Distributed Trace Flow Graphs for Critical Path Analysis
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Solution Overview
Problem
Existing tracing and performance analysis systems face challenges in large-scale distributed systems due to inconsistent trace data quality, dynamic tracing paths, and data loss, making it difficult to understand and visualize trace data for microservices.
Innovation Solution
A graph analysis and database system is used to process and store aggregated distributed trace data, generating process flow graphs to visualize system behavior, identify critical paths, and apply machine learning for anomaly detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If distributed tracing is implemented in large-scale microservices systems, then service monitoring and performance analysis capability is improved, but trace data quality becomes inconsistent and difficult to understand
Solution Approach 1:
The patent segments trace data into standardized spans with consistent fields (trace_id, span_id, parent_span_id, operation_name, start_time, end time, service_name, service_instance_name). This segmentation into uniform atomic units enables consistent aggregation and visualization across heterogeneous microservices, resolving the trace data quality issue while maintaining system complexity management.
Solution Approach 2:
The patent transforms raw trace data into aggregated flow data by changing parameters - grouping traces by service flow paths and aggregating metrics (count, latency, error rate). This parameter transformation simplifies the data representation and improves understandability while preserving essential monitoring capabilities.
2Quantity of substance
If raw trace data is collected and stored, then complete performance data is available, but data volume becomes huge and difficult to process
Solution Approach 1:
The patent extracts and aggregates only the essential flow information from raw traces - service flow paths, latency distributions, error rates - while discarding redundant individual span details. This extraction creates a condensed representation that maintains analytical value while dramatically reducing data volume for processing and visualization.
Solution Approach 2:
The patent merges multiple individual trace spans into aggregated flow records by grouping traces that share common service flow paths. This merging operation consolidates redundant information and produces a compact dataset that is easier to process and visualize while preserving complete performance insights.
3Reliability
If comprehensive trace collection is implemented, then complete performance insights are obtained, but data loss occurs due to incomplete or broken traces
Solution Approach 1:
The patent applies preliminary actions to trace data during aggregation - filtering out incomplete or broken traces before they enter the visualization system. By pre-processing and validating trace completeness, the system prevents information loss and ensures only valid flow data is aggregated and displayed, maintaining reliability while minimizing data loss.
Data Source
AI summary
Technologies are shown for generating process flow graphs from system trace data that involve obtaining raw distributed trace data for a system, aggregating the raw distributed trace data into aggregated distributed trace data, generating a plurality of process flow graphs from the aggregated distributed trace data, and storing the plurality of process flow graphs in a graphical store. A first critical path can be determined from the plurality of process flow graphs based on an infrastructure design for the system and a process flow graph corresponding to the first critical path provided for graphical display. Certain examples can determine a second critical path involving a selected element of the first critical path and provide the process flow graph for the second critical path for display. Some examples pre-process the aggregated distributed trace data to repair incorrect traces. Other examples merge included process flow graphs into longer graphs.