Event Data Compression via String Set Dictionary Indexing
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Solution Overview
Problem
Social networking systems face challenges in efficiently monitoring and managing large volumes of data, leading to high latency and unresponsive tools for operators, making it difficult to quickly analyze performance and user interaction metrics, behavioral events, and error reports.
Innovation Solution
The system employs a data processing system that compresses event data using string set dictionary indexes, Fibonacci encoding, and in-memory data storage to provide rapid access and querying capabilities, allowing for efficient storage and retrieval of event data, including performance, user interaction, and behavioral events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If large volumes of event data are stored and processed using conventional methods, then data capacity is maintained, but system latency increases and responsiveness deteriorates
Solution Approach 1:
The patent segments event data into discrete event types (performance events, user interaction events, behavioral events, error reports) and processes them through specialized compression pathways. Each event type is handled by specific modules that apply appropriate compression techniques, enabling efficient processing of large data volumes while maintaining system responsiveness.
Solution Approach 2:
The system dynamically changes data representation parameters by converting event data into compressed formats using Fibonacci coding and variable-length encodings. This parameter transformation reduces the storage space required for event data while enabling rapid retrieval and analysis, directly improving system speed and responsiveness.
2Productivity
If conventional data storage and processing methods are used, then data retention is achieved, but analysis time and operational efficiency deteriorate
Solution Approach 1:
The patent implements preliminary compression and organization of event data as it is generated and ingested into the system. By pre-processing data through compression algorithms and organizing it into efficient storage structures before analysis is needed, the system eliminates time-consuming data preparation steps during actual analysis operations, significantly improving productivity.
Solution Approach 2:
The system replaces conventional mechanical data storage and retrieval mechanisms with memory-mapped file structures and optimized data layouts. This substitution enables direct memory access patterns and reduces I/O overhead, allowing rapid querying and analysis of compressed event data without the latency associated with traditional storage systems.
3Reliability
If detailed event data is maintained for comprehensive monitoring, then monitoring completeness is improved, but system complexity and resource requirements increase
Solution Approach 1:
The patent extracts only the essential and relevant features from raw event data for storage and analysis. By identifying and retaining only the critical event attributes needed for monitoring and analysis while discarding redundant information, the system maintains comprehensive monitoring coverage with reduced data volumes and simplified processing requirements.
Solution Approach 2:
The compressed event data structure is designed to serve multiple monitoring and analysis functions simultaneously. The same compressed representation supports performance monitoring, user behavior analysis, error tracking, and system health assessment, reducing overall system complexity by eliminating the need for separate processing pipelines for different monitoring objectives.
Data Source
AI summary
Event data comprising an unordered string set may be received. String set dictionary indexes may be assigned for strings of the unordered string set in a string set dictionary. The unordered string set may be sorted to provide a sorted series based on the string set dictionary indexes for the unordered string set. A differential series may be computed from the sorted series. The differential series may be encoded into binary code words. In an embodiment, the event data also may comprise strings. A schema version associated with the strings in a row may be determined. Computing resources may be allocated based on the schema version.