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Optimize Program Performance and Reduce Latency
Profile analysis helps identify the exact functions and code lines causing performance bottlenecks (such as high CPU usage). By measuring execution times, call relationships, and memory usage of functions and methods within the application, it quickly locates which parts of the program or system consume the most time and resources, thereby improving performance and efficiency.
Code-Level Analysis
- Guance presents stack data in the form of flame graphs, making it easy to identify resource-intensive code paths and hotspots that affect user experience. It supports Python, Java, Go, and C/C++.
- It supports analysis of CPU, memory, or IO usage at the method level across different code types. Additionally, it allows analysis of execution time and proportion from various dimensions, helping us identify how code impacts application performance in production.
- It supports analysis of CPU, memory, or IO usage at the method level across different code types. Additionally, it allows analysis of execution time and proportion from various dimensions, helping us identify how code impacts application performance in production.
Trace Correlation Profile
Observability Cloud provides span-level correlation data analysis. When analyzing performance and viewing link spans, it automatically retrieves code hotspot information for the corresponding time period. This allows for quick viewing of the list of related code method calls and their execution time proportions, helping to identify the root cause of slow data requests.
