Distributed Event Processing System for Continuous Stream Updates
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Solution Overview
Problem
Traditional database systems are inadequate for processing continuous data streams, as they are designed for finite data sets and lack the processing flexibility required for modern event-stream-based applications such as sensor data, financial tickers, and network monitoring, which generate unbounded data sets.
Innovation Solution
A distributed event processing system that receives continuous data streams, registers continuous queries, and executes them in real-time, allowing for the deployment and execution of event processing applications across a cluster of machines, reducing delays associated with application updates and enabling quick delivery of results to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional database systems are used to store and process data, then data can be stored in structured tables and queried using SQL, but the system cannot efficiently process continuous unbounded data streams and lacks temporal processing capabilities
Solution Approach 1:
The system segments the data processing architecture into distinct components: a traditional database layer for structured data storage and a new temporal query processing layer for continuous stream processing. This segmentation allows each layer to handle its specialized function while maintaining overall system coherence through standardized interfaces.
Solution Approach 2:
The patent creates a universal query processing framework that can handle both traditional SQL queries on finite datasets and temporal continuous queries on unbounded streams. The system provides multi-functionality by enabling a single platform to serve both batch processing and real-time stream processing needs through its dual-capability architecture.
2Adaptability or versatility
If product suites are offered for event stream processing, then basic stream processing capabilities are provided, but processing flexibility required for handling today's event processing needs is still lacking
Solution Approach 1:
The system implements dynamic query execution where continuous queries are not statically compiled but are instead executed adaptively as data streams flow through the system. The query processing engine dynamically adjusts to varying data patterns, time windows, and stream characteristics, providing flexibility without requiring complex static configuration.
Solution Approach 2:
The patent enables flexible parameter adjustment in continuous queries, allowing users to modify time window sizes, aggregation parameters, and filtering criteria dynamically. The system supports parameter changes without requiring complete query redefinition, enabling adaptive processing of diverse event streams with varying characteristics.
3Productivity
If applications are deployed on a cluster of machines, then processing capacity and scalability are improved, but delays occur when updates to applications are received and deployed
Solution Approach 1:
The system performs preliminary actions by pre-compiling and validating application code before deployment to the cluster. Update packages are prepared and verified in advance, allowing for faster deployment when updates are needed. The system pre-establishes execution environments and resource allocations, reducing the time required to apply updates across the distributed cluster.
Solution Approach 2:
The patent implements a copying mechanism where application updates are replicated across multiple nodes in the cluster simultaneously. Instead of sequentially deploying updates node-by-node, the system creates and distributes copy instances of the updated application code to all relevant computing nodes in parallel, significantly reducing overall deployment time while maintaining consistency.
Data Source
AI summary
A distributed event processing system is disclosed that receives continuous data streams, registers a continuous query against the data streams, and continuously executes the query as new data appears in the streams. In certain embodiments, the distributed event processing system deploys and executes applications (e.g., event processing applications) by distributing the execution of the application on a cluster of machines within the system. In certain embodiments, the system receive updates to an application, processes the updated application against a continuous stream of data, and continuously generates updated results to a user. In certain embodiments, the system reduces delays associated with application deployment and execution when updates to an application are received. The system enables a user to view results pertaining to an updated application quickly without experiencing the typical deployment and execution delays that occur when updates to an application are made.