Code Profiling Call Stack Linking for Low-Overhead Performance Analysis
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Solution Overview
Problem
Existing systems lack effective methods for real-time monitoring and visualization of call stack data to identify performance issues and optimize function execution in computing environments.
Innovation Solution
Capturing and indexing call stack data, including span IDs and trace IDs, to generate visualizations such as flamegraphs, allowing for the retrieval and analysis of function-related data based on query criteria to identify performance bottlenecks and optimize function execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If call stack data is captured and indexed for real-time monitoring, then system performance visibility is improved, but data processing overhead increases
Solution Approach 1:
The patent applies preliminary action by pre-indexing call stack data during function execution, organizing span IDs and trace IDs into searchable structures before queries are issued. This upfront preparation reduces the computational burden during real-time monitoring queries, resolving the contradiction between monitoring precision and processing overhead.
Solution Approach 2:
The patent creates simplified copies of call stack data in indexed formats (span IDs, trace IDs) that can be quickly queried without accessing the full raw call stack data. These copied representations enable efficient performance monitoring while minimizing the processing overhead of handling complete call stack information.
2Measurement precision
If detailed call stack data is captured for analysis, then performance bottleneck identification is improved, but data storage requirements increase
Solution Approach 1:
The patent extracts only the essential identifying elements (span IDs and trace IDs) from complete call stack data for storage and indexing. This extraction approach maintains the ability to accurately identify performance bottlenecks through these identifiers while significantly reducing storage requirements compared to retaining full call stack information.
Solution Approach 2:
The patent segments call stack data into discrete, indexable units (individual span IDs and trace IDs) that can be stored and queried independently. This segmentation enables efficient storage of performance data by breaking down complex call stack information into manageable, reusable components that can be cached and reused across multiple queries.
3Productivity
If real-time call stack snapshots are captured, then function execution monitoring is improved, but system resource consumption increases
Solution Approach 1:
The patent applies partial action by capturing only the essential elements needed for performance monitoring (span IDs and trace IDs) rather than complete call stack state information. This partial capture approach provides sufficient data for identifying performance issues while reducing the resource consumption associated with capturing and processing full call stack snapshots in real-time.
Data Source
AI summary
Described are systems, methods, and techniques profiled call stack linking. Data relating to functions that are part of call stacks can be captured from a series of snapshots. Frame information for the identified functions (e.g., a span ID, trace ID) can be identified and indexed. Responsive to receiving a query for a visualization specifying one or more criteria (e.g., all frames that are part of a span), all frames corresponding with the criteria can be identified. An action can be performed using the identified frames, such as generating a visualization of the identified frames for use in deriving insights into the functions being executed as part of a call stack.