Skip to content

Latest commit

 

History

History
162 lines (115 loc) · 5.65 KB

Chapter11_Notes.md

File metadata and controls

162 lines (115 loc) · 5.65 KB

Chapter 11 - Design a autocomplete/typeahead service (Summary)

Mindmap for designing a autocomplete/typeahead service

System Design Architecture for Autocomplete

1. High-Level Architecture

The autocomplete system is composed of three major components:

  1. Data Ingestion System: Handles the logging and capture of user inputs.
  2. Data Processing Pipeline: Converts raw data into a structured format, such as a weighted trie.
  3. Query System: Interfaces with the user, processes input, and retrieves suggestions.

2. Component Details

2.1. Data Ingestion
  • Purpose: Collect and store user queries at high scale.
  • Implementation Options:
    • Shared Logging Service: Handles logs for multiple services, not just autocomplete.
      • Examples: Kafka, Elasticsearch, or Logstash.
    • Data Storage: Retain raw logs in HDFS or similar distributed systems.
  • Scalability Features:
    • Partitioning: Logs partitioned by topic (e.g., search) and timestamp.
    • Horizontal Scaling: Allows handling spikes in user input.

Key Design Pattern:

  • Command Query Responsibility Segregation (CQRS): Separate the ingestion (write-heavy) system from the query (read-heavy) system.

#DataIngestion #CQRS #Scalability

2.2. Data Processing
  • Goal: Transform raw logs into structured, meaningful data for autocomplete.
  • Key Stages:
    1. Log Fetching: Retrieve logs from sources like Kafka or Elasticsearch.
    2. Preprocessing:
      • Split search strings into words.
      • Filter inappropriate content using blacklists.
      • Normalize data (e.g., convert to lowercase).
    3. Word Count:
      • Aggregate word frequencies using MapReduce or Count-Min Sketch algorithms.
    4. Weighted Trie Generation:
      • Use word counts to assign weights to trie nodes.
      • Serialize the trie into JSON for storage and querying.

Design Pattern:

  • Batch ETL (Extract, Transform, Load):
    • Independent pipeline stages ensure modularity and easier debugging.
    • Example Frameworks: Apache Spark, Hive.

Optimization:

  • Sampling: Process only a subset of data for lower resource usage.
  • Rollup: Aggregate logs by hour, day, or month to reduce storage needs.

#DataProcessing #BatchETL #WeightedTrie

2.3. Query System
  • Purpose: Serve user requests by providing autocomplete suggestions in real time.
  • Key Operations:
    1. Receive partial input from the user (via a frontend interface).
    2. Match input against the weighted trie.
    3. Retrieve the top suggestions based on weight and relevance.
  • Performance Requirements:
    • P99 Latency: ~100ms.
    • Ensure low-latency trie lookups using in-memory storage.

Deployment:

  • Host trie in memory on backend servers (or distribute via CDNs for faster access).
  • Update trie periodically (e.g., daily) to reflect recent trends.

#QuerySystem #LowLatency #RealTime

3. Architectural Patterns

3.1. Weighted Trie
  • Functionality: Efficiently stores strings and retrieves suggestions based on input prefixes.
  • Advantages:
    • Fast lookups for user input.
    • Compact storage due to shared prefixes.
  • Trie Node Structure: 
    TrieNode:
  - Children: Links to child nodes.
  - Weight: Frequency or relevance score.
3.2. Batch Processing Pipeline
  • Purpose: Separate processing from querying to reduce complexity and increase scalability.
  • Implementation:
    • Use tools like Apache Spark or Hadoop for parallel processing.
    • Modularity: Each processing step (e.g., filtering, counting) is implemented as an independent task.
3.3. Content Moderation
  • Objective: Prevent inappropriate suggestions.
  • Approaches:
    • Maintain blacklists/whitelists in a database.
    • Use distributed joins to filter content during the word processing stage.
    • Incorporate machine learning for advanced filtering.
3.4. Sampling
  • Goal: Reduce resource consumption without compromising accuracy.
  • Stages for Sampling:
    1. Raw log entries.
    2. Processed words post-filtering.
    3. Popular words after frequency analysis.

#WeightedTrie #ContentModeration #Sampling

4. Advanced Features

  • Personalization:
    • Enhance user experience by tailoring suggestions based on user-specific history.
    • Requires integration of user profiles and demographics.
  • Handling Phrases:
    • Extend trie to store and suggest multi-word phrases.
    • Keep trie size manageable by including only top phrases.
  • Real-Time Updates:
    • Introduce a Lambda Architecture with separate batch and streaming pipelines for real-time trie updates.

#Personalization #RealTimeUpdates #PhraseHandling

5. Key Takeaways

  • Separate ingestion, processing, and querying to maintain modularity.
  • Leverage batch processing for scalability and maintainability.
  • Optimize for low-latency, high-throughput user interactions.
  • Use weighted tries for efficient and compact storage.
  • Design for future extensions (personalization, phrase handling, real-time updates).

References:

  1. Trie
  2. Bloom Filters
  3. Bloom Filter Code
  4. count-min sketch
  5. Map Reduce - Filter, Map, Reduce explained in less than 2 minutes
  6. A/B testing and multi-armed bandit