Asynchronous Log Enrichment for Error-Aware Data Processing
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Solution Overview
Problem
Existing log data processing systems face inefficiencies and inflexibilities, particularly with synchronous processing of large volumes of data, leading to bottlenecks and failure to identify and report errors, resulting in data loss and inefficient resource utilization.
Innovation Solution
Asynchronous processing of log data to validate configurations and detect errors, generating enriched log records with error annotations, and providing user interfaces for error correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If synchronous processing is used to process large volumes of log data, then data processing throughput is maintained, but computing resources are bottlenecked and processing efficiency deteriorates
Solution Approach 1:
The patent segments the synchronous processing workflow into separate asynchronous tasks. Log data validation, error detection, and enrichment operations are divided into independent tasks that can be processed concurrently without blocking the main processing pipeline, thereby maintaining throughput while reducing resource bottlenecks.
Solution Approach 2:
The system dynamically adjusts processing modes by implementing asynchronous processing for specific operations. The processing architecture transitions from rigid synchronous execution to flexible asynchronous execution, allowing the system to adapt resource allocation based on task priorities and computational demands.
2Loss of energy
If asynchronous processing is implemented to improve processing efficiency, then computing resource utilization improves, but error identification and validation capabilities may deteriorate
Solution Approach 1:
The patent implements feedback mechanisms where validation results and error detections from asynchronous processing are fed back into the system. Error information is captured, categorized, and communicated back to relevant components, ensuring that asynchronous processing does not compromise error identification accuracy while maintaining resource efficiency.
Solution Approach 2:
The system replaces traditional synchronous mechanical processing with asynchronous event-driven processing. Error detection and validation are handled through event streams and message queues rather than blocking operations, maintaining reliability while improving resource utilization through non-blocking architectures.
3Manufacturing precision
If error detection and validation are performed on all log records, then data accuracy improves, but processing time increases
Solution Approach 1:
The patent applies partial validation actions by prioritizing error detection for critical log records while using sampling or reduced validation for less critical data. This selective approach ensures high accuracy for important records while reducing overall processing time through differentiated validation strategies.
Solution Approach 2:
The system changes validation parameters dynamically based on log record characteristics. Validation depth, error checking intensity, and processing priority are adjusted as parameters based on the type, source, and importance of each log record, optimizing the balance between accuracy and processing speed.
4Productivity
If log data is processed without error identification, then processing speed is maintained, but data loss increases
Solution Approach 1:
The patent implements preliminary error detection mechanisms that identify and flag potential data loss conditions before they occur. Validation rules and error patterns are pre-configured to detect issues early in the processing pipeline, allowing corrective actions to be taken before data is lost or corrupted.
Solution Approach 2:
The system introduces intermediary error detection and validation components between data ingestion and processing. These intermediary layers act as mediators that monitor data quality, identify errors, and prevent data loss without significantly impacting processing speed through efficient error handling mechanisms.
Data Source
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AI summary
Log data that includes a plurality of log records is asynchronously processed to validate a configuration of each log record and data included in each log record. It is determined that one or more attributes of a particular subset of log records of the plurality of log records corresponds to one or more errors. Using the particular subset, one or more enriched log records are generated by augmenting each log record of the particular subset of log records with error information that indicates one or more categories corresponding to the one or more errors. A user interface is generated to facilitate correction of the one or more errors, the user interface comprising a plurality of interactive elements corresponding to a plurality of error metrics of different categories of errors, wherein the one or more categories of the one or more errors are included in the different categories of errors.