Event Serialization Service for Log Format Translation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data processing techniques face challenges in efficiently integrating and translating events from data streams into formats consumable by various services, leading to the proliferation of batch services and manual modifications, especially with proprietary log file formats, which limits the ability to automatically harvest and integrate data across services.
Innovation Solution
The method involves using meta information to detect and serialize events within data streams, translating them into desired formats, and transmitting them to services for action, allowing for dynamic integration and processing without requiring specific batch services for each format.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If batch processing services are used to process each unique log file data format, then the data can be processed and integrated with consuming services, but the number of batch services proliferates rapidly requiring extensive maintenance and support
Solution Approach 1:
The patent implements a universal event serialization service that can process multiple different log file formats through a single unified interface. The service uses configuration files to define event patterns for different log formats, allowing one service to handle diverse data formats without requiring separate batch services for each format.
Solution Approach 2:
The system changes the approach from creating multiple services to changing the configuration parameters of a single service. By modifying configuration files that define event patterns and data formats, the same serialization service can adapt to process different log formats dynamically without requiring service proliferation.
2Adaptability or versatility
If batch services are designed to interface with services based on evaluation of log file contents, then integration can be achieved, but the batch services need to be modified regularly when new services are integrated
Solution Approach 1:
The event serialization service automatically adapts to new consuming services through configuration files rather than requiring manual modification of the service code. The system evaluates log file contents against defined event patterns and automatically serializes events in the appropriate format for different consuming services, making the service self-adapting to new integrations.
Solution Approach 2:
The system implements dynamic configuration where event patterns and serialization formats can be modified at runtime through configuration files. This allows the service to adapt to new services and log formats without requiring service restarts or code modifications, enabling flexible integration while maintaining ease of maintenance.
3Measurement precision
If manual inspection of log files is performed, then detailed analysis can be conducted, but the process is time-consuming and not scalable
Solution Approach 1:
The patent replaces manual inspection processes with an automated event serialization service that uses pattern matching and configuration-based rules to detect and process events in log files. This automated approach maintains the detailed analysis capability of manual inspection while eliminating the time-consuming aspect by using algorithmic event detection rather than human review.
4Ease of manufacture
If proprietary data formats are used by services, then each service can optimize its data structure, but the ability to integrate and translate data between services is limited
Solution Approach 1:
The event serialization service acts as an intermediary between services using proprietary data formats and consuming services. It receives log data in various proprietary formats, processes them through defined event patterns, and serializes the events into a standardized format that can be consumed by multiple different services, thereby enabling integration without requiring changes to the original services' data structures.
Data Source
AI summary
Techniques are provided for serializing events of a data stream. Meta information defines information unit separators and context for events within the data stream. The data stream is parsed according to the instructions of the meta information and event data associated with the events of the data stream are retained. The event data is packaged into selective groupings of event data and transmitted to one or more services in data formats used by the services. The services perform one or more actions based on the received selective groupings of event data.