Real-time Data Stream Analysis with Delay Thresholds
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Solution Overview
Problem
Conventional techniques for instrumenting software are inadequate for complex, distributed systems as they struggle with network delays and system failures, leading to delays and inaccuracies in data processing and reporting.
Innovation Solution
An analysis system that evaluates data streams by determining the maximum expected delay for each stream, excludes late-arriving data values, and marks failed streams as dead to ensure accurate real-time processing and reporting, using a moving average to calculate delays and periodically updating these values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data values are processed in real-time from distributed systems, then reporting speed is improved, but accuracy deteriorates due to network delays and system failures causing late or missing data
Solution Approach 1:
The system pre-calculates and stores expected data arrival times and thresholds before processing begins. By establishing baseline expectations for data arrival patterns in advance, the system can quickly determine whether late-arriving data should be included or excluded without complex real-time analysis, thus maintaining both speed and accuracy
Solution Approach 2:
The system dynamically adjusts data inclusion criteria based on observed network conditions and system performance. By continuously monitoring data arrival patterns and adapting thresholds accordingly, the system maintains accuracy while responding to changing real-time conditions in distributed environments
2Measurement precision
If the system waits for all data values to arrive before processing, then accuracy is improved, but processing time increases due to network delays
Solution Approach 1:
The system pre-establishes data arrival thresholds and processing triggers before data collection begins. By having predetermined criteria for when sufficient data has been received, the system can initiate processing without waiting for all possible data values, balancing completeness with timely results
Solution Approach 2:
The system processes data when a sufficient portion has arrived rather than waiting for complete data sets. By accepting partial data meeting predefined thresholds, the system achieves timely processing with adequate accuracy without the excessive delay of waiting for all data values
3Quantity of substance
If the system includes all arriving data values in processing, then data completeness is improved, but reliability deteriorates due to inclusion of late-arriving or failed data from unstable systems
Solution Approach 1:
The system pre-defines reliability thresholds and data quality criteria before processing begins. By establishing baseline expectations for data arrival timing and system stability in advance, the system can filter out unreliable data points without complex real-time judgment, maintaining both data volume and trustworthiness
Solution Approach 2:
The system continuously monitors data arrival patterns and system performance, using this feedback to dynamically adjust inclusion criteria. By comparing actual data arrival against expected patterns and excluding anomalies, the system maintains reliability while preserving sufficient data volume for meaningful analysis
Data Source
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AI summary
An analysis system receives data streams generated by instances of instrumented software executing on external systems. The analysis system evaluates an expression using data values of the data streams over a plurality of time intervals. For example, the analysis system may aggregate data values of data streams for each time interval. The analysis system determines whether or not a data stream is considered for a time interval based on when the data value arrives during the time interval. The analysis system determines a maximum expected delay value for each data stream being processed. The analysis system evaluates the expression using data values that arrive before their maximum expected delay values. The analysis system also determines a failure threshold value for a data stream. If a data value of a data stream fails to arrive before the failure threshold value, the analysis system marks the data stream as dead.