Rule-Based Data Stream Processing Pipeline
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Solution Overview
Problem
Existing data collection, indexing, and visualization systems face challenges in efficiently processing and analyzing machine data from diverse sources due to the need for multiple data processing pipelines, which increases development and production costs and complexity.
Innovation Solution
The implementation of a rule-based data stream processing system that uses configurable rules for data processing operations, allowing a single pipeline to handle data from multiple sources by matching source types and applying specific actions, thereby reducing the need for multiple pipelines and simplifying the addition or removal of data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple data processing pipelines are used to handle data from diverse sources, then data processing capability is improved, but device complexity and development costs increase
Solution Approach 1:
The patent implements a universal data processing pipeline that can handle multiple data sources through a single unified structure. The pipeline uses configurable rules that can be dynamically assigned to different data sources, eliminating the need for separate dedicated pipelines for each source type while maintaining the ability to process diverse data formats and sources efficiently.
Solution Approach 2:
The patent introduces dynamic rule assignment where processing rules can be configured and assigned to data sources at runtime. This dynamic configuration allows the same pipeline infrastructure to adapt to different data sources without requiring structural changes, reducing complexity while maintaining versatile data processing capabilities.
2Adaptability or versatility
If multiple data processing pipelines are implemented, then data processing capability is improved, but development and production costs increase
Solution Approach 1:
By creating a universal pipeline that handles multiple data sources through a single codebase and infrastructure, the patent eliminates the need to develop and maintain multiple separate pipelines. This universality directly reduces development costs by avoiding code duplication while preserving the ability to process diverse data sources.
Solution Approach 2:
The patent implements self-service through configurable rules that can be dynamically assigned to data sources without requiring pipeline reconfiguration or code changes. This allows the system to adapt to new data sources using existing infrastructure, reducing both development and operational costs.
3Adaptability or versatility
If multiple data processing pipelines are used, then data processing capability is improved, but the system becomes more complex to manage
Solution Approach 1:
The universal pipeline design consolidates management of multiple data sources into a single unified system. Administrators manage one pipeline infrastructure that handles all data sources, simplifying operations compared to managing multiple separate pipelines while maintaining the capability to process diverse data types.
Solution Approach 2:
The dynamic rule assignment mechanism allows the system to automatically adapt to different data sources through configuration rather than structural changes. This dynamic approach simplifies management by enabling flexible rule-based processing without requiring complex pipeline reconfiguration for new data sources.
Data Source
AI summary
Systems and methods for rule-based data stream processing by data collection, indexing, and visualization systems. An example method includes: receiving, by the computer system, an input data stream comprising raw machine data; processing the raw machine data by a data processing pipeline that produces transformed machine data, wherein the data processing pipeline comprises an ordered plurality of pipeline stages, wherein a pipeline stage of the ordered plurality of pipeline stages applies a rule of a set of rules to an input of the pipeline stage, wherein the rule specifies an action to be performed on the input of the pipeline stage responsive to evaluating a conditional expression applied to the input of the pipeline stage, wherein the action generates an output of the pipeline stage, and wherein the rule is selected based on a source type associated with the input data stream; and supplying the transformed machine data to a data collection, indexing, and visualization system.