity.
  • Graph Representation:
    • Represents the web as a graph (nodes: web pages, edges: hyperlinks) to track relationships.
  • Politeness Algorithm:
    • Uses delay timers per domain to avoid overwhelming web servers.

Implementation Example (Politeness with Priority Queue):

python Copy code from queue import PriorityQueue class Crawler: def __init__(self): self.frontier = PriorityQueue() def add_url(self, url, priority): self.frontier.put((priority, url)) def fetch_next(self): priority, url = self.frontier.get() # Fetch content here while respecting politeness delay return url

Scaling for Peak Traffic:

  • Distributed Crawling:
    • Uses a cluster of crawling bots, each handling specific domains or regions.
  • URL Partitioning:
    • Partitions the frontier queue across nodes to ensure even load distribution.

Edge Cases:

  • Dead Links:
    • Detects and removes URLs returning HTTP 404 or 410.
  • Duplicate Content:
    • Avoids re-crawling by hashing content and comparing with existing hashes.

2. Indexing Service

End-to-End Working:

The Indexing Service organizes crawled content into an inverted index. It parses web pages, tokenizes text, and maps terms to the documents in which they appear. It computes metadata like term frequency and stores ranking signals (e.g., PageRank) to assist in query relevance.

Communication:

  • Protocols Used:
    • Kafka: Receives crawled data for processing.
    • gRPC: Communicates with the Ranking Service for scoring updates.
  • Inter-Service Communication:
    • Sends index updates to the Search Query Service.
    • Publishes metadata to the Real-Time Feedback Service for ranking adjustments.

Data Structures and Algorithms:

  • Inverted Index:
    • Maps terms to document IDs with metadata like positions and frequencies.
  • PageRank Algorithm:
    • Calculates a relevance score for each page based on inbound links.
  • Delta Indexing:
    • Updates only the affected parts of the index during incremental crawls.

Implementation Example (Inverted Index Construction):

python Copy code class InvertedIndex: def __init__(self): self.index = {} def add_document(self, doc_id, content): for term in content.split(): if term not in self.index: self.index[term] = [] self.index[term].append(doc_id)

Scaling for Peak Traffic:

  • Sharding:
    • Splits the index by term ranges or document IDs across nodes.
  • Replication:
    • Maintains replicas of the index for fault tolerance and high availability.

Edge Cases:

  • Ambiguous Terms:
    • Uses contextual metadata (e.g., location, user history) to disambiguate queries.
  • Stale Content:
    • Implements TTLs for index entries to refresh outdated documents.

3. Query Processing Service

End-to-End Working:

The Query Processing Service handles user queries by tokenizing, analyzing intent, and identifying synonyms or related terms. It generates a query plan to retrieve relevant documents from the inverted index.

Communication:

  • Protocols Used:
    • REST APIs: Accepts user queries and returns search results.
    • gRPC: Interacts with the Indexing Service to fetch document lists.
  • Inter-Service Communication:
    • Sends refined queries to the Ranking Service.
    • Retrieves suggestions from the Autocomplete Service.

Data Structures and Algorithms:

  • Trie for Autocomplete:
    • Stores query prefixes to efficiently suggest completions.
  • TF-IDF Scoring:
    • Calculates term importance within a document.
  • Synonym Matching:
    • Uses a hash map or graph for word relationships.

Implementation Example (Autocomplete Trie):

python Copy code class TrieNode: def __init__(self): self.children = {} self.is_end_of_word = False class Autocomplete: def __init__(self): self.root = TrieNode() def insert(self, word): node = self.root for char in word: if char not in node.children: node.children[char] = TrieNode() node = node.children[char] node.is_end_of_word = True def search(self, prefix): node = self.root for char in prefix: if char not in node.children: return [] node = node.children[char] return self.collect_words(node, prefix) def collect_words(self, node, prefix): words = [] if node.is_end_of_word: words.append(prefix) for char, child in node.children.items(): words.extend(self.collect_words(child, prefix + char)) return words

Scaling for Peak Traffic:

  • Caching:
    • Caches query results for frequently accessed queries.
  • Load Balancing:
    • Distributes incoming queries across multiple processing nodes.

Edge Cases:

  • Ambiguous Queries:
    • Uses NLP techniques to infer intent and prioritize results.
  • Misspelled Words:
    • Applies spell correction using edit distance algorithms like Levenshtein distance.

4. Ranking and Personalization Service

End-to-End Working:

The Ranking and Personalization Service scores documents based on relevance to the query, user preferences, and other contextual signals (e.g., location). It incorporates machine learning models for personalization.

Communication:

  • Protocols Used:
    • gRPC: Fetches metadata and user preferences.
    • REST APIs: Receives scoring updates from the Feedback Service.
  • Inter-Service Communication:
    • Retrieves ranking signals (e.g., PageRank) from the Indexing Service.

Data Structures and Algorithms:

  • Vector Embeddings:
    • Represents user queries and documents as vectors for similarity scoring.
  • Gradient Boosted Trees:
    • Combines multiple features to calculate a ranking score.
  • Click-Through Data:
    • Uses historical click data to refine ranking weights.

Implementation Example (Simple Ranking Function):

python Copy code def rank_documents(documents, query_terms): ranked = [] for doc in documents: score = sum(doc['tf_idf'][term] for term in query_terms if term in doc['tf_idf']) ranked.append((doc['id'], score)) return sorted(ranked, key=lambda x: x[1], reverse=True)

Scaling for Peak Traffic:

  • Distributed Scoring:
    • Scores documents in parallel across compute nodes.
  • Precomputed Rankings:
    • Precomputes scores for popular queries.

Edge Cases:

  • Bias in Rankings:
    • Uses fairness constraints to ensure diverse results.
  • Cold Start:
    • Applies default rankings for new users with no historical data.




Trade offs/Tech choices

Explain any trade offs you have made and why you made certain tech choices...



Distributed Crawling vs. Centralized Crawling:

  • Trade-off: Distributed crawling introduces complexity in managing duplicate URLs and politeness policies.
  • Reason: Enables scalability to handle the vast size of the web and faster content discovery.

Eventual Consistency in Index Updates:

  • Trade-off: Users might see slightly outdated results due to delays in index updates.
  • Reason: Prioritized system availability and scalability for handling billions of documents.

Vector Search for Ranking:

  • Trade-off: Higher computation cost compared to traditional keyword-based ranking.
  • Reason: Provides better relevance and personalization through semantic understanding.

Caching Frequently Queried Results:

  • Trade-off: Cached results might not reflect the latest content.
  • Reason: Improves query latency for popular queries and reduces load on backend services.



Failure scenarios/bottlenecks

Try to discuss as many failure scenarios/bottlenecks as possible.


Crawler Overloading Target Servers:

  • Issue: Crawling too aggressively can lead to server bans.
  • Mitigation: Implement politeness policies, rate limiting, and respect robots.txt.

Index Corruption:

  • Issue: Index data corruption can lead to incorrect or incomplete search results.
  • Mitigation: Use replication, periodic consistency checks, and backups.

Hot Queries:

  • Issue: Popular queries can overwhelm query processing nodes.
  • Mitigation: Cache results for hot queries and apply load balancing.

Ranking Bias:

  • Issue: Overemphasis on certain factors (e.g., click-through rates) might skew results.
  • Mitigation: Regularly tune ranking algorithms and incorporate fairness constraints.

Node Failures:

  • Issue: Failure of nodes in crawling, indexing, or querying services can disrupt operations.
  • Mitigation: Employ failover mechanisms, replication, and dynamic reallocation of tasks.




Future improvements

What are some future improvements you would make? How would you mitigate the failure scenario(s) you described above?


Real-Time Index Updates:

  • Improvement: Enable near real-time index updates for breaking news or trending content.
  • Mitigation: Use a delta index for fast updates while merging with the main index asynchronously.

Advanced NLP Models:

  • Improvement: Incorporate transformer-based models (e.g., BERT) for better query understanding and document ranking.
  • Mitigation: Use model distillation and distributed inference to manage computational overhead.

Dynamic Load Balancing:

  • Improvement: Dynamically allocate resources based on real-time query patterns and traffic.
  • Mitigation: Implement auto-scaling and adaptive caching mechanisms.

Geo-Distributed Indexing:

  • Improvement: Distribute indices across regions to reduce latency for global users.
  • Mitigation: Synchronize indices asynchronously and optimize query routing.

Enhanced Monitoring and Self-Healing:

  • Improvement: Use AI-driven anomaly detection for proactive system health management.
  • Mitigation: Automate recovery actions like node restarts, failovers, or task redistribution.