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Estimate the scale of the system you are going to design...
Define what APIs are expected from the system...
/api/crawl/start:{ seed_url: string, depth: int }.{ success: boolean }./api/crawl/status:{ task_id: string }.{ status: string, pages_crawled: int }./api/index/update:{ url: string, content: string }.{ success: boolean }./api/index/search:{ query: string, filters: object }.{ results: [ { url, snippet, rank } ] }./api/query/process:{ query: string, user_id: string, location: string }.{ results: [ { url, snippet, rank } ] }./api/query/suggest:{ partial_query: string }.{ suggestions: [string] }.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...
CrawlQueueurl (Primary Key): URL to be crawled.priority: Crawling priority.last_crawled: Timestamp of the last crawl.status: Crawling status (e.g., pending, completed).InvertedIndexterm (Primary Key): Search term or keyword.doc_list: List of document IDs containing the term.tf_idf: Term frequency-inverse document frequency score.UserPreferencesuser_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.RankingSignalsdoc_id (Primary Key): Document identifier.page_rank: PageRank score.click_through_rate: Historical CTR data.freshness_score: Recency of the content.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...
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.
Processes crawled pages by extracting key information and organizing it into an inverted index, enabling efficient search queries.
Interprets user search queries and matches them against the index to retrieve relevant documents.
Ranks the retrieved documents based on relevance, user preferences, and contextual factors such as location and query intent.
Enhances the user experience by providing real-time query suggestions and autocomplete results as users type.
Ensures the index remains up-to-date with real-time changes, and integrates user behavior data to improve relevance and ranking.
Handles user interactions and displays search results in a user-friendly interface.
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...
Objective: Process a user’s search query and return ranked results.
Objective: Discover new or updated web pages and update the search index.
Objective: Provide query suggestions and auto-completions.
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...
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.
Implementation Example (Politeness with Priority Queue):
python
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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
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.
Implementation Example (Inverted Index Construction):
python
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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)
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.
Implementation Example (Autocomplete Trie):
python
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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
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.
Implementation Example (Simple Ranking Function):
python
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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)
Explain any trade offs you have made and why you made certain tech choices...
Distributed Crawling vs. Centralized Crawling:
Eventual Consistency in Index Updates:
Vector Search for Ranking:
Caching Frequently Queried Results:
Try to discuss as many failure scenarios/bottlenecks as possible.
Crawler Overloading Target Servers:
robots.txt.Index Corruption:
Hot Queries:
Ranking Bias:
Node Failures:
What are some future improvements you would make? How would you mitigate the failure scenario(s) you described above?
Real-Time Index Updates:
Advanced NLP Models:
Dynamic Load Balancing:
Geo-Distributed Indexing:
Enhanced Monitoring and Self-Healing: