Telemetry Rollups

How telemetry rollups work

Telemetry rollups transform granular time-series measurements into consolidated summaries through windowed aggregation. The process typically involves:

1. Defining time buckets (e.g., 1-minute, 5-minute, hourly)
2. Specifying aggregation functions
3. Automatically rolling up raw data into summary statistics

Key benefits of rollups

Telemetry rollups provide several advantages for managing high-volume sensor and device data:

Storage optimization

By consolidating raw measurements into summary statistics, rollups dramatically reduce storage requirements while maintaining analytical value. This is especially important for high-frequency sensor data.

Query performance

Querying pre-aggregated rollups is significantly faster than processing raw data, enabling quick analysis across large time ranges.

Data lifecycle management

Rollups facilitate tiered storage strategies where detailed data can be aged out while maintaining historical summaries.

Implementation considerations

Time bucket selection

The choice of rollup intervals affects both storage requirements and query granularity. Common approaches include:

Aggregation functions

Typical rollup calculations include:

• Statistical measures (mean, median, standard deviation)
• Range indicators (min, max)
• Counts and rates
• Custom domain-specific aggregations

Data retention policies

Organizations often implement multi-tiered retention:

• Raw data: Short retention (hours/days)
• Detailed rollups: Medium retention (weeks/months)
• Coarse rollups: Long retention (months/years)

Real-world applications

Telemetry rollups are essential in various domains:

Industrial monitoring

Manufacturing facilities use rollups to track equipment performance while managing data volume from thousands of sensors.

Infrastructure monitoring

Cloud providers aggregate millions of performance metrics across their infrastructure using automated rollup processes.

IoT analytics

IoT platforms leverage rollups to provide insights across large device fleets while optimizing storage and query costs.

Best practices

1. Align rollup intervals with analysis needs
2. Consider data variability when choosing aggregation methods
3. Document rollup calculations for data lineage
4. Monitor rollup performance and storage impacts
5. Validate rollup accuracy through sampling

Summary

Telemetry rollups are a crucial mechanism for managing high-volume time-series data, enabling efficient storage and analysis while preserving valuable insights. By carefully considering implementation factors and following best practices, organizations can effectively balance data granularity, storage costs, and analytical capabilities.