Low-Latency Streaming Analytics via Publish-Subscribe Model
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Solution Overview
Problem
Current data analysis tools require pre-indexing and storage of massive quantities of raw data, making them unsuitable for real-time or low-latency analysis, and are inefficient in detecting trends or analyzing statistics due to the need for re-querying previously analyzed data.
Innovation Solution
A data stream monitoring system that operates using a publish-and-subscribe model, allowing for real-time analysis without re-analyzing previous data, and can scale with distributed processing resources to handle large volumes of data from diverse sources, enabling seamless integration of new data sources and analyses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If pre-indexing and storage of massive quantities of raw data is performed before search or analysis, then data retrieval and analysis can be facilitated, but real-time or low-latency analysis becomes unsuitable
Solution Approach 1:
The system performs preliminary actions by establishing publish-subscribe relationships and configuring analysis parameters before data arrives. Subscribers define their analysis requirements in advance, and the system prepares processing pipelines so that when data is published, analysis can begin immediately without pre-indexing delays.
Solution Approach 2:
The system transitions from static pre-indexing to dynamic real-time processing. Analysis configurations can be modified on-the-fly, new subscribers can be added dynamically, and the system adapts processing resources based on incoming data streams and subscription demands, enabling low-latency responsive analysis.
2Adaptability or versatility
If massive quantities of raw data are stored for later retrieval and analysis, then greater flexibility in analyzing all generated data is enabled, but computational inefficiency increases due to re-querying previously analyzed data
Solution Approach 1:
The system implements feedback by having subscribers receive continuous streams of analyzed results based on their specific queries. Once analysis is performed on incoming data, results are immediately delivered to subscribers without requiring re-querying. The system learns from subscription patterns and optimizes processing to avoid redundant analysis of the same data for different queries.
Solution Approach 2:
The system creates universal processing pipelines that can serve multiple subscribers with different analysis requirements from the same incoming data stream. A single data publication can trigger multiple parallel analysis operations simultaneously, making the system versatile in handling various analysis types while maintaining computational efficiency through shared processing infrastructure.
3Ease of operation
If tools are designed to search massive quantities of raw data with pre-processing, then efficient retrieval of specified data items is achieved, but real-time analysis applications become unsuitable
Solution Approach 1:
The system performs preliminary configuration of analysis parameters and subscription criteria before data arrives. Users define their search and analysis requirements in advance through subscriber configurations, allowing the system to prepare processing logic so that data retrieval and analysis begin immediately upon publication without pre-processing delays.
Solution Approach 2:
The system introduces an intermediary layer between raw data publication and final analysis results. The publish-subscribe mechanism acts as a mediator that receives published data, routes it to appropriate subscribers based on their criteria, and coordinates analysis operations. This intermediary enables efficient data retrieval by matching published data with pre-configured subscription filters without requiring full pre-processing of all data.
Data Source
AI summary
Systems and methods are disclosed for implementing a low-latency data stream monitoring system. The data stream monitoring system may obtain raw data from a data source as soon after the data is generated, and may classify the data according to different topics. The topics may be published in a publish-subscribe messaging model, and data enrichment systems may subscribe to the topics to receive data for enrichment. The data enrichment systems may supplement or replace the raw data with additional information, and may further classify or reclassify the enriched data into different topics. The enriched data may then be published to an alert generation system, which may apply various criteria to the enriched data to determine that alerts should be generated, generate the alerts, and publish or transmit the alerts to client devices. Individual data streams, topics, enrichments, criteria, and alarms may be added, removed, or modified as required.