Performance Measurement Indexing for Faster Checkpoint Delay Queries
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Solution Overview
Problem
Performance measurement systems face inefficiencies due to large data store sizes resulting from high-volume data transactions, leading to prolonged query response times when analyzing application performance metrics.
Innovation Solution
Implement a performance measurement indexing system that parses data stores to determine latency between checkpoints, allowing focused retrieval of congested application areas during queries, thereby reducing search fields and improving query speed and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If more data entries are collected in the data store, then the application performance data becomes more useful, but query response time drastically increases
Solution Approach 1:
The patent segments the data store into multiple partitions based on different criteria (e.g., time ranges, application modules, checkpoint locations). Each partition contains a subset of the total data entries. When a query is executed, the system identifies and queries only the relevant partitions rather than scanning the entire data store, thus maintaining measurement precision while reducing query response time proportionally to the number of partitions.
Solution Approach 2:
The patent implements preliminary indexing and organization of data entries before queries are executed. Data is pre-processed and organized into structured formats with indexes that enable rapid location of relevant data. This preliminary action allows the system to quickly identify and access only the necessary data subsets during query operations, avoiding full data store scans while preserving complete performance measurement capabilities.
2Loss of information
If the data store size increases to capture high-volume transactions, then performance analysis completeness improves, but search and retrieval efficiency deteriorates
Solution Approach 1:
The patent introduces additional organizational dimensions beyond simple data volume, such as temporal dimensions (time-based partitioning), spatial dimensions (checkpoint location-based partitioning), and functional dimensions (application module-based partitioning). These multiple dimensions enable the system to maintain complete performance data across all dimensions while allowing queries to efficiently navigate and filter along specific dimensions as needed, thus preserving data completeness while improving retrieval efficiency.
3Measurement precision
If all data fields are searched to ensure comprehensive analysis, then measurement accuracy is maintained, but query execution time increases significantly
Solution Approach 1:
The patent applies local quality by making different parts of the data store have different access characteristics. Frequently queried data fields and partitions are optimized with enhanced indexing and faster access paths, while less frequently accessed data maintains standard access patterns. This allows the system to maintain comprehensive measurement capability across all fields while significantly reducing query execution time for common performance metrics by focusing optimization on the most critical data access paths.
Data Source
AI summary
A performance measurement indexing system indexes a data store containing data entries indicative of message processing by an application. The application includes a plurality of checkpoints, and the data store contains data logged upon each message traversing the checkpoints in the application. The performance measurement indexing system determines which data entries relate to messages that satisfy a delay condition, and limits queries run on the data store to those data entries, thereby increasing the speed and efficiency with which queries can be serviced.