Design streaming mention analytics with search and alerts
System Design: Streaming Mention Analytics with Search and Alerts
Design a high-level system to ingest, process, and analyze a real-time stream of social media posts and news articles. The system must track mentions of companies/stocks (referred to as "entities") and provide various functionalities, including real-time analytics, search capabilities, and user-configurable alerts.
Requirements
Data Ingestion and Processing
- Input Stream: Ingest a real-time external stream of social-media posts and news articles.
- Data Structure: Each item contains raw text and metadata (timestamp, source, author/site, etc.).
- Entity Tracking: The system must identify and track mentions of companies/stocks ("entities") within the ingested content.
Analytics and Visualization
- Mention Analytics: Provide analytics on how many times each entity was mentioned over time, similar to impressions or mentions counts.
- Time-Windowed Charts: Users must be able to choose time spans for charts, ranging from 30 minutes to multiple days. Both tumbling and sliding windows are acceptable for aggregation.
- Latency: Chart data may be delayed by 10–30 minutes, but all data must be fully aggregated before display.
Search Functionality
- Entity Search: Users can search across hundreds of thousands of entities by company or stock name.
- Document Search: Users can search for the underlying documents (posts/articles) that mention specific entities.
- Flexible Search: Search must support any number of keywords and filtering by criteria such as entity, time range, and source.
- High Search Load: The search subsystem must handle a very high query load, potentially up to ~100k requests per second (RPS).
Subscriptions and Alerts
- User Subscriptions: Users can follow a specific set of entities.
- Filtered Analytics: Analytics views should be filterable to show only data for followed entities.
- Configurable Alerts: Users can configure alerts based on specific conditions, such as a spike in mentions for a followed entity.
System Characteristics
- Spiky Traffic Handling: The system must be robust and scalable enough to handle extreme bursts of incoming data and user activity, particularly during breaking news or "meme-stock" events.
- Storage Decisions: The design must explicitly address how both processed/aggregated data and raw documents will be stored.
Task
Design a high-level system architecture, including APIs, data flow, storage mechanisms, and a scaling strategy, that satisfies all the above requirements. Your explanation should clearly detail:
- How raw streaming data is ingested, processed, and aggregated.
- How time-windowed analytics are computed and served to users.
- How document and entity search functions at high queries per second (QPS).
- How user subscriptions and alerting mechanisms are implemented.
- How the system maintains reliability and cost-effectiveness, especially when subjected to traffic spikes.
Be sure to state any assumptions you make and discuss key trade-offs considered (e.g., consistency models, latency targets, storage formats, data retention policies).
AI interview prep
Core dilemma
Balancing the low-latency requirements for real-time stream processing and high-QPS search with the need for robust, scalable, and cost-effective data aggregation and indexing under unpredictable, spiky traffic.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Design streaming mention analytics with search and alerts
System Design: Streaming Mention Analytics with Search and Alerts
Design a high-level system to ingest, process, and analyze a real-time stream of social media posts and news articles. The system must track mentions of companies/stocks (referred to as "entities") and provide various functionalities, including real-time analytics, search capabilities, and user-configurable alerts.
Requirements
Data Ingestion and Processing
- Input Stream: Ingest a real-time external stream of social-media posts and news articles.
- Data Structure: Each item contains raw text and metadata (timestamp, source, author/site, etc.).
- Entity Tracking: The system must identify and track mentions of companies/stocks ("entities") within the ingested content.
Analytics and Visualization
- Mention Analytics: Provide analytics on how many times each entity was mentioned over time, similar to impressions or mentions counts.
- Time-Windowed Charts: Users must be able to choose time spans for charts, ranging from 30 minutes to multiple days. Both tumbling and sliding windows are acceptable for aggregation.
- Latency: Chart data may be delayed by 10–30 minutes, but all data must be fully aggregated before display.
Search Functionality
- Entity Search: Users can search across hundreds of thousands of entities by company or stock name.
- Document Search: Users can search for the underlying documents (posts/articles) that mention specific entities.
- Flexible Search: Search must support any number of keywords and filtering by criteria such as entity, time range, and source.
- High Search Load: The search subsystem must handle a very high query load, potentially up to ~100k requests per second (RPS).
Subscriptions and Alerts
- User Subscriptions: Users can follow a specific set of entities.
- Filtered Analytics: Analytics views should be filterable to show only data for followed entities.
- Configurable Alerts: Users can configure alerts based on specific conditions, such as a spike in mentions for a followed entity.
System Characteristics
- Spiky Traffic Handling: The system must be robust and scalable enough to handle extreme bursts of incoming data and user activity, particularly during breaking news or "meme-stock" events.
- Storage Decisions: The design must explicitly address how both processed/aggregated data and raw documents will be stored.
Task
Design a high-level system architecture, including APIs, data flow, storage mechanisms, and a scaling strategy, that satisfies all the above requirements. Your explanation should clearly detail:
- How raw streaming data is ingested, processed, and aggregated.
- How time-windowed analytics are computed and served to users.
- How document and entity search functions at high queries per second (QPS).
- How user subscriptions and alerting mechanisms are implemented.
- How the system maintains reliability and cost-effectiveness, especially when subjected to traffic spikes.
Be sure to state any assumptions you make and discuss key trade-offs considered (e.g., consistency models, latency targets, storage formats, data retention policies).