Introduction

A search autocomplete system (also known as typeahead or search suggestions) provides real-time search suggestions as users type their queries. Systems like Google Search, Amazon, and LinkedIn provide instant suggestions based on partial queries, requiring ultra-low latency, high throughput, and intelligent ranking.

This post provides a detailed walkthrough of designing a scalable search autocomplete system, covering key architectural decisions, Trie data structures, caching strategies, ranking algorithms, and scalability challenges. This is one of the most common system design interview questions that tests your understanding of data structures, caching, distributed systems, and handling high-frequency queries at scale.

Table of Contents

  1. Problem Statement
  2. Requirements
  3. Capacity Estimation
  4. Core Entities
  5. API
  6. Data Flow
  7. Database Design
  8. High-Level Design
  9. Deep Dive
  10. Summary

Problem Statement

Design a search autocomplete system similar to Google Search suggestions with the following features:

  1. Real-time suggestions as user types (each keystroke)
  2. Top K suggestions (typically 5-10 suggestions)
  3. Suggestions ranked by popularity/relevance
  4. Support for multiple languages
  5. Handle typos and misspellings (fuzzy matching)
  6. Personalization based on user history
  7. Trending queries support
  8. Query completion (complete partial queries)

Scale Requirements:

  • 1 billion+ users
  • 100 million+ daily active users
  • 10 billion+ search queries per day
  • Peak QPS: 100,000 queries per second
  • Response time: < 100ms (p99)
  • Average query length: 15 characters
  • Total unique queries: 100 million+

Requirements

Functional Requirements

Core Features:

  1. Real-Time Suggestions: Show suggestions as user types (each keystroke)
  2. Top K Results: Return top 5-10 suggestions
  3. Ranking: Rank suggestions by popularity, relevance, recency
  4. Query Completion: Complete partial queries
  5. Fuzzy Matching: Handle typos and misspellings
  6. Personalization: Show personalized suggestions based on user history
  7. Trending Queries: Include trending/popular queries
  8. Multi-Language: Support multiple languages
  9. Query History: Store and use user query history
  10. Click Tracking: Track which suggestions users click

Out of Scope:

  • Full search results (only autocomplete suggestions)
  • Image/video autocomplete
  • Voice input
  • Advanced NLP processing
  • Real-time query analytics dashboard

Non-Functional Requirements

  1. Availability: 99.9% uptime
  2. Reliability: No data loss, accurate suggestions
  3. Performance:
    • Response time: < 100ms (p99)
    • Response time: < 50ms (p50)
    • Handle 100K+ QPS
  4. Scalability: Handle 10B+ queries per day
  5. Consistency: Eventually consistent is acceptable
  6. Accuracy: Suggestions should be relevant and useful
  7. Real-Time: Suggestions should update in near real-time

Capacity Estimation

Traffic Estimates

  • Total Users: 1 billion
  • Daily Active Users (DAU): 100 million
  • Search Queries per Day: 10 billion
  • Average Queries per User: 100 queries/day
  • Peak QPS: 100,000 queries/second
  • Normal QPS: 10,000 queries/second
  • Average Query Length: 15 characters
  • Average Keystrokes per Query: 8 keystrokes (user types 8 chars before selecting)

Storage Estimates

Query Data:

  • 100 million unique queries × 50 bytes = 5GB
  • Query metadata (frequency, last_seen): 100M × 100 bytes = 10GB
  • Total query data: ~15GB

Trie Data Structure:

  • Compressed Trie: ~500MB (highly compressed)
  • With metadata: ~1GB

User Query History:

  • 100M users × 100 queries × 50 bytes = 500GB
  • With compression: ~250GB

Click Data:

  • 10B queries/day × 10% click rate × 100 bytes = 100GB/day
  • 30-day retention: ~3TB

Total Storage: ~3.3TB

Bandwidth Estimates

Normal Traffic:

  • 10,000 QPS × 2KB average response = 20MB/s = 160Mbps

Peak Traffic:

  • 100,000 QPS × 2KB = 200MB/s = 1.6Gbps

Core Entities

Query

  • query_id (UUID)
  • query_text (VARCHAR)
  • normalized_query (VARCHAR) - lowercase, trimmed
  • language (VARCHAR)
  • frequency (BIGINT) - total search count
  • click_count (BIGINT) - total click count
  • last_seen (TIMESTAMP)
  • created_at (TIMESTAMP)

Query Prefix

  • prefix (VARCHAR) - query prefix (e.g., “goo” for “google”)
  • top_queries (JSON) - top K queries for this prefix
  • updated_at (TIMESTAMP)

User Query History

  • user_id (UUID)
  • query_text (VARCHAR)
  • searched_at (TIMESTAMP)
  • clicked (BOOLEAN)
  • query_text (VARCHAR)
  • trend_score (DECIMAL)
  • time_window (VARCHAR) - hour, day, week
  • updated_at (TIMESTAMP)

API

1. Get Autocomplete Suggestions

GET /api/v1/autocomplete?q=googl&limit=5&user_id=uuid&language=en
Response:
{
  "query": "googl",
  "suggestions": [
    {
      "query": "google",
      "type": "completion",
      "score": 0.95
    },
    {
      "query": "google maps",
      "type": "suggestion",
      "score": 0.85
    },
    {
      "query": "google translate",
      "type": "suggestion",
      "score": 0.80
    }
  ],
  "response_time_ms": 45
}

2. Track Query Click

POST /api/v1/autocomplete/click
Request:
{
  "query": "google",
  "suggestion": "google maps",
  "user_id": "uuid",
  "position": 2
}

Response:
{
  "status": "tracked"
}

GET /api/v1/trending?time_window=day&limit=10
Response:
{
  "trending_queries": [
    {
      "query": "chatgpt",
      "trend_score": 0.95,
      "change": "+15%"
    }
  ]
}

Data Flow

Autocomplete Request Flow

  1. User Types Query:
    • User types “googl” in search box
    • Client sends autocomplete request after debounce (100-200ms)
    • Request includes partial query, user_id, language
  2. Request Processing:
    • API Gateway receives request
    • Routes to Autocomplete Service
    • Autocomplete Service:
      • Checks cache for prefix “googl”
      • If cache miss: queries Trie Service
      • Applies personalization (if user_id provided)
      • Ranks and filters results
      • Returns top K suggestions
  3. Trie Query:
    • Trie Service traverses Trie data structure
    • Finds all queries starting with “googl”
    • Returns candidates with scores
    • Results cached for future requests
  4. Response:
    • Autocomplete Service returns suggestions
    • Client displays suggestions to user

Query Update Flow

  1. User Searches:
    • User submits full query “google maps”
    • Search Service processes search
    • Publishes query event to Message Queue
  2. Query Processing:
    • Query Aggregator Service consumes query events
    • Updates query frequency in database
    • Updates Trie data structure
    • Invalidates cache for affected prefixes
  3. Trie Update:
    • Trie Builder Service rebuilds Trie periodically
    • Or updates Trie incrementally
    • Updates are propagated to Trie Service instances

Database Design

Schema Design

Queries Table:

CREATE TABLE queries (
    query_id UUID PRIMARY KEY,
    query_text VARCHAR(500) NOT NULL,
    normalized_query VARCHAR(500) NOT NULL,
    language VARCHAR(10) DEFAULT 'en',
    frequency BIGINT DEFAULT 0,
    click_count BIGINT DEFAULT 0,
    last_seen TIMESTAMP DEFAULT NOW(),
    created_at TIMESTAMP DEFAULT NOW(),
    INDEX idx_normalized_query (normalized_query),
    INDEX idx_frequency (frequency DESC),
    INDEX idx_language (language),
    INDEX idx_last_seen (last_seen DESC)
);

Query Prefixes Table (Cache Table):

CREATE TABLE query_prefixes (
    prefix VARCHAR(100) PRIMARY KEY,
    top_queries JSON NOT NULL,
    updated_at TIMESTAMP DEFAULT NOW(),
    INDEX idx_updated_at (updated_at)
);

User Query History Table:

CREATE TABLE user_query_history (
    user_id UUID,
    query_text VARCHAR(500),
    searched_at TIMESTAMP DEFAULT NOW(),
    clicked BOOLEAN DEFAULT FALSE,
    PRIMARY KEY (user_id, query_text, searched_at),
    INDEX idx_user_searched (user_id, searched_at DESC),
    INDEX idx_query_text (query_text)
);

Trending Queries Table:

CREATE TABLE trending_queries (
    query_text VARCHAR(500),
    time_window VARCHAR(20), -- 'hour', 'day', 'week'
    trend_score DECIMAL(10, 4),
    updated_at TIMESTAMP DEFAULT NOW(),
    PRIMARY KEY (query_text, time_window),
    INDEX idx_trend_score (trend_score DESC),
    INDEX idx_time_window (time_window, trend_score DESC)
);

Database Sharding Strategy

Queries Table Sharding:

  • Shard by normalized_query hash
  • 1000 shards: shard_id = hash(normalized_query) % 1000
  • Enables parallel queries and horizontal scaling

Shard Key Selection:

  • Hash of normalized_query ensures even distribution
  • Enables efficient lookups
  • Prevents hot-spotting

Replication:

  • Each shard replicated 3x for high availability
  • Master-replica setup for read scaling
  • Writes go to master, reads can go to replicas

High-Level Design

┌─────────────┐
│   Client    │
│  (Web App)  │
└──────┬──────┘
       │
       │ HTTP/HTTPS
       │
┌──────▼──────────────────────────────────────────────┐
│        API Gateway / Load Balancer                   │
│        - Rate Limiting                               │
│        - Request Routing                             │
└──────┬──────────────────────────────────────────────┘
       │
       │
┌──────▼──────────────────────────────────────────────┐
│         Autocomplete Service                         │
│  ┌──────────────┐  ┌──────────────┐                │
│  │   Cache      │  │ Personalization│               │
│  │  (Redis)     │  │   Service     │                │
│  └──────┬───────┘  └──────┬───────┘                │
│         │                  │                          │
│  ┌──────▼──────────────────▼──────────────┐        │
│  │         Trie Service                     │        │
│  │  - In-Memory Trie                        │        │
│  │  - Prefix Matching                       │        │
│  │  - Ranking                               │        │
│  └──────┬──────────────────────────────────┘        │
└─────────┼───────────────────────────────────────────┘
          │
          │
┌─────────▼───────────────────────────────────────────┐
│              Message Queue (Kafka)                    │
│              - Query events                           │
│              - Click events                           │
└─────────┬─────────────────────────────────────────────┘
          │
          │
┌─────────▼─────────────────────────────────────────────┐
│         Query Aggregator Service                      │
│         - Update query frequencies                    │
│         - Update Trie                                 │
│         - Calculate trending                          │
└─────────┬─────────────────────────────────────────────┘
          │
          │
┌─────────▼─────────────────────────────────────────────┐
│         Database Cluster (Sharded)                    │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐          │
│  │ Queries  │  │  User    │  │ Trending │          │
│  │ DB       │  │ History  │  │ Queries  │          │
│  └──────────┘  └──────────┘  └──────────┘          │
└───────────────────────────────────────────────────────┘

┌───────────────────────────────────────────────────────┐
│              Cache Layer (Redis)                      │
│  - Prefix cache (prefix → top queries)                │
│  - Query frequency cache                              │
│  - User history cache                                 │
│  - Trending queries cache                             │
└───────────────────────────────────────────────────────┘

Deep Dive

Component Design

1. Trie Data Structure

Responsibilities:

  • Store all queries in Trie format
  • Enable fast prefix matching
  • Support ranking and scoring
  • Handle updates efficiently

Key Design Decisions:

  • Compressed Trie: Use compressed Trie (Patricia Trie) to save memory
  • In-Memory Storage: Keep Trie in memory for fast access
  • Lazy Loading: Load Trie on service startup
  • Periodic Updates: Update Trie periodically (every few minutes)

Trie Node Structure:

class TrieNode:
    def __init__(self):
        self.children = {}  # char -> TrieNode
        self.is_end = False
        self.queries = []  # List of (query, score) tuples
        self.max_score = 0

Implementation:

class AutocompleteTrie:
    def __init__(self):
        self.root = TrieNode()
        self.max_suggestions = 10
    
    def insert(self, query, score):
        node = self.root
        for char in query:
            if char not in node.children:
                node.children[char] = TrieNode()
            node = node.children[char]
            
            # Update max score along path
            node.max_score = max(node.max_score, score)
            
            # Add query to node's queries list
            node.queries.append((query, score))
            node.queries.sort(key=lambda x: x[1], reverse=True)
            node.queries = node.queries[:self.max_suggestions]
        
        node.is_end = True
    
    def search(self, prefix):
        node = self.root
        
        # Traverse to prefix node
        for char in prefix:
            if char not in node.children:
                return []
            node = node.children[char]
        
        # Return top queries from this node and descendants
        results = []
        self._collect_queries(node, results)
        
        # Sort by score and return top K
        results.sort(key=lambda x: x[1], reverse=True)
        return [q[0] for q in results[:self.max_suggestions]]
    
    def _collect_queries(self, node, results):
        if node.is_end:
            results.extend(node.queries)
        
        for child in node.children.values():
            self._collect_queries(child, results)

2. Autocomplete Service

Responsibilities:

  • Handle autocomplete requests
  • Query Trie or cache
  • Apply personalization
  • Rank and filter results
  • Return top K suggestions

Key Design Decisions:

  • Cache-First: Check cache before querying Trie
  • Personalization: Apply user-specific ranking
  • Debouncing: Client-side debouncing to reduce requests
  • Rate Limiting: Limit requests per user/IP

Implementation:

def get_autocomplete_suggestions(prefix, user_id=None, language='en', limit=5):
    # Check cache first
    cache_key = f"autocomplete:{prefix}:{language}"
    cached_results = cache.get(cache_key)
    
    if cached_results:
        suggestions = cached_results
    else:
        # Query Trie
        candidates = trie_service.search(prefix, language)
        
        # Rank candidates
        suggestions = rank_queries(candidates, prefix)
        
        # Cache results (TTL: 1 hour)
        cache.set(cache_key, suggestions, ttl=3600)
    
    # Apply personalization
    if user_id:
        suggestions = personalize_suggestions(suggestions, user_id)
    
    # Return top K
    return suggestions[:limit]

3. Ranking Algorithm

Challenge: Rank suggestions by relevance and popularity

Solution:

  • Multi-Factor Ranking: Combine multiple signals
  • Frequency: Higher frequency = higher score
  • Recency: Recent queries get boost
  • Click-Through Rate: Higher CTR = higher score
  • Query Length: Prefer shorter queries
  • Prefix Match: Exact prefix match gets boost

Implementation:

def rank_queries(candidates, prefix):
    scored_queries = []
    
    for query in candidates:
        score = calculate_score(query, prefix)
        scored_queries.append((query, score))
    
    # Sort by score
    scored_queries.sort(key=lambda x: x[1], reverse=True)
    
    return [q[0] for q in scored_queries]

def calculate_score(query, prefix):
    # Base score from frequency
    frequency_score = query.frequency / 1000000.0  # Normalize
    
    # Recency boost
    days_since_seen = (now() - query.last_seen).days
    recency_score = 1.0 / (1.0 + days_since_seen / 30.0)
    
    # Click-through rate
    ctr = query.click_count / max(query.frequency, 1)
    ctr_score = ctr * 2.0  # Boost CTR
    
    # Prefix match bonus
    prefix_match_bonus = 1.0 if query.normalized_query.startswith(prefix.lower()) else 0.5
    
    # Query length penalty (prefer shorter queries)
    length_penalty = 1.0 / (1.0 + len(query.query_text) / 20.0)
    
    # Combine scores
    total_score = (
        frequency_score * 0.4 +
        recency_score * 0.2 +
        ctr_score * 0.2 +
        prefix_match_bonus * 0.1 +
        length_penalty * 0.1
    )
    
    return total_score

4. Query Aggregator Service

Responsibilities:

  • Process query events from message queue
  • Update query frequencies
  • Update Trie data structure
  • Calculate trending queries
  • Invalidate cache

Key Design Decisions:

  • Batch Processing: Process queries in batches
  • Incremental Updates: Update Trie incrementally
  • Async Processing: Process asynchronously
  • Cache Invalidation: Invalidate affected prefixes

Implementation:

def process_query_events(events):
    # Group by query
    query_counts = {}
    for event in events:
        query = event['query']
        query_counts[query] = query_counts.get(query, 0) + 1
    
    # Update database in batch
    for query, count in query_counts.items():
        update_query_frequency(query, count)
    
    # Update Trie incrementally
    for query in query_counts.keys():
        query_data = get_query_data(query)
        trie_service.insert(query, query_data.score)
    
    # Invalidate cache for affected prefixes
    for query in query_counts.keys():
        invalidate_prefix_cache(query)

Detailed Design

Caching Strategy

Challenge: Cache autocomplete results for fast retrieval

Solution:

  • Prefix-Based Caching: Cache results for each prefix
  • Multi-Level Cache: L1 (in-memory), L2 (Redis)
  • Cache Warming: Pre-warm cache for popular prefixes
  • Smart Invalidation: Invalidate only affected prefixes

Cache Structure:

Key: "autocomplete:{prefix}:{language}"
Value: JSON array of top K queries
TTL: 1 hour

Implementation:

def get_cached_suggestions(prefix, language):
    cache_key = f"autocomplete:{prefix}:{language}"
    
    # Try L1 cache (in-memory)
    if cache_key in l1_cache:
        return l1_cache[cache_key]
    
    # Try L2 cache (Redis)
    cached = redis.get(cache_key)
    if cached:
        suggestions = json.loads(cached)
        l1_cache[cache_key] = suggestions  # Populate L1
        return suggestions
    
    return None

def cache_suggestions(prefix, language, suggestions):
    cache_key = f"autocomplete:{prefix}:{language}"
    
    # Store in both caches
    l1_cache[cache_key] = suggestions
    redis.setex(
        cache_key,
        3600,  # 1 hour TTL
        json.dumps(suggestions)
    )

Personalization

Challenge: Show personalized suggestions based on user history

Solution:

  • User Query History: Track user’s past queries
  • Boost User Queries: Boost queries from user’s history
  • Collaborative Filtering: Use similar users’ queries
  • Real-Time Personalization: Apply personalization in real-time

Implementation:

def personalize_suggestions(suggestions, user_id):
    # Get user's recent queries
    user_history = get_user_query_history(user_id, limit=100)
    user_queries = {q.query_text for q in user_history}
    
    # Boost user's queries
    personalized = []
    for query in suggestions:
        score = query.score
        
        # Boost if in user history
        if query.query_text in user_queries:
            score *= 1.5
        
        personalized.append((query, score))
    
    # Sort by personalized score
    personalized.sort(key=lambda x: x[1], reverse=True)
    
    return [q[0] for q in personalized]

Fuzzy Matching

Challenge: Handle typos and misspellings

Solution:

  • Edit Distance: Use Levenshtein distance
  • Fuzzy Trie: Extend Trie with fuzzy matching
  • Soundex/Metaphone: Use phonetic matching
  • Tolerance Level: Allow 1-2 character differences

Implementation:

def fuzzy_search(trie, prefix, max_distance=1):
    # Exact match first
    exact_results = trie.search(prefix)
    if exact_results:
        return exact_results
    
    # Fuzzy match
    fuzzy_results = []
    all_queries = trie.get_all_queries()
    
    for query in all_queries:
        distance = levenshtein_distance(prefix, query[:len(prefix)])
        if distance <= max_distance:
            fuzzy_results.append((query, distance))
    
    # Sort by distance
    fuzzy_results.sort(key=lambda x: x[1])
    
    return [q[0] for q in fuzzy_results[:10]]

Trie Update Strategy

Challenge: Update Trie efficiently as queries change

Solution:

  • Incremental Updates: Update Trie incrementally
  • Periodic Rebuild: Rebuild Trie periodically (every hour)
  • Versioning: Use versioned Trie for zero-downtime updates
  • Delta Updates: Apply only changes

Implementation:

def update_trie_incremental(query, score):
    # Insert into Trie
    trie_service.insert(query, score)
    
    # Update all prefixes
    for i in range(1, len(query) + 1):
        prefix = query[:i]
        update_prefix_cache(prefix)

def rebuild_trie_periodic():
    # Get all queries from database
    all_queries = get_all_queries_from_db()
    
    # Build new Trie
    new_trie = AutocompleteTrie()
    for query in all_queries:
        score = calculate_query_score(query)
        new_trie.insert(query.query_text, score)
    
    # Atomic swap
    trie_service.swap_trie(new_trie)

Scalability Considerations

Horizontal Scaling

Autocomplete Service:

  • Stateless service, horizontally scalable
  • Load balancer distributes requests
  • Each instance has its own Trie copy
  • Trie updates propagated to all instances

Trie Service:

  • Multiple instances with Trie replicas
  • Trie loaded on startup
  • Periodic updates sync across instances
  • In-memory Trie for fast access

Caching Strategy

Multi-Level Cache:

  • L1 Cache: In-memory cache per service instance
  • L2 Cache: Redis cluster for shared cache
  • Cache Warming: Pre-warm popular prefixes
  • Cache Invalidation: Smart invalidation on updates

Cache Hit Rate Target:

  • Target: 90%+ cache hit rate
  • Popular prefixes: 99%+ hit rate
  • Long-tail prefixes: 70%+ hit rate

Load Balancing

Request Distribution:

  • Round-robin or least-connections
  • Health checks for backend services
  • Circuit breakers for failing services

Geographic Distribution:

  • Deploy services in multiple regions
  • Route requests to nearest region
  • Regional Trie replicas

Security Considerations

Rate Limiting

  • Per-User Rate Limiting: Limit requests per user
  • Per-IP Rate Limiting: Limit requests per IP
  • Global Rate Limiting: Limit total requests
  • Sliding Window: Use sliding window algorithm

Input Validation

  • Query Sanitization: Sanitize user input
  • Length Limits: Limit query length
  • Character Validation: Validate characters
  • SQL Injection Prevention: Use parameterized queries

Privacy

  • User Data: Don’t store sensitive user data
  • Query Anonymization: Anonymize queries for analytics
  • GDPR Compliance: Comply with data privacy regulations

Monitoring & Observability

Key Metrics

System Metrics:

  • Request rate (QPS)
  • Response latency (p50, p95, p99)
  • Cache hit rate
  • Trie query time
  • Error rate

Business Metrics:

  • Total queries per day
  • Unique queries
  • Click-through rate
  • Average suggestions per query
  • Popular queries

Logging

  • Structured Logging: JSON logs for parsing
  • Request Tracing: Trace requests across services
  • Query Logging: Log queries (anonymized)
  • Performance Logging: Log slow queries

Alerting

  • High Latency: Alert if p99 latency > 100ms
  • Low Cache Hit Rate: Alert if hit rate < 80%
  • High Error Rate: Alert if error rate > 1%
  • Trie Update Failures: Alert on Trie update errors

Trade-offs and Optimizations

Trade-offs

1. Trie Storage: Full vs Compressed

  • Full Trie: Faster queries, more memory
  • Compressed Trie: Less memory, slightly slower
  • Decision: Compressed Trie for memory efficiency

2. Cache TTL: Short vs Long

  • Short (5 min): More accurate, higher load
  • Long (1 hour): Lower load, less accurate
  • Decision: 1 hour TTL with smart invalidation

3. Personalization: Real-Time vs Batch

  • Real-Time: Better UX, higher latency
  • Batch: Lower latency, less personalized
  • Decision: Real-time with caching

4. Trie Update: Incremental vs Full Rebuild

  • Incremental: Lower latency, more complex
  • Full Rebuild: Simpler, higher latency
  • Decision: Incremental updates with periodic rebuild

Optimizations

1. Trie Compression

  • Use compressed Trie (Patricia Trie)
  • Reduce memory usage by 50-70%
  • Slight performance trade-off

2. Batch Processing

  • Batch query updates
  • Batch cache updates
  • Reduce database load

3. Precomputation

  • Precompute top K for popular prefixes
  • Precompute trending queries
  • Reduce computation at query time

4. Connection Pooling

  • Reuse database connections
  • Reuse Redis connections
  • Reduce connection overhead

What Interviewers Look For

Data Structures Knowledge

  1. Trie Implementation
    • Trie vs compressed Trie (Patricia Trie)
    • Memory efficiency
    • Prefix matching algorithm
    • Red Flags: No Trie knowledge, inefficient structure, wrong choice
  2. Algorithm Efficiency
    • O(m) prefix search where m = prefix length
    • Efficient updates
    • Memory optimization
    • Red Flags: Inefficient search, slow updates, high memory
  3. Ranking Algorithm
    • Multi-factor ranking
    • Frequency, recency, CTR
    • Personalization
    • Red Flags: Simple ranking, no personalization, poor algorithm

Distributed Systems Skills

  1. Caching Strategy
    • Multi-level caching (L1, L2)
    • Cache warming
    • High hit rate design
    • Red Flags: No caching, low hit rate, poor strategy
  2. Real-Time Updates
    • Incremental Trie updates
    • Periodic rebuilds
    • Red Flags: Full rebuild only, no incremental, slow updates
  3. Scalability Design
    • Trie replication
    • Horizontal scaling
    • Red Flags: Single instance, no scaling, bottlenecks

Problem-Solving Approach

  1. Latency Optimization
    • Sub-100ms target
    • Caching strategy
    • Precomputation
    • Red Flags: High latency, no optimization, slow queries
  2. Memory Efficiency
    • Compressed Trie
    • Bounded memory
    • Red Flags: High memory usage, no compression
  3. Edge Cases
    • Long prefixes
    • Rare queries
    • Cache misses
    • Red Flags: Ignoring edge cases, no handling

System Design Skills

  1. Component Design
    • Autocomplete service
    • Ranking service
    • Clear boundaries
    • Red Flags: Monolithic, unclear boundaries
  2. Update Strategy
    • Incremental updates
    • Batch processing
    • Red Flags: Full rebuild, no batching
  3. Personalization
    • User history tracking
    • Personalized ranking
    • Red Flags: No personalization, one-size-fits-all

Communication Skills

  1. Algorithm Explanation
    • Can explain Trie operations
    • Understands ranking
    • Red Flags: No understanding, vague explanations
  2. Trade-off Analysis
    • Memory vs speed
    • Accuracy vs latency
    • Red Flags: No trade-offs, dogmatic choices

Meta-Specific Focus

  1. Data Structures Mastery
    • Deep Trie understanding
    • Algorithm optimization
    • Key: Show CS fundamentals
  2. Search Systems Expertise
    • Autocomplete knowledge
    • Ranking algorithms
    • Key: Demonstrate search expertise

Summary

Designing a search autocomplete system at scale requires careful consideration of:

  1. Trie Data Structure: Efficient prefix matching with compressed Trie
  2. Caching Strategy: Multi-level caching for sub-100ms latency
  3. Ranking Algorithm: Multi-factor ranking combining frequency, recency, CTR
  4. Personalization: User-specific ranking based on history
  5. Scalability: Horizontal scaling with Trie replication
  6. Performance: Sub-100ms response time with 90%+ cache hit rate
  7. Real-Time Updates: Incremental Trie updates with periodic rebuilds
  8. Fault Tolerance: Handle failures gracefully

Key architectural decisions:

  • Compressed Trie for memory efficiency
  • Multi-Level Cache (L1 in-memory, L2 Redis) for performance
  • Incremental Updates with periodic rebuilds
  • Personalization based on user history
  • Horizontal Scaling with Trie replication
  • Batch Processing for query updates

The system handles 100,000 QPS with sub-100ms latency and maintains high accuracy through intelligent ranking and personalization.