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Analysis Runtime

The Analysis Runtime provides visibility and control over the resource usage and performance metrics of your analysis scripts within the TagoIO platform. Proper configuration and monitoring of analysis runtime parameters are essential for optimizing operational costs and maintaining system performance. Analyses are billed primarily based on their execution duration and the memory consumed during runtime.

Memory Configuration​

Memory allocation is a critical factor that directly impacts both the performance and cost of your analyses. Each analysis execution is provisioned with a configurable memory limit, which determines the maximum amount of RAM available during runtime. Consuming more memory, especially when processing large datasets or performing complex calculations, will result in higher costs.

  • Maximum Allocation: The highest memory limit you can assign to an analysis is 10GB. This is intended for advanced analytics or workloads that require substantial in-memory processing.
  • Default Allocation: By default, each analysis is allocated 512MB of memory. This configuration is typically sufficient for standard operations and applications that do not require intensive data processing.

Carefully assess the memory requirements of your analysis scripts. Allocate only as much memory as needed to prevent unnecessary cost increases. Monitor memory consumption patterns, especially after changes to data retrieval or processing logic.

Runtime Metrics​

To help you manage and optimize your analyses, the platform provides real-time metrics for both Python and Node.js runtimes:

Invocation Count​

This metric tracks the number of analysis executions initiated over a given period. Monitoring invocation trends can help you identify usage patterns, optimize scheduling, and anticipate scaling needs.

Execution Duration​

This metric records the average time (in seconds) taken for analyses to complete their execution. Tracking duration helps you detect performance bottlenecks, optimize script efficiency, and manage resource allocation more effectively.

Cost and Performance Optimization​

Since analysis billing is based on both execution duration and memory consumption, it is important to:

  • Regularly review runtime metrics to identify inefficiencies or unexpected resource usage.
  • Tune memory allocation settings according to the actual requirements of your analyses.
  • Refactor scripts to minimize execution time and avoid unnecessary data processing or retrieval.
  • Set up alerts or monitoring thresholds to proactively manage costs and ensure stable operations.