Distributed Event Processing with HBase Enrichment
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Solution Overview
Problem
Traditional database models are not well-suited for processing continuous data streams, as they are designed for bounded data sets rather than real-time, unbounded sequences of events, leading to inefficiencies in handling and processing large volumes of data from sources like sensors and financial tickers.
Innovation Solution
Distributing the processing of CQL queries across multiple processing nodes, using an HBase database store as a data source and sink, and employing Map-Reduce frameworks to handle event streams efficiently, allowing for load-balancing, partitioning, and broadcast distribution flows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional database models are used for processing data streams, then data storage and querying capability are provided, but processing efficiency and scalability deteriorate due to being designed for bounded data sets rather than real-time unbounded sequences
Solution Approach 1:
The patent segments the event processing system into multiple independent processing nodes that can handle different portions of the event stream in parallel. Each node maintains its own state and processes events independently, allowing the system to scale horizontally by adding more nodes without increasing overall system complexity significantly.
Solution Approach 2:
The patent transitions from a single-threaded sequential processing model to a multi-dimensional parallel processing architecture. By introducing the dimension of multiple processing nodes and using distributed state management, the system achieves scalability while maintaining processing efficiency for unbounded data streams.
2Measurement precision
If CEP queries are applied continuously to event streams, then real-time pattern detection is achieved, but processing speed deteriorates due to single-threaded execution required for state maintenance
Solution Approach 1:
The patent divides the continuous event processing task into multiple parallel CEP query executions across different processing nodes. Each node maintains its own state and executes CEP queries independently on its assigned event subset, enabling simultaneous pattern detection without sequential bottlenecks while preserving detection accuracy.
3Productivity
If multiple processing nodes are used for distributed event processing, then scalability and throughput are improved, but system complexity and state management difficulty increase
Solution Approach 1:
The patent implements a universal event processing framework where each processing node can handle multiple types of events and execute various CEP queries. This multi-functional design allows nodes to be replicated and distributed without requiring node-specific configuration, simplifying state management and reducing system complexity while maintaining high throughput.
4Adaptability or versatility
If HBase database store is used as data source and sink, then data enrichment capability is improved, but query complexity and processing overhead increase
Solution Approach 1:
The patent introduces an intermediary layer between the HBase database store and the event processing nodes. This intermediary handles data retrieval and enrichment operations centrally, allowing processing nodes to access enriched event data without directly querying HBase. This approach maintains data enrichment capability while reducing query complexity and processing overhead at each node.
Data Source
AI summary
Some event ordering requirements can be determined based on continuous event processing queries. Other event ordering requirements can be determined based on distribution flow types being used to distribute events from event streams to node executing the queries. Events from event streams can be ordered according to ordering semantics that are based on a combination of all of these event ordering requirements. Additionally, virtual computing nodes can be associated with constraints, and computing processors can be associated with capabilities. Virtual computing nodes for processing event streams can be assigned to execute on various computing processors based on both these constraints and capabilities. Additionally, for each of several events in an event stream, a ratio between a total latency and a communication latency can be for determined. Based on an average of these ratios, a quantity of reducing nodes that will be involved in a map-reduce operation can be selected.