bc-code-intelligence-mcp
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Markdown
---
title: "Telemetry Performance Considerations"
domain: "dean-debug"
difficulty: "advanced"
bc_versions: "14+"
tags: ["telemetry", "performance", "optimization", "monitoring"]
prerequisites: ["bc-session-logmessage-methods", "custom-dimensions-best-practices"]
samples: "samples/telemetry-performance.md"
---
Telemetry implementation significantly impacts system performance through CPU utilization, memory consumption, and network overhead. Understanding these performance characteristics enables effective telemetry design that balances diagnostic capability with system efficiency.
Telemetry overhead scales with event frequency, custom dimension complexity, and verbosity levels. High-volume telemetry scenarios require careful optimization to prevent performance degradation in production environments.
LogMessage calls incur CPU overhead for message string formatting regardless of verbosity filtering. Complex message formatting with string concatenation or expensive calculations impacts performance even when events are ultimately filtered.
Optimize message formatting by using simple string operations and pre-calculating expensive values. Consider lazy evaluation patterns for complex message content that may not be needed in all scenarios.
Custom dimension serialization consumes CPU resources proportional to dimension count and value complexity. Large dimension collections or complex dimension values can significantly impact telemetry performance.
Minimize dimension processing overhead by pre-formatting dimension values, reusing dimension collections, and avoiding expensive calculations within dimension generation code.
Telemetry performance impact scales directly with event frequency. High-frequency telemetry in performance-critical code paths can measurably impact overall system performance.
Consider telemetry frequency when implementing logging in loops, frequent business operations, or real-time processing scenarios. Use sampling strategies to reduce event volume while preserving analytical value.
The telemetry system buffers events before transmission, consuming memory proportional to event volume and buffering duration. High-volume telemetry can increase memory pressure, particularly in memory-constrained environments.
Monitor telemetry memory utilization and consider event volume in memory capacity planning. Implement backpressure mechanisms if telemetry volume threatens system memory stability.
Custom dimensions consume memory for key-value pair storage and serialization. Large dimension collections or string-heavy dimension values can contribute significantly to overall memory utilization.
Optimize dimension memory usage by using efficient data types, minimizing string allocations, and reusing dimension objects where possible.
Correlation mechanisms may maintain in-memory state to link related telemetry events across time. Long-running correlations or high-cardinality correlation scenarios can impact memory utilization.
Telemetry events consume network bandwidth for transmission to monitoring systems. High-volume telemetry can impact network performance, particularly in bandwidth-constrained environments.
Consider telemetry network impact in overall network capacity planning. Implement local buffering and batch transmission strategies to optimize network utilization efficiency.
Large telemetry volumes can overwhelm monitoring systems and impact query performance in Application Insights or other telemetry backends. Consider backend capacity when designing telemetry volume strategies.
Telemetry events consume storage space in monitoring systems with associated cost implications. Balance diagnostic capability with storage costs and retention requirements.
Use verbosity levels strategically to control telemetry volume in production environments. Filter high-verbosity events in production while maintaining essential diagnostic capability.
Implement telemetry sampling for high-volume scenarios to reduce data volume while preserving statistical significance. Consider business-aware sampling that maintains critical event capture.
Implement conditional telemetry patterns that enable detailed logging during specific scenarios without permanent performance overhead. Use feature flags or configuration settings to control telemetry behavior.
Group telemetry events into batches where possible to optimize transmission efficiency and reduce per-event overhead. Balance batching benefits with real-time monitoring requirements.
Monitor telemetry system performance including event volume, processing latency, and resource utilization. Telemetry overhead should be measured and tracked as part of overall system performance.
Regularly analyze telemetry performance impact through load testing and production monitoring. Understand telemetry overhead trends and optimize configurations based on actual impact measurements.