Event Time
Event time refers to the moment when a data point was actually created or when an event occurred, as opposed to when it was processed or received by a system. This concept is fundamental to time-series data processing, especially in systems handling real-world events where timing accuracy is crucial.
Understanding event time vs processing time
Event time represents the true timestamp of when something happened in the real world. This differs from processing time, which is when the data is actually handled by the system. This distinction is crucial for:
- Financial market data where trade execution times matter
- Industrial sensor readings where precise measurement timing is essential
- Audit trails requiring accurate event sequencing
Next generation time-series database
QuestDB is an open-source time-series database optimized for market and heavy industry data. Built from scratch in Java and C++, it offers high-throughput ingestion and fast SQL queries with time-series extensions.
Why event time matters
Event time is critical for:
- Temporal accuracy: Ensuring events are analyzed in their true chronological order
- Data consistency: Maintaining the correct sequence of related events
- Business logic: Supporting time-sensitive operations and analysis
For example, in financial markets, the exact timing of trades impacts:
- Price discovery
- Market surveillance
- Regulatory compliance
- Performance analysis
Next generation time-series database
QuestDB is an open-source time-series database optimized for market and heavy industry data. Built from scratch in Java and C++, it offers high-throughput ingestion and fast SQL queries with time-series extensions.
Handling event time challenges
Several challenges arise when working with event time:
Out-of-order events
Data points may arrive in a different order than they occurred, requiring systems to handle late arriving data:
Time skew
Different data sources may have varying clock synchronization, requiring:
- Timestamp normalization
- Clock drift compensation
- Synchronization protocols
Event time in streaming systems
Modern streaming systems must maintain both event time and processing time contexts:
- Windowing operations: Grouping events by their actual occurrence time
- State management: Tracking event order and handling late arrivals
- Watermarking: Setting bounds for late arriving data
This enables accurate:
- Time-series analytics
- Event correlation
- Temporal pattern detection
The relationship between these components ensures robust handling of real-world timing complexities while maintaining data accuracy and analytical integrity.