Data Stream Language for Dynamic Software Instrumentation Analysis
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Solution Overview
Problem
Conventional techniques for instrumenting complex software applications are inadequate for fast-paced development cycles, causing significant delays in data assimilation and analysis, and require vendor-based solutions with high overhead, making them unsuitable for modern software development practices.
Innovation Solution
A data stream processing language and system that processes data streams from instrumented software, allowing flexible and efficient analysis through a network of blocks with input and output ports, threshold comparisons, and metadata-based reporting, enabling real-time data processing and generation of reports without modifying the instrumented code.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional instrumentation techniques are used for complex distributed applications, then code can be instrumented with logging statements, but significant delays occur in data assimilation, storage, and analysis
Solution Approach 1:
The patent segments the instrumentation system into distributed agents deployed across multiple systems, each independently collecting and processing data locally. This segmentation enables parallel data collection and reduces centralization bottlenecks, thereby reducing delays in problem detection while maintaining reliable monitoring across complex distributed applications.
Solution Approach 2:
The patent implements preliminary action by pre-configuring instrumentation templates and data collection rules before deployment. Agents are pre-programmed with instrumentation logic that automatically activates upon deployment, eliminating the need for manual code instrumentation and enabling immediate data collection, thus reducing delays in problem detection.
2Adaptability or versatility
If vendor-based expert services are used for code instrumentation, then flexible and comprehensive instrumentation can be achieved, but significant overhead in time and cost is incurred
Solution Approach 1:
The patent enables self-service instrumentation by providing developers with automated agents and template-based configuration tools that allow them to instrument their own code without requiring external expert services. The system automatically handles data collection, processing, and analysis, eliminating vendor dependency and reducing overhead while maintaining instrumentation flexibility through configurable templates.
Solution Approach 2:
The patent uses parameter changes by allowing dynamic configuration of instrumentation behavior through configurable parameters and templates. Developers can adjust data collection frequency, thresholds, and analysis parameters without re-instrumenting code, providing flexibility while maintaining fast development cycles through automated parameter-based customization.
3Ease of manufacture
If standard logging techniques are used for simple applications, then implementation is straightforward, but the techniques are inadequate for complex distributed systems
Solution Approach 1:
The patent implements universality by designing a multi-functional instrumentation agent that can operate across simple and complex distributed systems. The agent provides unified data collection, processing, and analysis capabilities that adapt to different system complexities through configurable parameters, maintaining ease of implementation while enabling applicability to complex distributed environments.
Solution Approach 2:
The patent applies dynamics by creating an adaptive instrumentation system that automatically adjusts its behavior based on system complexity. The agent dynamically configures data collection strategies, processing intensity, and analysis depth according to the monitored system's characteristics, providing simplicity for simple applications while scaling to handle complex distributed systems.
Data Source
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AI summary
An instrumentation analysis system processes data streams by executing instructions specified using a data stream language program. The data stream language allows users to specify a search condition using a find block for identifying the set of data streams processed by the data stream language program. The set of identified data streams may change dynamically. The data stream language allows users to group data streams into sets of data streams based on distinct values of one or more metadata attributes associated with the input data streams. The data stream language allows users to specify a threshold block for determining whether data values of input data streams are outside boundaries specified using low/high thresholds. The elements of the set of data streams input to the threshold block can dynamically change. The low/high threshold values can be specified as data streams and can dynamically change.