Delayed Delivery
Delayed delivery refers to the phenomenon where time-series data arrives later than its event timestamp indicates. This latency between event occurrence and data arrival requires specific handling mechanisms in time-series databases and streaming systems to maintain data accuracy and consistency.
Understanding delayed delivery
Delayed delivery occurs when there's a gap between when an event happens and when its data reaches the processing system. This delay can range from milliseconds to hours, depending on factors like network conditions, device connectivity, or system load.
Common causes of delayed delivery
- Network latency and congestion
- Device connectivity issues
- Buffer queuing at various system points
- System processing overhead
- Physical distance between source and destination
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.
Impact on data systems
Delayed delivery affects several aspects of time-series data processing:
Data consistency
Systems must maintain temporal consistency while handling late-arriving data. This often requires specialized mechanisms like out-of-order ingestion and backfill capabilities.
Query accuracy
Historical queries need to account for potential late arrivals, especially when dealing with real-time analytics and aggregations.
Resource utilization
Systems must allocate resources to handle both current and delayed data, potentially impacting write throughput and overall performance.
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 strategies
Buffering and reordering
Systems often implement ingestion buffers to temporarily store and reorder data based on event timestamps rather than arrival time.
Watermarking
Using watermarking to track the progress of event time and manage the trade-off between completeness and latency.
Late arrival policies
Configurable policies determine how to handle data that arrives after its expected window:
- Reject late data
- Update existing aggregations
- Maintain separate late arrival statistics
Monitoring and optimization
Effective management of delayed delivery requires:
- Tracking delivery latency patterns
- Monitoring ingestion latency
- Adjusting buffer sizes and timeout parameters
- Implementing retry mechanisms for failed deliveries
Industrial applications
In industrial settings, delayed delivery handling is crucial for:
- Manufacturing sensor data collection
- Supply chain tracking systems
- Industrial process monitoring
- Equipment telemetry analysis
The system must balance real-time processing needs with data completeness requirements, especially in scenarios involving industrial IoT (IIoT) data.