Retention-aware Queries

How retention-aware queries work

Retention-aware queries leverage metadata about data retention periods and partition boundaries to optimize query execution. When processing a query, the database engine automatically:

1. Identifies relevant time ranges based on retention policies
2. Excludes expired or out-of-retention partitions
3. Optimizes partition pruning based on temporal boundaries
4. Adjusts query plans to account for data availability

Benefits for time-series workloads

Retention-aware queries provide several key advantages for time-series databases:

Improved query performance

By automatically excluding expired data partitions and optimizing for retention boundaries, these queries reduce unnecessary I/O and processing overhead. The storage engine can skip entire partitions that fall outside the retention period.

Resource optimization

Retention-aware queries help prevent wasteful processing of expired data, leading to better resource utilization. This is especially important for systems with real-time data ingestion where efficient processing of current data is critical.

Automatic compliance

These queries help enforce data retention policies by naturally working within defined retention boundaries, supporting compliance requirements and data lifecycle management.

Implementation considerations

Retention policy integration

Retention-aware queries require tight integration between the query engine and retention policy management:

SELECT *
FROM trades
WHERE timestamp > dateadd('d', -30, now())
SAMPLE BY 1h;

This query automatically considers the 30-day retention period, optimizing execution accordingly.

Partition alignment

For optimal performance, partition boundaries should align with retention policies. This enables more efficient partition pruning and query optimization.

Monitoring and optimization

Systems should track:

• Query performance relative to retention boundaries
• Partition access patterns
• Resource utilization across retention periods

Real-world applications

Financial data management

Financial institutions use retention-aware queries to efficiently process market data while maintaining compliance with retention requirements:

SELECT symbol, avg(price)
FROM trades_latest_1d
WHERE timestamp > dateadd('h', -24, now())
SAMPLE BY 5m;

Industrial monitoring

Manufacturing systems leverage these queries for efficient analysis of sensor data while managing storage costs:

SELECT avg(tempF)
FROM weather
WHERE timestamp > dateadd('d', -7, now())
SAMPLE BY 1h;

These examples demonstrate how retention-aware queries combine efficient data access with proper lifecycle management, making them essential for modern time-series applications.