Ingestion Interval
Ingestion interval defines the frequency at which a time-series database accepts and processes new data points. This critical configuration determines how often data is written to the database, affecting everything from system performance to data freshness and resource utilization.
Understanding ingestion intervals
Ingestion intervals establish the temporal boundaries for data acceptance in time-series systems. These intervals can range from milliseconds for high-frequency trading data to hours for less time-sensitive industrial metrics. The choice of interval directly impacts:
- System resource consumption
- Write throughput capabilities
- Data consistency guarantees
- Real-time analysis potential
For example, a trading system might require millisecond ingestion intervals to capture market microstructure, while an industrial sensor network might operate with minute-level intervals.
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.
Types of ingestion intervals
Fixed intervals
Fixed intervals maintain consistent timing between ingestion operations. This predictability helps with:
- Resource planning
- Performance optimization
- System monitoring
- Capacity management
Variable intervals
Variable or adaptive intervals adjust based on:
- Current system load
- Data volume
- Priority levels
- Resource availability
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 system performance
The choice of ingestion interval significantly affects database performance:
Write amplification
Shorter intervals can lead to increased write amplification, as the system performs more frequent disk operations.
Resource utilization
Interval settings influence:
- Memory usage
- CPU consumption
- Storage I/O patterns
- Network bandwidth requirements
Data freshness tradeoffs
Best practices for interval configuration
Factors to consider
- Data characteristics
- Volume
- Velocity
- Variability
- System capabilities
- Application requirements
- Resource constraints
Optimization strategies
- Align intervals with batch ingestion patterns
- Consider backpressure handling requirements
- Monitor and adjust based on performance metrics
- Balance real-time needs with system efficiency
Common use cases
Financial markets
- Sub-millisecond intervals for high-frequency trading
- Second-level intervals for market data aggregation
- Minute-level intervals for analytical processing
Industrial systems
- Regular intervals for sensor data collection
- Scheduled intervals for equipment monitoring
- Event-driven intervals for alarm conditions
IoT applications
- Battery-optimized intervals for remote devices
- Network-aware intervals for distributed sensors
- Adaptive intervals based on activity levels
Monitoring and maintenance
Key metrics to track
- Ingestion latency
- Queue depth
- Resource utilization
- System backpressure
Adjustment strategies
- Dynamic interval scaling
- Load-based adaptation
- Performance-driven optimization
- Resource-aware scheduling
The proper configuration of ingestion intervals is crucial for building efficient time-series data systems that balance performance, resource utilization, and data freshness requirements.