Trace Data Analysis Through Partitioned Summary Digests
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Processing large quantities of machine-generated data in IT environments is time and resource-intensive, posing challenges in efficiently responding to queries and deriving insights.
Innovation Solution
An observability system that integrates with diverse data sources to transform raw metrics, traces, and logs into actionable insights, using a unified environment for real-time monitoring and analytics, and employs a distributed query resolution method that divides data into partitions, incrementally assigns workers, and merges summary data structures to efficiently process trace data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large quantities of machine data are processed in response to queries, then comprehensive insights and accurate analysis are improved, but processing time and resource consumption increase
Solution Approach 1:
The system pre-processes trace data and maintains summary data structures (digests) that capture essential statistics and patterns. When a query arrives, the system can quickly retrieve and filter pre-computed summaries rather than scanning all raw trace data, dramatically reducing query response time while maintaining analysis accuracy.
Solution Approach 2:
The patent divides trace data into multiple partitions and processes them in parallel using distributed workers. Each worker independently processes a subset of partitions and contributes to the final result through incremental merging of summary data structures. This segmentation enables scalable processing of large data volumes without linearly increasing query time.
2Measurement precision
If detailed trace data is stored for comprehensive analysis, then measurement precision and insight quality are improved, but storage requirements and compute resources increase
Solution Approach 1:
The system extracts essential statistical information and patterns from detailed trace data to create compact summary data structures (digests). These digests contain aggregated metrics, quantiles, and other condensed representations that preserve analytical value while occupying minimal storage space. The full trace data can be discarded or archived after summary extraction.
Solution Approach 2:
The patent transforms detailed trace data into summary statistics by changing the representation parameters from individual data points to aggregated metrics such as quantiles, counts, and distributions. This parameter transformation maintains the ability to answer analytical queries while reducing storage requirements from storing every trace to storing only essential statistical parameters.
3Productivity
If distributed query resolution with multiple workers is used, then processing throughput and scalability are improved, but system complexity increases
Solution Approach 1:
The system uses mergeable summary data structures that can be incrementally combined from multiple workers. Each worker independently processes partitions and produces summaries that can be merged through simple operations (such as merging histograms or quantile structures). This merging capability enables linear scalability across distributed workers without requiring complex coordination or communication protocols.
4Speed
If real-time monitoring and analytics are implemented, then operational insights and response time are improved, but compute resource consumption increases
Solution Approach 1:
The system continuously pre-computes and maintains summary data structures from incoming trace data in real-time. These pre-computed summaries are kept updated as new data arrives, enabling the system to respond to queries immediately by filtering and combining existing summaries rather than performing heavy computation on demand. This preliminary action enables real-time responsiveness with reduced per-query compute resources.
Data Source
AI summary
Queries may be resolved against large quantities of collected data (such as traces) by dividing collected data into multiple time intervals and incrementally assigning multiple workers the query and the collected data over the multiple time intervals. For each time interval, these workers may conditionally update one or more summary data structures within the worker based on the query and the portion of collected data assigned to the worker. The summary data structures for each time interval may then be incrementally returned and merged with results from earlier time intervals to create a final merged query result.