I am developing a GridDB-based application that ingests large volumes of time-series data from IoT devices. My application uses TQL (GridDB Query Language) to query and aggregate this data in real-time. However, as the dataset grows, some queries—especially those performing aggregation—are becoming performance bottlenecks. For example, I use a query like:

SELECT COUNT(*), AVG(temperature)
FROM SensorData   WHERE timestamp >= '2025-03-01T00:00:00Z'

The query execution time increases noticeably with higher data volumes.

I have two specific questions:

1. What are the best practices to optimize TQL queries for high-frequency data ingestion? Should I be adjusting indexing or container partitioning strategies to improve performance?
2. Are there recommended query design patterns that can help minimize latency when filtering and aggregating massive time-series datasets in GridDB?

Additional Context:

I have read through the GridDB documentation on TQL and container design, but I haven’t found comprehensive guidance on query optimization for high ingestion rates. Insights, recommendations, or sample queries proven effective in similar environments would be extremely helpful. Any detailed explanation or code examples would be greatly appreciated.