Meaning
A technique involves collecting and transmitting only a subset of performance data or logs from a running application. Using telemetry sampling reduces the volume of data that must be stored and processed by monitoring systems. This helps manage the financial cost and network overhead associated with observability platforms.
Selection Method
Several algorithms can be used to decide which data points to keep and which to discard. In telemetry sampling, approaches include rate limiting, where only a fixed number of events are kept per second, or adaptive sampling, which keeps a higher percentage of rare events to ensure they are captured. This ensures that the most interesting data is not lost while common events are heavily reduced.
Data Retention
This selective retention of data points is critical for keeping monitoring costs manageable while still providing a clear view of system performance. With telemetry sampling, some information loss is expected, but the overall trends and patterns in the data remain visible. This allows for effective alerting and performance monitoring without the expense of storing every single event.
Operational Tradeoff
Engineers must carefully choose their sampling rates to avoid missing rare errors or transient issues. When telemetry sampling is configured too aggressively, it can make it difficult to troubleshoot intermittent problems because the relevant logs or traces may not have been collected. This requires a choice between the cost of data storage and the need for high-resolution diagnostic data when things go wrong, which must be evaluated regularly as the system grows.
