Push-Based Event Processing for Real-Time Stream Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional data analytics systems are limited by storage capacity and require extensive time to analyze large volumes of data, which can lead to delays in deriving timely insights from the vast amounts of data streamed across networks.
Innovation Solution
Implementing a push-based event processing system that evaluates events in real-time as they are streamed, allowing for immediate analysis without storage constraints by using filters and server components to identify and push relevant events to remote computer systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional data analytics systems store data before analysis, then data can be analyzed, but the analysis requires extensive time and is limited by storage capacity
Solution Approach 1:
The system performs preliminary filtering and evaluation of data streams in real-time before data is stored. Server components evaluate events as they arrive and push only relevant events to remote computer systems, eliminating the need to store and re-analyze entire data sets later. This preliminary action at the data ingestion stage resolves the contradiction by reducing both storage requirements and subsequent analysis time.
Solution Approach 2:
The system extracts only the relevant events from the data stream that match specific criteria, separating them from the bulk data. Instead of storing and analyzing all data, the system takes out only the necessary events for analysis, thereby reducing storage capacity requirements while maintaining analysis effectiveness and reducing time loss.
2Productivity
If data is analyzed after storage, then complete data sets can be analyzed, but the process is slow and storage-limited
Solution Approach 1:
The system performs preliminary filtering and evaluation of data streams in real-time before data is stored. Server components evaluate events as they arrive and push only relevant events to remote computer systems, eliminating the need to store and re-analyze entire data sets later. This preliminary action at the data ingestion stage resolves the contradiction by reducing both storage requirements and subsequent analysis time.
Solution Approach 2:
The system maintains continuous real-time analysis of data streams without interruption or batch processing delays. Events are evaluated continuously as they arrive, and relevant events are immediately pushed to remote systems for analysis, ensuring uninterrupted productive action throughout the data pipeline rather than periodic batch analysis that slows productivity.
3Reliability
If all events in a data stream are evaluated, then no relevant events are missed, but the processing time and resources increase
Solution Approach 1:
The system segments the evaluation process into multiple server components, each responsible for evaluating specific events or event types. This segmentation allows parallel processing of different event streams, maintaining comprehensive coverage and reliability while reducing the time burden on any single component. Each server component handles a subset of events, enabling scalable and efficient evaluation.
Solution Approach 2:
The system performs partial evaluation by applying filters that assess only specific characteristics of events rather than进行全面 analysis of all events. Server components evaluate events based on predetermined criteria and push only those events that meet the criteria, performing excessive action on filtering to enable partial action on detailed analysis, thereby maintaining reliability while reducing overall evaluation time.
Data Source
AI summary
Pushing an event that is identified within a data stream to a remote computer system. Event characteristic(s) that are to be searched for within the data stream are determined (e.g., in response to a query). The event(s) in the data stream is evaluated to determine if it includes the event characteristic mentioned above. If an event is determined to include the event characteristic, then the event may be pushed to the remote computer system. In this manner, the event may be responded to at the remote computer system. This process enables the events to be pushed in a very fast manner. Furthermore, the process of evaluating the events may happen without storing the events, which process further increases the speed of pushing the events.